Offshoring and Wages in French Manufacturing Firms

Amelie Schiprowski

This Version: May 21, 2012

Master Thesis

Academic Year 2011/2012

Supervisor: Denis Fougere

Second Reader: Thierry Mayer

Ecole Doctorale, Sciences Po

Economics and Public Policy, Phd Track

Offshoring and Wages in French Manufacturing Firms

Amelie Schiprowski

May 21, 2012

Abstract

This Master thesis empirically assesses the interaction between trade flows and wages in

a within-firm framework. By focusing on the role of offshoring flows, I propose to analyze

two channels through which offshoring has been predicted to impact wages at the firm

level. On the one hand, offshoring is expected to negatively affect wages by modifying

the composition of the firm’s input choice. This effect will depend on the elasticity of

substitution between the worker’s labor force and import flows; it is therefore supposed to

be skill-specific. On the other hand, offshoring can enhance firm productivity and thereby

increase the wages of all skill groups.

These predictions are analyzed empirically using panel data that matches information

on firm-level trade flows, balance sheet variables, and wage outcomes of four different

occupational groups. In a first step, the endogeneity of both offshoring and export flows

at the firm level is addressed with an instrumental variable strategy proposed by recent

contributions in the literature. On these grounds, it is in a second step possible to relate

the exogenous component of these two variables to wage outcomes, skill-specific labor

demand and productivity measurements. I find evidence that offshoring and exports both

positively affect productivity. Further, exports are positively associated with the demand

for labor of all four occupations, and offshoring has a slight negative impact on the demand

for low-skilled labor. However, none of these changes is found to translate into significant

wage responses within the firms in my sample.

I thank Denis Fougere for supervising this Master thesis. I thank him, Juan Carluccio and Erwan Gautier for

their help, and for sharing their expertise. I am grateful towards Laurent Baudry for all his help with the data.

3

Contents

1 Introduction 5

2 Theoretical Discussion 8

2.1 Exogenous and Endogenous Components in the Firm’s Decision to Offshore . . 8

2.2 Predictions on Counteracting Wage Effects . . . . . . . . . . . . . . . . . . . . 10

3 Data Sources and Characteristics 13

3.1 Data Sources and Main Variables of Interest . . . . . . . . . . . . . . . . . . . . 13

3.2 Merge of Data Sources and Resulting Sample . . . . . . . . . . . . . . . . . . . 15

3.3 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3.1 Firm Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3.2 Patterns of Trade Behavior . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3.3 Trade Intensity and Firm Characteristics: The ”Heterogeneous Firms

Phenomenon” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Empirical Strategy 22

4.1 Challenges to Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Instrumentation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2.1 Approaches in the Literature . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2.2 Construction of Instruments . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.3 Two Stage Estimation of Wage Equation . . . . . . . . . . . . . . . . . . . . . . 27

4.3.1 First Stage Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.3.2 Second Stage Wage Equations . . . . . . . . . . . . . . . . . . . . . . . . 31

4.3.3 The Role of Firm Control Variables in the Wage Equation . . . . . . . . 31

5 Main Results and Extensions 31

5.1 Baseline Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.2 Decomposition of Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.2.1 Productivity and Factor Demand . . . . . . . . . . . . . . . . . . . . . . 34

5.2.2 Skill-Specific Labor Demand . . . . . . . . . . . . . . . . . . . . . . . . 35

5.3 Alternative Adjustment Mechanisms: The Role of Union Bargaining . . . . . . 37

6 Conclusion and Discussion 39

A Appendix 45

A.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

A.2 Supplementary Table to Section 4.2.2 . . . . . . . . . . . . . . . . . . . . . . . . 46

A.3 Supplementary Table to Section 5.2 . . . . . . . . . . . . . . . . . . . . . . . . . 46

4

1 Introduction

In an assessment on the impacts of changing trade patterns on wage outcomes in developed na-

tions, Paul Krugman argued in 2008: ”How can we quantify the actual effect of rising trade on

wages? [...] The answer, given the current state of the data, is that we can’t.” This statement

was made before the emergence of a large empirical literature employing microeconomic data

to analyze the relationship between international trade and wages. The possibility of match-

ing different individual-level, firm-level and industry-level data sources has in the meantime

favoured a microeconomic approach towards the different channels through which increased

trade flows have affected worker outcomes in developed nations.

Among these channels, this Master thesis seeks to assess the relationship between offshoring

flows and wages at the firm level. In public debates, it is often unclear which exact firm

behaviors the term offshoring describes. Feenstra (2010) addresses this uncertainty by defining

”narrow offshoring” as the process that occurs ”when a firm sends a portion of its production

process abroad, but keeps it in-house [...].” He opposes this definition to a ”more common

definition of offshoring” which implies that offshoring ”encompasses both the multinational

strategy and foreign outsourcing, meaning it refers to any transfer of production overseas,

whether it is done within or outside the firm.” From now on, I retain this ”more common”

definition, which will allow to identify offshoring through firm-level import flows.

Through its focus on firm-level offshoring flows, this Master thesis is related to two different

strands of the empirical literature on labour market effects of trade. First, it is situated within

several contributions that have related both import and export flows at the firm level on

worker’s wages. As it will concentrate on those wage impacts that have been predicted to

be specific to offshoring trade flows, it is also linked to the works that relate industry-level

offshoring intensities to individual employment outcomes.

The different firm-level analyses on the relation between trade flows and wages can be fur-

ther distinguished according to their focus on the extensive or the intensive margin of trade.

Situated most often in a cross-sectional framework, the extensive margin of trade describes in

our context differences in trade statuses across firms. Empirical contributions in this frame-

work analyze how a firm’s status as a domestic or an international firm affects its worker’s

wage outcome, controlling for other observable worker and firm characteristics. For instance,

Baumgarten (2010) decomposes wage differentials between exporters and non-exporters. He

finds a conditional “exporter wage premium” that contributes to growing wage inequality, pre-

dominantly within skill groups. Martins and Opromolla (2011) assess the relationship between

exporting, importing, and wage premia. They show that while firm characteristics explain

the larger part of the exporter wage premium, the importer wage premium can be explained

primarily through worker characteristics. In a preliminary draft, Helpman et al. (2011) ar-

gue that wage inequality following increased trade occurs within sectors and occupations, but

between firms. The extensive margin can also be analyzed in a within-firm framework, which

has however been done less frequently. It then consists in a firm’s ”switching behavior”, i.e. in

its transition from a domestic to an international firm or vice versa. Martins and Opromolla

5

(2009) show that the transition of a domestic firm towards importing or exporting increases

its workers’ wages.

The intensive margin of trade is mostly conceptualized in a within-firm framework. Here,

the question is how a change in the intensity of firm-level trade flows affects wages. Amiti and

Davis (2008) assess theoretically and empirically how tariff cuts affect workers’ wages. They

show that a fall in output tariffs lowers wages at import-competing firms, while raising wages

at exporting firms. Macis and Schivardi (2012) show that the increase in Italian firms’ export

share of sales, induced by the 1992 devaluation, caused wages to be higher. They attribute

this to both a rent sharing effect and to changes in the market value of workers’ unobservable

skills.

Among these different approaches, this Master thesis is situated in the within-firm, intensive

margin framework. However, not all import flows will be of interest here. Indeed, the distinctive

feature of offshoring consists in the value added that is imported by the firm. It is therefore

expected not to have the same labour market outcomes as the import of intermediate inputs

or raw materials.

Grossman and Rossi-Hansberg (2008) formalize this distinction by characterizing offshoring

as the trade in tasks, opposing it to the exchange of goods that traditionally described trade

activities. They present a model that describes the determinants of the firm-level decision to

offshore and that allows decomposing the wage effects of a decrease in offshoring costs. This

theoretical specification represents the framework of several empirical contributions that assess

the impacts of industry-level offshoring on individual worker level outcomes.

For instance, Geishecker and Gorg (2008) relate industry-level offshoring intensities in the

UK service sector to individual-level data and identify a negative wage impact for low and

medium skilled individuals. Liu and Trefler (2011) show that industry-level offshoring in the

US service sector increases the frequency of worker-level occupational switching and associated

wage losses. Ebenstein et al. (2009) construct occupation-level measures of offshoring and

assess with US data how an increase in this measure impacts the movement of labor across

sectors and occupations. They find important wage effects resulting from these movements.

Baumgarten et al. (2010) adopt a task-based framework that implies analyzing individual

exposure to offshoring as a function of the task realized by the worker during the production

process. Using German data, they find substantial negative wage effects of offshoring for low-

and medium-skilled workers within a task group.

These contributions base their identification strategy on variations in the industry- or task-

specific offshoring flows. The firm-level equivalent would consist of assessing how firm-level

variations in offshoring flows affect workers employed in this firm. The empirical literature

employing such a framework is less exhaustive. A recent contribution by Hummels et al. (2011)

suggests a method of conceptualizing and identifying the within-firm dimension of interactions

between offshoring and wages. These authors propose to consider offshoring as changing the

composition of the firm’s input choice and therefore affecting skill-specific wages depending on

the worker’s substitutability by import flows. Further, offshoring can enhance firm productivity

6

and thereby increase the wages of all workers. They empirically confirm these mechanisms with

data on Danish firms and workers.

Similarly to Hummels et al. (2010), I seek in this Master thesis to explore the relation be-

tween offshoring and wages at the firm level. I also include firm-level exports as an explanatory

variable in the wage function. This is necessary in order to provide a complete picture of the

mechanisms at stake, since a firm’s exporter status has been shown to be a good predictor of

its importer status (e.g. Martins and Opromolla (2009)). However, the main motivation and

focus will consist in analyzing the impacts of offshoring as the import of added value by the

firm. This implies that a large part of the discussion will be exclusively devoted to this aspect.

The relation between wages and firm-level trade flows suffers from a simultaneity problem

due to unobserved, time variant factors that affect both the wage setting process and the

firm’s intensive margin of trade. For instance, unobserved shifts in demand or productivity

can impact both wages and trade flows; in which case the data will reflect a positive, but

non-causal correlation between offshoring, exporting and wages.

Besides addressing this problem with the help of an instrumental variable strategy, my

main objective will consist in identifying channels through which offshoring (and exporting)

affects skill-specific wages within the firm. Employer-employee data would allow me to observe

worker characteristics and to control for eventual changes in the firm’s workforce over the

sample period. While I do not have access to such data, I am still able to observe wages of four

different occupational groups on the firm level. These indirectly reflect different skill levels and

allow to differentiate wage impacts accordingly.

Bearing this in mind, two main mechanisms will be of particular interest: on the one hand,

I seek to identify whether offshoring substitutes firm-level labor and thus lowers wages through

a decrease labor demand. This effect is expected to vary according to the worker’s occupational

status. On the other hand, offshoring can lead to cost reductions or productivity gains and

therefore positively affect the demand for all production factors as well as the marginal product

of each worker. This should lead to a positive wage effect for all occupations.

In order to formalize these mechanisms and to derive implications for my empirical ap-

proach, I start this paper by outlining two theoretical frameworks that allow to understand

both exogenous and endogenous components of the firm decision to offshore, as well as the

channels through which offshoring can be expected to affect firm-level wages of the four differ-

ent skill groups that can be identified in the data (Section 2). I then move over to summarizing

the different firm-level panel data sources and the construction of the main variables of interest.

Further, the specific characteristics of my final sample will be described (Section 3). Having

these characteristics in mind, Section 4 presents the empirical strategy that allows in a first

stage to endogenize firm-level trade flows and in a second stage to assess the impact of these

flows on firm wages of four different occupational groups. Section 5 discusses the main results

of this two stage estimation. It will be shown that unlike other empirical contributions, my

data displays nearly no significant impact of firm-level trade flows on wages. This result leads

me to present two extensions to the analysis. First, I decompose the causal chain according to

7

which offshoring is supposed to affect wages, by assessing separately its impact on productivity,

the demand for production factors, and the demand for skill-specific labor. I am thereby able

to find evidence that an increase in the exogenous components of offshoring and exporting

positively affects the firm’s productivity, demand for capital and demand for the overall labor

force. Exports are positively associated with the demand for all skill groups and offshoring

negatively is negatively associated with the demand for blue-collar workers. It appears however

that these mechanisms do not translate into wage responses. Given this result, I then discuss

the potential role of non-competitive labor market forces and provide reduced-form evidence

for this role, within the bounds of what is possible with my data sources.

