Distributional Effect of International Trade and Comparative Advantage in Labor Markets

Transcription

1 Distributional Effect of International Trade and Comparative Advantage in Labor Markets Rodrigo Adão MIT September 20, 2015 Abstract This paper investigates the distributional consequences of international trade shocks in the context of the Brazilian labor market. First, I document a new set of facts about how differential exposure to commodity price shocks across educational groups and regions leads to differential outcomes in terms of sectoral employment and wages. Second, I show that such facts are qualitatively consistent with a two-sector Roy model where worker heterogeneity regarding comparative and absolute advantage in sector-specific tasks determines the structure of employment and wages. Third, I establish that the schedules of comparative and absolute advantage are nonparametrically identified from cross-regional variation in sectoral responses of employment and wages induced by sector demand shocks. Lastly, I build on this result to structurally estimate the model in the sample of Brazilian local labor markets. My structural estimates indicate that a 10% decrease in commodity prices causes counterfactual increases of 1.2% in the skill wage premium and of 5% in wage dispersion. Author contact information: rradao@mit.edu. I am extremely grateful to Daron Acemoglu, Arnaud Costinot and Dave Donaldson for invaluable guidance and support. I also thank Pol Antras, David Autor, Arthur Braganca, Ariel Burstein, Dejanir Silva as well as seminar participant at the MIT Labor Lunch and the MIT Macro-International Lunch. All errors are my own. 1

2 1 Introduction In an integrated world economy, shocks in a particular country have the potential to exert different effects across different workers within another country located on the other side of the globe. For example, if China implements a large-scale plan of infra-structure expansion, what is the effect on workers employed in the mining industry relative to those in the auto industry within the U.S.? Alternatively, if Saudi Arabia aggressively reduces oil production, what is the effect on workers residing in oil-rich regions relative to those in oil-poor regions within Brazil? More generally, if such shocks affect the relative world price of various products, which workers stand to gain or lose within a country? And how large are these distributional effects? Such distributional concerns would not be relevant if homogeneous workers were able to perfectly switch their sector of employment, their region of residence, and their level of skill. However, this is unlikely to be true. Consider how the negative shock to the world oil supply caused by Saudi Arabia s policy affects different workers in Brazil. In order to expand production in response to higher oil prices, firms compete for the residents of oil-rich coastal regions with the necessary skills to perform the daily tasks in the offshore drilling industry, pushing their wages upwards. In contrast, workers without these skills and those unable to move to the oil-rich areas are not directly affected by the higher labor demand in the Brazilian oil industry. This simple example suggests that heterogeneous workers are differentially exposed to international trade shocks due to their limited ability to respond to changes in sectoral labor demand. In this article, I propose a unified empirical and theoretical framework to investigate how differential exposure to sectoral shocks across educational groups and regional markets leads to differential outcomes in terms of employment and wages in the context of the Brazilian labor market. The first contribution of the paper is to provide a new set of empirical facts that connect changes in the international price of agriculture and mining commodities to changes in the Brazilian structure of wages and employment between 1980 and First, I establish that aggregate movements in Brazilian wage dispersion are negatively related to the evolution of international commodity prices with the correlation being mainly driven by changes in wage differentials associated with workers level of education, sector of employment and region of residence. Second, I explore the variation in the importance of different commodity-oriented industries in employment across educational groups and regional markets to establish a causal relation between commodity prices and labor market outcomes. In each region, I measure exposure to international commodity price shocks for two worker groups: High-School Graduates (HSG) and High-School Dropouts (HSD). The exposure of a groupregion pair is defined as the interaction between the change in commodities world prices and the initial participation of corresponding commodities in the group s labor payroll in the region. Armed with this measure, I document that regional economies more exposed to positive price shocks experienced stronger employment expansions in the commodity sector among both HSG and HSD. This employment expansion is associated with mixed responses in the commodity sector wage differential: it increases for HSG, but remains stable for HSD. Lastly, higher exposure to positive shocks is 1 Production of basic products constitutes an important part of the Brazilian economy. In 2010, agriculture and mining industries represented 58.5% of total exports and 19.9% of total employment. 2

