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
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(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.
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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
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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