2 Theoretical Discussion

Firm-level offshoring and wages are not connected by a clear-cut causal relationship. It is

therefore helpful to start with a discussion of theoretical contributions that illustrate the

mechanisms that an empirical analysis will try to identify. Although my empirical analysis

will include both offshoring and exports at the firm-level, the following section almost entirely

focuses on offshoring, which is the main topic and motivation of this paper.

Two aspects will be crucial in empirically identifying and interpreting the interaction be-

tween offshoring and wages: on the one hand, we are interested in the determinants of the

firm’s decision to offshore, which should particularly help understanding its endogenous com-

ponents (Section 2.1). On the other hand, potential channels through which offshoring can

impact wages need to be discussed before conducting the empirical analysis (Section 2.2).

For these two purposes and against the background of my dataset’s characteristics, which

will be outlined in Section 3, two contributions are particularly relevant. First, many empirical

studies on offshoring and wages rely their predictions on the fundamental contribution by

Grossman and Rossi-Hansberg (2008), who model origins and impacts of ”trade in tasks” at

the industry level. Second, the production function-based framework presented by Hummels

et al. (2011) allows to assess the role of firm-level exports at the intensive margin, which also

corresponds to the firm-level data I will be analyzing. I limit the following discussion to the

main intuitions from these two models and do not intend to give an exhaustive presentation

of their mechanisms.

2.1 Exogenous and Endogenous Components in the Firm’s Decision

to Offshore

The Task and Industry-Level Framework in Grossman and Rossi-Hansberg (2008)

Assuming perfectly competitive labor and product markets, Grossman and Rossi-Hansberg

(2008) present a two-industry model where production requires a continuum of tasks realized

by high-skilled workers (H−tasks) and low-skilled workers (L−tasks). During the production

process, skills are thus translated into tasks, which then produce output. Offshoring costs imply

that only L− tasks can be produced abroad, for instance because H − tasks require certified

8

skills that only be ensured by domestic workers, or because they can only be realized through

direct human interaction. 1

The firm faces two choices concerning the composition of its labor force: first, the production

technology for a given product k may allow a firm to choose the intensities of L− tasks, aLk,

and the intensities of H − tasks, aHk that it performs to produce a unit of k. Second, the firm

can decide to substitute domestic by foreign factors in order to realize a given L− task: a firm

which chooses aLk as the intensity of L− tasks to produce product k must employ aLk ∗ βt(i)units of foreign labor to perform task i offshore, where β represents a shift parameter for

exogenous offshoring costs, which could for instant be affected by the removal of tariffs or a

decrease in transportation costs. The equilibrium amount of offshoring occurs where the costs

of performing the task abroad equals its cost at home, w∗βt(I) = w, where I represents the

marginal task performed at home. In each industry, the marginal task performed at home is

assumed to be equal across firms.

The Grossman and Rossi-Hansberg model illustrates the importance of exogenous changes

in trade costs in order to understand the rise of offshoring. In this framework, a decrease

in both foreign wages w∗ and in offshoring costs β can raise industry-level offshoring levels,

holding domestic wages fixed. The level of firm-level offshoring is thus determined by an

exogenous shift to the cost of offshoring. However, the authors do not account for for the

endogenous components of the firm’s offshoring decision. While they stress the industry- and

task-specific dimension of offshoring intensities, they do not explain why we observe in the data

an important amount of heterogeneity across firms. The literature on heterogeneous firms in

international trade suggests that high productivity firms are more likely to pay higher wages,

export more and buy more imported inputs (e.g. Bernard and Jensen (1999); Melitz (2003)).

Accounting for this heterogenity in the firm-level decision to offshore and export is crucial

when analyzing firm-level data.

The Firm Decision to Offshore in Hummels et al. (2011) Having this heterogeneity in

mind, Hummels et al. (2011) derive their empirical analysis from a firm-specific Cobb-Douglas

function, in which output in firm j at time t is produced using capital Kjt, high-skilled labor

Hjt, and a composite input combining low-skilled labor Ljt and imported goods Mjt. Their

specification distinguishes between high- and low-skilled labor and, similar to Grossman and

Rossi-Hansberg (2008), relies on the assumption that only low-skilled labor can be substituted

by imported goods. However, they allow in their empirical specification for offshoring to affect

high-skilled labor, and present a generalization of their framework, in which any skill group can

see its labor force substituted by imported goods. I adopt this generalization to my dataset, in

which I can observe wages of four different occupational groups (blue-collar worker, white-collar

worker, intermediary profession, executives). I decide to specify the occupation of executives

to be non-substituable by imports, due to both its skill requirements and its interactive nature.

While an executive thus enters as a non-composite factor, all other three types of occupations

indexed by g = 1, 2, 3 are specified to be potentially substituable:

1This assumption will be relaxed by most empirical contributions, see for instance Baumgarten et al. (2010).

9

Yjt = AjtKαjtH

βjt

3∏g=1

Cγggjt

where each Cgjt is composite of

(Lσg−1

σg

gjt +Mσg−1

σg

jt

) σgσg−1

and where3∑g=1

γg = 1 − α− β

Before analyzing its predictions concerning the impacts of an increase in Mjt, two main dif-

ferences between this production-function based framework and Grossman and Rossi-Hansberg

(2008) must be noted.

First, the absence of tasks in Hummels et al. (2011) reflects the assumption of a perfect

mapping between skills and tasks. According to Acemoglu and Autor (2010), this assumption

would be simplifying. They define a task as a unit of work activity that produces output, while

a skill is a worker’s endowment of capabilities for performing tasks in exchange for wages. In

Grossman and Rossi-Hansberg (2008), skills can be substituted by offshored production only

through the intermediary of tasks: a firm does not offshore skills, but the activity to which

skills are allocated. The specification presented by Hummels et al. (2011) does not include

this intermediary step. Although this is certainly a simplifying assumption that leads to a

loss of information concerning the channels through which offshoring affects workers, it is well

adapted to my dataset. As I can neither identify individual workers employed in a given firm,

nor the tasks to which they are allocated, I retain the same assumption, bearing in mind its

potential associated shortcomings.

The second difference concerns the interaction between firm-specific productivity and the

decision to offshore. Here, Hummels et al. (2011) allow for a richer specification. Indeed, an

increase in the factor augmenting productivity Ajt can simultaneously affect the demand for

all input factors, including both imports of all kinds and labor of all skill groups. Thereby, the

decision to offshore is not purely exogenous, such as specified by Grossman and Rossi-Hansberg

(2008). While Mjt can increase through an exogenous shock to the cost of offshoring, it can also

increase due to a favorable shift in demand or productivity. In such a scenario, we would observe

a positive correlation between offshoring levels and labor demand that is non-causal. For the

empirical strategy, this implies that it will not be justified to consider firm-level offshoring flow

as exogenous in the wage equation and that an instrumentation strategy will be needed to

obtain unbiased estimates.

2.2 Predictions on Counteracting Wage Effects

Assuming that it has been possible to endogenize offshoring flows, what are the channels

through which wage impacts can be expected to operate? While Grossman and Rossi-Hansberg

10

(2008) and Hummels et al. (2011) were making different assumptions as to the firm-level de-

terminants of the decision to offshore, these assumptions lead to similar predictions concerning

the mechanisms through which this decision affects wages. In their formulation, the two ap-

proaches nevertheless remain distinct, since their mechanisms operate at different levels.

Grossman and Rossi-Hansberg (2008) pursue their analysis of comparative statics at the

industry-task level, which directly results from the assumption that in each industry, the

marginal task performed at home is equal across firms. That is, all firms in one industry have

the same task-specific level of offshoring. This assumption fits particularly well those empirical

works that exploit variations in offshoring levels on the industry-level, such as Liu and Trefler

(2011) and Baumgarten et al. (2010).

On the contrary, Hummels et al. (2011) remain within their firm-specific production func-

tion framework when analyzing wage impacts of offshoring. Here, each firm is allowed to

have different offshoring levels. As this assumption corresponds better to my firm-level data,

in which I will assess the impacts of firm-specific offshoring and export flows, I focus in the

following presentation of channels on this firm-level approach.

According to both contributions, an increase in offshoring can expect to affect wages through

a labor demand effect as well as through a productivity effect. Grossman and Rossi-Hansberg

(2008), who pursue a general equilibrium approach, further identify a potential Samuelson-

Stolper price effect. In the following, these channels are briefly outlined.

Labor Demand Effect Section 2.1 has shown that both presented frameworks assume the

substitutability of domestic labor by imported goods. An expansion of offshoring increases the

effective supply of low-skilled labor, which under the hypothesis of perfectly competitive labor

markets implies negative wage impacts.

Hummels et al. (2011) derive this effect by first writing the marginal product of a given

skill group. In a competitive framework, the firm takes product demand as given and chooses

its inputs accordingly. In the version of their model that is adapted to my data with four

different occupation groups, the marginal product of skill group a blue-collar worker (g=1)

writes:∂Yjt∂L1jt

= (1 − α− β)AjtKαjtH

βjtL

− 1σ1

1jt C1σ1

+γ1−1

1jt

3∏g=2

Cγggjt

Holding other factors constant, the labor demand effect of an increase in Mjt depends mainly

on the CES parameter σg: if 1σg

< (α + β), the demand for labor of type g decreases. The

higher the substitability of skill-specific labor by imports, the higher will be this decrease.

Having determined that an increase in Mjt implies a decrease in labor demand depending

on parameter σ1, the existence of a negative wage impact on the concerned skill group, depends

according to the authors, on the elasticity of labor supply. Under the plausible assumption

of an upward sloping labor supply curve, they show that the sign of the wage effect can be

expected to follow the same conditions as firm-level labor demand. I will in Section 6 discuss

circumstances under which changes in firm-level labor demand do not translate into wage

responses. But for now, I remain within the author’s reasoning.

11

Productivity Effect According to both contributions, the potential negative labor demand

effect can be offset by a positive productivity effect of offshoring, that impacts wages similarly

as would technological progress. On the one hand, the assumption of a perfectly competitive

framework implies that offshoring-related productivity increases are directly translated into

wage increases for domestic factors. On the other hand, an increase in factor-augmenting

productivity implies an increase in the demand for all production inputs.

Amiti and Wei (2006) name several possible channels through which service offshoring can

affect productivity; parts of these channels also concern the manufacturing sector. First, a

firm can be expected to offshore its relatively inefficient parts of the production process and to

thereby increase its average productivity through a compositional effect: the firm keeps those

production processes in-house that it performs most efficiently. Further, offshoring may allow

for structural gains, for instance through the rationalization of the production process. Finally,

offshoring can imply a productivity-enhancing diversification of inputs. These mechanisms all

imply an increase in Ajt in the presented production function.

Hummels et al. (2011) show that following this increase, that augments the demand for all

factors, the net wage effect of offshoring is alleviated, as compared to the direct labor demand

effect operating through the substitutability of Mjt and Ljt. These two different levels of wage

effects need to be born in mind when setting up the empirical approach. A detailed discussion

on how Hummels et al. (2011) propose to ensure their identification will be made in Section

4.3.

Industry-Level Price Effect While the firm-level analysis by Hummels et al. (2010) oper-

ates in a partial equilibrium framework, Grossman and Rossi-Hansberg’s (2008) general equi-

librium model implies a Stolper-Samuelson price effect. Cost savings induced by offshoring

concern mostly labor-intensive industries, which implies a fall in the relative price of the labor-

intensive good, which the authors expect to operate to the disadvantage of low-skilled labor.

This general equilibrium is difficult to identify and left aside by most empirical works.2 I will

not be able to consider it either.

Note on the Role of Union Bargaining All the presented mechanism operate under the

assumption of competitive labor and product markets. Obviously, such a framework looses

its validity with the introduction of non-competitive labor market forces. Kramarz (2008)

introduces the firm’s offshoring decision into a two-stage bargaining game, in which the firm

first decides on its level of outsourcing and then engages into strongly efficient bargaining with

its workers. In this framework, the impact of offshoring on worker’s bargaining power operates

through the firm’s threat point: credible outsourcing opportunities positively influence the

firm’s outside option, and thus its threat point.

Since I will limit my assessment of the role of union bargaining to the provision of prelimi-

nary reduced-form evidence, I do not provide a detailed discussion of these effects here.

2See for instance Liu and Trefler (2011).