3 related to stronger growth in the group s average wage which, in turn, translates into a reduction in the HSG-HSD wage premium because of the relatively higher employment share in the commodity sector among Brazilian HSD. In order to explain this rich response pattern in labor market outcomes across groups and regions, I propose a Two-Sector Roy Economy as in Heckman and Honore (1990). Within each group and region, individuals are heterogeneous with respect to a skill bundle that, ultimately, determines their efficiency in the sector-specific tasks demanded by the commodity and the non-commodity sectors. Conditional on the price of these sector-specific tasks, the heterogeneity in sector efficiency translates into heterogeneity in potential sector wages, generating endogenous selection of an individual to the sector yielding her the highest labor income. To be more precise, the sector employment decision is intrinsically related to the worker s comparative advantage defined as the worker s efficiency in the commodity sector task relative to her efficiency in the non-commodity sector task. Whenever the worker s comparative advantage is higher than the relative task price offered in the non-commodity sector, the worker decides to be employed in the commodity sector. Alternatively, the worker s wage, given her employment decision, depends also on the worker s absolute advantage defined as her efficiency in the task specific to the non-commodity sector. In this environment, an individual s exposure to sectoral demand shocks is centrally dependent on her level of comparative advantage. To see this, consider a partial equilibrium comparative statics exercise in which a demand shock increases the task price in the commodity sector while not affecting the task price in the non-commodity sector. The shock increases the wage of all commodity sector employees, but it only affects the wage of those non-commodity sector employees that decide to switch into the commodity sector. These sector-switchers are the non-commodity sector employees whose level of comparative advantage is similar to the pre-shock ratio of sector-specific task price. Accordingly, the magnitude of the change in sector employment is determined by the amount of such workers in the economy as controlled by the comparative advantage distribution. To the extent that sector-switchers are distinct from sector-stayers in terms of sector-specific efficiency, the change in employment composition affects the average efficiency in the sector with the potential to attenuate or reinforce the direct effect of the task price change. The magnitude of this compositional effect is directly related to the difference between the average absolute advantage of sector-switchers and sector-stayers. In a group and region, the average wage response combines the gains of workers employed in the two sectors, depending on both the sector employment composition and the comparative advantage distribution. In line with this discussion, I show that two functions are sufficient to evaluate the impact of sectoral shocks on the average and variance of the log-wage distribution in a group and region. Specifically, (i) the distribution of comparative advantage; and (ii) the average absolute advantage conditional on the level of comparative advantage. The second contribution of this paper is to provide a novel result that establishes the nonparametric identification of such functions using cross-regional variation in sector labor demand. For each worker group, I assume that regions are segmented labor markets with parallel schedules of comparative and absolute advantage; yet, I allow regions to have different skill shifters to accommodate regional variation in the level of sector-specific task supply. Under this assumption, the schedules of comparative and absolute advantage are nonparametrically identified, respectively, from responses of sector employment and average sector wage to changes in 3

4 sector-specific task prices induced by exogenous shocks to sector labor demand in the cross-section of regions. The identification result is valid for an arbitrary number of observable worker groups and it imposes no parametric restrictions on the skill distribution. This nonparametric identification result constitutes a mapping between population moments and the unknown schedules of comparative and absolute advantage. Its main importance relies on informing the source of variation in the data that uncovers the economic relation of interest without additional restrictions than those implied by the theory. 