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Do Crises Catalyze Creative Destruction?
Firm-level Evidence from Indonesia
Mary Hallward-Driemeier, DECRG
Bob Rijkers, DECRG1
April 10, 2010
Abstract: This paper uses Indonesian manufacturing census data from 1991-2001 to examine
the impact of the East Asian crisis on the efficiency of resource allocation. The hypothesis that
crises may have a silver lining by improving resource allocation is rejected. There was large
excess churning; gross job creation and destruction far exceeded the amount required to achieve
net employment reallocation and the correlation between productivity and employment growth
weakened. Moreover, firm productivity became a less important determinant of survival,
contradicting the idea that crises accelerate creative destruction by ―cleansing‖ out less
productive firms. Decomposing aggregate productivity growth reveals within firm adjustment
dominated, rather than reallocation between firms. Credit market imperfections could account
for the attenuated relationship between productivity and survival. Indeed, firms more exposed
to credit market volatility exited more and shed labor, but amongst them, relatively productive
firms were better capable of weathering the shock; the attenuation of the link between
productivity and exit was greatest for firms that lacked access to credit to start with. There does
not, however, appear to be long term scarring in the process of creative destruction. The link
between productivity and survival is restored post-crisis, although the link with employment
growth remains less strong.
Key words: Financial crisis, creative destruction, firm survival, productivity decompositions,
capital market imperfections
JEL Codes: G01, O47, J21, D21
1 We would like to thank Andrew Waxman for exceptional assistance in the preparation of the dataset and paper. We would also like to thank Luis Serven, Ana Fernandes, Fitria Fitrani, Leo Iacovone, Kai Kaiser, Alex Korns, David Newhouse, Rifa Rufiadi and William Wallace for their assistance in obtaining the data and in discussing the policy environment in Indonesia. We also thank the participants of the Economist Forum and a DECRG Macroeconomics and Growth seminar, particularly Pierella Paci and John Giles, for their useful comments. Jagadeesh Sivadasan kindly shared the code used to implement the Ackerberg Caves Frazer procedure. The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Board or member countries. Corresponding author: Mary Hallward-Driemeier, mhallward@worldbank.org.
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1. Introduction
Firm dynamics and resource allocation are increasingly recognized to be crucial determinants of
countries‘ comparative economic success and long-run productivity growth. Cross-country studies
based on aggregate data furthermore suggest that plant-dynamics are also key determinants of the
depth and duration of crises (Bergoeing et al. ,2004, Collier and Goderis, 2009) Yet, while crises are
periods of intensified adjustment, firm-level evidence on the impact of crises on resource allocation
is limited. Moreover, their impact is theoretically uncertain. The idea that crises may accelerate the
Schumpetarian (1939) process of creative destruction by ―cleansing‖ out unproductive arrangements
and freeing up resources for more productive uses features prominently in macro-models (see e.g.
Hall, 1995, Caballero and Hammour, 1994, 1999 and Gomes et al., 1997). On the other hand, some
recent papers suggest that, rather than being cleansing, crises ―scar‖ the economy and undermine
long-run productivity growth by exacerbating market imperfections and destroying productive firms
(see Barlevy (2002)).2 Which view is correct matters for policy, since at issue is whether or not there
is a tradeoff between minimizing the short-term impact of crises and maximizing long-run growth
prospects; if crises are cleansing policies to dampen short-term impacts may obstruct long-run
recovery and be not only costly, but also counterproductive. By contrast, if crises are scarring,
policies to minimize short-term impacts are consistent with maximizing long-run growth prospects
(see Paci et al., 2009).
Given these competing paradigms, empirical analyses become all the more important in
determining whether there is evidence of market imperfections and whether their effects are
exacerbated during crises such that the cleansing hypothesis is overturned. By examining how the
East Asian crisis affected manufacturing firm dynamics in Indonesia this paper attempts to
empirically discriminate between the cleansing and scarring hypotheses and to redress the lacuna in
the empirical evidence on the impact of crises on firm dynamics. The empirical basis for our analysis
is the Indonesian manufacturing census from 1991-2001, a uniquely detailed firm-level panel dataset
that spans the periods before, during and after the East Asian crisis. We provide both aggregate and
firm-level evidence to test for the effects of the crisis on the efficiency of resource allocation and the
reallocation process itself. We examine job flows and assess how the relative contributions of entry,
exit, and reallocation between and within firms to aggregate productivity growth change during and
2 In addition, Ouyang (2009) has argued that crises may undermine long-run productivity growth by destroying potentially productive firms during their infancy.
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after the crisis. These decompositions are complemented by firm-level analysis of exit and
employment growth patterns, to assess whether and how firm dynamics differed during the crisis
compared to pre- and post-crisis periods. The firm-level analysis enables us to disentangle the role of
productivity from that of other firm-characteristics, such as size, ownership, export-orientation and
sector. In addition, we explore whether firm dynamics were affected by exacerbating credit market
imperfections, by looking at how the impact of the crisis varied across sectors varying in terms of
their dependence on external finance, and within sectors depending on whether or not firms used
loans to finance investments. Furthermore, we examine whether crisis-induced changes in the
reallocative process persist during the recovery.
Previous work on allocative efficiency has largely avoided periods of crisis, and work on
crises has rarely examined firm responses. By using firm-level data to examine the impact of a major
crisis on resource allocation this paper thus combines and contributes to several strands of literature
that have evolved fairly separately up until this point. The available evidence on the impact of crises
on resource allocation typically relies on aggregate data. The few previous attempts to examine the
cleansing hypothesis using firm-level data – discussed in detail in section 2 - have either failed to
examine changes in the reallocative process itself or suffer methodological shortcomings. Second,
existing plant-level studies of reallocation dynamics have demonstrated that business cycles are
important determinants of both the pattern and pace of reallocation (see surveys by Bartelsman and
Doms, 2000, Caves, 1998, and Syverson, 2010) but typically deliberately exclude crisis-periods. It is
therefore not clear to what extent the conclusions based on them generalize to crisis times. Thirdly,
micro-studies of the impact of crises on labor market outcomes mostly rely on household and labor
market data (see e.g. McKenzie 2003, 2004; Fallon and Lucas, 2002; Manning, 2000, Beegle et al.
1999). While such data are useful for documenting who becomes unemployed and how much their
earnings decrease, these types of data are ill-suited for examining the impact of crises on labor
demand, which requires understanding how productively labor can be employed – and whether
shifts are coming from changes in net entry or incumbents adjusting. Moreover, by enabling us to
document and analyze heterogeneity in firm vulnerability, establishment-level data can help us assess
which workers were likely to be most impacted and how.
Examining the impact of the East Asian crisis on the Indonesian manufacturing sector
provides a very interesting case study of how financial crises reverberate through the real economy.
Strong manufacturing performance had been an important driver of growth until the onset of the
crisis and manufacturing was one of the hardest hit sectors. Examining the mechanisms by which
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the financial crisis affected manufacturing firms helps highlight transmission mechanisms likely to be
relevant in the current global crisis, which is also characterized by a sharp contraction of demand,
reduced access to credit, and uncertainty. However, the current crisis is not characterized by
depreciations of a magnitude similar to those observed during the East Asian crisis and, is global,
rather than regional in nature. Focusing on Indonesia is particularly revealing as the size and
archipelagic geography of Indonesia provides us with cross-sectional and temporal variation which
we can exploit in our econometric strategy. In addition, Indonesia has a uniquely rich longitudinal
census of manufacturing firms that allows us to examine these issues.
Our main findings can be summarized as follows: the data do not support the cleansing
hypothesis, which predicts that the link between productivity and survival should become stronger
during the crisis, and that reallocation should occur between rather than within firms (i.e. that the
crisis would hurt relatively unproductive firms, disproportionately). On the contrary, they reveal that
the dominant mode of adjustment was within firms, rather than across firms. While the crisis led to
a spike in exit and induced excessive employment reallocation, productivity was only weakly
correlated with survival and employment growth during the crisis. Rather than the crisis raising the
productivity threshold for survival, it was more indiscriminate in terms of the productivity of firms
driven out of business. Firms more exposed to changing credit market conditions were more likely
to exit and shed labor, but amongst them, relatively productive firms were better capable of
weathering the shock. Thus, the attenuation of the association between productivity and exit was
greatest for firms that lacked access to credit to start with. Overall, the crisis was clearly destructive
in the short-run. During the recovery, aggregate allocative efficiency improved somewhat, as did
entrants‘ productivity. The protective power of productivity against exit was restored, but the
correlation between productivity and employment growth remained weaker than it had been pre-
crisis.
The remainder of this paper is organized as follows. The next section reviews the related
literature and elaborates on our approach. Section three describes the data and the context.
Decompositions of aggregate productivity growth and job flows are presented in section four, while
section five presents firm survival and employment growth models. A final section concludes.
2. Relation to Existing Literature and Approach
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Before explaining how we aim to empirically discriminate between the cleansing and scarring
hypotheses, we briefly review the theoretical models that underpin the idea that crises improve the
efficient allocation of resources as well as empirical evidence for this hypothesis. In addition, we
discuss recent theoretical and empirical challenges to the cleansing hypothesis and review the
existing firm-level evidence on the impact of crises on resource allocation.
2.1 Related Literature
According to Schumpeter (1939), business cycles are driven by a process of creative destruction by
which innovative, high-productivity firms drive relatively unproductive firms out of business. Some
prominent macro-models predict that recessions may speed up this process by ―cleansing‖ out
unproductive firms and freeing up resources for more productive uses (see e.g. Hall, 1995, Caballero
and Hammour, 1994, 1999; and Gomes et al., 1997). The precise mechanism can vary, but the idea
is that the additional competitive pressure caused by crises facilitates efficiency enhancing
reallocation. For example, firms will fire the least productive employees first and may be able to
attract more highly skilled workers to their firms as the number of applicants rises; banks may
allocate credit more efficiently as a result of increased scrutiny and competition; labor unions may be
more willing to accept employment losses or wage cuts. To be clear, these models do not predict
that crises will enhance aggregate welfare or suggest they are inherently desirable, but rather suggest
that, by improving the efficiency of resource allocation, crises may have a silver lining.
Empirical work using firm-level data supports the general claim of the importance of
creative destruction in improving productivity. Indeed, the increased availability of firm-level micro-
data has led to a burgeoning body of evidence on the microeconomic determinants of growth. There
is now rich empirical support for the idea that differences in the allocation of resources across
establishments that differ in productivity may be an important factor not only in accounting for
cross-country differences in output per worker, but also in differences in long-run growth (see e.g.
Restuccia and Rogerson, 2008; Hsieh and Klenow, 2009).
Although they typically exclude crisis periods, these firm-level studies raise questions about
how the process of creative destruction varies over time, with more ambiguous results regarding
whether the cleansing effect holds, even in periods of mild downturns. Studies of reallocation and
productivity growth that use panel data on manufacturing firms (surveyed in Bartelsman and Doms,
2000 and Caves, 1998, and Syverson, 2010) indicate that the business cycle is an important
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determinant of both the pattern and the pace of aggregate productivity growth. However,
decompositions of aggregate productivity growth only weakly support the hypothesis that allocative
efficiency increases during normal cyclical downturns (see, inter alia, Griliches and Regev, 1995) and
Baily et al. (1992), who demonstrate that within-firm adjustment was the major driver of
productivity growth in Israeli and U.S. manufacturing industries).3 Since such studies typically
exclude extreme economic events, it is uncertain to what extent lessons based on them apply during
times of crisis. Moreover, it is not clear to what extent conclusions based on developed country
datasets generalize to developing country contexts (see e.g. Bartelsman, Haltiwanger and Scarpetta
(2004) and Loayza et al. (2005) for empirical evidence of differences in the reallocation process
across developed and developing countries).
This microeconomic evidence that challenges the view that downturns can be cleansing is
supported by theoretical models that demonstrate that, in the presence of market imperfections,
downturns may hamper, rather than facilitate, adjustments. Barlevy (2002) for example, has pointed
out that crises may well obstruct the process of creative destruction by exacerbating labor and credit
market imperfections, whereby the expected efficiency-enhancing adjustment that is supposed to be
facilitated does not materialize. He argues that credit market imperfections are more likely to be
binding for relatively efficient producers, since highly efficient production arrangements are more
vulnerable to financing constraints as a result of their typically higher fixed costs. Crisis-induced
exacerbation of credit constraints would therefore hurt efficient firms disproportionately,
contradicting the creative destruction hypothesis. In addition, crises may increase labor market
frictions by augmenting search costs, which lead to lower average worker-firm match quality.
Moreover, these models have shown that the efficiency of surviving jobs deteriorates, leading to a
sullying effect of recessions. Moreover, Ouyang (2009) has pointed out that crises may undermine
long-run productivity growth by destroying potentially productive firms during their infancy.
In view of the competing predictions generated by the cleansing and scarring hypotheses,
empirical evidence becomes all the more important in determining which view is correct. Cross-
country studies on the impact of crises and shocks on long-run growth using aggregate data
furthermore suggest that (policy induced) distortions and market imperfections are a crucial
determinant of both the short- and long-run impact of crises on productivity. Bergoeing et al.
3 Whether policy reforms that strengthen competitive pressures also serve to improve resource allocation has also been debated (Hallward-Driemeier and Thompson, 2009).
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(2004) have demonstrated that countries with more distortionary regulations4 have experienced more
severe recessions. Collier and Goderis (2009) assess how developing countries‘ ability to cope with
aggregate shocks varies depends on which structural policies they have implemented. They consider
a wide variety of policies—including those pertaining to trade, financial deepening, and labor
markets-and conclude that regulations that delay the speed of firm closure are the most important
determinant of short-term growth losses from adverse price-shocks in mineral exporting countries,
while labor market regulations play an important role in mitigating shocks due to natural disasters.
In addition, while not based on crisis periods, there is a burgeoning literature on the impact of the
investment climate on firm performance that provides empirical support for the idea that market
imperfections hamper growth. Hallward-Driemeier (2009), for example, shows that red tape,
corruption, cronyism and weak property rights may attenuate the link between productivity and exit
and thereby undermine the Schumpetarian process of creative destruction.
While firm-level evidence on the impact of crises on resource allocation is scant, there are a
few studies that shed light on the debate. Liu and Tybout (1996) compare the performance of
continuing plants and exiting plants in Chile and Colombia from 1980-1985 but find no evidence for
systematic covariance of the efficiency gap between these two groups of firms over the business
cycle in either country, even though Chile suffered a recession in 1992. Exit rates of Chilean firms
only increased modestly during the recession. Casacuberta and Gandelsman (2009) examine the
impact of the 2002 banking crises in Uruguay on resource allocation. Even during the crisis,
productivity was negatively correlated with exit. Although it is not the direct focus of their paper,
their estimates suggest that the crisis attenuated the link between productivity and exit somewhat.
