foreign direct investment in services and...
TRANSCRIPT
Foreign Direct Investment in Services and Manufacturing Productivity
Evidence for Chile
Ana M Fernandes a Caroline Paunov
b
The World Bank
OECD
March 2011
Journal of Development Economics forthcoming
Abstract
This paper examines the impact of substantial foreign direct investment (FDI) inflows in
producer service sectors on the total factor productivity (TFP) of Chilean manufacturing
firms Positive effects are obtained in firm fixed effects instrumental variables regressions
and show that forward linkages from FDI in services explain 7 of the observed increase
in Chilersquos manufacturing usersrsquo TFP Our findings also suggest that service FDI fosters
innovation activities in manufacturing Moreover we show that service FDI offers
opportunities for laggard firms to catch up with industry leaders
Keywords Total Factor Productivity Service Liberalization Foreign Direct Investment
Chile Firm Heterogeneity
JEL Classification codes D24 L8 L9 F21 F23
a Ana Margarida Fernandes (corresponding author) The World Bank Development Research Group 1818 H Street
NW Washington DC 20433 USA Email afernandesworldbankorg b Caroline Paunov OECD Directorate for Science Technology and Industry 2 rue Andreacute Pascal 75 775 Paris Cedex
16 France Email carolinepaunovoecdorg and carolinepaunovgmailcom
This paper is a modified version of the World Bank Policy Research Working Paper 4730 The authors would like to
thank Eric Verhoogen (the co-editor) and two anonymous referees as well as Richard Disney Ana Paula Fernandes
Jonathan Haskel Beata Javorcik Raimundo Soto Peter-Paul Walsh and seminar participants at Indiana University the
Chilean Central Bank the University of Chile Queen Mary University of London the 6th International Industrial
Organization Conference the OECD Development Centre the 2008 Empirical Investigations in International
Economics Conference in Slovenia the 2008 North American Summer Meetings of the Econometric Society the 2008
EEA ESEM Meetings the 2008 European Trade Study Group Conference 2008 LACEA-LAMES Meetings the 4th
MEIDE Conference in Estonia for valuable comments Support from the governments of Norway Sweden and the
United Kingdom through the Multi-Donor Trust Fund for Trade and Development is gratefully acknowledged The
findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank
or the OECD
1
1 Introduction
Foreign direct investment (FDI) inflows into the service sector experienced a boom
during the 1990s By 2002 services accounted for 60 of the world stock of FDI a four-
fold increase since 1990 (UNCTAD 2004) In developed and developing countries alike
the main recipients of FDI have been profit-seeking producer services which range from
network-intensive services such as electricity telecommunications and transport to
finance and business services These sectors are characterized by the facilitating and
intermediating role which they play for downstream user firms (Francois 1990) Thus
producer service sectors are an intricate component of a countryrsquos business environment
In emerging economies where manufacturing firms are constrained by cumbersome
business environments it is particularly relevant to understand how the performance of
service sectors can be improved and how that supports business development and thus
overall economic growth FDI is a potentially powerful means to achieve such
improvements as it might increase the quality and variety of services available as well as
lower their cost Manufacturing firms may benefit from their interaction with foreign
services suppliers through spillovers of management organizational marketing or
technological knowledge (Markusen 1989 Rivera-Batiz and Rivera-Batiz 1992)
Despite the relevance of this topic the effects of vertical linkages resulting from the
openness of producer services to FDI on downstream manufacturing firms have not been
widely documented (Hoekman 2006) This paper attempts to fill this gap by addressing
the following question did the increased penetration of FDI into producer service sectors
in Chile benefit total factor productivity (TFP) of manufacturing firms between 1995 and
2004 Chile is an interesting economy to study as its service sector received large FDI
2
inflows during the 1990s We show that foreign-owned service firms perform better in
terms of labor productivity and innovation than their domestic counterparts The fact that
this finding refers also to greenfield FDI - ie it is not simply foreign investors cherry-
picking the best-performing domestic service firms - suggests these firms introduced
superior standards in the Chilean services sector thus potentially offering improved
services for manufacturing firm users
Evaluating the causal impact of service FDI on manufacturing firm TFP is
challenging due to several valid endogeneity concerns discussed below Our strategy to
identify that impact is to estimate by instrumental variables (IV) a regression of firm TFP
on a service FDI linkage measure controlling for firm fixed effects as well as industry-
year and region-year fixed effects Our study uses TFP measures obtained as residuals
from production functions estimated following the methodologies of Levinsohn and
Petrin (2003) Olley and Pakes (1996) and Ackerberg et al (2006) to correct for the
endogeneity of input choices - including the choice of service inputs - with respect to
productivity Our service FDI linkage measure is defined as service FDI penetration
weighted by firm-level intensity of service usage The rationale underlying this novel
measure is the expectation that firms that use services more intensively benefit more from
any positive effects of service FDI Our measure of service usage intensity is based on
historic values for each firm following Blalock and Gertler (2009) to avoid the potential
endogeneity between firm TFP and service intensity Our choice of instrumental variables
estimation addresses another potential endogeneity concern related to fact that large FDI
inflows into Chilean service sectors may have responded to the strong performance of the
downstream manufacturing users Service FDI penetration weighted by the historic firm-
3
level intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile - Spain and the US - weighted
similarly The identification assumption is that while increases in total FDI outflows into
service sectors by those two countries affect service FDI penetration in Chilean service
sectors they are not correlated with the TFP of manufacturing firms in the country
through any other channel
We find evidence of a positive and significant effect of service FDI on Chilean
manufacturing firmsrsquo TFP This effect is robust to the control for differential productivity
trends across plants with different services intensity and to allowing the effects of service
FDI to operate with lags Alternative measures of service usage weights and service FDI
penetration including the use of industry-level weights from an input-output table
confirm our main findings The positive and significant effect of service FDI is also
obtained when considering a one-stage regression where output depends on inputs as well
as the service FDI linkage measure We also provide suggestive evidence supporting the
hypothesis that service FDI stimulates innovation by Chilean manufacturing firms
Moreover interestingly we find that service FDI offers an opportunity for laggard firms
to catch up in terms of TFP with industry leaders From a policy perspective this finding
is particularly relevant since it suggests that the benefits from service liberalization do not
accrue mostly to leading firms but seem to offer opportunities for firms that are further
behind
Our estimates suggest that a one-standard deviation increase in service FDI would
lead to an increase in TFP of Chilean firms by 3 all else constant The economic
magnitude of this impact is that forward linkages from FDI in services accounted for at
4
least 7 of the observed increase in manufacturing usersrsquo TFP in Chile during the sample
period This economic impact is quite meaningful in light of the finding by Haskel et al
(2007) that horizontal spillovers from manufacturing FDI explain a roughly similar share
of manufacturing TFP growth in the UK during the 1973-1992 period The positive
effects of service FDI on manufacturing firmsrsquo TFP may capture to some extent an
unmeasured decline in quality-adjusted services prices but also the spillover of
managerial and organizational knowledge from service providers to manufacturing users
The microeconomic evidence provided by our study contributes to the emerging
literature on the impact of services liberalization on growth and the performance of
services users At the macro level Mattoo et al (2006) and Eschenbach and Hoekman
(2006) show that countries with liberalized service sectors grow faster once all standard
growth correlates are controlled for Based on computable general equilibrium models
Konan and Maskus (2006) and Jensen et al (2007) argue that business services
liberalization could bring large GDP gains to Tunisia and Russia respectively1 The main
mechanism for these gains is the increase in the number of services available for
manufacturing users as a result of FDI2 At the industry level Francois and Woerz (2008)
show that the increased openness of business services through exports and FDI has strong
positive effects on exports value added and employment of manufacturing industries in
the OECD while Fernandes (2009) estimates positive and significant effects of
liberalization of finance and infrastructure on labor productivity of downstream
manufacturing industries in Eastern European countries At the firm-level Arnold et al
1 Markusen et al (2005) also show important GDP gains from services liberalization based on general equilibrium
simulations for a hypothetical country In their model the presence of foreign-owned service providers allows final
goods producers to rely on more specialized expertise 2 This increase in the number of services increases the TFP of manufacturing firms through a Dixit-Stiglitz-Ethier
framework (Dixit and Stiglitz 1977 Ethier 1982)
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Ethier W 1982 National and International Returns to Scale in the Modern Theory of
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Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
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Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
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Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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31
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57
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
1
1 Introduction
Foreign direct investment (FDI) inflows into the service sector experienced a boom
during the 1990s By 2002 services accounted for 60 of the world stock of FDI a four-
fold increase since 1990 (UNCTAD 2004) In developed and developing countries alike
the main recipients of FDI have been profit-seeking producer services which range from
network-intensive services such as electricity telecommunications and transport to
finance and business services These sectors are characterized by the facilitating and
intermediating role which they play for downstream user firms (Francois 1990) Thus
producer service sectors are an intricate component of a countryrsquos business environment
In emerging economies where manufacturing firms are constrained by cumbersome
business environments it is particularly relevant to understand how the performance of
service sectors can be improved and how that supports business development and thus
overall economic growth FDI is a potentially powerful means to achieve such
improvements as it might increase the quality and variety of services available as well as
lower their cost Manufacturing firms may benefit from their interaction with foreign
services suppliers through spillovers of management organizational marketing or
technological knowledge (Markusen 1989 Rivera-Batiz and Rivera-Batiz 1992)
Despite the relevance of this topic the effects of vertical linkages resulting from the
openness of producer services to FDI on downstream manufacturing firms have not been
widely documented (Hoekman 2006) This paper attempts to fill this gap by addressing
the following question did the increased penetration of FDI into producer service sectors
in Chile benefit total factor productivity (TFP) of manufacturing firms between 1995 and
2004 Chile is an interesting economy to study as its service sector received large FDI
2
inflows during the 1990s We show that foreign-owned service firms perform better in
terms of labor productivity and innovation than their domestic counterparts The fact that
this finding refers also to greenfield FDI - ie it is not simply foreign investors cherry-
picking the best-performing domestic service firms - suggests these firms introduced
superior standards in the Chilean services sector thus potentially offering improved
services for manufacturing firm users
Evaluating the causal impact of service FDI on manufacturing firm TFP is
challenging due to several valid endogeneity concerns discussed below Our strategy to
identify that impact is to estimate by instrumental variables (IV) a regression of firm TFP
on a service FDI linkage measure controlling for firm fixed effects as well as industry-
year and region-year fixed effects Our study uses TFP measures obtained as residuals
from production functions estimated following the methodologies of Levinsohn and
Petrin (2003) Olley and Pakes (1996) and Ackerberg et al (2006) to correct for the
endogeneity of input choices - including the choice of service inputs - with respect to
productivity Our service FDI linkage measure is defined as service FDI penetration
weighted by firm-level intensity of service usage The rationale underlying this novel
measure is the expectation that firms that use services more intensively benefit more from
any positive effects of service FDI Our measure of service usage intensity is based on
historic values for each firm following Blalock and Gertler (2009) to avoid the potential
endogeneity between firm TFP and service intensity Our choice of instrumental variables
estimation addresses another potential endogeneity concern related to fact that large FDI
inflows into Chilean service sectors may have responded to the strong performance of the
downstream manufacturing users Service FDI penetration weighted by the historic firm-
3
level intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile - Spain and the US - weighted
similarly The identification assumption is that while increases in total FDI outflows into
service sectors by those two countries affect service FDI penetration in Chilean service
sectors they are not correlated with the TFP of manufacturing firms in the country
through any other channel
We find evidence of a positive and significant effect of service FDI on Chilean
manufacturing firmsrsquo TFP This effect is robust to the control for differential productivity
trends across plants with different services intensity and to allowing the effects of service
FDI to operate with lags Alternative measures of service usage weights and service FDI
penetration including the use of industry-level weights from an input-output table
confirm our main findings The positive and significant effect of service FDI is also
obtained when considering a one-stage regression where output depends on inputs as well
as the service FDI linkage measure We also provide suggestive evidence supporting the
hypothesis that service FDI stimulates innovation by Chilean manufacturing firms
Moreover interestingly we find that service FDI offers an opportunity for laggard firms
to catch up in terms of TFP with industry leaders From a policy perspective this finding
is particularly relevant since it suggests that the benefits from service liberalization do not
accrue mostly to leading firms but seem to offer opportunities for firms that are further
behind
Our estimates suggest that a one-standard deviation increase in service FDI would
lead to an increase in TFP of Chilean firms by 3 all else constant The economic
magnitude of this impact is that forward linkages from FDI in services accounted for at
4
least 7 of the observed increase in manufacturing usersrsquo TFP in Chile during the sample
period This economic impact is quite meaningful in light of the finding by Haskel et al
(2007) that horizontal spillovers from manufacturing FDI explain a roughly similar share
of manufacturing TFP growth in the UK during the 1973-1992 period The positive
effects of service FDI on manufacturing firmsrsquo TFP may capture to some extent an
unmeasured decline in quality-adjusted services prices but also the spillover of
managerial and organizational knowledge from service providers to manufacturing users
The microeconomic evidence provided by our study contributes to the emerging
literature on the impact of services liberalization on growth and the performance of
services users At the macro level Mattoo et al (2006) and Eschenbach and Hoekman
(2006) show that countries with liberalized service sectors grow faster once all standard
growth correlates are controlled for Based on computable general equilibrium models
Konan and Maskus (2006) and Jensen et al (2007) argue that business services
liberalization could bring large GDP gains to Tunisia and Russia respectively1 The main
mechanism for these gains is the increase in the number of services available for
manufacturing users as a result of FDI2 At the industry level Francois and Woerz (2008)