12

3 Data Sources and Characteristics

To what extent will my data sources allow to sort out the described mechanisms at the firm

level? The following section first summarizes the different data bases of which my final sample

will be constituted as well as the main variables that will be of interest (3.1). In a second step,

I present some main characteristics of this final sample and discuss their implications for the

empirical analysis (3.2 and 3.3).

3.1 Data Sources and Main Variables of Interest

Declaration des Douanes The Declaration des Douanes is an exhaustive dataset that

allows to identify all import and export flows reported by French firms on the year-product-

country level. It can be assumed that all firms for which no trade flow is reported in a given

year did not trade in that year. Flows are reported in volume and in their value; however, only

values (in euros) will be employed for the analysis in this paper. Products are reported in the

HS6 Nomenclature defined by the World Customs Organization. This nomenclature has been

revised in 1992, 1996, 2002 and 2007. In order to ensure comparability across years and in order

to be able to match each year-product-country flow with Comtrade data when constructing

the instruments (cf. section 4.2), I convert all products into the 1992 Nomenclature, following

the conversion tables set up by UN Comtrade.3 This conversion leads to a loss of observations

ranging from 0.1% to 2.0% of the Douanes database, depending on the sample year.

My focus on offshoring requires to distinguish between two different categories of imports.

Imports of raw materials and intermediary inputs will be of only marginal interest, because

the main target consists in identifying those imports that have the potential of substituting

labor in the importing firm, as specified in Section 2.1.

The identification of offshoring flows in the data follows Kramarz (2008). The latter qualifies

an import flows as offshoring if the three first digits of the firm’s industry code matches with

the three first digits of the imported good. In order to be able to implement this approach, I

convert all HS6 codes from the nomenclature 1992 into the nomenclature 2007, then convert

these into the EU CPA 2002 (Statistical classification of products by activity) system, using a

conversion table of RAMON, provided by Eurostat.4 The CPA 2002 codes are, according to

INSEE, strictly identical to the CPF rev. 1, 2003 (Classification des produits francaise).5 As

a consequence, they can directly be matched with a correspondence table of INSEE, linking

each product code to one French industry (NAF 700, rev. 1).6 I am thereby able to code those

HS6 imports as offshoring whose three digits converted product identifier corresponds to the

importing firm’s three digits industry identifier.

Enquete Annuelle d’Entreprise As opposed to the Declaration des Douanes, the

Enquete Annuelle d’Entreprise is non-exhaustive. It results from a yearly survey realized

3downloaded 03/2012 at http : //unstats.un.org/unsd/trade/conversions/HSCorrelationandConversiontables.htm.4downloaded 04/2012 at http : //ec.europa.eu/eurostat/ramon.5downloaded 04/2012 at http : //www.insee.fr/fr/methodes.6downloaded 04/2012 at http : //www.insee.fr/fr/methodes.

13

by the INSEE and covering firms with at least 20 employees and five million euros of turnover.

It provides balance sheet data and allows to identify several firm characteristics that will be

essential in understanding the relationship between the within-firm margin of trade and wages.

I clean the EAE of missing values, outliers (99th percentile and 1st percentile of the distribu-

tion of capital stock and number of employees), and of observations with characteristics outside

the official coverage of the EAE (i.e. firms with less that 20 employees). Further, production

inputs and outputs as well as value added are deflated using industry (”branche”)-specific price

indexes provided by INSEE.7

The main variables of interest in the EAE are first the firm’s real capital stock and its

labor force (both identified at the beginning of the year). Further, labor productivity can be

approximated by dividing real value added by the labor force. Total factor productivity (TFP)

can be approximated by estimating the residual of a production function linking value added

to the inputs labor and capital. This estimation can take place either in a cross-sectional OLS

or in a firm fixed effects framework. However, such an approach has been shown to suffer

from a simultaneity bias: the firm observes its own TFP and reacts by modifying its choice of

factor inputs. As a consequence, regressors are not uncorrelated with unobserved productivity

shocks that enter the ideosyncratic error term, and OLS and FE estimators will be biased.

Different solutions have been proposed to address this issue. I apply the semi-parametric

method proposed by Levinson and Petrin (2003), who use intermediate inputs to control for

unobservable productivity shocks. These intermediate inputs will in my case be measured by

real material costs.

I estimate both a fixed effects and a Levinsohn-Petrin TFP residual with the entire EAE

sample. The redicuals obtained by Levinson-Petrin and through a simple fixed effects estima-

tion are highly correlated (0.972). In what follows, ”TFP” describes the parameter obtained

through the Levinson-Petrin estimation.

ACEMO The Enquete sur l’Activite et les Conditions d’Emploi de la Main d’Oeuvre

(ACEMO), made available by the French Ministry of Labor, also relies on a non-exhaustive

firm-level survey, realized among a sample of firms with at least 10 employees. ACEMO surveys

establishments with at least 250 employees on a permanent basis; firms with less employees

are surveyed during shorter periods. However, it appears that there is a large number of

non-responses, also for large firms, which makes ACEMO a highly unbalanced panel.

ACEMO allows to identify the firm average hourly wage, as well as the number of employees

and average hourly wage of four different occupation categories (ouvrier-blue-collar worker, em-

ploye-white-collar worker, profession intermediaire-intermediary profession, cadre-executive).8

This feature makes it suitable to the theoretical discussion in section 2. While it allows to

distinguish between these four skill groups, ACEMO does not provide any worker-specific

characteristics, such as would employer-employee data. Therefore, worker characteristics can-

not enter as controls in the empirical analysis, and eventual changes in a firm’s workforce

7I gratefully take price indexes and the Stata code for cleaning the data base from Juan Carluccio.8I thank Laurent Baudry for having constructed these hourly wage measures from the raw ACEMO data.

14

composition cannot be observed.

The final ACEMO sample will be cleaned of outliers. Further, I want to ensure the compa-

rability of outcomes for the four occupational groups and therefore limit my sample to those

firms that report observations for all of these four groups. This cleaning process leads to a

sample containing 46 382 observations distributed over the period 1998 to 2008 in the manu-

facturing sector. For the period 1998 to 2005, which I will be able to merge with the EAE file,

ACEMO has 34 528 observations.

Firm-Level Bargaining Outcomes Finally, the French Ministry of Labor collects all

bargaining outcomes reported by French firms in a year. The database which I have access to

allows, through a set of dummy variables, to identify all firms that declared their outcomes of

firm-level bargaining on wages, employment and the reduction du travail (RTT) reported to

the French Ministry of Labor. As firms are forced by law report all agreements they conclude,

I assume that firms that do not report an agreement in a given year did not bargain in that

year. Further, I assume following Fougere et al. (2011) that if a firm-level agreement is signed

in a given firm, all workers of this firm are covered by the agreement. Under these assumptions,

the database is exhaustive during the sample period.

3.2 Merge of Data Sources and Resulting Sample

In all four sources, each firm can be identified through a 9-digit identification number (SIREN),

which implies that the analysis will occur at the firm level, as opposed to the plant-level (SIRET

identifier).

Merging the non-exhaustive databases EAE and ACEMO results in a highly unbalanced

and severely reduced panel, containing 17 954 observations and 4282 firms with a sample

presence of at least two years (T ≥ 2), as will be required for the subsequent within-firm, fixed

effects analysis. It is necessary to make two main preliminary remarks on the consequences of

my sample’s characteristics for subsequent analysis.

First, it needs to be assumed that entries and exits out of the sample are random and result

from the ”survey nature” of the used sources (as opposed, for instance, to data collection for

the purpose of tax collection). This assumption is necessary in order to ensure that trade

intensities and the wage setting process are unaffected by unobserved evolutions in firms will

exit the market during the sample period. As limiting the analysis to those firms which stay

until the end of the sample period would lead to a clearly insufficient number of observations

(242 firms for the subsample used in my estimation), I am forced to exclude the possibility of

attrition biases due to firm exits. However, the focus of my analysis on the intensive margin

of trade within the firm will imply a nearly exclusive usage of fixed effects estimations. The

latter will remove firm-specific, unobserved factors that drive a firm’s exit, and thereby alleviate

potential selection problems due to attrition.

Second, it is necessary to comment the characteristics of those firms that will appear in

the final subsample. Indeed, merging two surveys that concentrate on the coverage of large

15

firms implies necessarily an over-representation of the latter. In this respect, my analysis

concentrates on firms that have ”survived” a double selection process: they have first selected

into the sample, which has a coverage that favors large firms; and then into their trade status.9

I now move over to further discussing the characteristics of firms in my sample.

3.3 Descriptive Statistics

3.3.1 Firm Characteristics

It is first of all interesting to know whether the characteristics of the firms that appear in the

merged subsample differ significantly from the remaining, unmerged observations in ACEMO

and EAE. Table 1 therefore includes comparative summary statistics for these three samples

(final merge, unmerged ACEMO and unmerged EAE). Exhaustive summary statistics on all

variables that will be used in the subsequent analysis can be found in Table 11 of Appendix

A.1.

A comparison of sample means illustrates the first step of the ”double selection process”

described above. Among those firms that have ”selected” into the subsample, there is a sig-

nificantly higher share of exporters, importers and exporter-importers than in both ACEMO

and EAE. This difference is much stronger for the firms remaining in EAE, which represents

a higher share smaller, domestic and less productive firm. For instance, there are on aver-

age 9.4% more ”exporter-importers” in the final sample than among the non-merged ACEMO

observations, and 22.8% more than among the non-merged EAE observations. Even more

strikingly, among those firms that import, firms in the final sample import on average 18.5%

more than ACEMO importers and 180.1% more than EAE importers.

This feature is likely to reflect large differences in firm size and productivity. Indeed, it can

be seen that the average firm represented in the final sample has 136% more employees and

179% more capital than the average among the remaining EAE firms. The difference in labor

productivity, measured here as value added over the number of employees, amounts to striking

352%. Skill-specific wages can only be compared the final sample and non-merged ACEMO

firms, since they are not reported in EAE. Here, the differences are much smaller (0.5% for the

average wage weighted by the number of employees in each occupational category), and partly

insignificant (e.g. blue collar).

The extreme differences in key firm characteristics between the final sample and the more

representative EAE sample illustrate that my analysis will take place within a subselection

of very large and productive firms. This has to be born in mind during the entire empirical

analysis.

9For instance, Eaton et al. (2011) show with French data that exporters are large and productive firms.

16

Tab

le1:

Com

pari

son

of

Sam

ple

Mea

ns

Mea

nD

iffer

ence

Diff

eren

ce

AC

EM

OO

ut

of

Sam

ple

EA

EO

ut

of

Sam

ple

Sam

ple

wit

hA

CE

MO

wit

hE

AE

Exp

ort

er0.6

96

0.6

08

0.7

71

-0.0

75***

(0.0

05)

-0.1

63***

(0.0

04)

Imp

ort

er0.7

21

0.6

10

0.7

89

-0.0

68***

(0.0

05)

-0.1

78***

(0.0

04)

Exp

ort

erand

Imp

ort

er0.6

57

0.5

23

0.7

51

-0.0

94***

(0.0

05)

-0.2

28***

(0.0

04)

NoofObservations

16574

117133

17954

Log

Imp

ort

s14.6

08

12.9

92

14.7

93

-0.1

85***

(0.0

28)

-1.8

01***

(0.0

20)

NoofObservations

11948

71486

14159

Log

Exp

ort

s14.7

35

12.8

76

14.9

32

-0.1

97***

(0.0

33)

-2.0

56***

(0.0

23)

NoofObservations

11528

71164

13833

Log

Lab

or

Forc

e3.9

37

5.2

53

-1.3

16***

(0.0

07)

Log

Gro

ssC

apit

al

Sto

ck1.3

51

15.9

06

-1.7

99***

(0.0

11)

VA

/L

ab

or

Forc

e45.6

82

49.2

02

-3.5

20***

(0.1

75)

NoofObservations

117133

17954

Aver

age

Wage

2.6

37

2.6

26

.011***

(0.0

03)

Aver

age

Wei

ghte

dW

age

2.3

53

2.3

58

-.005**

(0.0

02)

Blu

eC

ollar

2.1

37

2.1

34

.002

(0.0

02)

Whit

eC

ollar

2.2

33

2.2

36

-.004*

(0.0

02)

Inte

rmed

iary

2.4

94

2.5

13

-.018***

(0.0

02)

Exec

uti

ve

3.0

82

3.1

15

-.033***

(0.0

02)

NoofObservations

16574

17954

Ob

serv

ati

on

sA

CE

MO

/E

AE

”ou

tof

sam

ple

”co

nta

inall

those

ob

serv

ati

on

sth

at

are

pre

sent

inA

CE

MO

or

EA

E,

bu

tn

ot

inth

efi

nal

sam

ple

.O

bse

rvati

on

sin

the

Sam

ple

resu

ltfr

om

am

erge

bet

wee

nA

CE

MO

an

dE

AE

.M

ean

sof

exp

ort

an

dim

port

flow

sex

clu

de

zero

valu

es.