However, their estimation strategy makes it difficult to examine whether or not the link between
productivity and exit during the crisis was significantly different from what it was before the crisis.
In addition, they do not examine the relationship between productivity and employment
reallocation. Nishimura, et al. (2005) use a cohort analysis comparing productivity of entrants and
survivors to examine the impact of the Japanese recession on productivity growth and find that the
1996/97 banking crisis induced the exit of some relatively efficient firms among the youngest
cohorts. However, since their analysis essentially amounts to a comparison of means, it could suffer
from omitted variable bias; it is not clear whether the recession attenuated the link between
4 In terms of financial restrictions, trade barriers, firm entry costs, inefficient bankruptcy procedures, bureaucratic red tape, tax burden and labor regulations.
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productivity and exit, per se, or whether the above average productivity of young exiting firms was
associated with other factors. Nevertheless, their result is indicative the crises may not be cleansing.
A plausible explanation for the findings of Nishimura, et al. (2005) is that, consistent with
the scarring hypothesis, crises exacerbate credit market imperfections. Peek and Rosengren (2005)
demonstrate that Japanese banks levied additional credit to the weakest firms in order to avoid
balance sheet losses (also see Okada and Horioka, 2007). Empirical evidence supporting the idea
that credit market imperfections are important determinants of the reallocation process during
economic crises is provided by Gallego and Tessada (2009) who analyze job flows in Latin America
in response to sudden stops and demonstrate that sectors more dependent on external finance are
more heavily affected by the creation margin, while liquidity needs affect the destruction margin. In
addition, they found a negative correlation between firing and dismissal costs and labor destruction.
Moreover, using the same dataset as the one considered in this paper, Blalock, Gertler and Levine
(2008) compare the performance of foreign owned and domestically owned exporters in order to
test for the existence of liquidity constraints. The superior performance of foreign-owned firms,
which are likely to have had better access to finance, suggests that they were better capable of taking
advantage of the depreciation of the rupiah. This evidence of the existence of credit constraints
further raises the possibility that the potential for reallocation in post-crisis Indonesia could be
limited. However, crises need not always exacerbate credit market imperfections; Borensztein and
Lee (2002) find evidence that in response to the East Asian crisis Korean banks reallocated credit
from conglomerate (chaebol) firms to relatively more efficient firms.
Further evidence for the idea that market imperfections have particularly pernicious
consequences during times of crisis, which is the crucial assumption underlying the scarring
hypothesis, is that jobs created during recessions are usually less productive and less likely to last (see
Bowlus, 1993; Barlevy, 1998, Davis, Haltiwanger and Schuh, 1996). Fallon and Lucas (2002)
furthermore argue that crises are typically associated with excess churning of workers and jobs.
In short, theoretical models yield competing predictions regarding the impact of crises on
resource allocation, whilst the empirical evidence on the impact of crises on firm dynamics is limited
and ambiguous. Discriminating between the cleansing and scarring hypotheses requires firm-level
data as it requires one to assess the link between productivity and exit. There are only a handful of
studies that have used firm-level data to examine how resource allocation evolves during a crisis, yet
these studies have not tackled the scarring hypothesis directly, nor offered a comprehensive
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assessment that relates microeconometric-evidence on the link between productivity, employment
growth and survival to macro-trends in productivity growth.
2.2 Hypotheses and Approach
The cleansing and scarring hypotheses yield competing testable predictions at both the macro- and
the micro-level. If the cleansing view is correct, one would expect crises to accelerate the weeding
out of unproductive firms, resulting in a stronger association between productivity and survival at
the micro-level; in other words, one would expect unproductive firms to be disproportionately
affected. One might furthermore anticipate the correlation between firm‘ productivity and
employment growth to strengthen, as one would expect less productive firms to contract more in
response to shocks. At the macro-level, one would expect to see a corresponding increase in the
contribution of exit to aggregate productivity growth as well as stronger correlations between
productivity and changes in market share. By contrast, if crises are scarring, one would anticipate the
efficiency of resource allocation to deteriorate, and the link between productivity, exit and
employment growth to attenuate, undermining aggregate allocative efficiency. To the extent that
these scarring effects arise because of increased credit market imperfections, one might expect firms
more reliant on finance to be more severely affected by the crisis.
To test these competing hypotheses we combine analysis of aggregate reallocation patterns
with firm-level analysis and examine how reallocation dynamics evolved over time. Macro-level
decompositions of aggregate productivity growth are used to examine to what extent aggregate
productivity growth was due to adjustment within firms, reallocation between surviving firms and
changes in average productivity due to entry and exit. The decompositions help us assess whether
crises catalyze or retard efficiency enhancing re-allocation, and to analyze how industry dynamics
during crises differ from pre- and post crisis dynamics. Of particular interest are the contributions
due to exit and reallocation between surviving firms, both of which the cleansing hypothesis predicts
will rise.
These decompositions are complemented with firm-level analysis of survival and
employment growth. Such analysis i) helps us understand what is driving aggregate reallocation
patterns ii) enables us to document the heterogeneity in firm behavior (and what is driving this) and
to isolate the role of productivity from the impact of other firms characteristics and iii) helps explore
different dimensions of adjustment. Using a difference-in-difference approach, we offer a direct test
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of the cleansing hypothesis by examining whether or not the crisis induced changes in the
relationship between productivity, survival and employment growth. Moreover, we distinguish
between the short-run and medium-run impacts of crises on the reallocation process and examine
whether crisis induced changes in the reallocation process persist during the recovery. In addition,
we offer an indirect test of the scarring hypothesis by examining whether firms more reliant on
finance were hit harder by the crisis and whether exit and employment growth were less strongly
correlated with productivity for such firms; we compare the performance of firms across sectors that
vary in their dependence on external credit, and within sectors, we exploit information on the
composition of investment finance to examine whether there are differential impacts of the crisis.
3. Data and Context
3.1 Data
The Indonesian Manufacturing Census (1991-2001) provides the empirical basis for our analysis. It
is uniquely well-suited for a detailed examination of the impact of the East Asian crisis on firm
behavior since it contains information on all Indonesian manufacturing establishments with more
than 20 employees and spans the pre- and post- crisis periods, as well as the crisis itself. Moreover, it
has very detailed information on employment, inputs and outputs, industrial classification, exporting,
ownership and investment behavior. To examine how dynamics differ across sectors, we augment
the data with industry-level measures of financial dependence, employment turnover, the natural rate
of establishment entry and other salient sector characteristics.
As the data is an annual census of manufacturing establishments,6 we can not only track
performance over time, but also identify entry and exit.5 Our definitions of entry and exit are
affected by the fact that only establishments with 20 or more employees are included in the survey.
Entry is defined as entry into the survey. it is when establishments cross the 20 employee threshold,
not necessarily when they began operations. As establishments are not required to report their
closure, exit is inferred from establishments ceasing to file reports to BPS. We do not count as exits
5 Since our data track establishment, rather than firm behavior, we are unable to tell whether the behavior of one establishment relative to another represents a within-, rather than between-, firm reallocation. BPS submits a separate questionnaire to the head office for multi-establishment firms, but these data were unavailable for this study. A recent BPS study has suggested that less than 5% of establishments in the Manufacturing Census are owned by a multi-establishment firm (see Blalock, Gertler, Levine 2008 for discussion). For this reason, we believe that our results for establishments are likely to generalize to firms and we will use the terms ―firms‖ and ―establishments‘ interchangeably in the remainder of the text.
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temporary lapses in reporting. Exit is defined as exit from the survey; establishments that exit need
not discontinue their operations; they may simply be dropping below the 20 employee threshold.
This paper considers the period 1991-2001. Additional years could be available covering the
1980s. However, the sampling procedures changed during this period, with procedures more
rigorous and comparable over time beginning in 1991. As we are interested in understanding the
crisis period, this still provides adequate time pre-crisis for our comparisons. We extended the data
through 2001 to include a sufficient period post-crisis.6
There are two particular issues regarding response rates that need to be addressed. The first
regards a spike in exits in 2001, which coincides with the year in which administrative boundaries of
provinces changed as the decentralization laws passed after Suharto was removed from power came
into effect.7 Aggregate data on manufacturing GDP growth presented in figure 1 suggest that there
was a small downturn in GDP growth in 2001. However, this minor downturn is unlikely to account
for the jump we see. Coupled with lower than average exit rates in the preceding two years, it is
possible that some firms were ‗zombies,‘ that were carried forward for a year or two, with such a
practice then stopped in 2001. Discussions with officials in BPS indicate this may be part of the
explanation. The issue is whether the timing for some of these firms‘ exit should have been in 1999
or 2000 – and how this may affect our results.
The potential presence of such zombie firms in 1999 and 2000 likely makes it harder for us
to reject the cleansing hypothesis. There are two potentialtypes of zombie firms. The first type,
which we identify, are firms that only partially report their performance, effectively dropping out of
our sample as it is not possible to calculate their productivity. However, if we move up their date of
exit to the last year of complete reporting, it turns out these were relatively productive firms (see the
Appendix). Not including these firms as exiters would thus make it easier to find evidence consistent
with the cleansing hypothesis. The second possible type, which we cannot identify but may exist, do
report performance indicators but they may really only be achieving these on paper. To the extent
they are reporting productivity higher than their performance, they too would bias results in favor of
the cleansing hypothesis. Their inclusion among incumbents overestimates the productivity of
incumbents during the crisis and their existence during the recovery would make it appear that more
6 We also have data post 2001, but using these to compare firm dynamics with the crisis period is difficult because of changes in firm identifiers, changing definitions of key explanatory variables, and province splits that results in all the incumbents firms ‗exiting‘ from the old province and ‗entering‘ the new one. 7 We have worked with BPS to acquire the bridging concordance of id numbers. A number of provinces also divided then and it is possible that some firms are erroneously marked as exiting in one province and entering in another. This is a problem in later years, but seems unlikely to be of major significance in 2001. See the appendix for more details.
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productive firms are exiting then – the opposite of what we find. To test the robustness of our
results we also expand the window for analyzing firm survival so that the specific timing of exit is
not an issue, e.g. look at the probability of firms that operated in 1996 surviving to 2001.
A second issue regarding response rates pertains to the reporting of information on the
capital stock, for which our preferred proxy is the replacement value of machinery and equipment.
In 1996, the year immediately prior to the onset of the crisis, information on the capital stock was
not collected. In other years, measures of capital were included, although it is missing for some
firms in some years.8 We attempt to redress this issue by imputing the capital stock for firms for
which it is missing. To examine the robustness of our results, we present results with and without
controlling for the capital stock and examine how sensitive our results are to different proxies for
capital.
Our preferred measure of productivity is value-added per worker, which is not sensitive to the
imputation of the capital stock and robust to the endogeneity concerns that plague Total Factor
Productivity (TFP) estimation.9 Nevertheless, we examine how robust our results are to using
measures of TFP as proxies for productivity instead and use a variety of methods, including the
Solow method and the recent procedure proposed by Ackerberg, Caves and Frazer (2005) to
compute TFP. See the Appendices for more information on the construction of our data, key
explanatory variables and our productivity estimates.
3.2 Context: the Indonesian Crisis
The early 1990s saw Indonesia at the height of an extended period of industrialization and economic
growth under the ―New Order‖ government of Suharto, with labor-intensive exports being an
important driver of economic growth. A substantial reduction in trade protection and barriers to
foreign investment combined with effective macroeconomic and exchange rate management and a
flexible labor market helped fuel the growth of the manufacturing sector (Dwor-Frecault et al.,
1999).
In the wake of the devaluation of the Thai Baht, flows of foreign capital shrank drastically
and investors pulled resources out of the country as speculation against the rupiah mounted.
8 By contrast, all firms report labor, output and value added without breaks. 9 Since we do not observe firm-level prices, our productivity measures are revenue, rather than output based (see Foster, Haltiwanger, and Syverson (2008) for an illuminating comparison of revenue-based and output-based productivity measures and a discussion of the possible caveats of using revenue-based productivity measures).
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Between July 1997 and December 1998, the rupiah fell from 2400 to the dollar to a low of 217,000
during the political violence in 1998. Interest rates were raised to defend the currency, which
exacerbated the decline in demand. Inflation rates, which had been 12% shot up to close to 100%.
Figure 1 plots GDP over time, showing that it contracted severely in 1997, and fell by over 13% in
1998. The evolution of manufacturing output followed a similar pattern, though manufacturing
growth had already suffered a mild dip in 1996. The drop in manufacturing output was larger than
the corresponding dip in GDP as manufacturing was one of the hardest hit sectors due to its greater
reliance on imported inputs, exposure to changes in foreign demand (particularly given the
importance of intra-regional trade) and greater reliance on external financing, often in foreign
currency. Indeed, the growing trend of borrowing in foreign currency became an enormous burden
for firms post-devaluation. As sources for new credit froze, the inability to service loans helped
drive firms into bankruptcy, even ones that were otherwise productive. Unemployment rates also
increased, though they mask substantial employment reallocation across sectors (see e.g. Fallon and
Lucas, 2002).
The ensuing political crisis precipitated the end of the Suharto regime and led to a dramatic
change in the political and business environments. Large firms that had benefitted from ownership
connections to the Suharto family lost their privileged status and were subjected to greater
competitive pressures (Fisman, 2001; Mobarak and Purbasari, 2008). Policy priorities also shifted,
with greater labor protections put into place, and a move to a more flexible exchange rate regime
(that implied greater exchange rate volatility in the short run). While the new governments were
more populist, the increase in minimum wages and the severance payments regulations they
introduced to protect workers may have had the unintended consequence of discouraging hiring
workers. post-crisis there has been a lack of a resurgence of the labor-intensive sectors of the
economy which had driven growth prior to the crisis (see Aswicahyono et al. (2008) for a detailed
discussion). In short, post-crisis, labor markets were much less flexible than they had been in the
Suharto era.
4. A Bird’s Eye View of Reallocation: Job Flows and Aggregate Productivity Dynamics
We first present a bird‘s eye view of aggregate reallocation patterns and employment and
productivity growth prior to turning to a firm-level analysis of adjustment dynamics.
4.1 Job Flows, Entry and Exit
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Table 4.1 and Figures 2 and 3 present aggregate entry, exit and employment growth statistics.