show that the increased openness of business services through exports and FDI has strong
positive effects on exports value added and employment of manufacturing industries in
the OECD while Fernandes (2009) estimates positive and significant effects of
liberalization of finance and infrastructure on labor productivity of downstream
manufacturing industries in Eastern European countries At the firm-level Arnold et al
1 Markusen et al (2005) also show important GDP gains from services liberalization based on general equilibrium
simulations for a hypothetical country In their model the presence of foreign-owned service providers allows final
goods producers to rely on more specialized expertise 2 This increase in the number of services increases the TFP of manufacturing firms through a Dixit-Stiglitz-Ethier
framework (Dixit and Stiglitz 1977 Ethier 1982)
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
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Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
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180-203
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57
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
2
inflows during the 1990s We show that foreign-owned service firms perform better in
terms of labor productivity and innovation than their domestic counterparts The fact that
this finding refers also to greenfield FDI - ie it is not simply foreign investors cherry-
picking the best-performing domestic service firms - suggests these firms introduced
superior standards in the Chilean services sector thus potentially offering improved
services for manufacturing firm users
Evaluating the causal impact of service FDI on manufacturing firm TFP is
challenging due to several valid endogeneity concerns discussed below Our strategy to
identify that impact is to estimate by instrumental variables (IV) a regression of firm TFP
on a service FDI linkage measure controlling for firm fixed effects as well as industry-
year and region-year fixed effects Our study uses TFP measures obtained as residuals
from production functions estimated following the methodologies of Levinsohn and
Petrin (2003) Olley and Pakes (1996) and Ackerberg et al (2006) to correct for the
endogeneity of input choices - including the choice of service inputs - with respect to
productivity Our service FDI linkage measure is defined as service FDI penetration
weighted by firm-level intensity of service usage The rationale underlying this novel
measure is the expectation that firms that use services more intensively benefit more from
any positive effects of service FDI Our measure of service usage intensity is based on
historic values for each firm following Blalock and Gertler (2009) to avoid the potential
endogeneity between firm TFP and service intensity Our choice of instrumental variables
estimation addresses another potential endogeneity concern related to fact that large FDI
inflows into Chilean service sectors may have responded to the strong performance of the
downstream manufacturing users Service FDI penetration weighted by the historic firm-
3
level intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile - Spain and the US - weighted
similarly The identification assumption is that while increases in total FDI outflows into
service sectors by those two countries affect service FDI penetration in Chilean service
sectors they are not correlated with the TFP of manufacturing firms in the country
through any other channel
We find evidence of a positive and significant effect of service FDI on Chilean
manufacturing firmsrsquo TFP This effect is robust to the control for differential productivity
trends across plants with different services intensity and to allowing the effects of service
FDI to operate with lags Alternative measures of service usage weights and service FDI
penetration including the use of industry-level weights from an input-output table
confirm our main findings The positive and significant effect of service FDI is also
obtained when considering a one-stage regression where output depends on inputs as well
as the service FDI linkage measure We also provide suggestive evidence supporting the
hypothesis that service FDI stimulates innovation by Chilean manufacturing firms
Moreover interestingly we find that service FDI offers an opportunity for laggard firms
to catch up in terms of TFP with industry leaders From a policy perspective this finding
is particularly relevant since it suggests that the benefits from service liberalization do not
accrue mostly to leading firms but seem to offer opportunities for firms that are further
behind
Our estimates suggest that a one-standard deviation increase in service FDI would
lead to an increase in TFP of Chilean firms by 3 all else constant The economic
magnitude of this impact is that forward linkages from FDI in services accounted for at
4
least 7 of the observed increase in manufacturing usersrsquo TFP in Chile during the sample
period This economic impact is quite meaningful in light of the finding by Haskel et al
(2007) that horizontal spillovers from manufacturing FDI explain a roughly similar share
of manufacturing TFP growth in the UK during the 1973-1992 period The positive
effects of service FDI on manufacturing firmsrsquo TFP may capture to some extent an
unmeasured decline in quality-adjusted services prices but also the spillover of
managerial and organizational knowledge from service providers to manufacturing users
The microeconomic evidence provided by our study contributes to the emerging
literature on the impact of services liberalization on growth and the performance of
services users At the macro level Mattoo et al (2006) and Eschenbach and Hoekman
(2006) show that countries with liberalized service sectors grow faster once all standard
growth correlates are controlled for Based on computable general equilibrium models
Konan and Maskus (2006) and Jensen et al (2007) argue that business services
liberalization could bring large GDP gains to Tunisia and Russia respectively1 The main
mechanism for these gains is the increase in the number of services available for
manufacturing users as a result of FDI2 At the industry level Francois and Woerz (2008)
show that the increased openness of business services through exports and FDI has strong
positive effects on exports value added and employment of manufacturing industries in
the OECD while Fernandes (2009) estimates positive and significant effects of
liberalization of finance and infrastructure on labor productivity of downstream
manufacturing industries in Eastern European countries At the firm-level Arnold et al
1 Markusen et al (2005) also show important GDP gains from services liberalization based on general equilibrium
simulations for a hypothetical country In their model the presence of foreign-owned service providers allows final
goods producers to rely on more specialized expertise 2 This increase in the number of services increases the TFP of manufacturing firms through a Dixit-Stiglitz-Ethier
framework (Dixit and Stiglitz 1977 Ethier 1982)
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
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31
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57
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World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
3
level intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile - Spain and the US - weighted
similarly The identification assumption is that while increases in total FDI outflows into
service sectors by those two countries affect service FDI penetration in Chilean service
sectors they are not correlated with the TFP of manufacturing firms in the country
through any other channel
We find evidence of a positive and significant effect of service FDI on Chilean
manufacturing firmsrsquo TFP This effect is robust to the control for differential productivity
trends across plants with different services intensity and to allowing the effects of service
FDI to operate with lags Alternative measures of service usage weights and service FDI
penetration including the use of industry-level weights from an input-output table
confirm our main findings The positive and significant effect of service FDI is also
obtained when considering a one-stage regression where output depends on inputs as well
as the service FDI linkage measure We also provide suggestive evidence supporting the
hypothesis that service FDI stimulates innovation by Chilean manufacturing firms
Moreover interestingly we find that service FDI offers an opportunity for laggard firms
to catch up in terms of TFP with industry leaders From a policy perspective this finding
is particularly relevant since it suggests that the benefits from service liberalization do not
accrue mostly to leading firms but seem to offer opportunities for firms that are further
behind
Our estimates suggest that a one-standard deviation increase in service FDI would
lead to an increase in TFP of Chilean firms by 3 all else constant The economic
magnitude of this impact is that forward linkages from FDI in services accounted for at
4
least 7 of the observed increase in manufacturing usersrsquo TFP in Chile during the sample
period This economic impact is quite meaningful in light of the finding by Haskel et al
(2007) that horizontal spillovers from manufacturing FDI explain a roughly similar share
of manufacturing TFP growth in the UK during the 1973-1992 period The positive
effects of service FDI on manufacturing firmsrsquo TFP may capture to some extent an
unmeasured decline in quality-adjusted services prices but also the spillover of
managerial and organizational knowledge from service providers to manufacturing users
The microeconomic evidence provided by our study contributes to the emerging
literature on the impact of services liberalization on growth and the performance of
services users At the macro level Mattoo et al (2006) and Eschenbach and Hoekman
(2006) show that countries with liberalized service sectors grow faster once all standard
growth correlates are controlled for Based on computable general equilibrium models
Konan and Maskus (2006) and Jensen et al (2007) argue that business services
liberalization could bring large GDP gains to Tunisia and Russia respectively1 The main
mechanism for these gains is the increase in the number of services available for
manufacturing users as a result of FDI2 At the industry level Francois and Woerz (2008)
show that the increased openness of business services through exports and FDI has strong
positive effects on exports value added and employment of manufacturing industries in
the OECD while Fernandes (2009) estimates positive and significant effects of
liberalization of finance and infrastructure on labor productivity of downstream
manufacturing industries in Eastern European countries At the firm-level Arnold et al
1 Markusen et al (2005) also show important GDP gains from services liberalization based on general equilibrium
simulations for a hypothetical country In their model the presence of foreign-owned service providers allows final
goods producers to rely on more specialized expertise 2 This increase in the number of services increases the TFP of manufacturing firms through a Dixit-Stiglitz-Ethier
framework (Dixit and Stiglitz 1977 Ethier 1982)
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
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No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
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Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
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Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
4
least 7 of the observed increase in manufacturing usersrsquo TFP in Chile during the sample
period This economic impact is quite meaningful in light of the finding by Haskel et al
(2007) that horizontal spillovers from manufacturing FDI explain a roughly similar share
of manufacturing TFP growth in the UK during the 1973-1992 period The positive
effects of service FDI on manufacturing firmsrsquo TFP may capture to some extent an
unmeasured decline in quality-adjusted services prices but also the spillover of
managerial and organizational knowledge from service providers to manufacturing users
The microeconomic evidence provided by our study contributes to the emerging
literature on the impact of services liberalization on growth and the performance of
services users At the macro level Mattoo et al (2006) and Eschenbach and Hoekman
(2006) show that countries with liberalized service sectors grow faster once all standard
growth correlates are controlled for Based on computable general equilibrium models
Konan and Maskus (2006) and Jensen et al (2007) argue that business services
liberalization could bring large GDP gains to Tunisia and Russia respectively1 The main
mechanism for these gains is the increase in the number of services available for
manufacturing users as a result of FDI2 At the industry level Francois and Woerz (2008)
show that the increased openness of business services through exports and FDI has strong
positive effects on exports value added and employment of manufacturing industries in
the OECD while Fernandes (2009) estimates positive and significant effects of
liberalization of finance and infrastructure on labor productivity of downstream
manufacturing industries in Eastern European countries At the firm-level Arnold et al
1 Markusen et al (2005) also show important GDP gains from services liberalization based on general equilibrium
simulations for a hypothetical country In their model the presence of foreign-owned service providers allows final
goods producers to rely on more specialized expertise 2 This increase in the number of services increases the TFP of manufacturing firms through a Dixit-Stiglitz-Ethier
framework (Dixit and Stiglitz 1977 Ethier 1982)
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
5
(2007) show significant positive effects of services liberalization in the Czech Republic
on manufacturing firmsrsquo TFP while Arnold et al (2010) find significant positive effects
of banking telecommunications and transport reforms on Indian manufacturing firmsrsquo
TFP Finally Javorcik and Li (2008) estimate a positive effect of FDI in Romaniarsquos retail
sector on the TFP of manufacturing suppliers to that sector3
Relative to the existing literature the contribution of our study is three-fold First
we exploit the heterogeneity in service usage by considering firm-level measures of the
intensity of service usage Evidence on the usage of services shows considerable
heterogeneity across Chilean firms which suggests that the practice followed in all the
aforementioned studies of using industry-level usage measures based on input-output
tables may be inappropriate The advantage of our measures is that they enable us to
identify the intensive service users within each industry Second we follow a rigorous
empirical approach by relying on firm fixed effects IV estimation to identify the causal
effects of services FDI on TFP Hence our specifications exploit the within-firm variation
in TFP in response to instrumented changes in the service FDI linkage measure Third
we go beyond previous studies by exploring the nature of the effects of service FDI
allowing for heterogeneity across industries relating to their potential for innovation We
also focus on heterogeneous effects across firms relating to their distance to
technologically advanced firms
The remainder of the paper proceeds as follows Section 2 describes recent trends in
FDI in services in Chile and the effects of FDI in services Section 3 describes the data
3 By considering the potential role of knowledge spillovers from service providers to manufacturing users our study
also relates to the literature on vertical spillovers from manufacturing FDI which are shown to be more important than
horizontal spillovers by Javorcik (2004) Kugler (2006) Blalock and Gertler (2008) Damijan et al (2008) and Marcin
(2008) A rationale provided in this literature for vertical forward linkages is that foreign suppliers provide assistance
and complementary services to local buyers
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
Manufacturing Performance Evidence from India CEPR Discussion Paper 8011
Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
6
Section 4 describes our empirical specification Section 5 discusses our main results and
robustness checks Section 6 discusses extensions to our main results Section 7
concludes
2 FDI in Services Trends and Effects
21 Trends in FDI in Services in Chile
Over the last three decades liberalization privatization and deregulation reforms in
Chile opened its economy to trade and investment more than any other country in Latin
America (Moreira and Blyde 2006)4 In the 1980s most FDI inflows were related to the
extraction and processing of natural resources while in the 1990s inflows into service
sectors took on a leading role with electricity water transport telecommunications and
business services representing about 60 of net FDI inflows during the 1996-2001
period5 Figure 1 shows that these substantial inflows resulted in a growing FDI stock in
the main service sectors in Chile Also the ratio of FDI to output increased substantially
in most Chilean service sectors over the 1990s as shown in Figure 26
The large FDI inflows in Chile during the 1990s reflect first and foremost a
worldwide increase in FDI in services mainly motivated by the interest of multinationals
(MNCs) to become global service providers by gaining access to domestic and regional
markets particularly in the developing world (UNCTAD 2004) In sectors such as
