Offshoring and Wages in French Manufacturing Firms

Transcription

1 Offshoring and Wages in French Manufacturing Firms Amelie Schiprowski This Version: May 21, 2012 Master Thesis Academic Year 2011/2012 Supervisor: Denis Fougère Second Reader: Thierry Mayer Ecole Doctorale, Sciences Po Economics and Public Policy, Phd Track

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3 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 Fougère 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

4 Contents 1 Introduction 5 2 Theoretical Discussion Exogenous and Endogenous Components in the Firm s Decision to Offshore Predictions on Counteracting Wage Effects Data Sources and Characteristics Data Sources and Main Variables of Interest Merge of Data Sources and Resulting Sample Descriptive Statistics Firm Characteristics Patterns of Trade Behavior Trade Intensity and Firm Characteristics: The Heterogeneous Firms Phenomenon Empirical Strategy Challenges to Identification Instrumentation Strategy Approaches in the Literature Construction of Instruments Two Stage Estimation of Wage Equation First Stage Regressions Second Stage Wage Equations The Role of Firm Control Variables in the Wage Equation Main Results and Extensions Baseline Regressions Decomposition of Channels Productivity and Factor Demand Skill-Specific Labor Demand Alternative Adjustment Mechanisms: The Role of Union Bargaining Conclusion and Discussion 39 A Appendix 45 A.1 Summary Statistics A.2 Supplementary Table to Section A.3 Supplementary Table to Section

5 1 Introduction In an assessment on the impacts of changing trade patterns on wage outcomes in developed nations, 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 matching 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 further 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 framework 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, predominantly 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) argue that wage inequality following increased trade occurs within sectors and occupations, but between firms. The extensive margin can also be analyzed in a within-firm framework, which has however been done less frequently. It then consists in a firm s switching behavior, i.e. in its transition from a domestic to an international firm or vice versa. Martins and Opromolla 5

6 (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 Görg (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 lowand medium-skilled workers within a task group. These contributions base their identification strategy on variations in the industry- or taskspecific 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

7 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 between 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 approach, 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 different 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

8 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 between 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 components (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

9 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, a Lk, and the intensities of H tasks, a Hk 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 a Lk as the intensity of L tasks to produce product k must employ a Lk 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 K jt, high-skilled labor H jt, and a composite input combining low-skilled labor L jt and imported goods M jt. 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: 1 This assumption will be relaxed by most empirical contributions, see for instance Baumgarten et al. (2010). 9

10 where each C gjt is composite of Y jt = A jt K α jth β jt ( L σg 1 σg gjt + M σg 1 σg jt 3 g=1 C γg gjt ) σg σg 1 and where 3 γ g = 1 α β g=1 Before analyzing its predictions concerning the impacts of an increase in M jt, two main differences 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 A jt 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 M jt 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

11 (2008) and Hummels et al. (2011) were making different assumptions as to the firm-level determinants 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 approaches 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 function 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: Y jt = (1 α β)a jt K L jth α β 1 jt L σ 1 1jt C 1jt 3 1 σ +γ jt g=2 Holding other factors constant, the labor demand effect of an increase in M jt depends mainly 1 on the CES parameter σ g : if σ g < (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. C γg gjt Having determined that an increase in M jt implies a decrease in labor demand depending on parameter σ 1, the existence of a negative wage impact on the concerned skill group, depends according to the authors, on the elasticity of labor supply. Under the plausible assumption of an upward sloping labor supply curve, they show that the sign of the wage effect can be expected to follow the same conditions as firm-level labor demand. I will in Section 6 discuss circumstances under which changes in firm-level labor demand do not translate into wage responses. But for now, I remain within the author s reasoning. 11

12 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 A jt 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 M jt and L jt. 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) operates in a partial equilibrium framework, Grossman and Rossi-Hansberg s (2008) general equilibrium 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 laborintensive 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 preliminary reduced-form evidence, I do not provide a detailed discussion of these effects here. 2 See for instance Liu and Trefler (2011). 12

13 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 Déclaration des Douanes The Déclaration des Douanes is an exhaustive dataset that allows to identify all import and export flows reported by French firms on the year-productcountry 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 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 rév. 1, 2003 (Classification des produits française). 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, rév. 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. Enquête Annuelle d Entreprise Enquête Annuelle d Entreprise is non-exhaustive. As opposed to the Déclaration des Douanes, the It results from a yearly survey realized 3 downloaded 03/2012 at http : //unstats.un.org/unsd/trade/conversions/hscorrelationandconversiontables.htm. 4 downloaded 04/2012 at http : //ec.europa.eu/eurostat/ramon. 5 downloaded 04/2012 at http : // 6 downloaded 04/2012 at http : // 13

14 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 (99 th percentile and 1 st percentile of the distribution 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 estimation are highly correlated (0.972). In what follows, TFP describes the parameter obtained through the Levinson-Petrin estimation. ACEMO The Enquête sur l Activité 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, employé-white-collar worker, profession intermédiaire-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 cannot enter as controls in the empirical analysis, and eventual changes in a firm s workforce 7 I gratefully take price indexes and the Stata code for cleaning the data base from Juan Carluccio. 8 I thank Laurent Baudry for having constructed these hourly wage measures from the raw ACEMO data. 14

15 composition cannot be observed. The final ACEMO sample will be cleaned of outliers. Further, I want to ensure the comparability 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 observations distributed over the period 1998 to 2008 in the manufacturing sector. For the period 1998 to 2005, which I will be able to merge with the EAE file, ACEMO has 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 réduction 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 Fougère 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 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

16 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 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 significantly 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 average 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. 9 For instance, Eaton et al. (2011) show with French data that exporters are large and productive firms. 16

17 Table 1: Comparison of Sample Means Mean Difference Difference ACEMO Out of Sample EAE Out of Sample Sample with ACEMO with EAE Exporter *** (0.005) *** (0.004) Importer *** (0.005) *** (0.004) Exporter and Importer *** (0.005) *** (0.004) No of Observations Log Imports *** (0.028) *** (0.020) No of Observations Log Exports *** (0.033) *** (0.023) No of Observations Log Labor Force *** (0.007) Log Gross Capital Stock *** (0.011) VA /Labor Force *** (0.175) No of Observations Average Wage *** (0.003) Average Weighted Wage ** (0.002) Blue Collar (0.002) White Collar * (0.002) Intermediary *** (0.002) Executive *** (0.002) No of Observations Observations ACEMO/EAE out of sample contain all those observations that are present in ACEMO or EAE, but not in the final sample. Observations in the Sample result from a merge between ACEMO and EAE. Means of export and import flows exclude zero values. Value added is reported in 1000 of euros. The Average Wage results from dividing the firm s wage bill by the total number of employees, while the Average Weighted Wage is weighed by the number of employees in each occupational group. 17

18 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 productivity 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, % 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 exporterimporters (XM) have this status both at t and t-1. There exists thus nearly no withinfirm 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 Matrix t D X M XM Total D % X t-1 % M % XM % Total % Within firm transition frequencies and probabilities, pooled over the sample period 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

19 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 dynamics, 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 variation: 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 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 lnoff jt an estimated firm effect α t and an estimated time effect λ t. 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

20 Figure 1: Within Firm Variation in Trade Flows Trade Intensity and Firm Characteristics: The Heterogeneous Firms Phenomenon 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