Average exit over the entire period is 8.8% while average entry is 11.1%. Firm exit spiked during the
crisis - in 1997 10.8% of firms exited, while in 1998, 11.2% of all firms exited - and dropped
precipitously during the recovery in 1999 and 2000, to peak again in 2001 (see Section 3 and the
Appendix). Employment growth followed a similar trend, but did not spike in 2001; before the
crisis, manufacturing employment grew quite rapidly. The crises induced substantial job losses; on
average firms shrank employment by 3.30% in 1997 and by 0.93% in 1998. Employment growth
recovered in 1999 and 2000, but dropped in 2001. A possible explanation for the decline in
manufacturing employment growth is that more stringent labor regulations (e.g. the Manpower Act)
increased the cost of labor. These aggregate numbers mask heterogeneity among firms: some firms
cut employment more than others, and surprisingly many firms reported expanding employment: in
1997, 24% of firms grew, while in 1998, 38% of all firms grew. Nevertheless, a striking feature of the
data is how persistent employment is. Over the entire period considered in each year on average,
about a quarter of all firms did not change their labor force and firms that did change their labor
force, typically did so by minimal amounts (the inter-quartile range is 4%).
To examine patterns of aggregate employment reallocation, we examine gross employment
flows following the method proposed by Dunne et al. (1992). Job creation is measured as the sum of
employment gains at new and expanding plants, while job destruction is measured as the sum of
employment losses at contracting and closing plants. Gross job reallocation is the sum of
employment changes at contracting and closing plants. Excess job reallocation is defined as the
difference between gross job reallocation and net job reallocation (job creation minus job
destruction).
Figure 3 presents evidence on aggregate manufacturing employment growth and shows that
the crisis resulted in large employment losses, driven by both a slowdown in job creation as well as a
spike in job destruction. Interestingly, however, a substantial of number of firms expanded
employment during the crisis, pointing towards heterogeneity in firm performance. As a result the
amount of gross reallocation taking place far exceeded the amount required to achieve net
employment adjustment, leading to enormous excess churning.
The figure also shows a longer trend towards lower job creation, both in aggregate and net
creation. This has raised concerns about a ‗jobless recovery‘ (World Bank, 2009b). The firm-level
analysis presented in section 5 examines which are the firms that are expanding and contracting.
15
Figure 4 decomposes gross job flows by incumbents, entrants and exiters while Figure
presents gross entry and exit rates. While the crisis was associated with decreased entry and exit,
most of the employment adjustment was accounted for by changes in labor usage by existing plants.
Thus, in looking at overall adjustment, exit is clearly not the only relevant dimension to be
examined. In the crisis and immediate recovery, relatively more destruction and creation was
undertaken by incumbents.
4.2 Decomposing Aggregate productivity growth
To examine how crises and recovery affect industrial dynamics and firm behavior, we decompose
the evolution of aggregate productivity growth into adjustment within firms, reallocation between
surviving firms and changes in average productivity due to entry and exit. This enables us to assess
whether crises catalyze or retard efficiency enhancing re-allocation, and to analyze how industry
dynamics during crises differ from pre- and post crisis dynamics.
Our preferred decomposition methodology is due to Foster, Haltiwanger and Krizan
(1998)10, who demonstrate that the evolution of aggregate productivity, P, can be decomposed as:
(1)
Where Pt represents average productivity at time t, is a difference operator, itp represents
the productivity of firm i at time t, and it is the market share of firm i at time t. C denotes the set
of incumbent firms surviving from period t-1 to period t, N denotes the set of new entrants and X
the set of firms that exited. The first term in this decomposition represents the ―within‖ effect, the
contribution of within-establishment productivity growth of surviving firms, weighted by initial
market share. The second term reflects the ―between-effect‖, the contribution of market share
reallocation to productivity growth. This effect is of particular interest as it indicates the extent to
which relatively more productive firms are expanding relatively faster, hence improving allocative
efficiency. The third term represents the ―cross-effect‖ (i.e. the covariance between the within and
the between effect). The last two terms capture the contributions of entry and exit to average
productivity growth. The main advantage of the FHK decomposition is that it provides an
10
Additional decompositions have also been proposed (Basu and Fernald 2002, Petrin and Levinsohn 2008, Basu et al 2009.). However, we cannot compare revenue and output productivity. Our data does not contain the information on prices required to calculate measures of mark-ups which are necessary to implement these decompositions.
1 1 1 1 1 1 1( ) ( ) ( )it it it it t it it it it t it it t
i C i C i C i N i X
P p p P p p P p P
16
integrated treatment of entry and exit and enables us to separate out ―between‖ and ―within‖ effects
from ―cross‖ effects.
Note that if crises catalyze creative destruction, one would expect within-firm adjustment to
be relatively smaller than during less turbulent times. Rather, one would expect to see an increase in
the ‗between‘ term - relatively more productive firms gaining market share - as well as higher
contributions from exit and entry and potential increases in the ‗cross term‘ - firms that improve
productivity also growing relatively faster.
Figure 3 shows very strikingly the dominant effect of the ‗within‘ adjustment over the crisis
and recovery periods. Incumbent firms (especially large ones) experienced large declines in labor
productivity that was then (partially) regained during the recovery. In contrast, there is not much
evidence that either the ‗between‘ or ‗cross‘ terms contribution grew. Thus resources do not appear
to have been reallocated toward more productive firms.
In terms of exit, the cost to aggregate productivity was greater in 1997, consistent with
relatively more productive firms exiting during the crisis. As pointed out by Liu and Tybout (1996),
however, the small spike in exit rates does not mean that turnover has a minor long-run impact on
productivity. The rise in the contribution of entering firms is the one piece of evidence that points
to improved productivity.
A potential problem with the FHK decomposition is that it is rather sensitive to
measurement error. Suppose, for example, that output is measured with error. This will yield a
positive covariance between changes in productivity and changes in market share, thus resulting in a
spuriously low within-establishment effect and a spuriously high cross-effect. To examine how
robust our results are to measurement error, the Griliches and Regev (1995) decomposition is used:
(2)
Where horizontal bars denote time-averages. (e.g. 1i it it ). While the disadvantage of this is
that it is harder to interpret since it does not allow one to distinguish ‗within‘ and ‗between‘ effects
from ‗cross‘ effects, it has the advantage of being less sensitive to measurement error since the time-
averaging of market shares and productivity will mitigate the influence of classical measurement
error.
1 1( ) ( ) ( ))i it it i it it it it
i C i C i N i X
P p p P p P p P
17
Given the large contribution of ‗within‘ adjustment and small contribution of the ‗cross
effect,‘ it is not surprising that Figure 6 does not show the GR decomposition as being that
different. The ‗within‘ adjustment still clearly dominates. There is some increase in the between
effect after the crisis, while the increase in entry‘s contribution declines somewhat.
Three additional robustness checks are conducted. First, we use sales rather than
employment to determine market share. As Figure 7 shows, the effects are very similar. The ‗within
effect‘ still dominates. The effect of entry is again positive and rising. Exit‘s contribution falls
during the crisis and again in recent years.
A second robustness test comes from using other measures of firm productivity.
Decompositions based on TFP estimates (see the Appendix for details on how TFP was estimated)
yield very similar patterns.
The final robustness check is to expand the time window used to calculate the effects.11 So
far the decompositions have been calculated on an annual basis. This can increase the variability of
the components over time and can have the effect of underestimating the contributions of entry and
exit. If young firms face a steep learning curve, those that survive will likely be more productive a
few years from their start-up. On the exit side, if firms decline as they head toward closure, looking
only at the most recent data may underestimate the larger costs of losing that firm. We find that
lengthening the window to three years does smooth out the series, but there is not a dramatic
change in the contribution of entry (see Figure 8). For exit, the extent of the positive contribution
declines in the earlier years. This would be consistent with a ‗shadow of death,‘ but one where the
decline is relatively rapid. Rather than the contributions of entry or exit changing significantly, the
more striking change is that the cross effect becomes stronger and more positive. Allowing longer
adjustments to productivity and market share do show a positive correlation. This is one of the
more encouraging pieces of evidence that allocation is improving post-crisis.
5. Firm-level Analysis
While decompositions of productivity growth are useful to understand what is happening in
aggregate, examining the impact of crises on firm exit and employment growth requires firm-level
11
In addition, to examine whether the dominance of the within effect was related the data only covering firms with
more than 20 employees, we re-ran the decompositions separately for firms with fewer than 100 employees and
firms with more than 100 employees. The results are robust to splitting the sample this way; results are not presented
to conserve space, but available from the authors upon request.
18
analysis. Moreover, firm-level analysis helps us understand the remarkable heterogeneity in
enterprise performance observed in the aggregate data. We are especially interested in testing
whether or not productivity became a more important determinant of firm success as a result of the
crisis.
5.1 Firm Survival
5.1.1 Descriptive Statistics
To get a first idea of whether or not firms that exited were indeed the least productive ones, Table
5.1 presents summary statistics for survivors and exiting firms, disaggregated by time period. Over
the entire period considered, firms that exit are on average younger, smaller, less capital intensive,
employ more unskilled workers, less likely to be government owned, more likely to be foreign
owned, less likely to export and less productive than firms that survive (see column 1).
Comparing across columns to examine how the differences between surviving firms and
exiting firms over time. During the crisis, younger firms seem to have been especially vulnerable,
while exporters were less likely to exit (compared to other years), perhaps because of favorable
exchange rate movements. In addition, firms that have a high proportion of women in their
workforce were more likely to survive. During the crisis, survivors were not, on average, more likely
to operate in sectors highly dependent on external finance, whereas pre- and post-crisis they were,
suggesting that the crisis hit sectors more dependent on external finance relatively harder. The
productivity gap between exiting and surviving firms seems to have narrowed during the crisis; while
before the crisis the difference in the average log value-added of surviving firms compared to exiting
firms was about .31, it narrowed to .21 during the crisis, to increase again in the recovery.
Demeaning value-added per worker by sector or by sector-year yields a similar, but slightly less
dramatic, pattern. Thus, the aggregate attenuation effect is due to a combination of more productive
sectors being more severely impacted by the crisis, as well as a decrease in the productivity
differential between continuing and surviving firms within sectors. Bear in mind, however, that these
statistics do not condition on other firm characteristics; it need not be the case that productivity
offered relatively less protection during the crisis. For example, firm size is typically positively
associated with productivity and the typical firm that exited during the crisis was smaller than in
other years. It could thus be the case that it was not productivity, but size that accounts for the
19
diminution of the productivity gap between firms that exit and firms that survive. The next test
provides a more formal test of the cleansing hypothesis which controls for other firm characteristics.
5.1.2 Econometric Framework
To model firm survival a discrete-time proportional hazards survival model is used. In discrete time,
the survival function 𝑆 𝑡𝑖 𝒙 , which measures the probability that for a firm with a vector of
characteristics 𝒙 survival time is at least T, can be expressed as the product of the complements of
period specific hazard rates: 𝑆 𝑡𝑖 𝑥 = Pr(𝑇 ≥ 𝑡𝑖 |𝒙) = (1 − 𝜆 𝑡𝑖|𝒙 )𝑡𝑖=1 . The 𝜆 𝑡𝑖|𝒙 refer to
the period specific hazard rates, which measure the conditional probability of exiting at time 𝑡𝑖
conditional on surviving until time 𝑡𝑖−1 and are defined as 𝜆 𝑡𝑖|𝒙 = Pr(𝑡𝑖−1 ≤ 𝑇 < 𝑡𝑖|𝑇 ≥
𝑡𝑖−1, 𝑥𝑖−1) = 1 −𝑓(𝑡|𝒙)
𝐹(𝑡|𝒙)=
𝑓(𝑡|𝒙)
𝑆(𝑡|𝒙), where 𝐹 𝑡 𝒙 is the cumulative density function of time at risk
given the vector of characteristics 𝒙𝒊.12 Following Cox (1972) we assume that the hazard rate of a
firm characterized by covariates 𝑥𝑖 , is proportional to a baseline hazard 𝜆0:
𝜆𝑡 𝑡𝑖
𝜆0 𝑡𝑖 = exp(𝛽𝒙𝒊
′𝒙𝒊) <=> 𝑙𝑜𝑔 𝜆𝑡 𝑡𝑖 = 𝑙𝑜𝑔 𝜆0 𝑡𝑖 + 𝛽𝒙𝒊
′𝒙𝒊
This choice of the hazard function implies that each regression coefficient 𝛽𝑥𝑖 measures the
proportionate change in hazards associated with absolute changes in the corresponding covariate
𝑥𝑖 ∈ 𝒙𝒊. Consider the impact of productivity as an example. If productivity does not affect the
hazard rate then, exp 𝛽𝑃 = 1, whereas it increases the likelihood of exit (survival) if 𝑒𝑥𝑝𝛽𝑃 > 1 ,
(𝑒𝑥𝑝𝛽𝑃 < 1).
To examine whether crises catalyze creative destruction and to examine how the
determinants of firm-survival varied over time, we interact these explanatory variables with dummies
for the crisis and the recovery:
𝑙𝑜𝑔 𝜆𝑡 𝑡𝑖 = 𝑙𝑜𝑔 𝜆0 𝑡𝑖 + 𝛽𝒙𝒊
′𝒙𝒊
+𝛽𝑪𝒓𝒊𝒔𝒊𝒔∗𝒙𝒊
′𝐶𝑟𝑖𝑠𝑖𝑠 ∗ 𝒙𝒊 + 𝛽𝑹𝒆𝒄𝒐𝒗𝒆𝒓𝒚∗𝒙𝒊
′𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 ∗ 𝒙𝒊+ 𝛽𝑪𝒓𝒊𝒔𝒊𝒔𝒊
′𝐶𝑟𝑖𝑠𝑖𝑠 + 𝛽𝑹𝒆𝒄𝒐𝒗𝒆𝒓𝒚𝒊
′𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦
Where Crisis is a dummy variable for 1997 and 1998 and 𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 a dummy for the period 1999-
12 Note the last equality follows from the definition of the survivor function: 𝑆 𝑡 𝑥 ≡ 1 − 𝐹(𝑡|𝑥)
20
2001. The years leading up to the crisis will be referred to as the pre-crisis period. Effectively we are
testing how during the crisis and the subsequent recovery period the relationship between exit and
covariates differed from the pre-crisis process. The ―crisis‖ interactions examine the short-term
impact of the crisis, while the ―recovery‖ interactions examine its medium-term impact. This testing
strategy is very general as we allow all parameters of the hazard function to vary, thus minimizing
parameter constancy restrictions. The proportional hazard specification is very convenient since it
enables us to test whether firms with certain characteristics were disproportionately more or less likely
to exit in certain periods.