2 In practice, limited availability of exogenous variation in the data imposes the need of auxiliary functional form assumptions. With this in mind, the empirical application of the model builds on a parsimonious log-linear system with two structural parameters that capture separately dispersion in comparative and absolute advantage. The proposed system represents a strict generalization of the system implied by a skill distribution of the Frechet family that entails a single parameter to control both comparative and absolute advantage. 3 The combination of the nonparametric identification result and the auxiliary functional form assumption leads to an empirical strategy to estimate the structural parameters of comparative and absolute advantage using Generalized Method of Moments (GMM). As in the reduced-form analysis, I consider HSG and HSD that are differentially exposed to international commodity price shocks across Brazilian regional labor markets. The implementation of such strategy requires measurement of sector-specific task prices for all groups and regions. 4 An additional contribution of this paper is to provide a new methodology to estimate task prices based on the Roy model s predicted relation between wage growth and initial sector employment across quantiles of the wage distribution for each triple of group-region-period. This methodology can be easily implemented as a first-step regression using repeated cross-section data on wage and employment at the individual level. The structural estimation delivers two main insights. First, between-sector worker mobility is low with the elasticity of relative sector employment to relative sector task price estimated to be around one for both HSG and HSD. Second, the pattern of selection is qualitatively different in the two groups. For HSD, an expansion in sector employment reduces average efficiency in both sectors, generating the stability in sector wage premium documented in the reduced-form analysis. However, a sector employment expansion among HSG is associated with a decrease in average efficiency in the commodity sector and an increase in the non-commodity, creating the strong responses in the sector wage premium obtained in the reduced-form regressions. I conclude the paper by applying the framework to answer one counterfactual question: If commodity prices fall by 10%, how would Brazilian wage inequality be affected?" To answer this question, I combine the estimated schedules of comparative and absolute advantage with the sectoral allocation of workers across regions and groups in Since employment in the commodity sector is higher among HSD residing in poor regions, the model predicts an increase in wage inequality following the 2 As noted by Matzkin (2007), such identification results represent an important first step in the empirical analysis of almost any model as the credibility of empirical results would be significantly hindered if identification could only be achieved under restrictive parametric assumptions. 3 The Frechet distribution has been the basis of numerous recent applications of the Roy Model see e.g., Hsieh, Hurst, Jones, and Klenow (2013); Burstein, Morales, and Vogel (2015); and Galle, Rodriguez-Clare, and Yi (2015). 4 Sector-specific task prices are not immediately available in survey datasets that only contain information on individual labor income. In the model, observable labor income corresponds to the product of the unit task price and the individual s endowment of efficiency units. 4

5 shock. Precisely, the fall in commodity prices triggers a 1.2% increase in the HSG-HSD wage premium that is accompanied by a 5% increase in the between-component of log-wage variance in the country. The rest of the paper is organized as follows. Section 2 reviews the related literature. Section 3 presents the empirical facts about the adjustment of the Brazilian labor market following shocks to the international price of agriculture and mining commodities. Section 4 presents the Two-Sector Roy Economy and its implications for the equilibrium structure of employment and wages. Section 5 establishes the nonparametric identification of comparative and absolute advantage in a sample of segmented labor markets. Section 6 implements the identification strategy for High-School Graduates and High-School Dropouts differentially exposed to commodity price shocks across Brazilian regions. Section 7 presents the counterfactual exercise that quantifies the effect of changes in commodity prices on changes in Brazilian wage inequality. Section 8 offers some concluding remarks. 2 Related Literature Research on the distributive effects of international trade has traditionally built upon the stark predictions regarding the changes in relative wages across worker groups provided by neoclassical models of trade in Stolper and Samuelson (1941) and Jones (1975). Yet, empirical studies have failed to provide support for the predictions of these canonical models. For instance, a number of authors have documented (i) movements in wage inequality correlated in both developed and developing countries (Goldberg and Pavcnik, 2007), (ii) movements in the skill wage premium uncorrelated with changes in the relative price of skill intensive products (Lawrence and Slaughter, 1993) while correlated with changes in the skill intensity of production within industries (Bekman, Bound, and Machin, 1998), (iii) and diminished between-sector responses in employment and wages following trade shocks (Wacziarg and Wallack, 2004; and Goldberg and Pavcnik, 2007). Such evidence gave rise to a consensus that international trade shocks were, at best, secondary drivers of the changes in wage inequality between the early-1980s and the mid-1990s. However, the last decade has witnessed a renewed interest in the distributive effects of trade shocks motivated by, on the one hand, recent changes in the wage distribution within several countries and, on the other hand, the integration into the world economy of China and other developing countries. 