electricity Chilean firms were privatized before 1990 and later acquired by foreign
4 FDI in Chile is governed by Decree Law 600 in place since 1974 which regulates conditions for market entry
capitalization and foreign capital remittances (ECLAC 2000) The decree law grants equal treatment to foreign and
domestic investments in mining manufacturing and most service sectors the exceptions being professional services
such as engineering or legal services (Moreira and Blyde 2006) 5 FDI inflows reached a peak in 1999 in the electricity and water sector due to the purchase of Enersis and Endesa-
Chile by the Spanish electricity firm Endesa-Spain (ECLAC 1999) 6 The computation of the variables shown in Figures 1 and 2 is described in Section 4 and in the Appendix
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
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No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
7
players Global MNCs identified Chilersquos largely privately-owned firms as an attractive
investment opportunity to consolidate their positions in Latin America (ECLAC 2000)
22 Effects of FDI in Services
FDI in the services sector can have four effects within the sector price reductions
quality improvements increased variety and knowledge spillovers7 First FDI is likely
to increase competition in local markets resulting in price reductions as incumbent firms -
eg in electricity and telecommunications sectors - no longer retain the rents they
obtained from being previously monopoly providers The available evidence for banking
electricity and telecommunications confirms price decreases for Chile (Stehmann 1995
Claessens et al 2001 Pollitt 2004) Second FDI may lead to service quality
improvements due to competition and the superior technological organizational and
managerial know-how of foreign-owned service providers FDI can also provide the
necessary finance for major upgrades and the expansion of existing electricity and
telecommunications networks improving the reliability of provision UNCTAD (2004)
and World Bank (2004) report evidence of such developments for Latin America Third
FDI may increase the variety of services provided including new technologically
advanced services or services provided to new regions or new types of clients FDI had
such effects in the Chilean telecommunications sector (ECLAC 2000) Fourth FDI may
result in leaking of managerial marketing and organizational know-how and best
practices (eg linked to the environment or labor codes) from foreign-owned to
domestic-owned services providers (Miroudout 2006)
7 FDI can also entail costs (1) foreign ownership in inherently monopolistic sectors such as electricity or
telecommunications may result in higher prices unless the regulatory system is well defined and managed by the
government and (2) foreign ownership may crowd out domestic firms eg in the banking sector (UNCTAD 2004)
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
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Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
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Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
8
The aforementioned positive effects of service FDI are based on the premise that
foreign-owned firms are better performers offering superior services and being more
productive than their domestic counterparts However due to data limitations evidence of
the superiority of foreign-owned service firms is rare The Fourth Chilean Innovation
Survey is a notable exception covering 612 service firms in the electricity generation real
estate financial intermediation business activities and transport storage and
communication sectors8 The survey collected data for 2003-2004 on firm innovation
outcomes accounting variables and basic characteristics Using this data we examine
whether foreign ownership is associated with better performance for Chilean service
firms If foreign investors acquire the best performing domestic service firms then a
positive effect of foreign ownership on firm performance would simply indicate the
endogeneity of the ownership status rather than the intrinsic advantages - eg better
technology - of the foreign parent company However this problem would not arise for
greenfield FDI Hence for each Chilean service firm we obtained information on
whether foreign ownership is due to greenfield FDI or foreign acquisition Table 1 shows
the results from regressions of three firm performance variables - labor productivity and
indicators for product and for process innovations - on dummy variables for greenfield
FDI and for foreign acquisition along with several controls The results show that
foreign-owned firms particularly those resulting from greenfield FDI exhibit better
productivity and innovation outcomes than their domestic counterparts While the
evidence in Table 1 is not causal due to the cross-sectional nature of the data it is
8 The survey includes a census of firms generating more than 2 megawatts of electricity per hour (category E of the
ISIC Rev 3 classification) and representative samples of the real estate renting and business activities (ISIC Rev 3
category K) financial intermediation services (ISIC Rev 3 category J) and transport storage and communication
services (ISIC Rev 3 category I)
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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31
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World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
9
suggestive of better performance by foreign-owned service firms in Chile and thus
provides support for the potential for FDI spillovers onto the TFP of manufacturing firms
in the country
The crucial hypothesis tested in this paper is whether the aforementioned FDI-
induced improvements in service sectors benefit TFP of downstream manufacturing
users If present these gains could be classified as pecuniary (rent) spillovers a by-
product of market transactions (Griliches 1992) Manufacturing firms benefit from
pecuniary spillovers if increases in the quality or variety of the services they use due to
FDI are not fully appropriated by service providers In imperfectly competitive service
sectors providers may not appropriate the full surplus from better and more diversified
services because of their inability to perfectly price discriminate whereas in sectors where
FDI increases competition competitive pressures may prevent providers from
appropriating the surplus FDI in services can also benefit manufacturing firms through
spillovers of lsquosoft technologyrsquo linked to eg managerial know-how or technical skills
Learning by manufacturing firms could result from demonstration effects personal
contacts manager or worker turnover9 Griliches (1992) distinguishes knowledge
spillovers from pecuniary spillovers since in principle only the former allow
manufacturing firms to improve their innovation capabilities But in practice pecuniary
spillovers may become knowledge spillovers if downstream users of better services apply
the embodied knowledge to improve their TFP (Branstetter 2001) First for knowledge-
intensive business services such as information technology (IT) the actual service
provided is a knowledge-intensive input upon which firms rely to improve their
9 Kugler (2006) notes a larger potential for knowledge spillovers from vertical relative to horizontal FDI as for the
former foreign-owned service suppliers are not concerned about knowledge leakages since they operate in different
sectors than their domestic manufacturing clients
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
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Integration 21 64-98
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Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
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180-203
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Massachusetts Institute of Technology Center for Energy and Environmental Policy
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57
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
10
innovation capabilities and TFP (Kox and Rubalcaba 2007) Second the usage of newer
services (eg internet banking) may embody technological knowledge allowing
manufacturing firms to improve their production and operations (eg by increasing their
IT investmentsrsquo efficacy) Third the FDI-induced increased reliability of service
provision may allow firms to optimize their machinery usage (eg production processes
are less disrupted due to electricity outages) and encourage firms to use technologically
more advanced production processes which depend on telecom or internetdata
connection These possibilities capture multiple dimensions of technological change thus
motivating a positive effect of FDI in services on firm TFP and epitomize the overlap
between pecuniary and knowledge spillovers which will characterize our main results
3 Manufacturing Firm-Level Data
The main dataset used in our analysis is the Encuesta Nacional Industrial Annual
(ENIA) which is the annual manufacturing survey of Chilean firms with more than 10
employees10
The dataset is an unbalanced panel capturing firm entry and exit that
includes an average of 4913 firms per year for the 1992-2004 period classified into 4-
digit ISIC revision 2 industries The Appendix provides details on how the final sample
of 57025 observations is obtained The ENIA survey collects firm-level information on
sales employment raw materials investments (buildings machinery and equipment
transportation and land) which are used to construct output and inputs for the production
function discussed in Section 4 All nominal variables are expressed in real terms using
10 The Chilean Statistical Institute (INE) collects information on which plants
in the ENIA are part of a multi-plant firm ie a firm with at least two plants responding to the survey The information
was kindly provided by INE to the authors for the purposes of a related research project During the 1997-2003 period
on average 917 of plants are single-plant firms Thus plant data corresponds to a large extent to firm data and we
designate the units of observation as firms throughout the paper The composition of our sample across years and
industries as well as summary statistics for the variables used in our econometric analysis are provided in the working
paper Fernandes and Paunov (2008)
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Ethier W 1982 National and International Returns to Scale in the Modern Theory of
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Fernandes A 2009 Structure and Performance of the Service Sector in Transition
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Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
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Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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31
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Chile Telecommunications Policy 19 667-684
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
11
appropriate deflators and capital is constructed applying the perpetual inventory method
as described in the Appendix
A particularly interesting and novel feature of the ENIA survey is that it collects
information on firm-level expenditures on a variety of services advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services electricity and water This information allows us to include a bundle of
services (excluding electricity) appropriately deflated as inputs in the production function
discussed in Section 4 For electricity the quantity consumed is the input included This
information also enables us to construct firm-specific weights representing the intensity
of service usage as detailed in Section 42
4 Empirical Specification
41 Basic Framework
In this section we present the reduced form framework used to estimate the impact
of FDI in services on Chilean manufacturing firmsrsquo TFP We consider a Cobb-Douglas
production function in logarithms for firm i in industry j at time t as in
j
it
j
K
j
it
j
s
j
it
j
E
j
it
j
M
j
it
j
UL
j
it
j
SL
j
it
j
it KSEMULSLAY lnlnlnlnlnlnlnln (1)
where Y is output SL is skilled labor UL is unskilled labor M is materials E is
electricity S is services K is capital and A is a firm-specific index of Hicks-neutral TFP
measuring the firmrsquos efficiency in transforming inputs into output The hypothesis tested
in this paper is that FDI in services affects firm TFP This effect could result from
pecuniary spillovers showing up in measured TFP through unaccounted for increases in
services quality and variety Equally important is the possibility that FDI in services
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
12
generates knowledge spillovers for manufacturing users and pecuniary spillovers can
result in knowledge spillovers Thus we allow firm TFP Aln to depend on the service
FDI linkage measure sFDI _ as in (ignoring the industry subscript j)
ititZitsfdiit ZsFDIA _ln _ (2)
where Z is a vector of control variables discussed in Section 43 and is a stochastic
residual A positive sfdi_ indicates a beneficial impact of FDI in services on firm TFP
Next we present our service FDI linkage measure and discuss the econometric issues
associated with the estimation of equation (2)
42 Service FDI Linkage Measure and Endogeneity Issues
To estimate the effects of service FDI on manufacturing TFP we obtain a
composite measure interacting FDI penetration in services and a firm-level measure of
the intensity of services usage The measure reflects the rationale that Chilean firms that
are relatively heavy users of services should (ceteris paribus) benefit disproportionately
more from increases in service FDI than firms that are less heavy users of services11
To
capture the intensity of service usage by firms we compute the ratio of firm expenditures
on four categories of services (henceforth designated as lsquofour service sectorsrsquo) - (1)
electricity and water (2) transport and communications (3) financial insurance and
business services and (4) real estate - to firm sales12
Our final measures for each firmrsquos
intensity of usage of each of the four services are obtained taking the average of the
corresponding historic service expenditure to sales ratios over the first three years of data
11 This assumption is inspired by that made by Rajan and Zingales (1998) in the estimation of the benefits of access to
external finance for industry growth Our assumption implies that there are no restrictions (other than cost) preventing
access to certain services by certain users 12 Business services encompass advertising legal technical and accounting services licenses and foreign technical
assistance and other services The four categories of services are dictated by the availability of sectoral GDP data from
the Chilean Central Bank used below
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
America and the Caribbean
ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
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31
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Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
13
for each firm13
Our estimating sample for the effect of service FDI on TFP includes the
remaining years of data for each firm covering a total of 33390 observations The
separation of our panel dataset into these two groups addresses the potential endogeneity
of the services intensity measure with respect to firm TFP This approach follows the
study by Blalock and Gertler (2009) on how the effects of horizontal FDI on Indonesian
manufacturing firmsrsquo TFP are mediated by firm capabilities14
To capture the presence of FDI we compute for each service sector net FDI
inflows based on data from the Chilean Foreign Investment Committee by subtracting
from annual FDI inflows the corresponding annual FDI outflows (ie foreign investorsrsquo
repatriation of capital profits and dividends) Net FDI inflows do not adequately capture
the importance of FDI in a sector and year because they neither account for past
investments nor for the sectorrsquos size Thus we cumulate net FDI inflows using the
perpetual inventory method to construct an FDI stock for each sector Our measure of
FDI penetration in a service sector is given by the ratio of the sectorrsquos FDI stock to the
sectorrsquos output (GDP) obtained from the Chilean Central Bank15
Our final firm-level time-varying service FDI linkage measure that captures both
the presence of FDI in services and firm usage of those services is computed as
K
k
kt
k
iit FDIpensFDI1
_ where ktFDIpen is the FDI penetration ratio in service
13
Ideally we would like to measure a plantrsquos usage of foreign-provided services as separate from domestic-provided
services but such information is not available However to the extent that domestic service providers increase their
quality and variety and lower their prices due to the presence of FDI in their sector - through increased competition or
knowledge spillovers - measuring total service usage (whether services are purchased from foreign or domestic
suppliers) is adequate to capture the overall benefits from service FDI 14 The authors use the first three years of each firm in the sample to measure its capabilities (ie its technology gap
relative to the most productive firms) and use the remaining years of each firm for their main regression that includes
the interaction between horizontal FDI and firm capabilities 15 One caveat with this measure of FDI penetration is that it differs from that used in most studies on FDI spillovers in