Under the null hypothesis of no short-run differential effect of crises on creative destruction,
𝑒𝑥𝑝𝛽𝑪𝑹𝑰𝑺𝑰𝑺∗𝑷 = 1. If crises catalyze creative destruction, 𝑒𝑥𝑝𝛽𝐶𝑅𝐼𝑆𝐼𝑆∗𝑃 > 1, while 𝑒𝑥𝑝𝛽𝐶𝑅𝐼𝑆𝐼𝑆∗𝑃 <
1 if they hamper it. At the risk of belaboring the point if 𝑒𝑥𝑝𝛽𝑪𝑹𝑰𝑺𝑰𝑺∗𝑷 = 1, this does not mean that
crises are not weeding out productive firms; whether this is happening depends of course also on
𝛽𝑷. The interaction term tells us whether productive firms were overrepresented amongst the exiters
relative to other periods. Following Rajan and Zingales (1998) this strategy, which essentially
constitutes a difference-in-difference approach, can also be used to examine the importance of
sector characteristics such as financial dependence and the natural rate of employment turnover.
Testing whether the association between sector-level measures of financial dependence and exit
varies over time will help us assess whether firms dependent on external finance are especially (less)
likely to survive during crises.
5.1.3 Results
Our baseline specification models firm-survival as a function of the log of the age of the firm, its
size, the proportion of blue collar workers in the total workforce (the ―unskilled ratio‖), the
proportion of women, foreign and government ownership, whether or not the firm exports and
productivity, proxied by value added per worker. In addition, industry, year, and province dummies
are included to eliminate time, industry and location effects. Results are presented in Table 5.2. The
first column pools all years, while columns two, three and four estimate the model separately for the
pre-crisis13, crisis, and recovery periods. The shaded areas indicate that the coefficient estimate is
significantly different from the pre-crisis one at the 10% level, while a bolded coefficient indicates
that the coefficient is significant at the 5% level (based on pooling over all periods and interacting
13 Note that these specifications only cover the period from 1993 onwards since the share of women in the workforce is not available prior to 1993.
21
the time dummies with all parameters of the hazard function).
The results in column 1 are consistent with the descriptive statistics presented in the
previous section and other studies of firm survival in developing countries and this dataset (see
Blalock, Gertler and Levine, 2008, Harrison and Scorse, 2009, Frazer, 2005, Söderbom and Teal,
2006); size, age, and productivity all increase the probability of survival. Interestingly, foreign owned
firms are less likely to exit while exporters are more likely to exit, ceteris paribus.
However, these effects are not stable over time, as is evidenced by a comparison of columns
2-4. Starting with the result of focal interest, the link between productivity and crisis seems to be
attenuated by the crisis; the odds ratio associated with value added per worker is always significantly
below 1, yet moves much closer to 1 during the crisis, suggesting that the crisis attenuated the link
between productivity and exit. This is not consistent with the cleansing hypothesis; while more
productive firms remain less likely to exit – ceteris paribus – the protective impact of productivity is
significantly weaker than it was pre-crisis. This difference between the pre-crisis and crisis effect is
statistically significant (as indicated by the fact that the coefficient is bolded); Post-crisis the
correlation between productivity and survival strengthened. Again, the coefficient on value-added is
significantly different from its pre-crisis counterpart.
The crisis has benefited exporters, who may have benefited from increased international
competitiveness due to the depreciation of the rupiah; while exporting is associated with a higher
propensity to exit during other periods, exporters were not ceteris paribus more likely to exit during
the crisis; the association between exporting and exit during the crisis is significantly different from
the pre-crisis association. Somewhat surprisingly, exit during the crisis is also negatively correlated
with the share of female employees and again this association is statistically significantly different
from the association between the gender composition of the workforce and exit pre-crisis. This
result is a sector composition effect; sectors which employed proportionately more women were hit
harder.14
During the recovery, some of these effects were reversed; firm-age was less strongly
correlated with exit than it had been before the crisis, while exports became – ceteris paribus – more
likely to exit than non-exporters. Also, government owned firms were much less likely to exit during
the recovery. In addition, firms employing proportionally more blue collar employees were more
likely to exit.
14 Result are omitted to conserve space, but available from the authors upon request.
22
5.1.3.1 Robustness
Table 5.3 presents alternative specifications and robustness test. The set of explanatory variables is
identical to those reported in table 5.2, except that it also includes industry dummies. Again, shaded
areas indicate that the coefficient is significantly different from the pre-crisis one at the 10% level,
while bolded coefficients indicate the effect is significantly different at the 5% level. To conserve
space, we only report the coefficients on value-added per worker, the explanatory variable of focal
interest. To start with, the result is not driven by differences across sectors or how different sectors
were impacted by the crisis as we can see in rows 1 and 2 of table 5.3, which control for industry
dummies and industry-year dummies respectively. The attenuation of the link between productivity
and exit is thus not simply due to the fact that more productive sectors were more severely impacted
by the crisis (recall that the descriptive statistics presented in Table 5.1 showed that firms in more
productive sectors were more likely to exit during the crisis), but operates within as well as across
sectors. Nevertheless, sectors differ in their vulnerability to the crisis; a point which we will return to
below.
Second, the pattern of results is robust to including the anomalous observations (see
Appendix B) that we have excluded from our sample and to dropping the top and bottom 10% of
most productive firms by sector-year from our analysis. If anything, excluding these extreme values
strengthens our results. The result is thus not driven by outliers.
Third, to examine the sensitivity of our result to our choice of the hazard function, the
baseline specification was re-estimated using a complementary log-logistic hazard function (i.e.
𝜆𝑡 𝑡𝑖 = 1 − (1 − 𝑙𝑜𝑔 𝜆0 𝑡𝑖 )𝐸𝑥𝑝 (𝛽 ′𝒙𝒊), or log(−log(1 − 𝜆𝑡 𝑡𝑖 )) = 𝑎𝑖 + 𝛽′𝒙𝒊,
where 𝑎𝑖 = log 1 − (1 − log 𝜆0 𝑡𝑖 . The findings are robust to using this alternative
specification of the hazard function.
Fourth, we re-estimated the models using deeper lags of value-added per worker to alleviate
concerns that the weakened association between productivity and exit is an artifact of the difficulties
of measuring productivity during turbulent times. Since productivity is very strongly correlated over
time (the autocorrelation coefficient for value added per worker is .84 over a one year window15 and
.63 over a five year period), lagged productivity is a good proxy for current productivity.16 While the
differences in the protective impact of productivity become somewhat smaller, the pattern of results
15 During the crisis-years, the correlations remain roughly constant; over a one-year window the correlation between value-added and lagged value-added is .85, while it is .66 over a five-year period. 16 This is also true in the crisis years, when the correlation between contemporaneous productivity and lagged productivity is .8.
23
is robust to using lagged productivity measures. We interpret this as strong evidence that the
attenuation effect is genuine and not driven by measurement error.
Fifth, one might worry that our results are driven by the fact that we do not observe firms
with fewer than 20 employees. If firms shrink and drop below this threshold, we will not observe
them in our data and define them as having exited. While we obviously cannot trace which of the
firms that exit from our sample stop operating altogether and which ones continue, but operate at a
smaller scale, we can investigate whether and how the exclusion of this latter group of firms might
affect our results. We do this by setting arbitrary size thresholds for exit and examining how our
results change. We exclude firms with fewer than 30 (40 or 50) employees and define a firm as
having exited when it either disappears from the data altogether or fails to report having more than
29 (39 or 49) employees in any of the subsequent years. Although using higher thresholds for exit
reduces the attenuation effect, the results are robust to using different exit thresholds.
Sixth, the regressions thus far have not controlled for capital intensity since capital might be
inaccurately measured.17 This is likely to introduce omitted variable bias. However, controlling for
capital intensity does not alter the pattern of results. On the contrary, once capital intensity is
controlled for, productivity loses its protective power altogether during the crisis – regardless of
whether we use the observed capital stock, or a combination of observed and imputed values.
Seventh, one may be concerned that exit is a protracted process and that by focusing on short-
term effects, we are missing the action. To address this concern, we assess how robust the results
are to using longer time windows.18 The bottom row presents estimates of the probability of long-
run survival, defined as being in business 5 or years later, using our baseline specification, with the
exception of the share of women in the total workforce, as this variable only became available in
1993. Column 1 models the probability of remaining in business until 1996 for firms operating in
1991, column 2 models the probability of survival until at least 2001 for firms operating in 1996.
Column 3 models for the probability of staying in business until 1996 for firms active in 1993 while
column 4 models the likelihood that firms that were active in 1996 were still in existence in 1999.
Overall, the pattern of results does not change when longer time-horizons are considered;
productivity offers less protection during the crisis than it did pre-crisis, ceteris paribus.
These results also provide a robustness check on the concern that the spike in exits in 2001
includes firms that actually exited in the year or years prior to 2001 but remained on the books until 17 One concern is that the response rate is lower for measures of the capital stock than for other variables. It is also missing in the 1996 data, requiring it to be estimated for that year (see Appendix). 18 Note that the spike in exit rates during crisis years suggests that the crisis did have an immediate impact on firm exit.
24
then, i.e. the issue of ―zombie firms‖. With longer time windows, the exact timing of these firms‘
exit is not an issue, the results remain.
Finally, since value-added per worker is a partial productivity measure we examine how
robust our results are to using a multitude of measures of multifactor productivity. Results are
presented in Table 5.4. The estimated attenuation effect becomes stronger.19
Past productivity is no
longer a significant predictor of productivity during the crisis, regardless of whether we use
productivity proxies computed by means of OLS, fixed effects estimation, the Solow residual or the
Ackerberg-Caves-Frazer procedure.
5.1.3.2 Dependence on credit and exit: indirect evidence
The evidence for the attenuation effect discussed above thus seems robust and begs the question
what is driving this attenuation effect. The scarring hypothesis postulates that the attenuation effect
arises because of market imperfections. Distortions in the credit market feature particularly
prominently as a potential explanation for the attenuation of the link between productivity and exit.
However, since we cannot directly observe which firms are credit constrained and which are not,
testing them is difficult with the data at hand. Instead, we try to probe whether such imperfections
might help explain the attenuation effect by comparing the performance of firms that are likely to
differ in their exposure to changing credit market conditions, both across and within sectors. If
changing loan conditions are the explanation for the attenuation effect, then one would expect the
attenuation effect to be especially severe amongst firms that were most exposed to these changing
conditions. Examining the impact of changing loan conditions is of interest in its own right, since
together with the Mexican crisis in the early nineties the East Asian crisis was one of the first ―third
generation‖ crises, in which financial fragility, rather than fiscal imbalances or weaknesses in real
activity, appears to have played a big role (Chang and Velasco, 2001).
To identify firms‘ exposure to changing credit market conditions we exploit information on
differences in dependence of finance across sectors, as well as information on differences in the
composition of investment finance controlling for sector. To start with, we examine whether or not
firms operating in sectors more dependent on credit (as proxied by the Rajan-Zingales measures of
dependence on external finance - see Appendix B20) were more likely to exit during the crisis –
19 Note that this result is not driven by controlling for the capital stock (see Row 2 in Table 5.2). 20 We also experimented with using the inventories-to-sales ratio as proposed by Raddatz (2006) as proxy for liquidity needs. Using this indicator, qualitatively the same pattern of results obtains. Results are omitted to conserve space, but available upon request from the authors.
25
controlling for firm characteristics and other sector characteristics. Moreover, if the scarring
hypothesis is correct and changing loan conditions are driving the attenuation effect,21 one would
expect that productivity would offer less protection against exit for firms in sectors highly dependent
on external credit. For firms in such sectors, the ability to access or repay loans might become a
relatively more important determinant of survival than productivity per se during the crisis.
Secondly, we complement this analysis – which essentially compares firms in different
sectors – by exploiting information on firms‘ investment financing and examining how exit patterns
vary with exposure to credit market changes after controlling for sector characteristics. Comparing
how the exit propensity of firms that took out loans to invest evolved relative to that of firms that
financed their investment by other means helps us assess whether changing credit market conditions
might explain the attenuation effect. Again, if more stringent loan conditions explain the attenuation
effect, then one would expect the protective impact of productivity to be especially weak for firms
that took out loans to invest.
The results of our regressions are presented in Table 5.5. The top half of the table presents
our results on the impact of credit constraints across sectors. Firms‘ survival is modeled as a
function of firm characteristics included in the baseline survival regressions presented in Table 5.2
In addition, to minimize omitted variable bias, we not only control for a sector‘s financial
dependence, but also its competitiveness and contestability, proxied by the Herfindahl index, the
―natural‖ rate of entry and of employment turnover. Turning to the results, before and after the
crisis firms operating in industries more dependent on access to credit are less likely to exit – as is
evidenced by the odds ratio associated with financial dependence in column 1 that is significantly
below 1. However, during the crisis, they became much more likely to exit suggesting that the crisis
hit firms in sectors more dependent on external finance relatively harder. In addition, firms in
sectors with higher levels of employment turnover and – somewhat surprisingly – industries with a
higher concentration ratio, had a higher exit propensity. Note, however, that controlling for these
sector characteristics does not explain the attenuation effect, which is robust to including sector
controls.
When we interact measures of financial dependence with value-added per worker (see row
2), we find that, generally, productivity is a less important determinant of survival in industries with
high levels of financial dependence. During the crisis, however, this effect was reversed; while firms 21 Our proxy for financial dependence is essentially an indicator of how much finance firms in the sector of interest manage to obtain from external sources. It can thus roughly be interpreted as a measure of excess to credit. Paradoxically, sectors and firms that lack access to credit may be more insulated from the shock.
26
in industries more dependent on external finance were more likely to exit, this was especially true for
relatively unproductive firms. That is, the crisis hit firms in sectors highly dependent on credit
harder, but relatively productive firms in such sectors where better capable of weathering the shock.
Rather than explaining the attenuation of the link between productivity and exit, this effect is
consistent with cleansing in sectors with better access to credit. Overall, however, the attenuation
effect remains. Productivity was a less important determinant of survival for firms in sectors that
have fewer outside financing opportunities.
To examine whether a similar mechanism is at work within sectors, Table 5.6 presents
regressions that include sector dummies and include dummies that indicate whether or not a firm
took out loans to finance any of the investments made during the past three years or not. The
omitted category is the group of firms that did not invest at all. Firms that invest are generally less
likely to exit and this is especially true for firms that took out loans to finance their investment.22
Yet, during the crisis, the negative association between investing and exit became weaker.