5 Due to the disconnection between empirical evidence and predictions of neoclassical models, this emerging literature has expanded the scope of analysis by examining broader dimensions of workers exposure to international trade shocks. The literature has evolved on several directions. More closely related to this paper is the recent reduced-form literature that establishes the impact on labor market outcomes of heterogeneous exposure to import competition in terms of sector of employment (Autor, Dorn, Hanson, and Song, 2014), level of education (Dix-Carneiro and Kovak, 2015b), and region of residence (Topalova, 2010; Kovak, 2013, Autor, Dorn, and Hanson, 2013; and Costa, Garred, and Pessoa, 2014). In this paper, I build on their reduced-form strategy to provide new evi- 5 Acemoglu and Autor (2011) document a rich transformation pattern in the wage distribution of the U.S. and other OECD countries. As noted by Hanson (2012), low- and middle-income countries experienced not only an increase in international trade volumes but also pronounced changes in their profile of traded goods. In fact, emerging countries increased their participation in world trade from 13.5% in 1990 to 29.6% in

6 dence regarding the effect of differential exposure to commodity price shocks on sectoral employment and wages across educational groups and regional labor markets. This reduced-form evidence is then combined with the structure of the Two-Sector Roy Economy to provide a methodology to quantitatively investigate the impact of trade shocks on the wage distribution. Following the explosion of firm-level work in the field of international trade, another strand of the literature has investigated the effect of international trade shocks on workers employed in different firms within-industries. These papers focus on how market integrations affects the within-industry matching of workers to firms and the subsequent implications for wage inequality (see e.g. Verhoogen, 2008; Helpman, Itskhoki, and Redding, 2010; Frias, Kaplan, and Verhoogen, 2012; Helpman, Itskhoki, Muendler, and Redding, 2015; and Burstein and Vogel, 2015). Another approach has been to investigate dynamic effects of international trade shocks, paying particular attention to the welfare costs implied by the transitional dynamics in the reallocation of workers across sectors (Kambourov, 2009; Artuç, Chaudhuri, and McLaren, 2010; Dix-Carneiro, 2014; and Dix-Carneiro and Kovak, 2015a) and, more recently, regions (Caliendo, Dvorkin, and Parro, 2015). This paper is closely related to the growing body of literature connecting changes in the economic environment to self-selection of heterogeneous workers. On the theory side, recent papers demonstrate the potential of the Roy model to generate a rich response pattern in wage inequality see Ohnsorge and Trefler (2007), Costinot and Vogel (2010), Acemoglu and Autor (2011), and, for a comprehensive review, see Costinot and Vogel (2014). On the empirical side, the Roy model has been applied to investigate the consequences of selection to aggregate productivity and output (Lagakos and Waugh, 2013; Hsieh, Hurst, Jones, and Klenow, 2013; and Young, 2014). Closer in spirit, two papers impose a Frechet distribution of worker skills to quantify the portion of changes in between-group wage inequality associated with technological changes in the USA (Burstein, Morales, and Vogel, 2015) and import competition in Germany (Galle, Rodriguez-Clare, and Yi, 2015). I complement these recent quantitative applications by providing a characterization of wage inequality responses in a generic Two-Sector Roy Model where the skill distribution is completely unrestricted. The generality of the model sheds light on the different roles played by comparative and absolute advantage in determining the evolution of labor market outcomes both within- and between-groups. This increases the ability of the model to capture features of the data that are meaningful in the study of distributional effects of trade shocks. 