manufacturing that relies on the ratio of foreign affiliatesrsquo employment relative to the total employment in the sector
Unfortunately we are unable to compute such measure for Chilean services firms since no firm-level census of services
firms is available for our sample period
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
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Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
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Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
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Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Ethier W 1982 National and International Returns to Scale in the Modern Theory of
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Fernandes A 2009 Structure and Performance of the Service Sector in Transition
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Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
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Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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31
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57
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Chile Telecommunications Policy 19 667-684
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
14
sector k in year t which is weighted by k
i the historic intensity of usage of services from
sector k by firm i The sum is computed over the four aforementioned service sectors
More details on the construction of the measure are provided in the Appendix
Our service FDI linkage measure is inspired by the measures used by Javorcik
(2004) Blalock and Gertler (2008) and Arnold et al (2007a) but differs from those by
relying on a firm-level measure of service usage instead of service usage measures based
on input-output coefficients at the 4-digit industry level The latter measures provide
information on average industry usage which does not identify the heavy users of
services within an industry Figure 3 shows a significant degree of heterogeneity in the
average intensity of service usage across 2-digit industries in Chile However unreported
variance decompositions suggest that almost all the variation in the average intensity of
service usage is due to variation across firms within industries rather than across
industries This suggests that industry-level measures may be strongly misleading about
the service usage of firms Hence we choose to measure the intensity of service usage by
our average service expenditures to sales ratios based on historic values for each firm
Two issues could raise the possibility of endogeneity in the FDI penetration
component of the service FDI linkage measure with respect to TFP A first issue is that
manufacturing industries with higher TFP may lobby the government for services
liberalization However since Chilersquos FDI regime was liberal since the 1980s lobbying
by manufacturing industries for services liberalization would have occurred well before
our sample period and is thus unlikely to bias our estimates16
A second issue is that
manufacturing TFP in Chile in the 1990s may have been a driving force for service
16 In fact one may even question whether such lobbying played any role given that the privatization of service firms
starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez 1994)
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
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Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
15
MNCs to invest in the country in expectation of strong demand for their services17
ECLAC (2004) argues that foreign investors were attracted by the sound performance of
recently privatized service firms in Chile and the strategic global positioning of MNCs
played a crucial role in their FDI decisions However it may also be the case that the
intensity of FDI inflows into Chilean service sectors was related to manufacturing thus a
positive coefficient on the service FDI linkage measure in firm TFP regressions could
reflect the choice of foreign investors rather than positive effects of service FDI on TFP
As a first approach to this endogeneity problem we will experiment with using one- and
two-period lags of the service FDI linkage variable but we are fully aware that this only
attenuates the problem Thus our preferred approach is to use IV estimation We select
two related instruments for the intensity of FDI in service sectors in Chile the stocks of
FDI outflows of Spain and of the US in service sectors18
The instrumental service FDI
linkage measures used are the sum across the four service sectors of Spanish or US
stocks of FDI outflows in a service sector weighted by the historic firm intensity of usage
of services from that sector Spain and the US are the main sources of FDI inflows into
Chile Hence it is natural to expect their overall FDI outflows to have an impact on
services FDI penetration in Chile However it is highly unlikely that Spanish and US
overall FDI stocks abroad are correlated with Chilean manufacturing firmsrsquo TFP through
any other channel other than the Chilean service FDI penetration
43 Other Econometric Issues and Final Specification
17 Evidence of such mechanism is provided by Nefussi and Schwellnus (2010) who estimate a positive effect of
downstream demand for services by French manufacturing firms on the location choice business services firms 18 Details on the construction of the outward FDI stocks for these countries are provided in the Appendix
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
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Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
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Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
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Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Ethier W 1982 National and International Returns to Scale in the Modern Theory of
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Fernandes A 2009 Structure and Performance of the Service Sector in Transition
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Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
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Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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31
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
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758-777
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
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Rauch J 1999 Networks versus Markets in International Trade Journal of International
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Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
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57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
16
In order to identify the effect of service FDI on firm TFP we use unbiased TFP
estimates that correct for the potential endogeneity between input choices - particularly
the choice of service inputs - and firm unobserved productivity We estimate our
production function (equation (1)) following the methodologies of Levinsohn and Petrin
(2003) [below LP] Olley and Pakes (1996) and Ackerberg et al (2006)19
The main idea
behind these methodologies is that unobserved firm productivity shocks can be
approximated by a non-parametric function of observable firm characteristics ndash eg in
the LP case electricity and capital - and as a result unbiased estimates of the production
function coefficients are obtained To allow for differences in production technology
across industries a separate production function for each 2-digit industry is estimated by
each of the three methodologies The production function coefficient estimates are shown
in Appendix Table 1 The coefficients are generally significant and have magnitudes in
line with those in previous studies Based on the three unbiased sets of estimates we
obtain three firm-level time-varying logarithmic TFP measures as residuals from equation
(1)20
Simple summary statistics on our TFP estimates suggest that they are quite
sensible For example several patterns are consistent with the predictions from models of
industry dynamics where competitive pressures lead to market selection based on
productivity differences within-industry TFP dispersion is larger for more concentrated
industries and within industries firm TFP is negatively associated with firm exit
19 The latter addresses collinearity problems in the Olley and Pakes (1996) and Levinsohn and Petrin (2003)
methodologies that prevent the identification of the variable input coefficients The Appendix provides further details
on the production function estimation 20 Note that our TFP estimates are obtained from production functions where nominal sales revenues (input
expenditures) deflated with industry price indexes are used to measure output (inputs) and are therefore subject to the
criticism by Katayama et al (2009) that they are revenue-per-unit-input-bundle measures that combine true efficiency
and price-cost markups Eslava et al (2004) address this problem empirically using information on firm prices
Theoretically as long as mark-ups increase with efficiency revenue-based TFP estimates will be positively correlated
with firm efficiency The differentiated products model developed by Bernard et al (2003) predicts in equilibrium a
positive relationship between firm size (market shares) price-cost markups and efficiency Hence this model provides
a rationale to expect our revenue-based TFP estimates to be positively correlated with unobserved true firm efficiency
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
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Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
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180-203
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57
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
17
Moreover firm TFP is found to be positively correlated with firm export status and firm
size within industries21
It is important to note that our TFP estimates are purged from the
effects of services since service use is explicitly accounted for as an input in the
production function Correlations between firm TFP and service intensity suggest in fact
that firms that use services more intensively are allocated a lower value of TFP
Second we consider as controls some time-varying industry- or firm-level
observable factors that could be correlated with the service FDI linkage and firm TFP and
whose omission could bias our coefficient of interest We account for the potential
spillovers from FDI in manufacturing or mining by FDI linkage measures for
manufacturing and mining22
These variables are allowed to be endogenous in our IV
specifications Thus we add to the aforementioned service FDI linkage measure
constructed based on Spanish or US stocks of service FDI outflows manufacturing and
mining FDI linkage measures constructed based on Spanish or US stocks of
manufacturing and mining FDI outflows as instruments23
To allow for differences in
service usage and in TFP for entrants into the export market we include a control for
firm exporter status in our specification
Third to control for static firm unobservables such as managerial ability we allow
the residual in equation (2) to include a firm-specific component f such that
21 See Bartelsman and Doms (2000) for a discussion of empirical evidence related to models of industry dynamics The
finding on dispersion is obtained measuring for each 2-digit industry concentration by its normalized Herfindahl index
of firm market shares and TFP dispersion by the difference between the 10th and the 90th percentile of the TFP
distribution The evidence for firm exit firm export status and firm size is obtained in regressions that include either 2-
digit industry or 2-digit industry-year fixed effects that are necessary to account for the differences in technology across
industries (ie for the fact that TFP levels are not comparable across industries) 22
The mining and manufacturing FDI linkage variables are constructed using coefficients from the 1996 Chilean input-
output table as described in the Appendix Hence in equation (3) below they are indexed by the industry subscript j
instead of the firm subscript i 23 Specifically the stocks of Spanish or US outward FDI in manufacturing and mining are interacted with the 1996
Chilean input-output table weights
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
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Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
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Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
18
itiit uf where u is an independent and identically distributed (iid) disturbance
Hence our final specifications are estimated by firm fixed effects IV
Fourth industries differ in productivity levels technological progress experienced
and market structure dynamics Chilean regions may also exhibit differential performance
over time due to the evolving nature and importance of agglomeration economies To
account for these possibilities we add 2-digit industry-year interaction fixed effects and
region-year interaction fixed effects to equation (2)
The considerations above lead to our final empirical specification
itireg
inditzjtmnfdijtmfdiitsfdiit
ufyearreg
yearinddxmnFDImfFDIsFDIA
___ln ___
(3)
where mfFDI _ and mnFDI _ are manufacturing and mining FDI linkages dx is a
dummy for firm export status indyear and reg year are 2-digit industry-year and
region-year interaction fixed effects and u is iid
5 Results
51 Main Results
Our main results are shown in Tables 2 and 4 for TFP estimates obtained from
Levinsohn and Petrin (2003) (Panel A) Olley and Pakes (1996) (Panel B) and Ackerberg
et al (2006) (Panel C) In Table 2 column 1 presents the results from estimating by firm
fixed effects a variant of equation (3) with no controls while column 2 presents the results
from the exact specification in equation (3) The estimates show a positive and significant
effect of service FDI penetration on firm TFP In columns 3 and 4 of Table 2 a variant of
equation (3) is estimated by firm fixed effects including respectively the one- and two-
period lags of the service FDI linkage measure The significant positive effect of FDI in
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
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Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
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Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
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Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
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Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
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Ethier W 1982 National and International Returns to Scale in the Modern Theory of
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Fernandes A 2009 Structure and Performance of the Service Sector in Transition
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Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
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Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
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Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
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31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
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758-777
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
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Rauch J 1999 Networks versus Markets in International Trade Journal of International
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Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
19
services on firm TFP is maintained Recognizing that the use of lagged values only
attenuates any potential endogeneity concerns we proceed to our preferred method IV
estimation with firm fixed effects whose results are shown in Table 4 while the
corresponding first-stage results are shown in Table 3 The first-stage regression results
for the Chilean service FDI linkage measure show a strong correlation between that
endogenous variable and the instrument which is a service FDI linkage measure
constructed based on current stocks of Spanish outward FDI in services24
Note that to
ensure that the controls are not driving our IV results we show in columns 1-4 of Table 4
the estimates from intermediate specifications before showing the estimates from the full
specification in column 5 the specification in column 1 includes only the service linkage
those in columns 2 and 3 add only the manufacturing linkage and the manufacturing and
mining linkages and that in column 4 adds only the export dummy We find that the
service FDI linkage variable affects Chilean manufacturing firmsrsquo TFP positively The R-
squared from those regressions suggests that our instruments explain a substantial
fraction (38) of the variation in the service FDI linkage measure The standard errors in
Table 2 and all subsequent tables are robust and clustered at the firm-level to allow for
possible serial correlation across observations belonging to the same firm25
52 Magnitudes and Robustness Checks
Following standard practice in instrumental variables estimation we consider in
Table 5 specifications that use alternative instruments for the Chilean service FDI linkage
24 Note that in columns 2 3 and 5 of Table 4 FDI linkage measures based on the current stocks of Spanish outward