Interestingly, this weakening was more pronounced for firms that used loans to invest than for firms
that financed investment by other means, suggesting the former group of firms was hit harder by the
crisis.23 Note that the attenuation of the link between productivity and exit is robust to inclusion of
these investment proxies.
The bottom part of table 5.6 presents regressions in which we interact the investment
dummies with the log of value-added per worker. The odds ratio on the dummy for having invested
and taking out a loan to do so is not significantly different from one before or after the crisis, but
very high and significant during the crisis. The odds ratio associated with the interaction between
productivity and having financed investments by means of a loan is also not significantly different
from one either pre- or post-crisis, but much lower than one during the crisis. Thus, firms that took
out loans to invest were severely affected by the crisis, yet, amongst them, the most productive firms
were better capable of weathering the shock. Again, these findings are at odds with the idea that the
exit of firms more exposed to credit market conditions caused the attenuation effect and consistent
with the pattern we observed across sectors. The odds ratio associated with the interaction between
productivity and investing but not using loans for this purpose is always below 1, but especially low
during the crisis and the recovery, while the odds ratio associated with the dummy or having
22 This is reflected in a negative correlation between investing and exit, which is consistent with conventional economic theory; investing is a strong signal of future profitability. 23 These results are robust to controlling for the amount of investment spending; results are available upon request from the authors but omitted to conserve space.
27
invested without borrowing is always higher than 1, but only significantly different from 1 before the
crisis. The link between productivity and exit was thus somewhat attenuated for this category of
firms. The attenuation was strongest for firms that did not invest at all.
In sum, these results are consistent with the hypothesis that firms more exposed to
fluctuations in credit market conditions were hit harder by the crisis, yet these effects cannot
account for the attenuation of the link between productivity and exit during the crisis. If anything,
the crisis induced a cleansing effect amongst firms more exposed to the volatilities of the credit
market, as the negative association between productivity and exit strengthened for these firms. The
attenuation effect was thus most pronounced amongst firms that were less likely to have a loan. At
the risk of belaboring the point, this does not rule out the possibility that credit constraints may be
an important part of the explanation as to why we are observing an attenuation effect in the
aggregate. However, it seems unlikely that changing loan conditions for firms that had taken out
loans can account for the overall attenuation effect observed.
5.1 Employment Growth
5.2.1 Econometric Framework
To examine which firms grow fastest and to assess whether or not employment growth became
more strongly associated with productivity during the crisis, or whether, as is the case with survival,
the link between employment and productivity was attenuated, we estimate employment growth
models of the form; ∆𝐿𝑡𝑖 = 𝛽𝒙𝒕𝒊−𝟏
′𝒙𝒕𝒊−𝟏, where 𝒙𝒕𝒊−𝟏 is a vector of firm and sector characteristics,
including size, ownership, age, productivity. We follow a similar approach as proposed in section
5.1.2 and interact period dummies with this vector of explanatory variables to assess how the firm
growth process evolved over time. That is, we estimate an equation that enables us to examine
whether the crisis induced short- and medium-run changes in the process governing employment
growth;
∆𝐿𝑖𝑡 = 𝛽𝒙𝒕𝒊−𝟏
′𝒙𝒕𝒊−𝟏+𝛽𝑪𝒓𝒊𝒔𝒊𝒔∗𝒊𝒙𝒕𝒊−𝟏
′𝐶𝑟𝑖𝑠𝑖𝑠 ∗ 𝒙𝒕𝒊−𝟏 + 𝛽𝑹𝒆𝒄𝒐𝒗𝒆𝒓𝒚𝒊
′𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 ∗ 𝒙𝒕𝒊−𝟏 + 𝛽𝑪𝒓𝒊𝒔𝒊𝒔𝒊
′𝐶𝑟𝑖𝑠𝑖𝑠
+ 𝛽𝑹𝒆𝒄𝒐𝒗𝒆𝒓𝒚𝒊
′𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦
28
Thus, we use a difference-in-difference approach. Under the null hypothesis that the crisis
did not improve the allocative efficiency of employment re-allocation amongst continuing firms
𝛽𝑪𝑹𝑰𝑺𝑰𝑺∗𝑷 = 0, whereas 𝛽𝑪𝑹𝑰𝑺𝑰𝑺∗𝑷 > 0 (𝛽𝑪𝑹𝑰𝑺𝑰𝑺∗𝑷 <0) under the alternative hypothesis that the
crisis enhanced (diminished) the importance of productivity as a key determinant of firm growth.
Serial correlation in the error term, in conjunction with the presence of lagged size as an
explanatory variable would render OLS estimates of the employment growth equation biased. To
address this concern, we also estimate the employment growth equation using a Fixed Effects
estimator, thus removing time-invariant unobserved heterogeneity. Unfortunately, the fixed effects
transformation leads to a correlation between the transformed error and the transformed
explanatory variables, resulting in the well-known Nickell bias (1981). As pointed out by Bond
(2002), the OLS and Fixed Effects estimators are biased in opposite directions, thus providing us
with a confidence interval within which we might expect the true parameters to lie.24
Our growth regressions may suffer from selection bias, since we only observe employment
growth for surviving firms. While in principle it would be feasible to correct for selection bias using
a control function or instrumental variable approach, it is difficult to think of variables that affect
employment growth but not exit or vice versa. As a result, selection corrections would have to rely
on stringent functional form assumptions. Instead, we follow the approach suggested by Dunne, et
al. (1989) and try to gauge the magnitude of survivor bias by comparing the results obtained by
estimating our models on the entire sample with those obtained by estimating the models on a
sample of firms that remained in the data until 2001 only. Comparison of the two sets of results is
informative about the likelihood of survivor bias, as estimates obtained from the latter sample suffer
from maximum survivor bias.
5.2.2 Results
Firm survival and employment growth are determined by the same data generating process,
notably the one determining firm-size, and we therefore use the same explanatory variables as in the
firm survival models. That is, we model employment growth from period t-1 to period t as a
function of the age of the firm, its size and size squared to allow for non-linear size effects,
24 While the Difference and Systems GMM estimators developed by Arellano and Bond (1991) are in principle capable of yielding unbiased estimates, these estimators are not well suited for our data; the difference GMM estimator is likely to result in poorly behaved estimates when variables are highly persistent as is the case with our data (for surviving firms the correlation between lnLt and lnLt-1 is 0.98 – both in crisis and non-crisis years), while the Systems GMM estimators relies on a mean stationarity assumption that is palpably undesirable in the context of a crisis (See Roodman, 2006, for a discussion). We therefore eschew this approach.
29
productivity proxied by value-added per worker, the proportion of women it employed, whether it
exported, and ownership structure at time t-1. In addition, industry, year, and province dummies are
included to eliminate time, industry and location effects.
The results of this baseline specification are presented in Table 5.7. The first column pools
all years, while columns two, three and four present model estimates for the pre-crisis,25 crisis, and
recovery periods. Shaded areas indicate that the coefficient estimates are significantly different from
the corresponding pre-crisis estimate at the 5% significance level. It is very difficult to predict
employment growth as evidenced by the consistently low R2s, even though we are including a rich
set of firm characteristics and dummy variables. The results are generally consistent with the
literature on firm growth: younger firms, smaller firms and more productive firms grow faster (see
for example, Bigsten and Gebreeyesus, 2007). Productivity is also correlated with faster employment
growth. Interestingly, firms with a higher proportion of women are also ceteris paribus more likely
to grow over the entire period, perhaps because women are paid lower wages.
Examining differences in these effects over time we find that the crisis did not only attenuate
the link between productivity and exit, but that the association between productivity and
employment growth was even weaker after the crisis. The finding that more productive firms that
survived were not less likely to shed labor, ceteris paribus, suggests that employment reallocation
amongst surviving firms was not especially efficiency enhancing during the crisis.
Of course, our estimates exclude exiting firms. If we include these, see Table 5.8, then we
again find evidence of an attenuation of the link between productivity and employment growth
during the crisis and recovery of this link after the crisis – consistent with our survival models.
We also find evidence that firms in sectors more dependent on external finance contracted
relatively faster (see Table 5.9). Firms that invested did not shed fewer jobs than firms that did not
(see Table 5.10), while before the crisis they had been growing faster, especially if they had taken out
loans to finance investment; these firms suffered a slightly higher reduction in growth rates
compared to the pre-crisis period than firms that financed investment by other means, though the
differences are marginal. Post-crisis firms that took out loans to invest grew, ceteris paribus, faster
than firms that did not invest. However, the growth rates of firms that invested but did not finance
such investments by taking out loans remained lower than that of firms that did not invest.
25 Note that these specifications only cover the period from 1993 onwards since the share of women in the workforce is not available prior to 1993.
30
Table 5.6 also presents a host of robustness checks. The observed pattern is robust to
controlling for sector and sector-year effects. Survivor bias is unlikely to be a major issue as the
estimates on the sample of firms that survive until 2001 are not radically different from those
obtained for the sample that includes firms that exit at one point or another. The results are
furthermore robust to using TFP instead of value-added per worker, to using lags of value-added per
worker, to controlling for the capital stock, to using longer growth windows and to controlling for
fixed effects (see Table 5.11).
6 Conclusion
While crises are recognized to be periods of intensified adjustment and aggregate studies suggest that
firm dynamics are a key determinant of the depth and duration of crises, firm-level evidence on their
impact on resource allocation is scant. Perhaps because of the paucity of the empirical evidence,
there is an active debate as to whether or not crises have a silver lining by improving resource
allocation. On the one hand, a host of macroeconomic models are predicated on the idea that the
additional competitive pressure induced by the crisis will hurt inefficient producers
disproportionately. They predict that the crisis will ―cleanse‖ out unproductive firms and reallocate
resources towards more efficient producers. On the other hand, a series of recent papers point out
that, in the presence of market imperfection, these conclusions may be overturned and that crises
may ―scar‖ the economy by driving productive firms out of business.
Using the Indonesian manufacturing census data from 1991-2001 to examine the impact of
the East Asian crisis on resource allocation, this paper finds strong evidence against the cleansing
hypothesis. Decompositions of aggregate productivity growth reveal that rather than the crisis
resulting in massive reallocation between firms, widespread within firm adjustment dominated.
Moreover, the link between productivity and exit was attenuated during the crisis and there was less
regard for prior productivity in terms of which firms adjusted labor. These findings are especially
concerning given the spike in exit and the excessive amount of job reallocation that took place
during the crisis.
Market imperfections (and possible aggravation thereof) provide a potential explanation for
the attenuation of the link between firm productivity, survival and employment growth. Consistent
with this explanation, firms more exposed to changing credit market conditions were more severely
impacted. Firms in sectors more dependent on external finance were more likely to exit compared
31
to other periods and shrank more during the crisis. In addition, firms that used loans to finance their
investments were hit harder by the crisis than firms that financed investment by other means. Yet,
amongst firms more exposed to changing credit market conditions, productive firms did better. The
attenuation effect was thus most severe for firms that had worse access to credit to start with. Our
data do not allow us to rule out the possibility that credit market imperfections account for the
attenuation effect, yet do suggest that it was not purely driven by firms with pre-existing loans being
hammered. To what extent our results regarding the importance of access credit generalize to other
crises is uncertain, since the East Asian crisis was one of the first crises in which financial fragility
appears to have played a dominant role.
An important question, particularly for policy makers deciding whether or not to support
firms in the short-run, is whether the efficiency of the resource allocation improved over time.
Fortunately, crisis-induced changes in the reallocative process did not last; post-crisis the link
between firm survival and productivity was restored suggesting that the crisis did not permanently
scar the Schumpetarian process of creative destruction. The attenuation of the link between
productivity and employment growth had greater persistence post-crisis. We speculate that this
might be due to the introduction of more stringent labor-market regulations in the immediate
aftermath of the crisis. Investigating whether this is indeed the case, assessing the role of specific
market imperfections and examining to what extent our findings generalize to other crisis-episodes
are promising areas for further research.
32
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Figure 1 : Growth in Indonesia (1991-2001)
Figure 2: Firm Entry and Exit
-20
-15
-10
-5
0
5
10
15
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
% C
han
ge, A
nn
ual
Growth in Production for total manufacturing, sa GDP growthSources: OECD.stat, WDI
0
0.05
0.1
0.15
0.2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Exit rate
Entry rate
38
Figure 3: Aggregate Job Flows
Figure 4: Aggregate Job Flows Incumbents, Entrants and Exiters
-500000
-250000
0
250000
500000
750000
1000000
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Job Creation
Job Destruction
Net Job Creation
Excess Churning
-400000
-300000
-200000
-100000
0
100000
200000
300000
400000
500000
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
Job Creation IncumbentsJob Creation - Entry
Job Destruction IncumbentsJob Destructions Exiters
39
Figure 5: FHK Decomposition of the growth of real value added per worker, employment market share
Figure 6: Griliches-Regev decomposition of the growth of real value added per worker, employment market share
Figure 7: FHK Decomposition of Total Factor Productivity, using employment market
share
-0.150
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
within
between
cross
entry
exit
-0.150
-0.100
-0.050
0.000
0.050
0.100
0.150
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
within
between
entry
exit
-0.030
-0.020
-0.010
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
within
between
cross
entry
exit
40
Figure 8: FHK Decomposition, using sales to define market shares
Figure 9: FHK Decomposition, using 3 year window
-0.150
-0.100
-0.050
0.000
0.050
0.100
0.150
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
within
between
cross
entry
exit
-0.200
-0.150
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
1994 1995 1996 1997 1998 1999 2000 2001
within
between
cross
entry
exit
41
Table 3.1: Average Entry, Exit and employment growth
Entry, Exit and Employment Growth
Entry Exit
Employment Growth (Survivors)
Employment Growth (Survivors)
mean sd % negative emp change
No emp change
% positive emp change
1992 14.54% 9.74% 2.69% 24.41% 24.88% 14.64% 36.97%
1993 10.51% 9.02% 6.61% 25.38% 20.10% 7.08% 52.18%
1994 11.70% 7.41% 2.66% 24.53% 25.92% 16.88% 37.18%
1995 17.96% 6.19% 1.32% 22.38% 24.49% 17.50% 33.39%
1996 14.41% 8.97% 0.72% 22.56% 28.42% 19.29% 32.26%
1997 8.07% 10.78% -3.30% 22.24% 46.05% 13.59% 23.81%
1998 7.93% 11.21% -0.93% 22.94% 32.30% 10.61% 38.03%
1999 7.61% 5.06% 0.41% 21.19% 27.09% 23.00% 28.87%
2000 5.65% 5.20% 1.11% 19.85% 23.27% 31.38% 27.85%
2001 9.36% 13.66% -0.23% 22.05% 27.32% 26.04% 23.21%
Note: entry and exit are defined as entry into the survey and exit from the survey.