Valu

ead

ded

isre

port

edin

1000

of

euro

s.T

he

”A

ver

age

Wage”

resu

lts

from

div

idin

gth

efi

rm’s

wage

bil

lby

the

tota

lnu

mb

erof

emp

loyee

s,w

hile

the

”A

ver

age

Wei

ghte

dW

age”

isw

eigh

edby

the

nu

mb

erof

emp

loyee

sin

each

occ

up

ati

onal

gro

up

.

17

3.3.2 Patterns of Trade Behavior

Extensive Margin My analysis will focus on firms that report positive trade flows in the

sample, which is due to my main goal of identifying intra-firm labor substitution and produc-

tivity effects of offshoring on wages. It could from a theoretical perspective be justified to work

on those firms that start offshoring or exporting during the sample period, and to analyze

how this transition affects their workers’s wages through the channel of the described effects.

However, such an approach would require substantial variations at the within-firm extensive

margin of trade. In other words, it would be necessary to observe firms that switch their

importer or exporter status during the sample period.

Table 2 provides descriptive evidence that my sample does not fulfill this requirement. For

instance, 96.91 % of firm-year observations whose trade status was ”domestic”(D) at year

t-1, remain within this status during the subsequent year t. Similarly, 97.97% of ”exporter-

importers” (XM) have this status both at t and t-1. There exists thus nearly no within-

firm variation at the extensive margin of trade. This phenomenon is consistent with various

contributions that show a firm’s exporter status to be a good prediction of its importer status

(Martins and Opromolla (2009)).

Those few observations in which a given firm reported being exporter or importer in a given

year t-1 are those with the highest probability of becoming either a fully domestic (D) or a

fully international (XM) firm. The number of these transitions is however clearly insufficient

for conducting an empirical analysis.

Table 2: Trade Status Transition Matrixt

D X M XM Total

D 2 537 39 23 19 2 618

% 96.91 1.49 0.88 0.73 100

X 39 119 8 75 241

t-1 % 16.18 49.38 3.32 31.12 100

M 30 12 279 173 494

% 6.07 2.43 56.48 35.02 100

XM 8 55 146 10 110 10 319

% 0.08 0.53 1.41 97.97 100

Total 2 614 225 456 10 377 13 672

% 19.12 1.65 3.34 75.90 100

Within firm transition frequencies and probabilities, pooled over the sample period 1998-2005. Reported trade

statuses are D= domestic, M= importer, X=exporter and XM=exporter importer. The import status can

include both flows coded as intermediate inputs and flows coded as offshoring.

18

Intensive Margin Given the rare occurrence of within firm variations at the extensive

margin, I decide to exclusively focus at the intensive margin of trade. This decision, which

implies focusing on firms that both offshore and export during their presence in the sample,

requires further steps concerning the constitution of my final sample.

I start by dropping all firms that are domestic, only exporter or only importer (understood

here as engaged in offshoring according to the definition of Section 3.1) during the entire sample

period. However, if a firm starts being both importer and exporter during the sample period

and reports positive trade flows in at least two subsequent years, I consider this firm’s presence

in my sample to begin with the year in which positive export and offshore flows are reported

for the first time. Hummels et al. (2011) might follow a more consisting strategy in considering

the first positive export or import flow reported by a firm as a ”pre-sample observation”, which

allows them to consider the first year to be unrepresentative of the firm’s actual trade behavior.

However, as my sample suffers from an insufficient number observations, I allow the first year

to be present in the sample. If a firm in the sample stops importing or exporting during some

point in the sample, I omit observations from this year on. As my sample is mostly constituted

of very large and productive exporters and importers, this constitutes a very small number of

observations. I remain with a small and unbalanced panel of 9981 observations, composed of

2325 firms. Only 242 firms are present during all eight years.

Having presented descriptive statistics on the extensive margin of within firm trade dynam-

ics, I now move over to discussing how within-firm trade flows vary at the intensive margin.

In their analysis, Hummels et al. (2011) provide evidence for substantial within firm vari-

ation: on average, firm-year observations vary by 82% with respect to the firm mean (46%

for exports). This term is likely to be smaller for my sample, since I observe many firms for

a very small period, some of them only during two years. Therefore, I cannot build on the

variations as Hummels et al. (2011), who dispose of a balanced panel ranging from 1995 to

2006. In addition, I think that it is useful to substract time trends from the firm-year specific

deviation, which further decreases my deviations as compared to those reported by Hummels

et al. (2011).

I thus want to identify how much a trade flow of firm j at time t deviates from its firm

mean, excluding time trends that are common to all firms in the sample. For this purpose.

I substract from the log Offshoring flow lnOffjt an estimated firm effect αt and an estimated

time effect λt. The distributions of this residual value, and of its equivalent for exporting

flows, are displayed in Figure 1. An observation situated at -1 on the x-axis is 100% smaller

than its corresponding firm mean. On average and considering deviations in absolute values,

a firm-year offshoring flow deviates by 28% from the firm mean flow (13% for exports).

19

Figure 1: Within Firm Variation in Trade Flows

3.3.3 Trade Intensity and Firm Characteristics: The ”Heterogeneous Firms Phe-

nomenon”

Before starting the empirical analysis on within-firm trade flows and wages, it is useful to

provide cross-sectional descriptive evidence confirming the ”heterogenous firms phenomenon”.

Melitz (2003) has rationalized this phenomenon by describing a selection process according to

which firms select into their trade status on the grounds of their productivity, which implies

that a drop in trade costs between countries will not affect all firms in these countries equally.

Eaton et al. (2011) confirm this phenomenon with French data, showing that the entry on

export markets ban to the largest part be attributed to differences in firm efficiency. While

this paper’s topic is not to analyze between-firm heterogeneity, it is important to acknowledge

the latter in order to illustrate that a cross-sectional analysis of trade flows and wages would

not be adapted to our purposes.

Therefore, Table 3 illustrates that a between-firm analysis of offshoring and wages that

operates in a cross-sectional framework would yield results that are dominated by across-firms

differences. It reports the results of descriptive regressions linking a firm’s offshoring activity to

its characteristics and wages. The first row shows that, controlling only for year and industry

effects, firms engaged in offshoring, are on average larger, more productive, and pay higher

average wages. The coefficients are however relatively small. For instance, firms enaged in

offshoring have on average exp(0.301)=1.35 more employees. This is presumably due to the

fact that my sample already reflects a strong selection towards large firms.

In the second row, it is shown that among those firms that offshore, higher offshoring levels

implies are associated wither higher productivity levels and the payment of higher wages. For

instance, the elasticity of executive’s wages to offshoring is on average 0.9% Again, only industry

and year effects are controlled for, and offshoring flows are not normalized by the firm’s size

or production levels, so there is no causal inference to make from this set of regressions.

20

Tab

le3:

Des

crip

tive

Evid

ence

on

Off

shori

ng

an

dth

eH

eter

ogen

ous

Fir

ms

Ph

enom

enon

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Log

Log

Aver

age

Wei

ghte

dB

lue

Collar

Wh

ite

Collar

Inte

rmed

iary

Em

plo

ym

ent

Kap

ital

Sto

ckT

FP

Wage

Aver

age

Wage

Work

erW

ork

erP

rofe

ssio

nE

xec

uti

ve

Cro

ss-S

ecti

on

Reg

ress

ion

Off

shori

ng

0.3

01***

0.4

24***

0.1

05***

0.0

38***

0.0

11*

0.0

01

-0.0

04

-0.0

03

0.0

17**

wit

hIn

du

stry

FE

Du

mm

y(0

.045)

(0.0

54)

(0.0

19)

(0.0

13)

(0.0

06)

(0.0

05)

(0.0

05)

(0.0

05)

(0.0

08)

R2

0.0

84

0.1

46

0.2

22

0.2

68

0.3

62

0.3

40

0.2

50

0.1

71

0.0

91

N17954

17954

17954

17954

17954

17954

17954

17954

17954

log

Off

shori

ng

0.1

69***

0.2

48***

0.0

50***

0.0

24***

0.0

10***

0.0

06***

0.0

04***

0.0

03*

0.0

09***

(0.0

14)

(0.0

19)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

R2

0.2

30

0.2

96

0.2

82

0.2

97

0.3

93

0.3

72

0.2

88

0.1

82

0.1

10

N9918

9918

9918

9918

9918

9918

9918

9918

9918

Wit

hin

Fir

mR

egre

ssio

nln

Off

0.0

14***

0.0

08***

0.0

07***

0.0

03

0.0

01

0.0

00

-0.0

01

0.0

02**

-0.0

00

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

R2

0.0

28

0.3

99

0.1

35

0.2

97

0.5

70

0.6

70

0.5

91

0.4

91

0.3

14

N*T

9918

9918

9918

9918

9918

9918

9918

9918

9918

N2325

2325

2325

2325

2325

2325

2325

2325

2325

*p<

0.1

0,

**

p<

0.0

5,

***

p<

0.0

1

All

regre

ssio

ns

incl

ud

eyea

rfi

xed

effec

ts.

Th

e”A

ver

age

Wage”

resu

lts

from

div

idin

gth

efi

rm’s

wage

bill

by

the

tota

lnu

mb

erof

emp

loyee

s,w

hile

the

”W

eighte

dA

ver

age

Wage”

isw

eigh

edby

the

nu

mb

erof

emp

loyee

sin

each

occ

up

ati

on

al

gro

up

.

21

The third row reports results from a within firm regression, in which all time invariant firm

characteristics are absorbed by fixed effects. The endogeneity of offshoring is not addressed in

this regression. Compared to the cross-sectional framework, all coefficients decrease or become

insignificant, which confirms that results from the second row nearly exclusively reflected het-

erogeneity across firms. In particular, it appears that there is barely any reaction of wages to

offshoring. This does at this stage not yet imply the absence of firm-level wage effects from

offshoring. The reason is that the regression does not control for other firm characteristics,

and, more importantly, does not account for the simultaneity problem between offshoring and

wages that will be addressed in the following section.

4 Empirical Strategy

4.1 Challenges to Identification

The previous subsection has illustrated that in a cross-sectional framework, observed and

unobserved firm characteristics influence both trade flows and the wage determination process,

which leads to the positive correlations between firm trade flows and firm characteristics that

are displayed in Table 3. Without firm controls or firm fixed effects, the correlations that could

be found in this cross-section analysis are most likely to be non-causal and to reflect simply

a string heterogeneity across firms that simultaneously affects variables at both sides of the

relation of interest.

For the following identification strategy, this phenomenon of across-firms heterogeneity will

not play a role, since the pursued aim consists in identifying potential interactions between firm-

level trade flows, productivity, the demand for production factors, and wages. This approach

requires an exclusive focus on within-firm evolution and thus the inclusion of firm-specific fixed

effects in the regression analysis. As these effects will absorb the time invariant component of

the endogeneity of trade flows in their relation with wages, heterogeneity across firms does not

represent a further issue in the subsequent analysis.

Nevertheless it remains necessary to address the existence of time variant factors that in-

troduce a simultaneity bias in our relation of interest by affecting both firm wages and firm

trade flows. For instance, firm-level variations in productivity or product demand are likely to

influence both variables, thereby inducing positive, but non-causal correlations between firm-

level wages and trade-flows. Parts of these variations can be captured through the introduction

of firm control variables such as total factor productivity or the firm’s size. However, these

variables do not eliminate the existence of unobservable shocks, for instance in demand, that

are likely to simultaneously affect wages and the firm’s trade activities. Therefore, it will be

necessary to introduce instrumental variables that impact the firm’s intensive margin of off-

shoring and exporting while being exogenous to other firm characteristics, such as productivity,

the demand for factors and worker wages. These instruments will be required to vary at the

firm-year level.

22

4.2 Instrumentation Strategy

4.2.1 Approaches in the Literature

All main contributions on labor market impacts of offshoring, import competition or exports

recognize the explained simultaneity problem and propose different strategies that exploit

exogenous variations in a firm’s or an industry’s trade intensity.