FDI in manufacturing and mining are also used as instruments since Chilean manufacturing and mining FDI linkages
are included in the second-stage regressions and also allowed to be endogenous Table 3 presents only one set of first-
stage regressions for the service FDI linkage given that the specifications and the sample used for IV estimation are the
same for the second-stage IV regressions shown in all panels of Table 4 (only the dependent variable TFP changes
across panels) 25 The use of non-clustered robust standard errors does not affect the significance of our results
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
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Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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30
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31
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Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
20
measure and re-estimate its effects on our three TFP measures Column 1 presents the
results when the service FDI linkage instrument is constructed based on the current US
stocks of outward FDI in services column 2 combines that measure with the measure
used in Table 4 based on current Spanish FDI in services as instruments and column 3
shows the results from using both current and one-period lagged services FDI linkage
measures constructed based on both Spanish and US stocks of outward FDI in services
as instruments26
In all cases the estimated effects of the service FDI linkage measure on
TFP are positive and significant It is also important to point out that our specifications do
not suffer from weak instrument problems as reflected by the p-values for the
Kleibergen-Paap under-identification test Also our instruments appear to be adequate as
indicated by the p-values from the Hansen over-identification tests
Thus our findings indicate that increased FDI in services in Chile during the
sample period led to a significant increase in TFP for firms using the services more
intensively The coefficients in column 5 across all panels of Table 4 suggest that a one-
standard deviation increase in service FDI (ie a 0085 increase in the FDI linkage
measure) would bring a 3 increase in TFP of Chilean firms all else constant To
quantify further the economic magnitude of the impact of FDI in services we use the
lowest point estimate in columns 1-3 of Table 5 (ie 0224 in column 3 of Panel C) as a
lower bound on that impact According to our estimates TFP in the Chilean
manufacturing sector increased by about 12 over the sample period and the service FDI
linkage variable increase over the sample period was 003827
Thus the forward linkages
26
Manufacturing and mining FDI linkage measures constructed analogously are also used as instruments 27
In order to obtain TFP growth for the manufacturing sector over the sample period for each of our TFP estimates we
proceed in four steps First we compute for each 2-digit industry and year a weighted average TFP level which weighs
each firmrsquos TFP by the firmrsquos share in total sales of the 2-digit industry in that year Second across every two
consecutive years we calculate the growth rates in this 2-digit industry TFP measure Third for each year we compute
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
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Economics 48 7-35
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57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
21
from FDI in services explain about 7 of the observed increase in Chilersquos manufacturing
usersrsquo TFP28
This economic impact is quite meaningful in light of the finding by Haskel
et al (2007) that horizontal spillovers from manufacturing FDI explain 5 of
manufacturing TFP growth in the UK between 1973 and 1992
To verify the robustness of our main results in Table 4 we conduct an extensive set
of tests including the control for differential TFP trends across plants with different
services usage changes in the dynamic pattern of the effects outlier exclusion variations
in the definition of the service FDI linkage measure and the consideration of a one-stage
regression IV estimation is used for all these robustness tests and the results are shown in
columns 4-11 of Table 5 in Panels A-C for each of the TFP measures and in column 12 of
Table 5 in Panel D for the one-stage regression
First column 4 considers the possibility that the service FDI linkage variable could
be picking up differential TFP trends across firms with different services intensity rather
than an effect of services FDI on firm TFP Within each industry we divide firms into
quartiles according to the distribution of firm initial services intensity Equation (3) is re-
estimated including four interaction terms corresponding to the interaction of a dummy
identifying each of those quartiles and a time trend29
The results show that the effect of
services FDI is maintained
TFP growth of the manufacturing sector as the weighted average of the 2-digit industry TFP growth rates where the
weights are given by the share of the 2-digit industry in manufacturing sector sales Fourth we sum this weighted
average TFP growth over all sample years to obtain a measure of the total TFP growth of the manufacturing sector over
the sample period 28 The 7 figure is obtained as the ratio of (i) the product of the regression coefficient (0224) and the increase in the
service FDI linkage variable over the sample period (0038) to (ii) the total TFP growth in the manufacturing sector
over the sample period (12) 29 Firm initial services intensity is computed as the ratio of the sum of expenditures on all services to sales averaged
across the first three years for each firm and a firm-specific time trend is used We thank Eric Verhoogen (the co-
editor) for suggesting this specification
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
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Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
America and the Caribbean
ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
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31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
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Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
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57
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World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
22
Second while some effects of service FDI are likely to be immediate such as
pecuniary price spillovers knowledge spillovers may take time to materialize Thus
columns 5 and 6 show the results from a variant of equation (3) including our services
FDI linkage measure in period t-1 or t-2 and adjusting the instruments accordingly to be
also lagged The results are qualitatively maintained
Third columns 7 and 8 investigate whether our results are driven by outliers in our
TFP estimates We re-estimate equation (3) for a sample where the observations whose
TFP values are above or below 15 times the inter-quartile range (the difference between
the 75th
and the 25th
percentile) of the TFP distribution in each 2-digit industry and year
are removed and for a sample where the top and bottom 1 of the TFP distribution in
each 2-digit industry and year are eliminated In both cases our results are maintained
Fourth while we believe that our service FDI linkage measure captures the
importance of FDI in services in Chile we verify in columns 9-10 whether our results are
robust to modifications in the two components of that measure the weights and the
service FDI penetration Column 9 reports the results from using 4-digit industry service
usage coefficients from the 1996 Chilean Input-Output table as weights for the service
FDI penetration The effect of service FDI on firm TFP is positive and significant at the
5 confidence level As noted in Section 1 relying on 4-digit industry service usage
weights has the caveat of assuming that all firms within an industry use services in the
same proportion even though that is highly disproved by the Chilean data However that
very assumption can be used as a check to our main results in the following sense If
there was a systematic relationship between a firm characteristic (say firm size) and TFP
and between that characteristic and the intensity of usage of services then our findings in
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
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Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
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Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
America and the Caribbean
ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
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Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
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31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
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Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
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Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
23
Table 4 could be conflating the firm size effect with the effect of service FDI However
that does not seem to be the case given our finding of a positive effect of service FDI on
firm TFP relying on the FDI linkage measure based on 4-digit industry service usage
weights that are uncorrelated with any firm characteristics In column 10 we use dummy
variables that identify firms whose service usage intensity (in the first 3 sample years) is
above the sample median to multiply service FDI penetration in the four service sectors
The magnitude of the IV estimate is naturally very different from those in Table 4 but
still shows a positive and significant effect of service FDI on TFP of higher-than-median
service users Column 11 reports results using the logarithm of service FDI penetration
stocks without output denominator with the same firm-level weights as in Table 4 The
positive effect of the service FDI linkage measure on firm TFP is maintained 30
Fifth equations (1) and (3) can be viewed as two steps in a two-step estimation
procedure for the effect of services FDI on TFP which assumes that the covariates from
the first step are not correlated with the covariates in the second step To address the
possibility of correlation we show in column 12 of Panel D of Table 5 the results from a
one-stage regression which is an augmented version of equation (1) that includes all the
regressors in equation (3) in addition to the production inputs The IV fixed effects
estimates show that the positive and significant effect of services FDI is maintained31
30 While the industry-year interaction effects included in our specifications account in general terms for industry-
specific technological progress and competition we also experimented with replacing those by observable measures of
the degree of competition the normalized Herfindahl index of firm market shares and the market share of the top 4
firms both at the 3-digit industry level The unreported effects of service FDI on firm TFP are maintained 31 We conducted another unreported robustness check changing the dependent variable in equation (3) to be a firm TFP
index computed following Aw et al (2001) The estimates show a positive effect of service FDI on the firm TFP index
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
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Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
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Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
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Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
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Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
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Coe D Helpman E 1995 International RampD Spillovers European Economic Review
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Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
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Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
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Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
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758-777
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180-203
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57
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World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
24
Sixth the coefficients on services shown in Appendix Table 1 are generally lower
that the observed input shares shown in Figure 332
Thus firms with higher service input
usage may have been assigned a higher TFP level than they would if the estimated
coefficients were more in line with the observed input shares If service input usage was
correlated with service sector FDI then this would raise the concern that the effect of FDI
in services was an artifact of estimation rather than a true productivity effect To address
this concern we re-estimated our IV regressions using TFP index measures computed
following Aw et al (2001) using the average service input shares in Figure 3 The results
which are available from the authors upon request are qualitatively maintained 33
6 Characterizing the Effects of FDI in Services
Our findings so far concern the average impact of FDI in services on firm TFP
across the Chilean manufacturing sector However the importance of service FDI for
firm TFP may differ across industries One reason for potential differences is that
services namely knowledge-based services - may play a particularly important role for
innovation Indeed for OECD countries Francois and Woerz (2008) show that the
openness of service sectors (in particular to FDI) enhances the performance of skill-
intensive and technology-intensive industries To address the possibility that FDI in
services is a contributor to innovation we follow two approaches The first approach is to
32 This statement refers to the bundle of business real estate and transport and communications services only The
coefficient on electricity shown in Appendix Table 1 is not comparable to the electricity input share shown in Figure 3
given that the electricity quantity rather than deflated expenditure (as is the case for other services) is the input entering
the production function 33 We thank a referee for pointing to this issue The bundle of electricity and services is obtained as the sum of
electricity expenditures and services expenditures deflated by a weighted average of the price deflators for these two
categories of services (using 3-digit industry input-output weights) Since TFP index measures impose the restrictive
assumption of constant returns to scale and perfect competition we examine the robustness of those results by
obtaining TFP measures as residuals from production functions that include this bundle of electricity and services by
OLS and by Levinsohn and Petrin (2003) and using them to re-estimate Equation (3) The results are again qualitatively
maintained
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
25
estimate a variant of equation (3) where the effect of service FDI on firm TFP is allowed
to vary with an industry variable related to its potential for innovation We consider the
definition of differentiated product industries proposed by Rauch (1999) and the RampD
intensity of industries used by Kugler and Verhoogen (2008)34
Columns 1 and 2 of
Panels A-C of Table 6 show the corresponding IV estimates The effects of service FDI
on firm TFP are stronger in differentiated product industries and in RampD intensive
industries The F-tests shows that the difference in the effects of FDI in services across
the different types of industries is statistically significant The coefficients in column 1 of
Panel B imply that a one-standard deviation increase in the service FDI linkage would
lead to a TFP increase of 3 for firms in differentiated product industries and of 2 for
firms in other industries all else constant The economic magnitude of these effects is
that the forward linkages from FDI in services explain about 16 of the increase in
manufacturing usersrsquo TFP in differentiated product industries but only 3 of the increase
in TFP in other industries
The second approach is to consider an indirect measure of firm innovation its
investment-capital ratio Huggett and Ospina (2001) argue that substantial increases in
investment-capital ratios represent the adoption of new technology thus process
innovation by a firm Column 3 of Panel D shows the results from estimating equation
(3) using firm-level machinery and vehicle investment-capital ratios as the dependent
variable The estimated effect of service FDI on investment-output ratios is positive and
34 Differentiated products are defined to be those that are neither (i) homogenous products traded in organized
exchanges (eg steel) nor (ii) reference-priced products which have listed prices in trade publications (eg some
chemical products) and require a more important degree of buyer-seller interaction To apply Rauchrsquos definition we
establish a correspondence between his classification of products based on 4-digit SITC rev 2 codes and our 4-digit
ISIC rev 2 codes For the printing industry (ISIC 342) we are unable to establish an unambiguous correspondence and
thus drop it from the regressions in Table 5 Kugler and Verhoogen (2008) use data from the US 1976 Federal Trade
Commission Line of Business Survey to compute the ratio of RampD expenditures to total sales for large firms in US