6 Lastly, this paper is related to the large literature on selection models in labor markets. Inspired by the seminal paper of Roy (1951), this literature investigates the consequences of self-selection based on unobservable characteristics to observable components of labor income. Evidence in favor of selection driven by multivariate skills is provided by Gibbons and Katz (1992), Neal (1995), Parent (2000), and Gibbons, Katz, Lemieux, and Parent (2005). In order to establish the Roy model s identification in a single market, the most traditional approach was to impose parametric restrictions on the skill distribution as in Heckman and Sedlacek (1985). Yet, this approach relies heavily on the functional form imposed on the skill distribution since Heckman and Honore (1990) show that the Roy model cannot 6 Although remaining tractable in the multiple sector setting, the Frechet distribution has features that are unappealing in labor market analysis. Not only it requires a high degree of between-sector mobility but also it is unable to generate responses in between-sector wage differentials. In contrast, these restrictive properties are not implied by the log-linear system used in the empirical application of this paper. 6

7 be nonparametrically identified in a single cross-section of individuals. Following this negative result, they propose two main alternatives. The first assumes observable shifters in the location of the skill distribution for individuals in the same market; the so called identification at infinity reviewed in French and Taber (2011). The second relies on the variation of skill prices across markets with an identical skill distribution. In its majority, the selection literature builds on the first approach to identify hedonic wage regressions (e.g., Mulligan and Rubinstein, 2008); alternatively, this paper builds upon the second approach to investigate distributional consequences of sectoral labor demand shocks. To this end, I provide a nonparametric identification result that complements the elegant result of Heckman and Honore (1990). By allowing for regional skill shifters, I introduce task supply shocks that affect sector employment and sector wages across markets while endogenously correlated with sectorspecific task prices in equilibrium. For this reason, identification requires an instrumental variable that generates exogenous variation in sector-specific task prices. Accordingly, the assumption in this paper is weaker than the existence of a single skill distributions in all market assumed by Heckman and Honore (1990) to establish their nonparametric identification result. In this sense, I provide a generalization of Heckman and Honore s (1990) result when regions are subject to parallel shocks in the skill distribution. 3 International Commodity Prices and the Brazilian Labor Market 3.1 Data and Background Brazil, as primarily an exporter of basic commodities, is particularly exposed to shocks in the world price of agricultural and mining goods. Such exposure pattern is captured by the relative price of six commodity categories: Grains, Livestock, Soft Agriculture, Metals, Precious Metals, and Energy. Together, these commodity groups account for a large share of Brazilian exports, representing 53.8% of total exports in 1980 and 60.4% in As described in Appendix A, I construct price indices for each category using data on commodity transactions in the main exchange markets of the United States between 1980 and To replicate relative prices faced by producers in Brazil, international commodity prices are converted to Brazilian currency and deflated by the Brazilian consumer price index. The analysis of labor market outcomes rely on wage and employment data from the Brazilian Census performed by the Brazilian Institute of Geography and Statistics (IBGE) for 1980, 1991, 2000 and To focus on individuals with strong labor force attachment mainly affected by marketoriented forces, I consider a benchmark sample of full-time white male employed individuals aged Individuals are divided into two observable groups: High-School Dropouts (HSD) and High- School Graduates (HSG). These two groups reflect the relevant margin of education in Brazil; among male white workers, the high-school graduation rate was 16.0% in 1980 and 51.4% in I restrict the benchmark sample to include only white and male individuals because of the strong declines in gender and race wage differential between 1995 and 2010; see e.g. Ferreira, Firpo, and Messina (2014). The model presented below is not intended to speak directly to the behavior of these components of the wage structure and, therefore, I exclude their behavior from the analysis in this section. Appendix B provides robustness exercises including female and non-white individuals, indicating that all results are essentially the same when these additional worker groups are considered. 7