42
Table 5.1.A Descriptive Statistics, by period
Period precrisis crisis recovery ’91-‘95 ’96-‘97 ’98-‘01
Firmage
All 11.41 11.68 13.32 Surviving(1) 11.56 11.97 13.15 Exiting(2) 9.82 9.29 12.39 Difference(1-2) 1.74*** 2.68*** 0.77***
lnL
All 4.22 4.15 4.19 Surviving(1) 4.25 4.21 4.23 Exiting(2) 3.80 3.64 3.65 Difference(1-2) 0.46*** 0.57*** 0.58***
Ln(K/L)
All 6.84 6.47 6.15 Surviving(1) 6.86 6.51 6.21 Exiting(2) 6.64 6.17 5.53 Difference(1-2) 0.22*** 0.34*** 0.69***
Unskilled ratio
All 0.86 0.86 0.86 Surviving(1) 0.86 0.86 0.86 Exiting(2) 0.88 0.88 0.89 Difference(1-2) -0.02*** -0.02*** -0.03***
%Women
All 0.39 0.39 0.40 Surviving(1) 0.39 0.39 0.39 Exiting(2) 0.40 0.36 0.40 Difference(1-2) -0.01*** 0.03*** -0.01***
Foreign-owned
All 0.05 0.06 0.07 Surviving(1) 0.05 0.06 0.07 Exiting(2) 0.03 0.03 0.04 Difference(1-2) 0.03*** 0.03*** 0.04***
Government Owned
All 0.03 0.03 0.04 Surviving(1) 0.03 0.03 0.04 Exiting(2) 0.02 0.01 0.05 Difference(1-2) 0.01*** 0.01*** -0.01***
Exporter
All 0.17 0.16 0.14 Surviving(1) 0.17 0.17 0.13 Exiting(2) 0.15 0.10 0.10 Difference(1-2) 0.02*** 0.06*** 0.04***
ln (V/L)
All 7.88 7.90 7.95 Surviving(1) 7.91 7.93 7.91 Exiting(2) 7.60 7.72 7.54 Difference(1-2) 0.31*** 0.20*** 0.37***
ln (V/L) (demeaned by sector)
All -0.02 -0.01 0.03 Surviving(1) 0.00 0.01 -0.02 Exiting(2) -0.25 -0.19 -0.29 Difference(1-2) 0.25*** 0.20*** 0.28***
ln (V/L) (demeaned by sector-year)
All -0.00 0.00 0.00 Surviving(1) 0.02 0.02 0.03 Exiting(2) -0.23 -0.16 -0.29 Difference(1-2) 0.25*** 0.18*** 0.32***
*=90%confidence interval, **=95% confidence interval, ***=99% confidence interval for two-tailed t-test. Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level.
43
Table 5.1.B Descriptive Statistics (continued), by period
Period precrisis crisis recovery ’91-‘95 ’96-‘97 ’98-‘01
TFP
All 2.69 2.71 2.67 Surviving(1) 2.68 2.70 2.58 Exiting(2)
2.80 2.83 2.63 Difference(1-2)
-0.11*** -0.14*** -0.05***
TFP (demeaned by sector)
All -0.00 0.02 -0.01 Surviving(1) -0.00 0.02 -0.10 Exiting(2)
-0.01 0.03 -0.06 Difference(1-2)
0.01 -0.01 -0.03***
TFP (demeaned by sector-year)
All 0.00 -0.00 -0.00 Surviving(1) 0.00 -0.00 0.00 Exiting(2)
-0.01 0.02 -0.03 Difference(1-2)
0.01 -0.02** 0.03***
turnover
All 19.27 19.30 19.25
Surviving(1) 19.20 19.19 19.20 Exiting(2)
20.02 20.17 19.75 Difference(1-2)
-0.82*** -0.97*** -0.56***
Rajan & Zingales – financial dependence
All 0.16 0.17 0.23 Surviving(1) 0.16 0.17 0.23 Exiting(2)
0.15 0.17 0.19
Difference(1-2) 0.02*** -0.00 0.04***
Natural rate of entry
All 6.05 6.02 5.95 Surviving(1) 6.04 6.01 5.94 Exiting(2)
6.20 6.12 6.06 Difference(1-2)
-0.16*** -0.11*** -0.12***
Herfindahl index
All 0.03 0.04 0.04 Surviving(1) 0.03 0.04 0.04 Exiting(2)
0.03 0.04 0.04 Difference(1-2)
-0.001*** 0.00 0.00***
Invested (since t -2) - loan
All 0.27 0.27 0.21 Surviving(1) 0.27 0.27 0.24 Exiting(2)
0.20 0.21 0.16 Difference(1-2)
0.07*** 0.06*** 0.08***
Invested (since t -2) - no loan
All 0.34 0.35 0.33
Surviving(1) 0.34 0.35 0.32 Exiting(2)
0.34 0.34 0.34 Difference(1-2)
0.00 0.00 -0.01** *=90%confidence interval, **=95% confidence interval, ***=99% confidence interval for two-tailed t-test. Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from precrisis. Bold and underline indicates to the 95% confidence level. TFP estimates based on a four-factor sector-specific gross output production function estimated by OLS (see Appendix A)
44
Table 5.2 : Survival model
Logistic Survival model -Odds Ratios: Relative Probability of Exit -
Period all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
coef/se coef/se coef/se coef/se
Firmage (log) 0.793*** 0.791*** 0.697*** 0.932***
(0.009) (0.015) (0.013) (0.020)
lnL 0.250*** 0.288*** 0.237*** 0.209***
(0.016) (0.029) (0.028) (0.023)
lnL2 1.099*** 1.087*** 1.101*** 1.115***
(0.007) (0.012) (0.014) (0.013)
Unskilled ratio 1.123 0.941 1.100 1.460***
(0.081) (0.118) (0.137) (0.187)
%Women 1.014 1.096 0.903* 1.087
(0.036) (0.069) (0.056) (0.067)
Foreign-owned 0.988 0.817* 0.970 1.275***
(0.057) (0.089) (0.098) (0.118)
Government Owned 0.793*** 1.060 1.123 0.718***
(0.050) (0.132) (0.154) (0.062)
Exporter 1.214*** 1.498*** 1.047 1.109*
Ln Value-added per worker
(0.041) (0.082) (0.063) (0.069)
0.863*** 0.801*** 0.953*** 0.844***
(0.009) (0.015) (0.017) (0.014)
Province dummies Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Number of observations 145,882 51,797 39,034 54,986
chi2 (df) 5,792 (41) 1,493 (36) 1,804(34) 2,831 (36)
Log-Likelihood -39,448 -13,190 -12,482 -13,422
ll_0 -42,344 -13,936 -13,384 -14,838
Pseudo R2 0.068 0.054 0.067 0.095
note: - *** p<0.01, ** p<0.05, * p<0.1 - Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level.
45
Table 5.3: Robustness Checks and Alternative Specifications
Logistic Survival model -Odds Ratios: Relative Probability of Exit -
Robustness checks and alternative specifications
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01 Odds ratio/se Odds ratio/se Odds ratio/se Odds ratio/se
SECTOR AND SECTOR-TIME EFFECTS Including industry & industry*year 0.885*** 0.828*** 0.964* 0.870***
dummies (0.010) (0.016) (0.019) (0.016)
ln(V/L) normalized by sector 0.890*** 0.831*** 0.964* 0.869***
and year (0.010) (0.016) (0.019) (0.016)
OUTLIERS AND ANOMALOUS OBSERVATIONS
Including anomalous observations 0.867*** 0.805*** 0.941*** 0.859***
(0.009) (0.015) (0.017) (0.015)
Excluding top and bottom 10% of ln(V/L)
0.943*** 0.926** 0.996 0.929**
10% of ln(V/L) by sector-year (0.017) (0.031) (0.033) (0.028)
ALTERNATIVE SPECIFICATION: C-LOGLOG MODEL C-loglog Specification 0.867*** 0.805*** 0.941*** 0.859***
(0.009) (0.015) (0.017) (0.015)
LAGGED VALUE ADDED PER WORKER ln(V/L)- t-2 0.879*** 0.837*** 0.929*** 0.859***
(0.011) (0.019) (0.020) (0.017)
N 129,709 44,427 34,491 50,732
ln(V/L)- t-3 0.904*** 0.900*** 0.936*** 0.873***
(0.012) (0.023) (0.024) (0.018)
N 115,918 39,262 29,318 47,285
DIFFERENT EXIT TRESHHOLDS Exit- 30 people treshhold 0.862*** 0.814*** 0.928*** 0.847***
(0.008) (0.014) (0.016) (0.013)
Exit-40 people treshhold 0.858*** 0.809*** 0.922*** 0.850***
(0.008) (0.014) (0.016) (0.013)
Exit 50 people treshhold 0.867*** 0.822*** 0.924*** 0.863***
(0.008) (0.014) (0.016) (0.014)
CONTROLLING FOR CAPITAL INTENSITY Ln (K/L) - Observed capital 1.011 1.060*** 0.973 0.987
(0.009) (0.015) (0.017) (0.017)
ln(V/L) 0.862*** 0.790*** 0.967 0.823***
(0.013) (0.020) (0.025) (0.024)
N 103,708 40,804 29,777 33,075
Ln (K/L) - Imputed capital 1.012 1.060*** 0.988 0.980
(0.009) (0.014) (0.017) (0.013)
ln(V/L) 0.875*** 0.799*** 0.968 0.877***
(0.011) (0.020) (0.024) (0.022)
N 145,882 51,797 39,034 54,986
LONG-RUN SURVIVAL Period 1991-1996 1996-2001 1993-1996 1996-1999 Survival time 5 year 5 years 3 years 3 years ln(V/L) 0.756*** 0.868*** 0.728*** 0.953***
(0.015) (0.015) (0.017) (0.018) - *** p<0.01, ** p<0.05, * p<0.1 - unless otherwise indicated all specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, Province dummies and Year dummies -Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. - Standard errors for regressions including imputed capital are bootstrapped.
46
Table 5.4: TFP and survival
Estimated impact of TFP on survival
Logistic Survival model -Odds Ratios: Relative Probability of Exit -
all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
Odds ratio/se Odds ratio/se Odds ratio/se Odds ratio/se
TFP – OLS 0.841*** 0.780*** 1.008 0.755***
(0.038) (0.074) (0.077) (0.070)
TFP-OLS 0.842*** 0.769*** 1.008 0.762***
No controls for capital (0.042) (0.072) (0.078) (0.071)
TFP – OLS 1.096*** 1.138*** 1.237*** 0.938**
Without industry dummies (0.023) (0.033) (0.046) (0.030)
TFP – OLS 0.873*** 0.780*** 1.028 0.791***
normalized by sector-year (0.046) (0.074) (0.089) (0.068)
TFP – FE 0.821*** 0.728*** 1.012 0.728***
(0.038) (0.061) (0.076) (0.064)
TFP – Solow procedure 0.984 0.829* 1.206** 0.929
(0.059) (0.088) (0.093) (0.115)
TFP- ACF 0.743*** 0.649*** 0.890* 0.704***
(0.032) (0.048) (0.062) (0.060)
TFP – VA - OLS 0.875*** 0.797*** 0.988 0.869***
(0.022) (0.030) (0.038) (0.038)
N 75,593 29,734 20,424 25,380
Note: - *** p<0.01, ** p<0.05, * p<0.1 -unless otherwise indicated all specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, Industry Dummies, Province dummies and Year dummies -Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. - Standard errors are bootstrapped
47
Table 5.5: Industry Characteristics and Exit
Logistic Survival model -Odds Ratios: Relative Probability of Exit -
all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
Odds
ratio/se Odds
ratio/se Odds
ratio/se Odds ratio/se
Industry Characteristics RZ_fin_dependence 0.898** 0.803*** 1.319*** 0.805***
(0.040) (0.068) (0.109) (0.057)
turnover 1.005** 1.002 1.021*** 1.002
(0.003) (0.005) (0.005) (0.004)
nat_rate_entry 1.090*** 1.122*** 1.050** 1.063***
(0.013) (0.025) (0.022) (0.022)
Herfindahl 0.829 1.237 3.179*** 0.536***
(0.129) (0.455) (1.197) (0.111)
ln(V/L) 0.873*** 0.820*** 0.960** 0.853***
(0.009) (0.016) (0.018) (0.015)
Number of observations 145,882 51,797 39,034 54,986
Industry Characteristics Interacted with ln(V/L) RZ_fin_dep*ln(V/L) 1.054* 1.135** 0.902* 1.144***
(0.032) (0.064) (0.052) (0.053)
RZ_fin_dependence 0.611** 0.310*** 2.923** 0.289***
(0.142) (0.135) (1.297) (0.104)
turnover 1.008*** 1.004 1.018*** 1.003
(0.003) (0.005) (0.005) (0.005)
nat_rate_entry 1.078*** 1.115*** 1.062*** 1.056**
(0.014) (0.025) (0.023) (0.023)
Herfindahl 1.022 0.887 3.592*** 0.317**
(0.249) (0.366) (1.471) (0.155)
ln(V/L) 0.871*** 0.808*** 0.975 0.840***
(0.010) (0.017) (0.020) (0.016)
Number of observations 141,903 50,333 37,888 53,626 Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. All specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, Industry Dummies, Province dummies and Year dummies
48
Table 5.6: Dependence on Credit and Exit
Logistic Survival model -Odds Ratios: Relative Probability of Exit -
Investment Invested (since t-2) -used loans 0.815*** 0.789*** 0.889*** 0.743***
(0.022) (0.036) (0.040) (0.035)
Invested (since t-2) - no loans 0.871*** 0.893*** 0.876*** 0.837***
(0.020) (0.036) (0.034) (0.033)
ln(V/L) 0.884*** 0.833*** 0.962* 0.867***
(0.010) (0.016) (0.019) (0.016)
Number of observations 145,851 51,785 39,026 54,975 Financial Characteristics Interacted with ln(V/L)
Invested (since t-2) -used loans 1.172 0.829 3.376*** 0.829
(0.204) (0.254) (1.056) (0.248)
Invested (since t-2) - no loans 1.473** 2.309*** 1.436 1.306
(0.229) (0.662) (0.405) (0.328)
Invested (since t-2) –loans * ln(V/L) 0.954** 0.993 0.843*** 0.985
(0.021) (0.039) (0.033) (0.038)
Invested (since t-2) - no loans * ln(V/L) 0.933*** 0.882*** 0.938* 0.942*
(0.019) (0.033) (0.034) (0.031)
ln(V/L) 0.916*** 0.873*** 1.025 0.888***
(0.014) (0.025) (0.028) (0.022)
Number of observations 145,851 51,785 39,026 54,975 Note: Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level.