Instruments for Industry-Level Imports Geishecker and Gorg (2008) employ a GMM

approach which relates current to lagged offshoring intensities on the UK industry level. Their

underlying assumption is that given their industry control vector, wages at time t are not related

to offshoring flows at t-1 other than through current offshoring flows. In their paper using

German data, Baumgarten, Geishecker and Gorg (2010) employ their UK data to instrument

the offshoring intensity in German industries with the UK equivalent. This strategy assumes

that industry-level trends in offshoring in both countries are driven by similar global trends,

while the offshoring of UK industries remains orthogonal to the wage setting process in German

industries.

Autor et al. (2010) instrument import penetration in U.S. regions with the help of weighted

industry-level Chinese import growth in other high-income markets, thereby exploiting the

supply-driven component of U.S. imports from China. As an alternative approach, which

they show to deliver highly similar results, they propose to also take account of the demand

component. They estimate the residuals from a regression that relates the difference between

country-specific exports from China and the U.S. to fixed effects of both this destination and

China. Thereby, the authors capture those Chinese comparative advantages vis-a-vis the U.S.

that also induce U.S. imports from China to increase.

To cite a final example, Liu and Trefler (2011) analyze the labor market effects of trade

in services with China and India. They use a gravity approach when instrumenting offshoring

and exporting flows reported by industries in the U.S. service sector. In order to predict

industry-level trade with China and India, they estimate the elasticity of U.S. trade flows to

GDP growth in 28 countries and then relate these elasticities to GDP growth in India and

China.

Instruments for Firm-Level Flows The contributions cited so far construct their instru-

ments at the industry level. They all rely their identification strategy on either evolutions in

industry-specific propensities to trade on a global level, or evolutions at the level of the trading

partner. It is thus assumed that both are without the reach of the industry whose labor market

outcomes are analyzed. At the same time, they are supposed to affect the industry’s trade

behavior, which creates their potential as a strong instrument.

This assumption of exogeneity can be extended, and even strengthened, when analyzing

firm behavior. Indeed, world-level or country-level evolutions are unlikely to be affected by

firm-level activities, which implies their strict exogeneity to firm-level wages. Nevertheless, it is

not obvious to relate these trends to firm-level variations in offshoring and exporting activities.

23

Industry-level instruments, such as constructed by the cited articles, can be expected to provide

insufficient variations when predicting firm-level outcomes. Therefore, the correlation between

an industry-level instrument and a firm-level trade flow will be insufficient for the requirements

towards a strong instrument. One example of a weak instrument problem resulting from

associating industry-level instruments with firm-level outcomes can be found in Abowd and

Lemieux (1993), who instrument firm-level quasi-rents with U.S. industry-level export prices.

Manning (2011) quotes this example as reflecting a choice of instruments that occurred before

researchers have become aware of the weak instrument problem.

The challenge of obtaining an instrument that varies at the firm-level while being unrelated

to other firm activities than trade has convincingly been adressed by Hummels et al. (2011) and

Berman et al. (2011). These contributions decompose a firm’s trade balance at the product-

country level. They proceed by endogenizing these flows with the help of world trade dynamics,

before re-aggregating them at the firm level. More precisely, they construct two year-product-

country specific measures of world demand and supply. While the demand-specific component

of a firm’s country-product import flow is likely to be simultaneously affected with wages, their

demand-side counterpart in the exporting country can be assumed exogenous. Similarly, the

supply side of firm exports is endogenous, while its demand side at the importing country is

exogenous.

As the Douanes database allows to decompose firm-year level trade flows on the firm-

product-country-year level, I am able to follow this method. In the following, I thus explain

their strategy in its application to my dataset.

4.2.2 Construction of Instruments

Variables at the Product-Country-Year Level The target being to capture these ex-

ogenous supply and demand components, I follow the authors in constructing the following

two product-country-year varying variables:

• Product-Country Level Export Supply: In order to capture changes in the compar-

ative advantage of country c in selling a given product k on the world market, all exports

of product k from country c to the world are aggregated for a given year. Exports to

France are excluded from this aggregation, in order not to contaminate the instrument.

When using this variable as a basis for the construction of an instrument for firm-level

offshoring, the underlying assumption consists in stating that a French firms’ decision

to change the intensive margin of its offshoring intensity is significantly related to the

supply side of the offshored good.

• Product-Country Level Import Demand In order to capture demand shocks to the

consumption or a loss of comparative advantage in the production of product k in country

c, all imports of product k by country c from the world are aggregated for a given year.

Again,France is not included in this aggregate measure.

24

It is assumed that a French firm increases its extensive margin as a result to product-

country specific demand shocks that are reflected in country c’s imports of product k

from the world.

The construction of these variables requires exhaustive data on worldwide inter-country trade

at the product level. The Base pour l’Analyse du Commerce International (BACI) made

available by the Centre d’Etudes Prospectives et d’Informations Internationales (CEPII) and

containing a cleaned version of Comtrade data on the product-country level gives access to

this data.10 It covers my entire sample period in the HS6 nomenclature of 1992, into which I

convert the Douanes data in order to merge the two sources.

Both Hummels et al. (2010) and Berman et al. (2011) realize the aggregation of product-

level demand and supply at the world level. One concern to this approach is that the resulting

variable could not capture well how French firms will react to a given dynamic on the world

market. For instance, one could imagine that French firms react differently to a supply shock

in country c than firms in a developing country. In order to address this concern, an alternative

approach would consist in aggregating only countries that have similar characteristics as France.

I implement this idea by first adding up flow of countries classified as High Income by the World

Bank.11 Second, I aggregate at the level of countries that are members of the Euro zone.

Table 12 in Appendix A.2 shows that the instruments resulting from these different aggre-

gation are nearly perfectly correlated. Therefore, it is justified to pursue the analysis with the

world-level aggregation. The variables’ high correlation also excludes the option of using the

three of them to overidentify the first stage regression.

Characteristics of product-country level trade flows in the sample Before translating

the two constructed variables into a firm-year varying instrument, it is useful to present some

descriptive statistics on product-country level flows in the sample. It is particularly interesting

to analyze the distribution of product-country pairs, in order to identify how many firms are

affected by a change in product-country specific world trade dynamics.

In the optimal case, one country-product pair would correspond to a unique firm obser-

vation, i.e. a firm would be the only one to buy or sell a given product from or to a given

country. In this case, each firm would be affected differently by product-country specific dy-

namics. Despite its exhaustiveness, the sample employed by Hummels et al. (2011) is close

to such a scenario, since the median product-country pair has one buyer/seller among Danish

firms (three at the 90th percentile). For the larger country France, a country-product pair

will be traded by a more important number of firms. However, my sample represents only

a very small subsample of the population of French firms. As Table 4 shows, the median

product-country offshoring pair is in this subsample imported by three firms in a given year.

Interestingly, offshored goods are more often imported by several firms than other type of

imports. Further, at the 90th percentile of the ”number of firms offshoring a product-country

10Gaulier and Zignago (2010) provide a detailed documentation on the construction of BACI.11This approach is similar to Autor et al.(2010), who instrument U.S. imports from China with imports from

China in 28 high-income countries.

25

pair”-distribution, 21 firms purchase the same good from the same country in a given year.

On the contrary, few product-country destinations are served by several French firms in the

sample: the median product-country pair is exported by only one firm per year.

Table 4: Distribution of Product-Country Pairs

Mean St. Dev. 10% 50% 90% Observations

No of Country-Product Pairs Imported by Firm 74.010 83.477 14 50 156 9918

No of Product-Country Pairs Offshored by Firm 68.650 82.887 10 44 150 9918

No of Product-Country Pairs Exported by Firm 106.768 181.662 9 52 246 9918

No of Firms Importing one Product-Country Pair 3.103 6.426 1 3 6 236876

No of Firms Offshoring one Product-Country Pair 9.457 22.924 1 3 21 163786

No of Firms Exporting one Product-Country Pair 2.094 2.926 1 1 4 505815

Observations are pooled over the sample period 1998-2005. The number of the observations concerns the

number of firms in the first three lines and the number of product-country pairs in the last three lines.

Imports include all imports reported by firms in the sample, both intermediate inputs and final goods.

It has thus been shown that for some product-country pairs, several firms will be affected

simultaneously by a change in the world demand and supply variables simultaneously, in par-

ticular in their offshoring activity. However, it must be noted that even when several firms

export or import one product-country pair, each firm does so within a firm-specific mix of

different product-county pairs. Therefore, not all firms are affected equally by world market

dynamics for a given pair and firm-level variation will remain ensured.

Aggregation of Country-Product Flows at the Firm Level It thus remains to weigh

each country-product flow on the firm-level, in order to translate variations on the product-

country-year level into an instrument that is correlated with firm-level aggregated imports and

exports per year. The weight of each product-country flow should reflect its importance in the

firm’s export or import ”portfolio”. The question is how to define this importance, and how

to translate it into the weighting strategy.

Hummels et al. (2011) propose to relate their country-product-year instruments to the

pre-sample share of a country-product pair of a firm’s total imports or exports. For each firm,

the pre-sample observation concerns the first year the firm appears in the sample. They justify

this strategy by the fact that 64.4 % of country-product offshoring flows in their sample also

appear in their pre-sample (77.7% for exports).

As the size of my small sample should not further decrease, it is not an option to follow this

approach and classify some observations as ”pre-sample” in the given case. A valid alternative

might be to use the share of each firm’s first year in the sample, without excluding it from the

sample. In my sample, the mean share of firm-level country-product pairs that also appear in

first year of firm’s presence in the sample is 47.47 % for imports. Therefore, using this share

26

would mean that half of country-product flows do not appear in the instrument.

As a consequence, I apply the weighting process proposed by Berman et al. (2011), using the

share of each product-country pair in the firm’s total exports over the entire sample period.

According to this reasoning, an import or export flow of product k from or to country c,

occurring at any time during the sample period is weighted by the following share within each

firm j:

∑tXkct∑

t,kc

Xkct.

A third alternative consists in using the share of the firm’s country-product pair at t-1,

similar to Autor et al. (2011). Their aim is to instrument regional import penetration; and

they weigh their industry-level instrument with the share of this industry’s employment in the

region’s total manufacturing employment at t-1. I apply this approach to the firm-product-

country dimension by weighing each country-product flow in t by its share in the firm’s exports

or imports in t-1: Xkct−1∑kc

Xkct−1. To not further decrease my sample size, I make the simplifying

assumption that in the first period of a firm’s presence in the sample, its country-product mix

in t-1 was the same as it is in t.

I will realize the first stage estimation using instruments resulting from both of these weight-

ing strategies. This will allow to compare these two instrument’s strength and to make a choice

on which strategy to retain for the subsequent analysis.

4.3 Two Stage Estimation of Wage Equation

Having constructed firm-year varying instruments, it is possible to specify the two-step frame-

work which will allow to relate occupation-specific wage outcomes to the firm’s intensive margin

of offshoring and exporting. Considering the theoretical discussion presented in section 2, the

aim consists in identifying how an increase at the intensive margin of offshoring and exporting

flows affects the evolutions of firm-level wages of four different occupation groups. Regressions

at both stages will thus be realized within a firm fixed effects framework.

4.3.1 First Stage Regressions

The wage equation to be estimated at the second stage includes two endogenous variables that

will be instrumented separately, namely offshoring flows and exporting. Knowing that the

firm’s propensity to trade is determined both by exogenous world demand or supply shocks

and by time variant and invariant characteristics, it is possible to specify the following two

equations for offshoring and exporting flows:

ln Offjt = γOff1 IV Offjt + γOff2 + λt + uOffjt , with ujt = ϕOffj + εOffjt

ln Expjt = γExp1 IV Expjt + γExp2 Zjt + λt + uExpjt , with ujt = ϕExpj + εExpjt

27

The subscript jt denotes firm j at time t. The error term ujt is composed of a firm-specific

fixed effect ϕj and an idiosyncratic error term εjt. λt denotes a time effect. The firm-specific

control vector Zjt includes for both regressions the firm’s level of employment and capital

stock at the beginning of year t, as well as Total Factor Productivity approximated with a

Levinsohn-Petrin estimation.12 Productivity has been shown to be a crucial determinant of

both firm-level trade flows and wages. Hummels et al. (2011) include total production, but

not productivity in their control vector. My results are not affected when I implement this

slightly different choice of control variables.