industries and we use those ratios as a benchmark for our Chilean 4-digit industries RampD-intensive industries are
defined as those with higher than median RampD intensity in the sample
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Functions UCLA mimeo
Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
Manufacturing Performance Evidence from India CEPR Discussion Paper 8011
Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
Data Journal of Economic Literature 38 569-594
Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
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Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
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Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
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Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
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Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
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Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
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Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
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Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
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31
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Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
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180-203
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Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
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Chile Telecommunications Policy 19 667-684
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
26
significant The findings in Table 6 are suggestive of a role of services FDI for innovation
outcomes in manufacturing This reinforces the importance of services liberalization in
view of their providing essential knowledge services required for firms to engage in
innovative activities
The differences across industries just described are stark but another important
heterogeneity dimension concerns the variability in effects across firms within a given
industry It is important to identify which manufacturing firms benefit more from FDI in
services in Chile In Table 7 we allow the effect of the service FDI linkage measure on
firm TFP to vary with the firmrsquos technology gap relative to the lsquotechnological leadersrsquo in
its industry In column 1 the closeness to lsquotechnological leadersrsquo is defined as the average
distance between a firmrsquos TFP and the average TFP of firms in the top 10th
percentile of
the TFP distribution in the first three years of the firm whereas in column 2 it is defined
as the average distance between a firmrsquos TFP and the average TFP of the top 10th
percentile of the TFP distribution of foreign-owned firms in the first three years of the
firm Both results suggest that firms that are more distant from the technological frontier
experience stronger TFP benefits from service FDI The economic magnitude of the
effects in column 1 of Panel A of Table 7 is that an increase in the service FDI linkage by
one standard deviation would lead to an increase of 7 in TFP for firms in the 10th
percentile of the closeness to frontier distribution to no change in TFP for firms in the
50th
percentile of the closeness to frontier distribution and to a decline of 3 in TFP for
firms in the 90th
percentile of the closeness to frontier distribution35
The interpretation
35 The 7 figure is obtained by multiplying one standard deviation in the service FDI linkage (0066) by the total effect
of the linkage for the 10th percentile of the closeness to frontier distribution (10741) the latter being equal to the sum
of the coefficient on the linkage (1176) and the product of the coefficient on the interaction (-1657) and the 10th
percentile of the closeness to frontier distribution (0059)
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Arnold J Javorcik B Mattoo A 2007 Does Services Liberalization Benefit
Manufacturing Firms Evidence from the Czech Republic World Bank Policy Research
Working Paper No 4109
Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
Manufacturing Performance Evidence from India CEPR Discussion Paper 8011
Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
Differentials and Turnover in Taiwanese Manufacturing Journal of Development
Economics 66 51-86
Bartelsman E Doms M 2000 Understanding Productivity Lessons from Longitudinal
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Bernard A Eaton J Jensen J Kortum S 2003 Plants and Productivity in
International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
Katholieke Universiteit Leuven LICOS Discussion Paper No 2182008
Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
American Economic Review 67 297-308
ECLAC 1999 Summary and Conclusions in ECLAC Foreign Investment in Latin
America and the Caribbean
ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
ECLAC Foreign Investment in Latin America and the Caribbean
ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
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Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
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Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
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180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
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Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
27
for these findings is that the technologically less advanced Chilean firms have an
opportunity to catch-up by learning about advanced managerial and organizational
techniques optimizing their machinery usage and improving their production as a result
of the increased reliability of service provision and the knowledge embodied in new
services brought by FDI Technologically more advanced firms by contrast have less to
gain since they already use better lsquosoftrsquo as well as lsquohardrsquo technologies Our findings echo
those by Blalock and Simon (2010) who show that Indonesian firms with better
capabilities gain less from supplying foreign multinationals and that the least productive
firms have most room for improvement and thus to gain from new technology adoption
In face of the large degree of heterogeneity across firms it is interesting to see that one of
the contributions of services FDI in Chile was to provide opportunities for lsquolaggardrsquo firms
to catch up
7 Conclusion
This paper finds that FDI in services had a positive effect on Chilean manufacturing
firms TFP In the estimation particular care was taken to measure the intensity of service
usage by firms given the large degree of heterogeneity of usage found within industries
Also we employ three different methodologies to obtain unbiased TFP estimates
Moreover endogeneity concerns that inevitably arise when attempting to estimate causal
effects of service FDI on manufacturing firmsrsquo TFP are addressed using firm fixed effects
instrumental variable estimation whereby service FDI penetration weighted by historic
firm intensity of service usage is instrumented using values of the outward FDI stocks in
service sectors of the two major foreign investors in Chile Spain and the US weighted
by historic firm intensity of service usage
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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30
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Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
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31
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Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
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57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
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World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
28
While governments spend large sums to attract FDI inflows in expectation of
spillovers the literature focusing the manufacturing sector has provided mixed evidence
relatively weak for horizontal spillovers and strong for vertical spillovers This is not
altogether surprising as foreign-owned firms have strong incentives to avoid technology
spillovers to local competitors in their industry whereas the same does not apply for
downstream providers and upstream clients of their products and services Our study
suggests that the service sector might turn out to be a particularly valuable source of
positive spillover effects from FDI Reducing the barriers that still protect FDI in services
in many emerging and developing economies may help improve TFP in their
manufacturing sectors Our evidence also hints at a role of services FDI in stimulating
innovation by manufacturing firms It is not surprising that we find evidence of such
effects as innovative activities require access to leading knowledge services that foreign
affiliates are particularly specialized at providing Services FDI can thus be seen as a
vehicle to stimulate innovative activities of manufacturing firms in countries behind the
technology frontier complementing catch-up opportunities provided by trade relations
More direct evidence on the impacts of service FDI on innovation would be valuable
Interestingly we also find that services FDI offers opportunities for lagging firms to
catch up with industry leaders This contradicts the frequently held idea that only the
most advanced firms in emerging economies can benefit from increased relations with
foreign-owned firms via FDI or trade Our results suggest that in some cases learning
opportunities can benefit more those further behind Concerns about opening emerging
economies further to FDI and trade based on alleged negative effects on less advanced
firms do not appear to be therefore a valid general objection
29
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Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
29
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Arnold J Javorcik B Lipscomb M Mattoo A 2010 Services Reform and
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Aw Y-B Chen X Roberts M 2001 Firm-Level Evidence on Productivity
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International Trade America Economic Review 93 1268-1290
Bitran E Saez R 1994 Privatization and Regulation in Chile in Bosworth B
Dornbush R Laban R (Eds) The Chilean Economy Policy Lessons and Challenges
The Brookings Institution Washington DC pp 329-367
Blalock G Gertler P 2008 Welfare Gains from Foreign Direct Investment through
Technology Transfer to Local Suppliers Journal of International Economics 38 402-421
Blalock G Gertler P 2009 How Firm Capabilities Affect Who Benefits from Foreign
Technology Journal of Development Economics 90 192-199
Blalock G Simon D 2009 Do All Firms Benefit Equally from Downstream FDI The
Moderating Effect of Local Suppliers Capabilities on Productivity Gains Journal of
International Business Studies 40 1095-1112
Branstetter L 2001 Are Knowledge Spillovers International or Intranational in Scope
Microeconometric Evidence from the US and Japan Journal of International
Economics 53 53-79
Claessens S Demirguc-Kunt A Huizinga H 2001 How Does Foreign Entry Affect
Domestic Banking Markets Journal of Banking and Finance 25 891-911
Coe D Helpman E 1995 International RampD Spillovers European Economic Review
39 859-887
Damijan J Rojec M Majcen B Knell M 2008 Impact of Firm Heterogeneity on
Direct and Spillover Effects of FDI Micro Evidence from Ten Transition Countries
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Dixit A Stiglitz J 1977 Monopolistic Competition and Optimum Product Diversity
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ECLAC 2000 Chile Foreign Direct Investment and Corporate Strategies Chapter 2 in
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ECLAC 2004 Energiacutea eleacutectrica Inversiones y estrategias empresariales en los paiacuteses del
Cono Sur in ECLAC La Inversioacuten Extranjera en Ameacuterica Latina y el Caribe
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
30
Eschenbach F Hoekman B 2006 Services Policy Reform and Economic Growth in
Transition Economies 1990-2004 Review of World Economics 142 746-764
Eslava M Haltiwanger J Kugler A Kugler M 2004 The Effects of Structural
Reforms on Productivity and Profitability Enhancing Reallocation Evidence from
Colombia Journal of Development Economics 75 333-371
Ethier W 1982 National and International Returns to Scale in the Modern Theory of
International Trade American Economic Review 72 389-405
Fernandes A 2009 Structure and Performance of the Service Sector in Transition
Economies Economics of Transition 17 467-501
Fernandes A Paunov C 2008 Foreign Direct Investment in Services and
Manufacturing Productivity Growth Evidence for Chile World Bank Policy Research
Working Paper No 4730
Francois J 1990 Trade in Producer Services and Returns due to Specialization under
Monopolistic Competition Canadian Journal of Economics 23 109-124
Francois J Woerz J 2008 Producer Services Manufacturing Linkages and Trade
Journal of Industry Competition and Trade 8 199-229
Griliches Z 1992 The Search for RampD Spillovers Scandinavian Journal of Economics
94 29-47
Haskel J Pereira S Slaughter M 2007 Does Inward Foreign Direct Investment
Boost the Productivity of Domestic Firms Review of Economics and Statistics 89 482-
496
Hoekman B 2006 Liberalizing Trade in Services A Survey World Bank Policy
Research Working Paper No 4030
Hugget M Ospina S 2001 Does Productivity Growth Fall After the Adoption of New
Technology Journal of Monetary Economics 48 173-95
Javorcik B 2004 Does Foreign Direct Investment Increase the Productivity of
Domestic Firms In Search of Spillovers through Backward Linkages American
Economic Review 94 605-627
Javorcik B Li Y 2008 Do the Biggest Aisles Serve a Brighter Future Global Retail
Chains and Their Implications for Romania World Bank Policy Research Working Paper
No 4650
Jensen J Rutherford T Tarr D 2007 The Impact of Liberalizing Barriers to Foreign
Direct Investment in Services The Case of Russian Accession to the World Trade
Organization Review of Development Economics 11 482-506
Katayama H Lu S Tybout J 2009 Firm-level productivity studies Illusions and a
solution International Journal of Industrial Organization 27 403-413
Konan D Maskus K 2006 Quantifying the Impact of Services Liberalization in a
Developing Country Journal of Development Economics 81 142-162
Kox H Rubalcaba L 2007 Business Services and the Changing Structure of European
Economic Growth Munich Personal RePEc Archive Paper No 3750
Kugler M 2006 Spillovers from Foreign Direct Investment Within or Between
Industries Journal of Development Economics 80 444-477
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
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Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
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758-777
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Integration 21 64-98
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Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
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180-203
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57
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Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
31
Kugler M Verhoogen E 2008 The Quality-Complementarity Hypothesis Theory and
Evidence from Colombia NBER Working Paper 14418
Levinsohn J Petrin A 2003 Estimating Production Functions Using Inputs to Control
for Unobservables Review of Economic Studies 70 317-341
Marcin K 2008 How Does FDI Inflow Affect Productivity of Domestic Firms The
Role of Horizontal and Vertical Spillovers Absorptive Capacity and Competition
Journal of International Trade and Economic Development 17 155-173
Markusen J 1989 Trade in Producer Services and in Other Specialized Intermediate
Inputs American Economic Review 79 85-95
Markusen J Rutherford T Tarr D 2005 Trade and Direct Investment in Producer
Services and the Domestic Market for Expertise Canadian Journal of Economics 38
758-777
Mattoo A Rathindran R Subramanian A 2006 Measuring Services Trade
Liberalization and Its Impact on Economic Growth An Illustration Journal of Economic
Integration 21 64-98
Mirodout S 2006 The Linkages between Open Services Markets and Technology
Transfer OECD Trade Policy Working Paper No 29
Moreira M Blyde J 2006 Chilersquos Integration Strategy Is There Room for
Improvement INTAL-ITD Working Paper 21
Nefussi B and C Schwellnus 2010 Does FDI in Manufacturing Cause FDI in Business
Services Evidence from French Firm-Level Data Canadian Journal of Economics 43
180-203
Olley S Pakes A 1996 The Dynamics of Productivity in the Telecommunications
Equipment Industry Econometrica 64 1263-1297
Pollitt M 2004 Electricity Reform in Chile Lessons for Developing Countries
Massachusetts Institute of Technology Center for Energy and Environmental Policy
Research Working Paper 0416
Pombo C 1999 Productividad Industrial en Colombia Una Aplicacioacuten de Nuacutemeros
Indice Revista de Economiacutea del Rosario 2 107-139
Rajan R Zingales L 1998 Financial Dependence and Growth American Economic
Review 88 559-586
Rauch J 1999 Networks versus Markets in International Trade Journal of International
Economics 48 7-35
Rivera-Batiz F Rivera-Batiz L 1992 Europe 1992 and the Liberalization of Direct
Investment Flows Services Versus Manufacturing International Economic Journal 6 45-
57
Stehmann O 1995 Network Liberalization and Developing Countries The case of
Chile Telecommunications Policy 19 667-684