49
Table 5.7: Employment Growth
Employment Growth Model – OLS -
all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
coef/se coef/se coef/se coef/se
Firmage (log) -0.018*** -0.018*** -0.020*** -0.016***
(0.001) (0.001) (0.002) (0.001)
lnL -0.155*** -0.214*** -0.163*** -0.089***
(0.010) (0.023) (0.009) (0.008)
lnL2 0.014*** 0.019*** 0.014*** 0.007***
(0.001) (0.002) (0.001) (0.001)
Unskilled ratio 0.025*** 0.052*** 0.020** 0.006
(0.006) (0.010) (0.010) (0.008)
%Women 0.029*** 0.046*** 0.038*** 0.009**
(0.003) (0.005) (0.005) (0.004)
Foreign-owned 0.014*** 0.026*** 0.020*** 0.003
(0.003) (0.006) (0.007) (0.005)
Government Owned -0.009* -0.013 0.007 -0.010
(0.005) (0.009) (0.010) (0.007)
Exporter 0.014*** 0.004 0.036*** 0.004
(0.002) (0.004) (0.005) (0.004)
Ln (V/L) 0.024*** 0.034*** 0.021*** 0.019***
(0.001) (0.002) (0.001) (0.001)
Province dummies Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Number of observations 132,586 47,520 34,464 50,602
R2 0.038 0.054 0.042 0.020
Adjusted R2 0.038 0.053 0.041 0.019
Note: -*** p<0.01, ** p<0.05, * p<0.1 -Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence
level.
50
Table 5.8: Employment Growth – Alternatives and Robustness
-*** p<0.01, ** p<0.05, * p<0.1Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. -Specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, and Province, year and industry dummies. - Standard errors for regressions including controls for TFP or imputed capital are bootstrapped
Employment Growth Models - OLS Robustness checks and alternative specifications all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01 coef/se coef/se coef/se coef/se
INCLUDING EXITING FIRMS (e.g set dlnL=--log(Lt-1-20) if exited) Min ( set dlnL=--log(Lt20) if
exited
0.047*** 0.065*** 0.033*** 0.042***
If exited (0.003) (0.005) (0.006) (0.004)
Max set dlnL=--log(Lt) iexited 0.063*** 0.088*** 0.040*** 0.056***
If exited (0.003) (0.006) (0.007) (0.005)
SECTOR AND SECTOR-TIME EFFECTS
Controlling for industry & 0.025*** 0.034*** 0.022*** 0.019***
Industry-time effects (0.001) (0.002) (0.002) (0.001)
ln(V/L) normalized by sector
and year
0.025*** 0.034*** 0.022*** 0.019***
(0.001) (0.002) (0.002) (0.001)
SURVIVOR BIAS – LONG-RUN SURVIVORS ONLY
ln(V/L) 0.025*** 0.039*** 0.022*** 0.019***
(0.001) (0.003) (0.002) (0.001)
N 103,318 30,999 26,466 45,853
TFP
TFP 0.023*** 0.030*** 0.024*** 0.014***
(0.003) (0.005) (0.006) (0.005)
LAGGED VALUE-ADDED PER WORKER
Lag ln(V/L) 0.017*** 0.022*** 0.017*** 0.014***
(0.001) (0.002) (0.002) (0.001)
CONTROLLING FOR CAPITAL INTENSITY
Ln (K/L) - Observed 0.008*** 0.009*** 0.003** 0.010***
(0.001) (0.001) (0.001) (0.001)
ln(V/L) 0.019*** 0.026*** 0.019*** 0.014***
(0.001) (0.002) (0.002) (0.002)
N 95,061 37,727 26,543 30,791
Ln (K/L) -Imputed 0.008*** 0.010*** 0.004*** 0.010***
(0.001) (0.001) (0.001) (0.001)
ln(V/L) 0.018*** 0.027*** 0.018*** 0.012***
(0.001) (0.002) (0.002) (0.001)
N 132,586 47,520 34,464 50,602
LONGER WINDOWS
Period 1991-1996 1996-2001 1996-1999 1999-2003 Survival time 5 year 5 years 3 years 3 years
ln(V/L) 0.065*** 0.053*** 0.070*** 0.035***
(0.007) (0.005) (0.006) (0.004)
N 9,947 13,251 12,735 15,398
51
Table 5.9: Dependence of Employment Growth on Industry Characteristics and Exit
Employment Growth Model – OLS -
all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
coef/se coef/se coef/se coef/se
Industry Characteristics RZ_fin_dependence 0.001 -0.013** -0.025*** 0.015***
(0.003) (0.006) (0.007) (0.004)
turnover -0.001*** -0.002*** -0.001** -0.000
(0.000) (0.000) (0.000) (0.000)
nat_rate_entry 0.001 0.002 0.000 0.001
(0.001) (0.002) (0.002) (0.001)
Herfindahl 0.084*** 0.121*** -0.055* 0.116***
(0.016) (0.027) (0.030) (0.026)
ln(V/L) 0.025*** 0.035*** 0.021*** 0.019***
(0.001) (0.002) (0.002) (0.001)
Number of observations 132,556 47,508 34,456 50,592 Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, and Province, year and industry dummies.
Table 5.10: Dependence of Employment Growth on Credit and Exit
Employment Growth Model – OLS -
all precrisis crisis recovery
‘93-‘01 ’93-‘96 ’97-‘98 ’99-‘01
coef/se coef/se coef/se coef/se
Investment Invested (since t-2) -used loans 0.004** 0.008*** -0.002 0.004*
(0.002) (0.003) (0.003) (0.003)
Invested (since t-2) - no loans 0.001 0.006** -0.002 -0.002
(0.001) (0.003) (0.003) (0.002)
ln(V/L) 0.025*** 0.034*** 0.022*** 0.019***
(0.001) (0.002) (0.002) (0.001)
Number of observations 132,556 47,508 34,456 50,592 Shaded differences indicate 90% confidence level in rejecting that difference in indicated period is not different from pre-crisis. Bold and underline indicates to the 95% confidence level. Note: specifications include the following controls: firmage (log), lnL, lnL2, Ln(K/L), Unskilled ratio, %Women, Foreign-owned, Government Owned, Exporter, and Province, year and industry dummies.
52
Table 5.11: Fixed Effects Regression with Period Interactions
Employment Growth – Fixed Effects
Coef/se Coef/se
lnfirmage 0.023*** Crisis*ln(V/L) 0.003
(0.005) (0.002)
lnL -0.715*** Recovery*lnfirmage -0.011***
(0.027) (0.004)
lnL2 0.030*** Recovery*lnL 0.015
(0.003) (0.020)
Unskilled ratio 0.022
Recovery*lnL2 -0.002
(0.014) (0.002)
%Women 0.031*** Recovery*skratio -0.003
(0.011) (0.015)
Foreign-Ownership -0.005 Recovery*%Women -0.027***
(0.011) (0.008)
Government-owned -0.017 Recovery*Foreign-
Ownership 0.005
(0.013) (0.009)
exporter -0.009* Recovery*Government-
owned 0.004
(0.005) (0.015)
ln(V/L) 0.014*** Recovery*exporter 0.015**
(0.002) (0.007)
Crisis*lnfirmage -0.007*** Recovery*ln(V/L) 0.001
(0.003) (0.003)
Crisis*lnL -0.018 Year dummies Yes
(0.016)
Crisis*lnL2 0.001 Number of
observations 132,586
(0.002) R2 0.261
Crisis*skratio -0.017 Adjusted R2 0.261
(0.014)
Crisis*%Women -0.008
(0.008)
Crisis*Foreign-Ownership 0.015*
(0.009)
Crisis*Government-owned 0.020
(0.013)
note: *** p<0.01, ** p<0.05, * p<0.1
53
Appendix A: Estimating Productivity
A.1 Econometric Framework
Our starting point is a four-factor industry-specific gross-output Cobb-Douglas production
function:
𝑦𝑖𝑗𝑡 = 𝛽𝑘𝑗 𝑘𝑖𝑗𝑡 + 𝛽𝑚𝑗 𝑚𝑖𝑗𝑡 + 𝛽𝑏𝑗 𝑏𝑖𝑗𝑡 + 𝛽𝑤𝑗 𝑤𝑖𝑗𝑡 + 𝜔𝑖𝑗𝑡 + 𝑣𝑖𝑗𝑡
Where 𝑦𝑖𝑗𝑡 is the log of output of firm i operating in sector j at time t, 𝑘𝑖𝑗𝑡 is the log of capital, 𝑏𝑖𝑗𝑡
the log of blue-collar workers, 𝑤𝑖𝑗𝑡 is the log of white-collar labour and 𝜔𝑖𝑗𝑡 denotes the log of
TFP, and 𝑣𝑖𝑗𝑡 is a random error term. To account for differences in technology across sectors,
factor-coefficients are allowed to vary by sector, as indicated by the sector subscripts j.
A well known problem is that unobserved productivity 𝜔𝑖𝑗𝑡 may be correlated with inputs,
causing OLS estimates of the production function to be biased. To assess how robust our
productivity estimates are to the resulting endogeneity bias, we contrast OLS estimates with those
obtained by means of Fixed Effects (FE) estimation. In addition, we compute input coefficients
using the Solow method and estimate productivity using the Ackerberg-Caves-Frazer (2006)
(henceforth ACF) procedure.
The Fixed-Effect estimator eliminates bias that may arise because of a correlation between
time-invariant unobservable productivity and input choices. Unfortunately, Fixed effects estimators
suffer from the Nickell bias, arising because the transformed explanatory variables and the
transformed error term are negatively correlated by construction. In addition, the empirical
performance of Fixed Effects Estimator has been disappointing, often resulting in implausibly low
estimates of returns to scale (see e.g. Gricliches and Mairesse, 1996).26 As pointed out by Bond
(2002), OLS and Fixed Effects estimators are biased in opposite directions and thus provide a
confidence interval within which we may expect our productivity-estimates to lie.27
Factor shares are also computed using the Solow method, a non-parametric procedure to
obtain output elasticities. Under the assumption of constant returns to scale and perfectly
competitive markets, 𝛽𝑘𝑗 , 𝛽𝑚𝑗 , 𝛽𝑏𝑗 and 𝛽𝑤𝑗 are equal to the shares of capital costs, materials costs
and the wagebills for blue-collar and white collar workers, respectively, in total costs (see Hall,
26 Estimating the production function in first-differences would circumvent the Nickell bias and eliminate fixed effects, yet exacerbates measurement error. 27 The Difference and Systems GMM methods (see Arelano and Bond, 1991, Arellano and Bover, 1995, Blundell and Bond, 1999) are in principle very well-suited to tackle potential endogeneity problems. Unfortunately, however, we were not able to identify a suitable specification; none of our instrumenting sets were statistically satisfactory and we therefore eschew this approach.
54
1990). That is:
𝑆𝐾 =rK
rK + wbB + ww W + mM= 𝛽𝑘𝑗
𝑆𝑀 =mM
rK + wb B + ww W + mM= 𝛽𝑚𝑗
𝑆𝑊 =ww W
rK + wbB + ww W + mM= 𝛽𝑤𝑗
𝑆𝐵 =wbB
rK + wb B + ww W + mM= 𝛽𝑏𝑗
where 𝑆𝐾 is the capital cost share, 𝑆𝑀 is the material inputs cost share, while 𝑆𝑊 and 𝑆𝐵 are
the share of the wagebill for white and blue collar workers in total cost, respectively, r is the rental
cost of capital, m is the unit price of material inputs ww is the average wage of white collar workers
and wb is the average wage for blue collar workers. If the assumptions of perfect competition and
constant returns to scale hold the average of these capital-input and labor shares should not differ
from the capital- and labor-output elasticities estimated by means of OLS.28 If they differ, this is a
sign that endogeneity may be a problem, although it might also be that the assumptions of constant
returns and perfect competition are false. Since the data lack reliable information on expenditure on
capital, in our empirical implementation, the value of total output is used as a measure of rK +
wbB + ww W + mM; information on expenditure on labor and material inputs enables us to
retrieve an estimate of rK.
In addition, we follow the estimation procedure outlined by Ackerberg, Caves and Frazer
(2006).29 Assuming that materials are a perfectly flexible input, chosen at time t, (when productivity
is observed) and that the prices of raw-materials are constant in the cross-section enables us to
obtain a value-added production function:30
𝑣𝑎𝑖𝑗𝑡 = 𝛽𝑘𝑗 𝑘𝑖𝑗𝑡 + 𝛽𝑏𝑗 𝑏𝑖𝑗𝑡 + 𝛽𝑤𝑗 𝑤𝑖𝑗𝑡 + 𝜔 ∗𝑖𝑗𝑡 + 𝑣𝑖𝑗𝑡
If we are furthermore willing to assume that capital, 𝑘𝑖𝑗𝑡 k, is chosen at period t-1 (since
28 Of course, it is quite restrictive to assume that the production function exhibits constant returns to scale and that markets are competitive. These assumptions are not needed if the production function is estimated by means of regression. 29 Note: the exposition here borrows heavily from Ackerberg et al. (2006), Chan (2009) and Balasubramanian and Sivadasan (2009). We are grateful to the latter Jagadeesh Sivadasan for sharing the code used to implement the ACF procedure. 30 Note that these are very strong assumption and that, in general, returns to scale estimated on the basis of value-added production functions are biased unless i) competition is perfect ii) the substitutability between material inputs and other inputs is zero (see Fernald and Basu, 1996) .