Offshoring and Exports are likely to be determined by similar observed and unobserved

factors. Thereby, the two equations correspond to the concept of a ”Seemingly Unrelated

Regressions” (SUR) model as proposed by Zellner (1962). This term describes a system of

equations in which each equation has its own vector of coefficients, which is why the equa-

tions are ”seemingly unrelated” with each other and can in principle be estimated separately.

However, there is potential correlation across the errors of different equations, since their de-

pendent variables are likely to be affected by similar unobserved influences. For the case of

the specified first stage exporting and offshoring equations, it is most likely that those time

variant, unobserved disturbances that affect lnOffjt will, at least partly, also affect lnExpjt.

In this case, Zellner (1962) suggests to exploit the variations across error terms in order to

improve estimator efficiency using a Feasible Generalized Least Squares (FGLS) estimator.13

Despite this inter-equation correlation of error terms, most works employing instrumental

variable strategies for more than one endogenous variable do not make use of a SUR framework.

Instead, endogenous variables are estimated in a reduced-form first stage framework in which

the exact same vector of instrumental and control variables are introduced in each first stage

regression. In this case, there are no more efficiency gains to be obtained by FGLS estimation

(e.g. Wooldridge (2002), p.164).

Using this result, it is necessary to set the vector of independent variables in (1) and (2)

such that XOffjt = XExp

jt . As the firm control vector is already specified to be identical for both

the offshoring and the exporting equation, the only change concerns the vector of instrumental

variables, which now includes both instruments in both equations:

ln Offjt = γOff1 IVjt + γOff2 Zjt + λt + uOffjt , with ujt = ϕOffj + εOffjt

ln Expjt = γExp1 IVjt + γExp2 Zjt + λt + uExpjt , with ujt = ϕExpj + εExpjt

where the vector of instruments IVjt contains both IV Offjt and IV Expjt .

12This method has been summarized in Section 3.1.13As I will in the end not apply this method, I do not explain it in any further detail.

28

Results of First Stage Regressions The first stage two-equation system is just identified,

with one instrumental variable for one endogenous variable. The instrumental variable for off-

shoring consists of a proxy for world export supply and the instrumental variable for exporting

consists of a proxy for world import demand, as outlined in Section 4.2. Two potential weight-

ing strategies have been presented: weighing over the sample period or weighing at t-1. Table

5 compares the strength of the instrumental variables resulting from these two approaches.

Both of them have a positive and significant correlation with their corresponding endogenous

variable. However, variables resulting from weighing flows over the sample (columns (1) and

(2)) display correlations with the endogenous variables that are significantly larger than those

resulting from weights at t-1. This is a first evidence in favour of the first weighing strategy.

The bottom of Table 5 gives F-statistics for the strength of instruments. Stock and Yogo

(2002) develop a method to test the null hypothesis that a given group of instruments is weak

against the alternative that it is strong. They construct a set of critical values that the Cragg-

Donald F Wald statistic needs to exceed in order to reject the hypothesis of weak instruments.

This statistic depends on the number of endogenous regressors, the number of instrumental

variables, and on the maximum accepted bias of 2SLS, relative to OLS. In our 2SLS estimation,

we relate two endogenous variables to two instruments. In this case, if we were to accept a

bias of our 2SLS estimation that, compared to the OLS estimation, is at maximum 10%, the

Stock and Yogo (2002) critical value equals 7.03.

The Cragg-Donald F statistics exceed this critical value for both weighting strategies. How-

ever, the statistic for weight 1 by far exceeds the one for weight 2. Together with the size of the

correlations between the endogenous regressors and the instrumental variable, this indicates

that weight 1 has led to a stronger instrument, which will be used in the subsequent analysis.

29

Table 5: First Stage Regressions

(1) (2) (3) (4)

log Exports log Offshoring log Exports log Offshoring

World Demand 0.291*** 0.041***

(weight 1) (0.013) (0.011)

World Supply 0.006 0.261***

(weight 1) (0.004) (0.008)

World Demand 0.022*** -0.016***

(weight 2) (0.004) (0.005)

World Supply -0.002 0.062***

(weight 2) (0.003) (0.006)

Labor Force 0.329*** 0.353*** 0.703*** 0.686***

(0.064) (0.102) (0.099) (0.146)

Capital Stock 0.164*** 0.032 0.281*** 0.059

(0.045) (0.062) (0.071) (0.082)

TFP 0.284*** 0.176** 0.427*** 0.312***

(0.055) (0.085) (0.077) (0.109)

R2 0.465 0.357 0.084 0.098

N*T 9918 9918 9918 9918

N 2325 2325 2325 2325

Kleibergen-Paap F Statistic 497.193 14.289

Cragg-Donald F Statistic 1548.364 42.706

Stock-Yogo Critical Value 10% 7.03 7.03

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 All regressions include firm and year fixed effects. Standard errors are

clustered at the firm level.

The Stock-Yogo critical calues are for the Cragg-Donald F statistic. Weight 1 follows Berman et al. (2011) in

weighting each flow by its share over all flows during the sample period. Weight 2 weights each offshoring flow

in t according to its share over total flows at t-1.

30

4.3.2 Second Stage Wage Equations

Having endogenized firm-level offshoring and exporting flows, it is now possible to assess their

correlation with log wages at the firm level. The relation of interest is the following:

lnwgjt = β1lnImpjt + β2lnExpjt + β3Zjt + δt + µjt,

where µjt = αj + ξgjt

The superscript g indicates that the wage of four different occupational groups will be

analyzed. The firm control vector Zjt is the same as in the first stage regression. Again, the

error term is composed of a firm-specific fixed effect and an idiosyncratic error term.

4.3.3 The Role of Firm Control Variables in the Wage Equation

The theoretical discussion in Section 2 has underlined that an impact of offshoring on wages

is expected to operate through two main channels: on the one hand, offshoring decreases the

demand for workers according to their skill’s substitutability. On the other hand, offshoring

can increase factor-augmenting productivity, thereby augmenting the demand for all factors,

including labor of all skill groups.

The firm control vector Zjt includes both input factors labor and capital, as well as a proxy

for total factor productivity. As pointed out by Hummels et al. (2011), these variables will in

the structural wage equation capture all potential impacts of offshoring on wages through the

productivity effect. If an increase in offshoring increases productivity, and thereby the demand

for factors, we will thus observe a positive coefficient for productivity as well as for labor and

capital. There will however not be any visible effect for the offshoring variable. The same

holds for firm-level exports.

As a consequence, the authors propose to remove the firm control vector when trying to

capture this second channel. This is valid under the assumption that there are no variations in

productivity that can operate through trade flows in their impact on wages, which would result

in a non-causal positive correlation between these exports, offshoring and wages. Given that

trade flows have been instrumented and can therefore be considered as exogenous to firm-level

variables, this assumption can be supposed to hold.

Therefore, all following regressions are, following Hummels et al. (2011), estimated both

with and without the firm control vector Zjt. When Zjt is excluded from the second stage

regressions, it is also excluded from the first stage regressions.

5 Main Results and Extensions

5.1 Baseline Regressions

Table 6 displays the results for the second stage regression including the vector of firm controls,

and instrumenting offshoring and export flows as described in the previous section. All standard

31

errors are clustered at the firm level.

It is not possible to detect any significant impact of neither export nor offshoring flows on

wages at the firm level. The only exception concerns Intermediary Professions, whose wages

have a slightly positive reaction to an increase in offshoring at the 10% significance level. These

results imply that among the firms in the sample, there is no direct effect of offshoring on the

ACEMO wage measures. Given the discussion on the role of the firm control vector in Section

4.3, this does not yet exclude the existence of an effect that operates through productivity or

factor demand. It could be for instance that the positive correlation between TFP variations

and average wages and on wages of executives and intermediary professions displays an indirect

impact of trade flows.

Table 6: Second Stage Regression Including Firm Control Vector

(1) (2) (3) (4) (5) (6)

Wage Average Weighted Blue Collar White Collar Intermediary Executive

log Exports 0.004 0.001 0.000 -0.001 -0.004 0.004

(0.005) (0.003) (0.002) (0.003) (0.003) (0.004)

log Offshoring 0.004 0.003 0.001 0.001 0.004* -0.001

(0.004) (0.002) (0.001) (0.002) (0.002) (0.003)

log Labor Force 0.005 0.012 0.023*** 0.010 0.015 0.035**

(0.018) (0.009) (0.007) (0.010) (0.009) (0.015)

Log Capital Stock -0.021 -0.009 -0.004 0.004 0.002 -0.004

(0.013) (0.006) (0.005) (0.006) (0.006) (0.009)

TFP 0.066*** 0.002 0.003 0.003 0.014* 0.020*

(0.016) (0.007) (0.005) (0.007) (0.008) (0.011)

R2 0.301 0.570 0.671 0.591 0.491 0.316

N*T 9918 9918 9918 9918 9918 9918

N 2325 2325 2325 2325 2325 2325

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 All regressions include firm and year fixed effects. Standard errors are

clustered at the firm level.

”Average” results from dividing the firm’s wage bill by the total number of employees, while ”Weighted”

considers the number of employees in each occupational group.

Table 7, which represents second stage results in which the firm control vector has been

removed, shows that this is not the case. Indeed, coefficients display identical patterns, which

further confirms that the theoretical predictions and the results of Hummels et al. (2011)

cannot be reproduced with my dataset. The non-significant coefficients have been found to be

32

robust to the introduction of lagged regressors that could capture a retarded effect of offshoring

and exports on wages. Further, results are not affected when the sample is reduced to those

firms that can be observed for at least three or four years.

Table 7: Second Stage Regression Excluding Firm Control Vector

(1) (2) (3) (4) (5) (6)

Wage Average Weighted Blue Collar White Collar Intermediary Executive

log Exports 0.004 0.002 0.001 -0.000 -0.003 0.006

(0.005) (0.003) (0.002) (0.003) (0.003) (0.004)

log Offshoring 0.005 0.003 0.001 0.001 0.004* -0.000

(0.004) (0.002) (0.001) (0.002) (0.002) (0.003)

R2 0.297 0.570 0.670 0.590 0.491 0.314

N*T 9918 9918 9918 9918 9918 9918

N 2325 2325 2325 2325 2325 2325

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 All regressions include firm and year fixed effects. Standard errors are

clustered at the firm level.

”Average” results from dividing the firm’s wage bill by the total number of employees, while ”Weighted”

considers the number of employees in each occupational group.

This absence of significant results is striking in several respects. First, it contradicts the

intuitions formulated in the theoretical discussion. This implies that either all or parts of the

mechanisms through which offshoring has been predicted to affect wages does not occur in

the firms that are present in my sample, or that an effect cannot be measured with the help

of the ACEMO wage variables. It therefore seems necessary to decompose the relationship

between wages and offshoring, and to assess separately how offshoring affects on productivity,

the demand for production factors, and the demand for skill-specific labor. This will be done

in the next subsection (5.2).

Second, my results are at odds with several previous empirical contributions, not only on the

impacts of offshoring, but also on the impacts of exports on wages. Indeed, it is puzzling that

the export regressors cannot be identified to significantly impact any of the wage categories.

This result opposes an increasing literature reporting worker-level wage raises following an

increased firm-level export intensity. For instance, Macis and Schivardi (2012) explore the

relationship between firm-level exports and wages and show that increased firm-level exports

both change the market value of workers’ unobservable skills and allocate workers a rent-sharing

premium. Amiti and Davis (2008) find that a fall in tariffs decreases wages in import-competing

firms while raising wages in exporting firms. Martins and Opromolla (2009) report positive

wage effects of both firm-level imports and export flows. Hummels et al. (2011) find results

that corresponds to their theoretical predictions: increased offshoring lowers the wage of all

workers, except high-skilled workers, whose wages react positively to offshoring. Further, they

33

show exports to be positively correlated with wages of all workers.

One difference between my sample and the data sources used by these contributions is that

they construct their analysis using employer-employee data, which allows them to control for

observed and unobserved worker characteristics. However, it is unlikely that this constitutes

the main reason for the non-significance of results. Given the firm fixed effects, a change

in worker characteristics would occur through a compositional change in the workforce. It

has been shown that a firm that expands its exporting activity will be likely to hire more

experienced and skilled workers (e.g. Molina and Muendler (2009)). Therefore, the lack of

worker controls in my analysis would rather be expected to bias upwards the effects of trade

flows on wages than affecting their significance. Indeed, in Martins and Opromolla (2009)’s

wage regressions, the import and export coefficients decrease significantly when worker controls

are introduced. Instead of explaining my non-significant results, the absence of employer-

employee data therefore further increases the puzzle: given potential compositional effects,

at least the non-weighted average wage should be expected to react positively to enhanced

firm-level trade.