UNCTAD 2004 World Investment Report The Shift Towards Services New York and
Geneva
World Bank 2004 Reforming Infrastructure Privatization Regulation and Competition
Washington DC
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
32
Figure 1 Stocks of FDI in Chilean Service Sectors
Source Authorrsquos calculations based on data from the Chile Foreign Investment Committee
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Stocks of FDI in services in million 2000 US$
Electricity and Water Transport and Communications
Business and Real Estate Services
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
33
Figure 2 Average Ratio of Sectoral FDI Stocks to Sectoral GDP
Source Authorrsquos calculations based on data from the Chilersquos Foreign Investment Committee and the Central Bank
Note The figures show the average ratio of sectoral FDI stocks to sectoral GDP in each of the two time periods
003
221
000
050
100
150
200
250
1989-1996 1997-2004
Electricity and Water
015
044
000
010
020
030
040
050
1989-1996 1997-2004
Transport and Communication
016
027
000
010
020
030
1989-1996 1997-2004
Business and Real Estate
Services
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
34
Figure 3 Intensity of Service Usage across Industries
Source Authorsrsquo calculations based on ENIA survey data
Notes The figure shows the average ratio of service usage to sales computed across firms over the sample period in each
of the ISIC rev 2 2-digit industries
002 001003 001 002 002 003
001 001
002002
004002
003 004004
002 003
009014
014
011011
011010
012
015
000
010
020
Electricity and Water Transport and CommunicationsBusiness and Real Estate Services
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
35
Table 1 Foreign Ownership and Firm Performance in Service Sectors
Notes Robust standard errors in parentheses and indicate significance at 1 5 and 10 confidence
levels respectively Firm controls include an exporter dummy and size and age dummies Industry fixed effects are at
the 2-digit ISIC Rev 3 level
OLS Estimation
Firm-Level Log
of Labor
Productivity
Firm-Level
Indicator of
Product
Innovation
Firm-Level
Indicator of
Process
Innovation
(1) (2) (3)
Greenfield FDI Dummy 0919 0884 0767
(0481) (0409) (0414)
Foreign Acquisition Dummy 1912 0358 0440
(0297) (0351) (0325)
Firm Controls Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Region Fixed Effects Yes Yes Yes
Observations 492 487 498
R-Squared 032
Dependent Variable
Probit Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
36
Table 2 Effect of FDI in Services on Firm TFP - OLS Estimation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively
(1) (2) (3) (4)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0278 0277
(0095) (0094)
Service FDI Linkage t-1 0274
(0068)
Service FDI Linkage t-2 0180
(0047)
Manufacturing FDI Linkage t -0485 -0217 -0365
(0651) (0738) (0821)
Mining FDI Linkage t -0176 -0140 -0203
(0112) (0114) (0123)
Export Dummy t 0021 0027 0030
(0010) (0012) (0013)
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0211 0210
(0069) (0068)
Service FDI Linkage t-1 0236
(0053)
Service FDI Linkage t-2 0175
(0045)
Manufacturing FDI Linkage t -0715 -0225 -0312
(0616) (0696) (0772)
Mining FDI Linkage t -0239 -0189 -0237
(0107) (0108) (0115)
Export Dummy t 0034 0041 0045
(0010) (0011) (0013)
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0228 0227
(0086) (0085)
Service FDI Linkage t-1 0233
(0071)
Service FDI Linkage t-2 0163
(0043)
Manufacturing FDI Linkage t -0592 -0135 -0163
(0648) (0738) (0823)
Mining FDI Linkage t -0227 -0179 -0221
(0109) (0109) (0117)
Export Dummy t 0027 0034 0037
(0010) (0012) (0013)
Firm Fixed Effects Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes
Observations 33390 33390 26925 21502
Number of Firms 5817 5817 5103 4354
OLS Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
37
Table 3 First-Stage IV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses indicates significance at the 1 confidence
level
(1) (2) (3) (4) (5)
Service FDI Linkage Based on Service FDI Stocks of Spain 0239 0239 0239 0239 0239
(0065) (0065) (0065) (0065) (0065)
Manufacturing FDI Linkage Based on Manufacturing FDI
Stocks of Spain 0002 0002 0002
(0002) (0002) (0002)
Mining FDI Linkage Based on Mining FDI Stocks of Spain -0002 -0002
(0003) (0003)
Export Dummy 0001 0001
(0001) (0001)
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
R-Squared 038 038 038 038 038
Observations 32682 32682 32682 32682 32682
OLS Estimation
First Stage Regressions Corresponding to Table 4
Dependent Variable Service FDI Linkage
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
38
Table 4 Effect of FDI in Services on Firm TFP ndashIV Estimation Results
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively FDI linkage variables are instrumented using service manufacturing and mining FDI
linkage measures constructed based on service manufacturing and mining FDI stocks of Spain at t
(1) (2) (3) (4) (5)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0422 0422 0422 0423 0423
(0131) (0130) (0131) (0131) (0130)
Manufacturing FDI Linkage t -1517 -1597 -1629
(1419) (1460) (1462)
Mining FDI Linkage t -0122 -0125
(0163) (0163)
Export Dummy t 0021 0022
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0384 0384 0384 0385 0385
(0125) (0125) (0125) (0126) (0125)
Manufacturing FDI Linkage t -1394 -1444 -1494
(1417) (1461) (1461)
Mining FDI Linkage t -0077 -0082
(0160) (0160)
Export Dummy t 0033 0034
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0362 0362 0362 0363 0363
(0124) (0124) (0124) (0124) (0124)
Manufacturing FDI Linkage t 0321 0304 0265
(1520) (1566) (1570)
Mining FDI Linkage t -0026 -0030
(0167) (0167)
Export Dummy t 0026 0026
(0010) (0010)
R-Squared of Service FDI Linkage First Stage
Regression038 038 038 038 038
Kleinbergen LM Statistic (under-identif test) 002 000 000 002 000
Firm Fixed Effects Yes Yes Yes Yes Yes
IndYear Fixed Effects Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes
Observations 33390 33390 33390 33390 33390
Number of Firms 5817 5817 5817 5817 5817
IV Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
Table 5 Robustness Results ndash Part 1
Alternative
Instruments -
Stocks US
at t
Alternative
Instruments -
Stocks
Spain US
at t
Alternative
Instruments -
Stocks
Spain US
at t and t-1
Including
Service-
Intensity
Trends
Service FDI
Linkage
at t-1
Service FDI
Linkage
at t-2
(1) (2) (3) (4) (5) (6)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0465 0393 0299 0673
(0177) (0108) (0098) (0202)
Service FDI Linkage Lagged 0274 0211
(0109) (0091)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)080 049
Observations 33390 33390 26925 33390 26925 21502
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0445 0338 0277 0636
(0174) (0104) (0092) (0189)
Service FDI Linkage Lagged 0275 0235
(0108) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)032 014
Observations 33390 33390 26925 33390 26925 21502
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0427 0320 0224 0600
(0172) (0104) (0093) (0185)
Service FDI Linkage Lagged 0221 0176
(0105) (0093)
Alternative Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression021 046 052 039 045 047
Hansen J Statistic (over-identif test of all
instruments)000 000 000
Kleinbergen LM Statistic (under-identif test) 017 012 000 000 000
Observations 33390 33390 26925 33390 26925 21502
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t
R-Squared of Service FDI Linkage First Stage
Regression
Kleinbergen LM Statistic (under-identif test)
Observations
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
40
Table 5 Robustness Results ndash Part 2
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 5
and 10 confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and
mining linkage variables The latter two variables are endogenous in the IV estimation In column 1 the instruments for
the Chilean FDI linkage variables are service manufacturing and mining FDI linkage measures constructed based on
the corresponding FDI stocks of the US at t In column 2 the instruments for the Chilean linkage variables are service
manufacturing and mining FDI linkage measures constructed based on the corresponding FDI stocks of Spain and the
US at t In column 3 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain and the US at t and t-1 In columns 4 7
8 1011 the instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI linkage
measures constructed based on the corresponding FDI stocks of Spain at t while in columns 5 and 6 the instruments are
dated t-1 and t-2 respectively The service intensity trends and the alternative service FDI linkage measures are
described in the text
Drop TFP
Above Below
15Interq
Range by
Industry-Year
Drop
TopBottom
1 TFP by
Industry-
Year
Service FDI
Linkage
Using Input-
Output
Weights
Service FDI
Linkage
Using
Median
Values
Service FDI
Linkage
Using Log
FDI Stocks
One-Stage
Equation (1)
Adding
Regressors of
Equation (3)
(7) (8) (9) (10) (11) (12)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 0318 0351
(0142) (0156)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0668 0024 0196
(0304) (0008) (0057)
R-Squared of Service FDI Linkage First Stage
Regression026 026 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 32038 32889 52630 33390 33390
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 0194 0207
(0107) (0116)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0617 0017 0178
(0296) (0007) (0057)
R-Squared of Service FDI Linkage First Stage
Regression031 031 034 026 064
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Hansen J Statistic (over-identif test of all
instruments)
Observations 31701 32869 52630 33390 33390
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0292 0305
(0104) (0112)
Service FDI Linkage Lagged
Alternative Service FDI Linkage t 0786 0017 0167
(0329) (0008) (0056)
R-Squared of Service FDI Linkage First Stage
Regression032 032 034 026 064
Hansen J Statistic (over-identif test of all
instruments)
Kleinbergen LM Statistic (under-identif test) 000 000 000 000 000
Observations 32112 32854 52630 33390 33390
Panel D Dependent Variable - Firm-Level Output
Service FDI Linkage t 0386
(0139)
R-Squared of Service FDI Linkage First Stage
Regression038
Kleinbergen LM Statistic (under-identif test) 000
Observations 33390
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes Yes Yes Yes
IV Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
41
Table 6 Services FDI and Innovation
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The instruments for the Chilean FDI linkage variables are service manufacturing and mining FDI
linkage measures constructed based on the corresponding FDI stocks of Spain all at t and t-1 for the results in columns
(1)-(2) and based on the corresponding FDI stocks of Spain and the US at t for the results in column (3)
Differentiated products industries and RampD intensive industries are defined in the text Columns (1)-(2) show the R-
squares of two first stage regressions corresponding to the service FDI linkage variable interacted with each of the
groups
(1) (2) (3)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t Differentiated Products 0538
(0180)
Service FDI Linkage t Non-Differentiated Products 0232
(0118)
Service FDI Linkage t RampD - Intensive 0391
(0156)
Service FDI Linkage t Non RampD-Intensive -0037
(0198)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 009 001
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t Differentiated Products 0534
(0173)
Service FDI Linkage t Non-Differentiated Products 0240
(0114)
Service FDI Linkage t RampD - Intensive 0399
(0156)
Service FDI Linkage t Non RampD-Intensive -0047
(0191)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 008 001
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t Differentiated Products 0518
(0169)
Service FDI Linkage t Non-Differentiated Products 0146
(0113)
Service FDI Linkage t RampD - Intensive 0331
(0153)
Service FDI Linkage t Non RampD-Intensive -0118
(0205)
R-Squared of Service FDI Linkage First Stage Regressions 050 amp 043 046 amp 044
Kleinbergen LM Statistic (under-identif test) 000 000
P-value for F-Test of Difference in Coeff across Groups 003 002
Panel D Dependent Variable - Investment-Capital Ratio
Service FDI Linkage t 0070
(0032)
R-Squared of Service FDI Linkage First Stage Regression 046
Kleinbergen LM Statistic (under-identif test) 000
Firm Fixed Effects Yes Yes Yes
IndustryYear Fixed Effects Yes Yes Yes
RegionYear Fixed Effects Yes Yes Yes
Observations 25752 26925 34426
Number of Firms 4900 5103 6017
IV Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
42
Table 7 Firm Heterogeneity in the Effects of Service FDI on TFP
Notes Robust standard errors clustered at the firm level in parentheses and indicate significance at 1 and 5
confidence levels respectively All regressions include a firm export dummy and Chilean manufacturing and mining
linkage variables The latter two variables are endogenous in the IV estimation The instruments for the Chilean FDI
linkage variables are service manufacturing and mining FDI linkage measures constructed based on the corresponding
FDI stocks of Spain at t Closeness to leading performers and to top foreign firms is defined in the text The R-squared
shown are for two first stage regressions corresponding to the service FDI linkage variable by itself and interacted with
the measure of closeness to leading performers (column 1) and to top foreign firms (column 2)
(1) (2)
Panel A Dependent Variable - Firm-Level TFP Estimates Levinsohn and Petrin (2003)
Service FDI Linkage t 1172 1175
(0357) (0358)
Service FDI Linkage t Closeness to Leading Performers -1657
(0651)
Service FDI Linkage t Closeness to Top Foreign Firms -1699
(0671)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel B Dependent Variable - Firm-Level TFP Estimates Olley and Pakes (1996)
Service FDI Linkage t 1056 1059
(0342) (0343)
Service FDI Linkage t Closeness to Leading Performers -1531
(0608)
Service FDI Linkage t Closeness to Top Foreign Firms -1571
(0627)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Panel C Dependent Variable - Firm-Level TFP Estimates Ackerberg et al (2006)
Service FDI Linkage t 0905 0908
(0319) (0320)
Service FDI Linkage t Closeness to Leading Performers -1210
(0588)
Service FDI Linkage t Closeness to Top Foreign Firms -1244
(0608)
R-Squared of Service FDI Linkage First Stage Regressions 022 amp 032 022 amp 032
Kleinbergen LM Statistic (under-identification test) 000 000
Firm Fixed Effects Yes Yes
2-Digit IndustryYear Fixed Effects Yes Yes
RegionYear Fixed Effects Yes Yes
Observations 33017 33017
Number of Firms 5749 5749
IV Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
43
Appendix
A Sample Details
From 1992 to 2002 the ENIA survey gives each firm a unique identifier that allows
us to link firms over time to generate a panel dataset In 2003 the firm identifier changed
We established a correspondence between the old and the new firm identifier by merging
two versions of the 2001 dataset (one including the pre-2003 identifier and one including
the post-2003 identifier) according to more than 100 variables We confirm the
correspondence by merging two versions of the 2002 dataset (one including the pre-2003
identifier and one including the post-2003 identifier) Thus we are able to create a panel
of firms from 1992 to 2004 In cases where the correspondence between the old and the
new firm identifier was ambiguous we kept the firm with the old identifier and the firm
with the new identifier in the sample as separate firms The sample includes some firms
with discontinuous data over the sample period For those firms we consider only the
observations across consecutive years for which yearly growth rates can be computed
Since the ENIA survey data is judged to be of high quality and has been widely
used in research only minor data cleaning procedures are applied First we exclude from
the analysis firms with missing identifiers missing output or input variables or missing
industry affiliation Second we impute output and inputs to correct for non-reporting by a
firm in a single year (occurring in fewer than 30 firm-year observations) Third we
eliminate from the sample for production function estimation outliers in output and
inputs firms whose output growth is larger than (smaller than) 400 and those whose
output growth ranges between 100 and 300 (-300 and -100) but is not
accompanied by corresponding high (low) growth rates of inputs After applying these
data cleaning procedures the final sample for 1992-2004 consists of 57025 firm-year
observations However the estimating sample consists of 33390 observations since the
first three years of each firm are excluded to be used for the construction of firm-level
historic service usage intensities
B Production Function Variables
Output is measured by deflated sales The output price deflator is based on information
on indexes of total sales and indexes of physical production for each 3-digit industry from
the Chilean Statistical Institute Based on the equality total sales=physical production
price one obtains growth in total sales=growth in output + growth in prices Using this
formula we compute an industry output price deflator using 2002 as the base year For
years 1992-2002 the price deflator is obtained for 3-digit ISIC Rev 2 industries while for
2003-2004 it is obtained for 3-digit ISIC Rev 3 industries
Skilled and unskilled labor are measured by the number of workers in the following
occupational categories (a) skilled owners managers administrative personnel and
specialized production workers and (b) unskilled workers directly or indirectly involved
in the production process and home workers
Materials is measured by deflated materials expenditures The materials price deflator is