55
investment takes time to materialize; capital is assumed to follow the law of motion 𝑘𝑖𝑗𝑡 =
(1 − 𝛿)𝑘𝑖𝑗𝑡 −1 + 𝑖𝑖𝑗𝑡 −1 , where 𝛿 represents depreciation and 𝑖 represents investment), and that
white and blue collar labour are also chosen at time t-1), that is, before material inputs are chosen,
and that productivity 𝜔 ∗𝑖𝑗𝑡 evolves according to a first-order Markov process between periods t-1
and t: i.e 𝑝(𝜔 ∗𝑖𝑗𝑡 𝐼𝑡−1 = 𝑝(𝜔 ∗𝑖𝑗𝑡 𝜔 ∗𝑖𝑗𝑡 −1 , material input demand will be a function of
productivity, capital and labour inputs; 𝑚𝑖𝑗𝑡 𝑘𝑖𝑗𝑡 , 𝑏𝑖𝑗𝑡 , 𝑤𝑖𝑗𝑡 , 𝜔 ∗𝑖𝑗𝑡 , that is monotonically increasing
in 𝜔 ∗𝑖𝑗𝑡 . Inverting this demand function enables us to rewrite the production function as an
equation of the form:
𝛷𝑖𝑗𝑡 = 𝛽𝑘𝑗𝑘𝑖𝑗𝑡 + 𝛽𝑏𝑗 𝑏𝑖𝑗𝑡 + 𝛽𝑤𝑗 𝑤𝑖𝑗𝑡 + 𝑓𝑗𝑡−1 𝑘𝑖𝑗𝑡 , 𝑏𝑖𝑗𝑡 , 𝑤𝑖𝑗𝑡 , 𝜔 ∗𝑖𝑗𝑡 + 𝑣𝑖𝑗𝑡
ACF propose estimating this equation in two stages. In the first stage, output is estimated as
polynomial of capital, blue-collar labor, white-collar labor, raw materials and output, enabling us to
get rid of 𝑣𝑖𝑗𝑡 .
In the second stage, estimates of 𝛽𝑘𝑗 , 𝛽𝑤𝑗 and 𝛽𝑏𝑗 can be retrieved. The timing assumptions
on capital and labour input choices in conjunction with the supposition that productivity follows a
Markov process – i.e. that 𝜔 ∗𝑖𝑗𝑡 = 𝐸𝑡−1 𝜔 ∗𝑖𝑗𝑡 + 𝝃𝒊𝒋𝒕 = 𝐸 𝜔 ∗𝑖𝑗𝑡 |𝜔 ∗𝑖𝑗𝑡 −1 + 𝝃𝒊𝒋𝒕 - enable us to
formulate three orthogonality conditions:
𝐸 𝑘𝑖𝑗𝑡 𝝃𝒊𝒋𝒕 = 0
𝐸 𝑏𝑖𝑗𝑡 1𝝃𝒊𝒋𝒕 = 0
𝐸 𝑤𝑖𝑗𝑡 𝝃𝒊𝒋𝒕 = 0
where 𝝃𝒊𝒋𝒕 is the productivity innovation. Appealing to the analogy principle allows us to replace
these population moments by their sample counterparts and to retrieve consistent estimates of 𝛽𝑘𝑗
and 𝛽𝑙𝑗 by minimizing the criterion function:
𝝃𝒊𝒋𝒕
𝑵
𝒊=𝟏
𝑻
𝒕=𝟏
𝑘𝑖𝑗𝑡
𝑏𝑖𝑗𝑡
𝑤𝑖𝑗𝑡
𝑻
𝑪 𝝃𝒊𝒋𝒕
𝑵
𝒊=𝟏
𝑻
𝒕=𝟏
𝑘𝑖𝑗𝑡
𝑏𝑖𝑗𝑡
𝑤𝑖𝑗𝑡
Empirical estimates of the productivity innovation 𝝃𝒊𝒋𝒕 can be retrieved by non-parametrically
regressing 𝜔 ∗𝑖𝑗𝑡 on 𝜔 ∗𝑖𝑗𝑡 −1 , and 𝜔 ∗𝑖𝑗𝑡 can be obtained by computing 𝛷𝑖𝑗𝑡 -𝛽𝑘𝑗
𝐺𝑘𝑖𝑗𝑡 − 𝛽𝑤𝑗𝐺𝑤 −
𝛽𝑏𝑗𝐺𝑏𝑖𝑗𝑡 , where 𝛽𝑘𝑗
𝐺𝑚, 𝛽𝑤𝑗𝐺
and 𝛽𝑏𝑗𝐺
denote initial values for 𝛽𝑘𝑗 , 𝛽𝑤𝑗 and 𝛽𝑏𝑗 . We use OLS
estimates as initial values.
56
A.2 Results31
Table A.1: Correlation matrix between different productivity measures
TFP-Gross output-OLS
TFP-Gross output-FE
TFP-VA-OLS
TFP-VA-ACF
TFP-SOLOW ln(Y/L) ln(VA/L)
TFP-Gross output-OLS 1 TFP-Gross output-FE 0.940 1
TFP-VA-OLS 0.538 0.588 1 TFP-VA-ACF 0.403 0.424 0.679 1
TFP-SOLOW 0.719 0.686 0.559 0.286 1 ln(Y/L) 0.039 0.243 0.521 0.515 0.184 1
ln(VA/L) 0.242 0.434 0.728 0.357 0.322 0.863 1 - All correlation are significant at the 0.001 significance level.
Table A.1 presents a correlation matrix between the different demeaned TFP measures. All
measures are positively correlated with each other and with output and value-added per worker.
Overall, then, the relative ranking of firms in terms of productivity is rather robust to using different
methods of estimating productivity.
31
Sector-specific production function estimates are available from the authors upon request, but suppressed to
conserve space.
57
Appendix B: Data Appendix
B.1 Construction of panel
Survei Industri (SI) from Indonesia‘s statistical bureau (Badan Pusat Statistik—BPS) provides the
plant-level data used in this paper. The SI is an annual census of all manufacturing establishments with more
than 20 employees. While the annual survey has been conducted going back to 1975, field procedures for
identifying new firms were dramatically improved over 1985-1990, making the accuracy of measuring entry
prior to 1990 problematic (see Ascwicahyono, 2008). As a result, this paper only considers the data post
1990. We use data up until 2001, since data from later years are not immediately comparable due to changes
in firm identifiers, changing definitions of key explanatory variables, and province splits.1
B.2 Construction of Key Explanatory Variables
Sector
Sector of main product In order to classify establishments by industry, BPS records the five-digit
International Standard Industrial Classification (ISIC) for firms based on the product with the largest
production value in any given year. In 2001, BPS changed the classification of plants from the second
revision of the ISIC to the third. A consistent bridge between these different coding systems was constructed
based upon the inclusion of both codings in the dataset for the 2000 survey database. This bridge was
corroborated using a bridge provided by BPS. In many cases, the industry code provided in the dataset was
truncated to four or fewer digits. Where possible, if an adjacent year‘s reported industry for the same firm
was available that was used to fill in the truncated digits. Where plants produce multiple products or the
production processes allow changing from one product to another, we may expect to see the coded industry
of production change from year to year. In such cases, the mode sector code is used (with ties going to the
initial sector code reported).
Labor
Total labor for an establishment was defined as the sum of all paid and non-paid workers, whether
production or non-production workers.32 Production workers were defined by BPS as all workers who work
directly in the production process or activities connected with production process, and non-production
workers were defined to be all other workers. These definitions roughly correspond to traditional definitions
of blue- and white-collar workers, respectively (see below).
Blue collar workers the sum of all production and unpaid workers
White collar workers: the sum of all non-production workers
Unskilled ratio. The unskilled ratio was constructed as the share of blue collar workers (including unpaid
production workers) as a share of the total establishment employment.
The establishment-level total wage bill was constructed as the sum of cash wages/salary and in-kind benefits
for production and non-production workers deflated to 1993 rupiah using the national consumer price index
obtained from the World Development Indicators.
32
Although BPS does not explicitly define it, this is understood to include both permanent and temporary workers.
58
Capital and materials
Capital was defined using the provided estimated value of machinery and equipment for each establishment
in a given year. Where the estimated value was not available, the book value was used as provided. These
values were deflated to 1993 rupiah values on an annual basis using the reported GDP deflator for machinery
for 1993-2001 provided by BPS and using the manufacturing sector deflator for 1990-1992. The
manufacturing survey does not include data on the value of the capital stock for 1996, so these values were
interpolated using predicted values constructed from a regression of the capital stock on contemporaneous
output, material inputs, labour usage, ownership characteristics, whether or not the firm exports, province
and lagged capital for 1991-1995. In a separate imputation procedure, we also imputed capital for firms that
did not report their capital stocks.
Materials were defined using the total reported value of raw materials used by the firm. These were deflated
to 1993 rupiah using a deflator constructed from 2-digit industry GDP deflators weighted by the shares of
input indicated from input-output tables obtained from the OECD Input-Output Table Database for 1995
and 2000.
Value-added and gross output
Value-added and gross output were calculated by BPS on the basis of the total value of production (and
net of total expenditure for value added). Both variables were deflated to 1993 rupiah using a 2-digit industry
level GDP deflator.
Firm age
Firm age was constructed using the difference between the survey year and the year the firm reported the
start of production in that province
Sector characteristics: Financial Dependence, turnover and the natural rate of entry
3-digit ISIC revision 3 industry-level measures for financial dependence, turnover, the natural rate of entry are
based on those presented in Rajan and Zingales (1998) and Micco and Pages (2004). Note that these proxies
are based on US industries.
The financial dependence measure is equal to the industry-level median of the ratio of capital expenditures
minus cash flow over capital expenditures. The numerator and denominator are summed over all years for
each firm before dividing collected from Standard and Poor‘s Compustat database. Cash flow is defined as the
sum of funds from operations, decreases in inventories, decreases in receivables, and increases in payables.
Capital expenditures include net acquisitions of fixed assets.
Natural rate of entry is measured as the mean number of new firms between periods t-1 and t , divided by
number of firms in period t, by two digit SIC code for the United States over the period 1963-1982. Source:
Table 5 in Dunne et al (1988).
Turnover (Excess employment reallocation) is defined as the sum of job creation and job destruction at 4
digit SIC code, minus the absolute value of net job creation. As with job reallocation, we compute the time
average of the annual measures. Time average by sector for period 1973-1993. Source: Job Flows data Davis,
Haltiwanger and Schuh (1996)
59
Export Status and ownership
Exporters were identified based upon reporting that the share of output exported was nonzero and
nonmissing, and foreign owned firms based upon whether foreign ownership was reported to be nonzero
and was nonmissing. Government owned firms were defined the same way based on both local and
national government.
Entry and exit
Entry is defined as the first year an establishment is observed in the data. Firms may have been in operation
for several years prior to crossing the minimum threshold of 20 employees to be included in the survey.
Firm exit is defined as permanent exit from the dataset. Note that, since the survey only includes firms with
20 or more employees, firms whose employment falls below this treshhold will be defined as having exited in
case they do not reappear in the data
B. 3 Cleaning of outliers and nonsensical values
As mentioned earlier, the problem of non-persistent extreme values for many variables is widely
discussed in published work using the SI. In some cases, these values may be the result of key punch errors,
where, for example, ownership share is recorded as 340 percent rather than 34 percent. Where shares
(exports and ownership) were reported, these were easily corrected, but for balance sheet variables, more
extensive cleaning was required.
These likely key punch errors and other highly volatile trends were corrected in the data by
identifying firms with implausibly large non-persistent changes over one or two years and replacing values
with those of preceding and succeeding years (or just one or the other of the observation was at beginning or
end of the series). Based on close observation of normal variation of these variables, the thresholds for
identifying large nonpersistent shifts were a 100% change in Labor, a 200% in real value added and real
output, a 150% change in real inputs, and 100% change in capital and average wages. Next, firms were
dropped from the data when they had such a significant number of non-persistent jumps that it was
impossible to interpolate (this constituted less than 1% of the sample). Finally remaining outliers were
identified by eyeballing plots of key relationships (e.g. inputs per worker) and spot interpolating obvious
remaining outliers.
60
Appendix C: Assessing the robustness of our results against excluding zombie firms.
The SI dataset shows a noticeable spike in exit in 2001.33 A large number of firms that exited in 2001
reported exactly identical values on key explanatory variables (i.e. having the same, nonmissing values for
labor, output, value-added, capital, materials, and wages) for the period 1998-2000 prior to exiting. Since this
is unlikely to be an accurate reflection of their true performance, it is likely that these firms may only be
achieving these results on paper.
To examine how these ―zombie‖ firms might affect our estimates of entry and exit, we distinguish between two different types of ―zombie‖ firms: (1) zombie1: firms that exit the year after next and have the same values next year as this year (2) zombie2: firms that exit in 3 years and have the same values for intervening years, Table C.1 documents the annual prevalence of each type as well as adjusted exit rates that define firms as
exiting when they become ―zombies‖ rather than when they exit from the data. Each year there are a small
but nontrivial number of zombie firms. However, the number of zombie firms spikes during the crisis. When
we correct exit rates for this problem, we see quite a different pattern in exit rates, not just for 2001.
EXIT ADJUSTED FOR ZOMBIES
year zombie1 zombie2 exit exit (corrected
for zombie 1) exit (corrected for
zombie 1 and 2) 1992 3.7% 2.8% 8.6% 12.2% 11.4% 1993 3.1% 2.1% 7.8% 7.4% 9.9% 1994 2.7% 3.4% 6.8% 6.5% 7.7% 1995 5.8% 4.0% 5.8% 8.2% 7.9% 1996 8.7% 4.7% 8.7% 6.4% 10.4% 1997 2.0% 1.0% 10.0% 10.0% 10.0% 1998 1.9% 2.3% 10.5% 10.5% 10.3% 1999 2.7% 6.5% 4.5% 4.5% 5.3% 2000 8.2%
4.7% 4.7% 10.3%
2001
12.4% 12.4% 4.2% Total 2.8% 2.4% 8.0% 8.0% 8.0%
To examine how exclusion of such zombie firms might affect our results, we re-estimated our model using
alternative definitions of exit; the attenuation of the link between productivity and exit turns out to be robust
to exclusion of these zombie firms.
Results with uncorrected exit measure:
all precrisis Crisis recovery
Odds
ratio/se
Odds
ratio/se Odds
ratio/se Odds
ratio/se
Exit (uncorrected)
0.881*** 0.827*** 0.961** 0.866***
(0.010) (0.016) (0.019) (0.016) Exit-corrected for zombie1 0.897*** 0.847*** 0.975 0.875***
(0.010) (0.017) (0.019) (0.017)
Exit-corrected for zombie1 and zombie2
0.897*** 0.857*** 0.972 0.884*** (0.010) (0.015) (0.021) (0.017) (0.010) (0.015) (0.018) (0.020)
33
For this table, exit in 2001 means that 2000 was the last year the firm appeared in the data.
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