5.2 Decomposition of Channels

Both the productivity effect and the labor demand effect predicted in Section 2.2 occur in two

steps: first, offshoring affects productivity, the demand for production factors and the demand

for skill-specific labor; then, in a second step, these changes translate into skill-specific wage

impacts. Given my results, either one or both of these steps cannot be identified to occur in

my sample. It is thus useful to assess the first step separately. I therefore proceed by first

empirically assessing the impact of instrumented export and offshoring flows on productivity

and factor demand (Section 5.2.1), and then on skill-specific labor demand (Section 5.2.2).

5.2.1 Productivity and Factor Demand

According to the ”Productivity Effect” predicted by Grossman and Rossi-Hansberg (2008) and

Hummels et al. (2011), offshoring affects productivity and the demand for production factors.

While we could not observe any wage impacts of such a mechanism, the following analysis

shows that the mechanism itself is present in my data.

Table 8 displays the results of a fixed effects estimation that relates firm-level trade flows

to different proxies of factor demand and productivity. Obviously, the relation between these

variables suffers of a simultaneity problem, similar to the one outlined when setting up the

wage equation (c.f. Section 4). In order to prevent this simultaneity from biasing the offshoring

and export coefficients, estimation is again realized in a 2SLS framework, where the first stage

regression is conducted with the instruments specified in section 4.3. Each first stage regression

includes the same control variables as its corresponding second stage regression.

Table 8 shows that three elements of the production function have a significant positive

correlation with the exogenous part of firm-level exports. This result is robust to the removal

of the firm control vector, which is composed of those two variables that are not the depen-

34

Table 8: Effects on Factor Demand and Productivity, Second Stage Regression

(1) (2) (3) (4) (5) (6)

Dependent Variable Log Labor Force Log Capital Stock TFP

log Exports 0.054*** 0.041*** 0.050*** 0.023** 0.011* 0.018***

(0.008) (0.008) (0.011) (0.011) (0.006) (0.006)

log Offshoring 0.009* 0.009* 0.002 -0.002 0.007* 0.008**

(0.005) (0.005) (0.006) (0.006) (0.004) (0.004)

Firm Controls No Yes No Yes No Yes

R2 0.059 0.207 0.398 0.494 0.135 0.152

N*T 9918 9918 9918 9918 9918 9918

N 2325 2325 2325 2325 2325 2325

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 2SLS estimation. All regressions include firm and year fixed effects.

Standard errors are clustered at the firm level. Offshoring and Exports are instrumented according to the first

stage specified in Section 4.3 Firm controls contain those two firm control variables that do not constitute the

dependent variable.

dent variable in the concerned regression (i.e. Capital Stock and Labor Demand in the TFP

equation). As a consequence, it seems that the productivity effect as proposed by Grossman

and Rossi-Hansberg (2008) is in my data a partial one: offshoring and export positively affect

the demand for factors, but wages do not respond to these mechanisms.

5.2.2 Skill-Specific Labor Demand

Similar to the productivity effect, it is useful to detect whether skill-specific labor demand,

which according to theory should translate into wage changes, reacts to offshoring and exporting

of the firm.

Table 9 results from a fixed effects estimation that relates firm-level trade flows to the firm’s

skill-specific labor force. The dependent variables are thus the number of workers of each of the

four skill groups, employed at the firm at the end of each year t. Again, estimation is realized in

a 2SLS framework, where the first stage regression is conducted with the instruments specified

in section 4.3. As the skill group-specific labor force was not available for about 2% of the

sample, the number of observations shrinks slightly. In order to compare the comparability of

results across skill groups, I realize all regressions with those observations for which information

on the labor force of all four skill groups is available.14

Table 9 displays a positive association between a firm’s exporting activity and the demand

14It has been tested that the significance of these results is not due to some kind of sample selection due

to the omission of those observations for which no skill-specific labor force is available. Regressing the wage

equation for this same set of observations does non affect any coefficient’s significance.

35

Table 9: Effects on Skill-Specific Labor Demand, Second Stage

(1) (2) (3) (4) (5) (6) (7) (8)

Labor Force Blue Collar White Collar Intermediary Executive

log Exports 0.010 0.047*** 0.039* 0.066*** 0.040*** 0.067*** 0.009 0.038**

(0.011) (0.013) (0.020) (0.021) (0.012) (0.014) (0.014) (0.015)

log Offshoring -0.017* -0.011 0.001 0.005 -0.000 0.003 0.007 0.011

(0.010) (0.011) (0.013) (0.014) (0.009) (0.010) (0.010) (0.011)

Firm Controls Yes No Yes No Yes No Yes No

R2 0.099 0.041 0.065 0.037 0.040 0.003 0.054 0.016

N*T 9124 9124 8991 8991 9124 9124 9124 9124

N 2198 2198 2164 2164 2198 2198 2198 2198

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01. 2SLS estimation. All regressions include firm and year fixed effects.

Standard errors are clustered at the firm level. Offshoring and Exports are instrumented according to the first

stage specified in Section 4.3. Firm controls contain log capital stock, log total labor force and TFP.

for labor of all four occupational groups. When productivity, capital stock and the overall

labor stock are controlled for, this elastisicity of labor demand to exports is significant only for

white-collar workers and intermediary professions, and amounts to 4.0%. That is, the direct

impact of exports on the demand for skills concerns primarily medium skilled workers. When

the control vector is removed, and the impact of an increase in exports is allowed to operate

through firm productivity or through the demand for other factors, the elasticity becomes

significant for all four groups. This is plausible: an expansion in output due to an increasing

exporting activity augments the demand for labor of all skills.

Offshoring is found to affect solely the demand for blue-collar workers. This elatisticity of

1.7% remains significant only under the presence of firm control variables. It can according to

the reasoning of Hummels et al. (2011) thus be considered to result from a direct substitution

effect between imported goods and unskilled labor. The demand for none of the other skill

groups reacts significantly to offshoring flows. Following the theoretical predictions, this would

reflect the fact that on average, only the labor of white collar workers can be substituted by

imported goods.

In order to assess the robustness of these results, Table 13 in Appendix A.3 reports results

from the same regression using instrumented intermediate inputs instead of offshoring as an

explanatory variable. The table confirms the supposition that the negative elasticity of the

demand for blue collar workers could be due to a substitution effect. Indeed, an increase in

the import of intermediate inputs augments the firm’s demand for labor of all skill groups.

36

In sum, this and the previous section have shown that the first part of the causal chain,

through which firm-level trade flows are supposed to affect wages, can be shown to occur within

the firms in my sample. It appears however that these mechanisms do not translate into wage

responses.

5.3 Alternative Adjustment Mechanisms: The Role of Union Bar-

gaining

Therefore, I finish this discussion on the origins of my puzzling results by providing descriptive

evidence on the role of union bargaining. Kramarz (2008) suggests in a theoretical and empirical

analysis on the interaction between union bargaining and offshoring that strong unions provide

incentives for a firm to engage into increased offshoring. This opportunity to offshore operates

as a threat for unions and therefore lowers their wage claims. Bastos and Wright (2010) show

that exchange rate fluctuations impact wages mostly through the channel of the ”wage cushion”

negotiated by unions, and that low-skill workers are the most affected by this mechanism. Such

an approach represents a clear alternative to the competitive vision on the interaction between

offshoring and wages, such as proposed by Grossman and Rossi-Hansberg (2008) and Hummels

et al. (2011).

Unfortunately, my sample, in which only 710 firms (3844 observations) bargain over their

wages at least twice over the sample period, does not allow to consistently analyze the role of

firm-level union bargaining on the interaction between wages and offshoring. I therefore do not

proceed into a detailed analysis of union strength, firm-level offshoring and wages. Still, I try to

give some purely descriptive evidence on whether workers engaged into wage bargaining with

their firms are differently affected from increased offshoring flows. For this purpose, I make use

of the set of dummies indicating whether a firm reached an agreement after bargaining with

its workers on wages, employment or the RTT.

The most plausible approach for providing this evidence would be to introduce the dummy

for wage agreement into the regression, as well as its interaction with offshoring and export

flows. However, such an approach is not adopted to the fixed approach that I am pursuing.

Suppose a firm bargains over wages during the whole sample period. In this case, the additional

effect of bargaining on the relation between offshoring and wages in this firm is completely

absorbed by the fixed effect and cannot be identified. This problem concerns only 148 firms

(482 observations) in the sample; which is however a considerable number given the small

overall number of observations.

I thus decide to run the same wage equation as specified in Section 4.3 only on the subset

of firm-year observations for which a bargained wage agreement is reported. Under usual

conditions, such an analysis should be done only with those firms bargaining over the whole

period. This would ensure that the decision to bargain is not endogenous to other firm-level,

time-varying conditions, such as the firm’s profitability. Since the number of firms bargaining

during the whole sample period is limited to 148 firms, this is not an option either. I therefore

37

make the simplifying assumption that the firm’s decision to engage into offshoring is perfectly

exogenous, and run the regression on all observations for which bargaining is reported in a given

year. The number of observations remains small, which for sure limits the result’s robustness.

Despite these reservations, it is worth noting from Table 10 that the wage of blue-collar

workers has a significant negative elasticity to offshoring of 0.6% I interpret this not as a robust

result from which we can draw any definite conclusions, but at a first evidence that calls for

future assessment of the role of non-competitive forces in the interaction between firm-level

offshoring and wages.

Table 10: Subsample of Firms Engaged in Wage Bargaining at t

(1) (2) (3) (4) (5) (6)

Average Weighted Worker Employee Intermediary Manager

log Exports 0.003 0.008 0.003 0.005 -0.003 0.011

(0.014) (0.006) (0.005) (0.006) (0.007) (0.010)

log Offshoring 0.004 -0.006 -0.006* -0.005 -0.001 -0.000

(0.010) (0.004) (0.003) (0.004) (0.004) (0.007)

log Labor Force 0.015 0.035** 0.024** 0.031 0.019 0.011

(0.040) (0.016) (0.012) (0.020) (0.016) (0.028)

Log Capital Stock 0.032 -0.025** -0.014 0.002 -0.001 -0.008

(0.026) (0.012) (0.009) (0.013) (0.014) (0.022)

TFP 0.120*** 0.003 0.003 0.005 -0.014 -0.017

(0.033) (0.011) (0.009) (0.013) (0.012) (0.019)

R2 0.320 0.626 0.719 0.653 0.598 0.402

N*T 2277 2277 2277 2277 2277 2277

N 710 710 710 710 710 710

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01. 2SLS estimation. All regressions include firm and year fixed effects.

Standard errors are clustered at the firm level. Offshoring and Exports are instrumented according to the first

stage specified in Section 4.3. Firm controls contain log capital stock, log total labor force and TFP.

38

6 Conclusion and Discussion

This Master thesis has assessed the interaction between trade flows, in particular offshoring,

and wages in a firm-level framework. Two mechanisms have been of particular interest: a

potential positive impact of offshoring on wages of all skill groups, operating through firm-level

productivity; and a skill-specific labor demand effect, whose sign depends on the elasticity of

substitution between a worker’s labor force and imported goods.

The analysis of these mechanisms has been done in a two-step framework. First, the

simultaneity of firm-level trade flows and wages has been addressed with an instrumentation

strategy that exploits exogenous variations in product-level demand and supply on the world

market. On these grounds, it was then possible to relate the exogenous part of the firm’s

intensive margin of trade to wages of four different occupational groups employed at the firm.

This empirical implementation has been realized with a relatively small subsample, in which

large and productive firms were over-represented. On the hand hand, my two main data sources

were non-exhaustive; and on the other hand, the focus on the intensive margin of firm-level

trade flows required to concentrate on firms which were both exporting and offshoring during

the entire sample period.

Within this subsample of French firms, neither average wages, nor occupation-specific wage

indicators could be shown to significantly react to variations in firm-level trade flows. This

absence of significant results has proven robust to different specifications and sample composi-

tions. In order to assess the origins of an absent correlation between firm-level offshoring and

wages in my sample, I decomposed the causal chain according to which offshoring is supposed

to affect wages; by analyzing separately its impact on productivity, the demand for production

factors in general, and the demand for skill-specific labor. I could thereby find evidence that

offshoring and exports both positively affect productivity. Exports are positively associated

with the demand for labor of all four occupations and offshoring has a slight negative impact on

the demand for low-skilled labor. However, none of these changes has been found to translate

into significant wage responses within the firms in my sample.