based on a weighted average of the aforementioned 3-digit output price deflators where
the weights are given by the share that each 3-digit industryrsquos output represents in total
manufacturing intermediates used by all 3-digit industries based on an input-output table
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
44
For years 1992-2002 [2003-2004] the weights are based on the 1986 [1996] Chilean
input-output table
Electricity is the quantity of electricity bought plus the quantity of electricity generated
minus the quantity of electricity sold in thousands of kilowatts
Services is measured by the deflated sum of expenditures on advertising banking
commissions and interest payments communications insurance legal technical and
accounting services licenses and foreign technical assistance rental payments transport
other services and water The services price deflator is based on GDP deflators for 4
groups of services from the Chilean Central Bank (i) electricity and water (ii) transport
and communications (iii) financial services insurance and business services and (iv)
real estate We calculate a weighted average of these GDP deflators where the weights
are given by the share that each of these 4 groups of services represents in total
intermediate expenditures (manufacturing plus services) for each 3-digit industry based
on the 1996 Chilean input-output table
Investment is computed as the sum of deflated net investment flows for each type of
capital in the ENIA survey where net investment flows are the sum of purchases of new
capital purchases of used capital and improvements to capital minus the sales of capital
and are deflated by an investment price deflator constructed as the ratio of current gross
capital formation to constant gross capital formation (in local currency units) from the
World Development Indicators with base year 2002 The ENIA survey provides
information on four types of capital buildings machinery and equipment transport
equipment and land
Capital is computed using the perpetual inventory method (PIM) for each of the four
types of capital in the ENIA survey and summed across the four types of capital For each
type of capital the PIM formula Kit+1 = (1 ndash δ) Kit + Iit is applied where Iit are real net
investment flows and δ is a depreciation rate Since detailed studies of depreciation rates
in Chile are unavailable we use the following rates proposed by Pombo (1999) who
studied the same type of capital goods in Colombia 3 for buildings 7 for machinery
and equipment and 119 for transport equipment Land is assumed not to depreciate
We also experimented with alternative rates of depreciation but did not find this to make
a substantial difference to the final capital stock values nor to our main results The initial
value of the capital stock needed to apply the PIM formula is given by the book value of
each of the four types of capital in the first year of firm presence in the sample Whenever
the book value is available only in a latter year we back out that value until the firmrsquos
first year in the sample taking into account the investment price deflator and the
corresponding depreciation rate
C FDI Linkage Measures
The main service FDI linkage measure is obtained based on the following five steps
1) For each service sector k net FDI inflows NI are given by FDI
kt
FDI
ktkt OINI where I
are sectoral inflows and O are outflows for each year t between 1974 and 2004 obtained
from the Chilean Foreign Investment Committee
2) Using the PIM formula we compute an FDI stock FDIS for each service sector k in
year t as FDI
kt
FDI
kt
FDI
kt SNIS )1( where δ is the depreciation rate assumed to be equal
to 565 which is the average of the depreciation rates for the capital goods machinery
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
45
buildings vehicles and land used in the construction of the capital stock for Chilean
manufacturing firms The initial value of the FDI stock needed to apply the PIM formula
is given by the net FDI inflows in 1974 for each service sector k Using these inflows as
initial value is reasonable given that FDI inflows into service sectors prior to 1974 were
minor While FDI stocks are calculated for the 1974-2004 period only the values for the
1995-2004 period are used in Steps 3 and 5 The construction of the FDI stock using the
PIM formula follows that used by Coe and Helpman (1995) to construct RampD stocks
3) For each service sector k we calculate a measure of FDI penetration in year t as
kt
FDI
ktkt GDPSFDIpen where GDP is total sectoral output obtained from the Chilean
Central Bank
4) For firm i the intensity of service usage in year t is given by it
k
it
k
it salesspending
ie firm expenditures on services from sector k as a ratio to sales We compute the
average of this intensity of service usage in the first three years of firm presence in the
sample to obtain a historic service usage intensity for each firm i k
i For the few firms
whose service usage intensity from sector k is unusually large (above 2) we replace the
firm intensity by the median service usage intensity in its 3-digit industry
5) We use FDI penetration from Step 3 and historic firm intensity of service usage from
Step 4 to construct the weighted sum which gives the main service FDI linkage measure
as
K
k
kt
k
iit FDIpensFDI1
_ where the K=4 services are (1) electricity and water (2)
transport and communications (3) financial insurance and business services and (4) real
estate
The service FDI linkage measure based on input-output weights used in column 9 of
Table 5 is obtained as follows We calculate the share that each service sector k
represents in total intermediate inputs (mining plus manufacturing plus services) kj used
by 4-digit manufacturing industry j based on the 1996 Chilean input-output table We use
the FDI penetration from Step 3 above and the formula for the weighted sum in Step 5
replacing k
it by kj to construct the service FDI linkage measure
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed as follows First we compute FDI penetration ratios for manufacturing
and mining sectors as described in Steps 1-3 above for service sectors Second for
manufacturing we calculate the share that each 4-digit manufacturing industry m
represents in total intermediate inputs (mining plus manufacturing plus services) mj used
by a 4-digit manufacturing industry j based on the 1996 Chilean input-output table We
interact each manufacturing industryrsquos share with the corresponding manufacturing FDI
penetration to obtain the FDI linkage variable as m
mtmjjt mfFDIpenmfFDI __
The mining FDI linkage measure is obtained analogously
To construct the instruments for the Chilean service manufacturing and mining FDI
linkages we follow the following 4 steps
1) For each service sector k of country c (ie Spain or US) we use data on overall real
service FDI outflows between 1993 and 2004 respectively provided by the OECD
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
46
2) Using the PIM formula we compute the stock of service FDI outflows FDI
cktO for each
service sector k in year t in country c as FDI
ckt
FDI
ckt
FDI
ckt ONIO )1( where δ is the
depreciation rate set equal to 10 Note that experimenting with alternative depreciation
rates produced no substantial differences The initial value of the FDI outflow stock
needed to apply the PIM formula is given by the FDI outflows in 1993
3) Is the same as Step 4 above
4) We use FDI penetration from Step 2 and historic firm intensity of service usage from
Step 3 to construct the weighted sum which gives the main service FDI instrumental
measure as
K
k
FDI
ckt
k
iit OcsFDI1
_ where the K=4 services are the same as above
The manufacturing and mining FDI linkage measures are based on input-output weights
and are computed analogously
D Production Function Estimation Methodologies
For production function estimation following Levinsohn and Petrin (2003) [below LP]
and Olley and Pakes (1996) [below OP] we point the reader to those two papers for
details In LP estimation we proxy for unobserved productivity using the variable input
electricity In OP estimation we proxy for unobserved productivity using investment and
thus only firms with positive investment are included in the estimating sample
For production function estimation following Ackerberg et al (2006) [below ACF] the
details are as follows We consider for expositional simplicity a three input logarithmic
Cobb-Douglas production function (instead of the six input production function in
equation (1))
itititkitmitlit kmly (D1)
where y is log output l is log labor m is log intermediates k is log capital for firm i at
time t ω are shocks observable to the firm but unobservable to the econometrician andis an iid residual Labor and intermediates are assumed to be perfectly variable inputs
therefore they are chosen by the firm after it observes ω Thus OLS estimates of the input
coefficients are biased To address this endogeneity LP assume that a firmrsquos profit-
maximizing demand for intermediates is a monotonically increasing function of its
productivity shock ω conditional on capital )( itittit kfm hence it can be inverted to
express the unobserved ω as a function of observable firm-level variables as in
)(1
itittit kmf Two other assumptions made by LP and retained by ACF are
(1) productivity ω follows a first-order Markov process (as in OP)
11 itititit pIp where 1itI is the information set available to the firm at t-1 so
one can write itititit E ][ 1 where it is the unexpected part of productivity
which is mean independent of all information known at t-1
(2) capital for use at t is decided at t-1 it is a state variable which can only be affected by
the expected value of productivity it conditional on 1it (but not by it )
LP propose that the function )(1
itittit kmf be inserted into equation (D1) to control
for the unobserved ω resulting in the first stage semi-parametric equation
ititittitlit kmly )( (D2)
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
47
where )()( 1
itittitlitmititt kmfkmkm
According to LP Equation (D2) is estimated by OLS approximating the unknown
function ()t by a third-degree polynomial on )( itit km This generates an unbiased
estimate for βl as well as for ()t since the error term is assumed to be uncorrelated with
the regressors
ACF identify a crucial identification problem with this first-stage estimate of βl They
argue that a consistent estimate of βl requires that labor varies independently (ie is not
collinear) with the non-parametric function )(1
ititt kmf
Since LP assume that intermediates and labor choices are made simultaneously and the
two inputs are perfectly variable it is natural to expect that they are determined in very
similar ways ie in the same vein of )( itittit kfm a function )( itittit kgl also
exists
Although ()tf and ()tg are likely to be different functions due to differences in input
prices both functions depend on the same two state variables )( itit k It follows that
)())(( 1
itittitititttit kmhkkmfgl This means that there is a serious collinearity
problem in the first stage of the estimation making it impossible to separately identify βl
from the non-parametric function ()t given that both depend on the same variables
To solve this collinearity problem ACF propose the following modification to LP labor
is assumed to be chosen at sub-period t-b (0ltblt1) after capital is known but before
intermediates are chosen at t Thus labor (like capital) can be affected by the expected
value of productivity it conditional on 1it ACF assume that it evolves according to
a first-order Markov process between the sub-periods t-1 t-b and t
bititbitit pIp and 11 itbititbit pIp A rationale for this timing
choice and for the fact that labor inputs are less variable than intermediates may be for
example restrictions in hiring or firing of workers
The fact that in ACF intermediates and labor are chosen with different information sets
generates independent variation But in this case the profit-maximizing firm demand for
intermediates will depend on labor )( ititittit lkfm
Assuming the conditions for invertibility ACF propose that the function
)(1
ititittit lkmf be inserted into equation (D1) to control for the unobserved ω
resulting in the ACF first stage semi-parametric equation
itititittit lkmy )( (D3)
where )()( 1
ititittitlitmitlitititt lkmfkmllkm
Equation (D3) is estimated by OLS approximating ()t by a third-degree polynomial
on )( ititit lkm ACF propose that the first stage is used to obtain unbiased estimates of
()t only not of βl The estimate for ()t is given by the polynomial expression
evaluated at the estimated polynomial coefficients and represents output net of the
untransmitted shock it By conditioning on a firmrsquos choice of intermediates this
procedure allows us to isolate the portion of output that is determined by unanticipated
shocks or measurement error at time t
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
48
In the second stage of the estimation ACF obtain the input coefficients using three
independent moment conditions Capital used at t is uncorrelated with the unexpected
part of productivity it so the moment condition that identifies the coefficient on capital
is given by 0][ itit kE A similar moment condition for labor does not identify its
coefficient given that labor is chosen at t-b and thus can be correlated with at least part of
it (in addition to being possibly correlated with the conditional expected value of it )
Lagged labor was chosen at t-b-1 and is part of 1itI and therefore is uncorrelated with
the unexpected part of productivity it Hence the moment condition that identifies the
coefficient on labor is 0][ 1 itit lE The moment condition that identifies the
coefficient on intermediates is similar to that in LP and is given by 0][ 1 itit mE
In order to operationalize these moment conditions we proceed as follows First for any
given set of candidate coefficients mll we use the first stage estimate for ()t to
obtain an estimate for kmlit as itkitmitltkmlit kml ()
Second we regress non-parametrically kmlit on kmlit 1 (obtained
similarly as the current value) and a constant The residuals from this non-parametric
regression are an estimate for kmlit Then we construct the sample analog to the
three moment conditions as
t i
it
it
it
kmlit
l
k
m
NT1
1
11
The minimization of this sample analog using an iterative procedure obtains consistent
estimates for the coefficients The standard errors for the coefficients are obtained by
block- bootstrap (ie if a firm is randomly selected to be part of the bootstrap sample
then all years of the firm are included in the bootstrap sample)
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation
49
Appendix Table 1 Production Function Estimates
Notes Bootstrapped standard errors in parentheses and indicate significance at 1 5 and 10
confidence levels respectively
Food
(ISIC Rev 2
31)
Textiles
Apparel (ISIC
Rev 2 32)
Wood
Furniture
(ISIC Rev 2
33)
Paper
Printing (ISIC
Rev 2 34)
Chemicals
(ISIC Rev 2
35)
Nonmet
Minerals
(ISIC Rev 2
36)
Basic Metals
(ISIC Rev 2
37)
Machinery
(ISIC Rev 2
38)
Other
Manuf (ISIC
Rev 2 39)
Log of Skilled Labour 0077 0135 0085 0144 0142 0105 0061 0122 0142
(0007) (0013) (0011) (0017) (0021) (0024) (0039) (0014) (0052)
Log of Unskilled Labour 0038 0058 0044 0020 0018 0017 -0025 0052 0084
(0005) (0007) (0009) (0008) (0009) (0012) (0020) (0008) (0024)
Log of Materials 0772 0657 0724 0657 0680 0644 0576 0642 0612
(0011) (0012) (0016) (0020) (0018) (0023) (0039) (0013) (0058)
Log of Services 0009 0072 0040 0031 0014 -0006 -0006 0059 0011
(0003) (0009) (0008) (0011) (0007) (0003) (0010) (0009) (0020)
Log of Electricity 0160 0140 0080 0080 0220 0030 0290 0160 0030
(0022) (0016) (0044) (0050) (0095) (0086) (0136) (0016) (0066)
Log of Capital 0010 0010 0010 0090 0010 0010 0050 0010 0120
(0004) (0000) (0023) (0068) (0176) (0032) (0236) (0003) (0120)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Log of Skilled Labour 0077 0157 0100 0146 0140 0097 0113 0127 0197
(0009) (0014) (0014) (0019) (0034) (0034) (0063) (0014) (0074)
Log of Unskilled Labour 0049 0072 0071 0022 0027 0041 0002 0062 0106
(0007) (0010) (0014) (0010) (0013) (0018) (0031) (0009) (0036)
Log of Materials 0770 0642 0707 0629 0690 0574 0552 0634 0656
(0012) (0017) (0023) (0019) (0025) (0026) (0044) (0014) (0075)
Log of Services 0006 0075 0040 0044 0020 -0012 -0002 0056 0044
(0008) (0010) (0014) (0013) (0010) (0016) (0038) (0011) (0043)
Log of Electricity 0031 0051 0053 0058 0015 0169 0145 0074 0020
(0004) (0014) (0010) (0012) (0008) (0005) (0019) (0010) (0055)
Log of Capital 0045 0016 0121 0137 0140 0139 0211 0057 -0083
(0020) (0017) (0029) (0040) (0056) (0066) (0073) (0026) (0097)
Observations 9433 4620 3153 2126 4478 1268 552 5420 336
Log of Skilled Labour 0090 0160 0120 0110 0170 0160 0170 0150 0140
(0008) (0019) (0014) (0034) (0023) (0034) (0040) (0028) (0064)
Log of Unskilled Labour 0170 0090 0040 0080 0020 0080 0020 0100 0100
(0014) (0027) (0013) (0090) (0027) (0074) (0022) (0030) (0054)
Log of Materials 0771 0666 0709 0640 0695 0622 0614 0646 0610
(0012) (0018) (0015) (0016) (0020) (0026) (0042) (0031) (0052)
Log of Services 0010 0074 0040 0031 0015 -0004 -0002 0062 0028
(0003) (0011) (0009) (0009) (0007) (0008) (0014) (0017) (0015)
Log of Electricity 0160 0020 0080 0120 0060 0140 0160 0100 0070
(0009) (0024) (0012) (0058) (0016) (0033) (0035) (0033) (0041)
Log of Capital 0120 0042 0070 0050 0130 0052 0050 0040 0140
(0006) (0018) (0011) (0026) (0014) (0026) (0027) (0018) (0071)
Observations 16917 8676 5448 3503 6589 2293 841 9405 696
Panel C Ackerberg et al (2006) Estimation
Dependent Variable Log of Output
Panel A Levinsohn and Petrin (2003) Estimation
Panel B Olley and Pakes (1996) Estimation