Given the described features of my sample, I do at this stage not consider my results

to question the validity of the presented channels for French firms. However, it is clearly

necessary to reassess them with a more exhaustive and diverse firm panel. For instance, my

sample included nearly no firms whose trade activity started during the sample period. It can

be imagined that wage impacts of trade flows occur rather in those kinds of firms, which are

still in the period of adjusting their factor use and remuneration to their international activity.

When employing a more exhaustive sample, including firms whose selection into international

trade occurs within the sample period, this possibility could be analyzed.

Further, it would be an important extension to analyze whether in France, the firm-level

interaction between offshoring in wages occurs more than in other countries through the in-

termediary of the union wage bargaining process. Kramarz (2008) presents results that point

towards such a hypothesis. In order to assess this option, and in order to obtain results that

39

are more robust than the descriptive evidence given in Section 5.3, it would be necessary to

observe a more important number of firms that are engaged in wage bargaining, and to be able

to address the endogeneity of the firm’s decision to bargain a wage agreement in a given year.

Beyond the discussion of my results, and beyond the need to include non-competitve labor

market forces into the analysis, it is appropriate to place the interaction between offshoring

and wage determination back into the context of the offshoring and labor market literature

summarized in the Introduction. As the contributions preceding Hummels et al. (2011) have

conducted their analysis in a industry- or task-level framework, it should be discussed whether

a firm-level view has proven to be a plausible alternative to these approaches. The implications

of analyzing the interaction between offshoring and wages on different levels have been pointed

out in the theoretical discussion of Section 2, which compared the task-based, industry-level

approach of Grossman and Rossi-Hansberg (2008) to the firm-level approach of Hummels et

al. (2011). Both frameworks predict productivity and labor demand effects of offshoring that

are expected to translate into wage responses.

When discussing the productivity effect, it has been argued that the assumptions made

in the industry-level framework were not able to account for within-industry, between-firm

heterogeneity in offshoring levels and in productivity impacts of offshoring. In this respect, the

firm-level view is an important complement to an industry-level or task-level framework: it is

particularly well adapted to capture the productivity effect of offshoring, as well as its impact

on wages that can be expected to occur at the level of the firm.

Is it however equally adapted to capture those channels that translate labor demand effects

of offshoring into wage responses? Hummels et al. (2011) assume wages to react with the

same sign as labor demand to variations in offshoring flows at the firm level (cf. Section

2.2). They consider their prediction confirmed after observing a negative direct effect of firm-

level offshoring flows on wages of low- and medium-skilled workers. Nevertheless, it appears

justified to question the plausibility of this channel in capturing wage effects that respond to

changes in the equilibrium of labor demand and supply after an increase in offshoring. Indeed,

this view does not account for the fact that workers can be employed by other firms in the

economy whose trade behavior differs from the employing firm. Under those circumstances,

the employing firm will not be able to lower wages after substituing parts of its labor with

imported goods. This indicates that exploiting industry-level variations of offshoring, and

specifying workers to be mobile across firms, allows for an approximation of the interaction

between labor supply and demand that is closer to a general equilibrium view. Ebenstein et al.

(2009) and Baumgarten et al. (2010) implement a task-based framework in which workers are

allocated to specific tasks, but allowed to move across industries. Such an approach further

improves the general equilibrium dimension of the interaction between offshoring and wages,

and it overcomes the simplifying assumption of a perfect mapping between skills and tasks in

the production function.

Therefore, the firm-level view is certainly an important complement to an industry-level or

task-level framework, since it is particularly well adapted to capture the productivity effect of

40

offshoring and its impacts on wages. It is however not an alternative, since it cannot present

a complete picture on the interaction between wages and offshoring as reflecting changes in

skill-specific labor demand. It would, under the availability of the according data sources, be

interesting to further explore this complementarity in a simultaneous assessment of the firm-,

the industry- and the task-level.

41

References

Abowd, J. M. and Lemieux, T. (1993). The effects of product market competition on collective

bargaining agreements: The case of foreign competition in canada. The Quarterly Journal

of Economics, 108(4):983–1014.

Acemoglu, D. and Autor, D. (2011). Skills, tasks and technologies: Implications for employment

and earnings. Handbook of labor economics, Elsevier.

Amiti, M. and Davis, D. R. (2008). Trade, firms, and wages: Theory and evidence. NBER

working papers, National Bureau of Economic Research, Inc.

Amiti, M. and Wei, S. (2006). Service offshoring and productivity: Evidence from the united

states. NBER Working Paper 11926, National Bureau of Economic Research, Inc.

Autor, David; Dorn, D. and Hanson, G. (2011). The china syndrome: Local labor market

effects of import competition in the united states.

Avouyi-Dovi, S., Fougere, D., and Gautier, E. (2010). Wage rigidity, collective bargaining and

the minimum wage: Evidence from french agreement data. CEPR Discussion Paper 7932,

C.E.P.R. Discussion Papers.

Bastos, P. and Wright, P. (2010). Exchange rates and wages in unionised labour markets.

Discussion Paper 10/15, University of Nottingham, GEP.

Baumgarten, D. (2010). Exporters and the rise in wage inequality a evidence from german

linked Employer-Employee data. Ruhr Economic Paper 0217, Rheinisch-Westfaelisches In-

stitut fuer Wirtschaftsforschung.

Baumgarten, D., Geishecker, I., and Goerg, H. (2010). Offshoring, tasks, and the Skill-Wage

pattern. IZA discussion papers, Institute for the Study of Labor (IZA).

Bernard, A. B. and Bradford Jensen, J. (1999). Exceptional exporter performance: cause,

effect, or both? Journal of International Economics, 47(1):1–25.

Biscourp, P. and Kramarz, F. (2007). Employment, skill structure and international trade:

Firm-level evidence for france. Journal of International Economics, 72(1):22–51.

Cameron, A. C. and Trivedi, P. K. (2005). Microeconometrics: Methods and Applications.

Cambridge University Press.

Cameron, A. C. and Trivedi, P. K. (2010). Microeconometrics Using Stata. Stata Press, revised.

edition.

Eaton, J., Kortum, S., and Kramarz, F. (2011). An anatomy of international trade: Evidence

from french firms. Econometrica, 79(5):1453–1498.

Feenstra, R. C. (2010). Offshoring in the global economy: Microeconomic structure and macroe-

conomic implications.

42

Gaston, N. and Trefler, D. (1995). Union wage sensitivity to trade and protection: Theory and

evidence. Journal of International Economics, 39(1-2):1–25.

Gaulier, G. and Zignago, S. (2010). BACI: international trade database at the Product-Level

(the 1994-2007 version). CAPII Working Paper.

Geishecker, I. and Goerg, H. (2008). Services offshoring and wages: Evidence from micro data.

IZA Discussion Paper 3593, Institute for the Study of Labor (IZA).

Grossman, G. M. and Rossi-Hansberg, E. (2008). Trading tasks: A simple theory of offshoring.

American Economic Review, 98(5):1978–97.

Helpman, E., Itskhoki, O., Muendler, M.-A., and Redding, S. (2011). Trade and inequality:

From theory to estimation. preliminary draft.

Hummels, D., Jorgensen, R., Munch, J. R., and Xiang, C. (2011). The wage effects of offshoring:

Evidence from danish matched Worker-Firm data. NBER working papers, National Bureau

of Economic Research, Inc.

Krugman, P. (2008). Trade and wages, reconsidered. Brookings Paper on Economic Activity.

Levinsohn, J. and Petrin, A. (2003). Estimating production functions using inputs to control

for unobservables. Review of Economic Studies, 70(2):317–341.

Liu, R. and Trefler, D. (2011). A sorted tale of globalization: White collar jobs and the rise of

service offshoring. NBER working papers, National Bureau of Economic Research, Inc.

Manning, A. (2011). Imperfect competition in the labor market. Handbook of labor economics,

Elsevier.

Martins, P. and Opromolla, L. D. (2011). Why Ex(Im)porters pay more: Evidence from

matched Firm-Worker panels. Working Paper w201123, Banco de Portugal, Economics and

Research Department.

Martins, P. S. and Opromolla, L. D. (2009). Exports, imports and wages: Evidence from

matched Firm-Worker-Product panels. IZA Discussion Paper 4646, Institute for the Study

of Labor (IZA).

Melitz, M. J. (2003). The impact of trade on Intra-Industry reallocations and aggregate in-

dustry productivity. Econometrica, 71(6):1695–1725.

Molina, D. and Muendler, M. (2009). Preparing to export.

Schaffer, M. E. (2010). XTIVREG2: stata module to perform extended IV/2SLS, GMM and

AC/HAC, LIML and k-class regression for panel data models.

Stock, J. H. and Yogo, M. (2002). Testing for weak instruments in linear IV regression. NBER

Technical Working Paper 0284, National Bureau of Economic Research, Inc.

43

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.

Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests

for aggregation bias. Journal of the American Statistical Association, 57(298):348–368.

44

A Appendix

A.1 Summary Statistics

Table 11: Summary Statistics for Final Subsample

Variable Obs Mean Std. Dev.

Log Average Wage 9918 2.666 0.298

Log Average Weighted Wage 9918 2.379 0.207

Log Wage Blue Collar 9918 2.138 0.156

Log Wage White Collar 9918 2.240 0.162

Log Wage Intermediary 9918 2.510 0.173

Log Wage Executive 9918 3.128 0.236

Wage Agreement 9918 0.271 0.445

Log No of Blue-Collar Workers 9246 4.753 1.035

Log No of White-Collar Workers 9146 2.655 1.192

Log No of Interm. Professsion 9246 3.584 1.258

Log No of Executives 9246 2.975 1.211

Log No of Employees 9918 5.432 0.898

Log Capital Stock 9918 16.160 1.408

TFP (Levinsohn-Petrin) 9918 4.134 0.372

VA/No of Employees 9918 52.273 22.248

Log Exports 9918 15.548 2.041

Log Imports 9918 15.366 1.691

Log Offshoring 9918 13.689 2.403

Log Interm. Imports 9918 14.399 2.239

Exports, detrended log deviation from firm mean 9918 0.272 0.422

Offshoring, detrended log deviation from firm mean 9918 0.509 0.646

VA is reported in 1000 euros. The distributions of log deviations from firm mean consider deviations in

absolute values.

45

A.2 Supplementary Table to Section 4.2.2

Table 12: Pairwise correlations between IVs resulting from three different levels of aggregation

X IV, world X IV, high inc. X IV, Euro Off IV, world Off IV, high inc. Off IV, Euro

X IV, world 1

X IV, high income 0.9972 1

X IV, Euro zone 0.9837 0.9889 1

Off IV, world 0.2236 0.2258 0.2322 1

Off IV, high income 0.2263 0.2286 0.2358 0.9987 1

Off IV, Euro zone 0.2257 0.2297 0.2424 0.9906 0.993 1

X= Exports, Off= Offshoring. The three different levels of aggregation refer to the construction of

product-country specific proxies for demand and supply (cf. Section 4.2.2).

A.3 Supplementary Table to Section 5.2

Table 13: The Import of Intermediates and Skill-Specific Labor Demand, Second Stage

(1) (2) (3) (4) (5) (6) (7) (8)

Labor Force Blue Collar White Collar Intermediary Executive

log Exports -0.000 0.033*** 0.034* 0.059*** 0.038*** 0.062*** 0.009 0.036**

(0.011) (0.012) (0.020) (0.020) (0.012) (0.013) (0.013) (0.015)

log Imports 0.024** 0.045*** 0.025** 0.039*** 0.010 0.025** 0.010 0.027***

of Intermediates (0.011) (0.011) (0.012) (0.012) (0.011) (0.012) (0.009) (0.010)

Firm Controls Yes No Yes No Yes No Yes No

R2 0.098 0.035 0.064 0.036 0.046 0.002 0.058 0.015

N*T 8991 8991 8991 8991 8991 8991 8991 8991

N 2164 2164 2164 2164 2164 2164 2164 2164

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01 2SLS estimation. All regressions include firm and year fixed effects.

Standard errors are clustered at the firm level. Intermediate Imports and Exports are instrumented according

to the first stage specified in Section 4.3. Firm controls contain those two firm control variables that do not

constitute the dependent variable.

46