8 Log of deflated international commodity prices (BRL) Variance of log wages 3.0 Figure 1: International Commodity Prices and Wage Dispersion in Brazil, International Relative Commodity Prices, Wage Dispersion in Brazil, Commodity Price Index Grains (corn, soybeans, wheat) Energy (crude oil) Soft (coffee, cacoa, sugar, and others) Metals (copper, lead, steel, tin, zinc) Livestock (cattle, hogs, and others) Precious Metals (gold and silver) Residual Between (education, sector, region, experience) Note. Left panel: six-month moving average of log international product price converted to Brazilian currency and deflated by the Brazilian consumer price index (Sept-1994 = 1). Right panel: variance of log wages among male white full-time workers in the Brazilian Census of 1980, 1991, 2000 and Wage decomposition implied by the regression of log real wage on a full set of dummies for high school graduation, employment in the commodity sector, and microregion of residence, and years of experience (0-39yrs). Details in Appendix A. Throughout the analysis, I classify individuals into a sector and a regional labor market. According to their self-reported industry of employment, I map workers either to the commodity sector or to the non-commodity sector. Industries specialized in the production of agricultural and mining products are included in the commodity sector, covering 13.9% of the national labor income in 1980 and 12.9% in All manufacturing and service industries are included in the non-commodity sector. In addition, I allocate individuals to a regional labor market based on their microregion of residence. Between 1991 and 2010, the Census contains a balanced panel of 558 microregions where each microregion corresponds to a set of economically integrated municipalities. In the 1980 Census, only a subset of these microregions can be constructed due to changes in administrative boundaries of municipalities and states between 1980 and Appendix A discusses details on the construction and measurement of labor market outcomes. 8 Let us now turn to the aggregate trends in international commodity prices and Brazilian wage dispersion presented respectively on the left and right panels of Figure 1. Between 1980 and 2012, the evolution of relative commodity prices is U-shaped: the strong price decline of the 1980s was followed by a recovery of equivalent magnitude from the late 1990s until Notice that, while the price drop was homogeneous across commodity categories, the price recovery exhibited great betweencategory heterogeneity. In opposite direction, wage dispersion, after increasing in the 1980s, began a compression process in the 1990s that accelerated in the 2000s. This movement in wage dispersion can be decomposed into observable and residual components by regressing log wages on a full set of dummies for years of experience, level of education, sector of employment and microregion of residence. The decomposition indicates that observable worker attributes account for a sizeable part of the wage dispersion movement i.e., 44.5% of the change in and 78.1% in Table A1 presents the allocation of industries to sectors in each year of the Census. Also, Appendix A describes both the construction of microregions in the 1980 Census and the consequences of the changing municipality boundaries to the panel of microregions across years. 8

9 Variance of log wages Wage Differentials Figure 2: Components of Wage Dispersion in Brazil, Decomposition of Between-Component of Wage Dispersion Return to Observable Worker Characteristics HSG Wage Premium Region Wage Premium Sector Wage Premium Covariance HSG Wage Premium P90/P10 Region Wage Premium Sector Wage Premium Note. Left panel: decomposition of the between-component of the log-wage variance associated with the set of dummies for high school graduation, employment in the commodity sector, and microregion of residence. Right panel: estimated return to observable worker attributes implied by the log-wage regression. The dispersion in the 558 regional wage differentials is represented by the difference between 90 th and 10 th percentiles of the estimated regional fixed effects. To investigate further the negative aggregate correlation between commodity prices and the betweencomponent of wage dispersion, Figure 2 presents the full decomposition of the portion of log-wage variance associated with worker s affiliation in terms of education-sector-region. The left panel indicates that the substantial contribution of this component to the decline in wage dispersion is distributed across all its terms: 34.0% is related to the education dummy, 10.7% to the sector dummy, 23.9% to regional dummies, and 31.4% to the covariance of these terms. Importantly, the right panel shows that this fall in log-wage variance was driven by strong reductions in the differential return to observable worker attributes. This discussion can be summarized as follows. Fact 1. Between 1980 and 2010, international commodity prices and Brazilian wage dispersion are negatively correlated. A large fraction of the change in wage dispersion in the period is related to observable worker attributes with substantial movements in wage differentials associated with the worker s level of education, sector of employment and region of residence. 3.2 Exposure to Commodity Price Shocks and Labor Market Outcomes The similarity between the aggregate trends in wage dispersion and commodity prices is suggestive of their interconnection. Yet, this correlation is potentially driven by confounding shocks afflicting the Brazilian economy in the period. This section addresses the causal effect of commodity prices on labor outcomes separately for High-School Dropouts and High-School Graduates. To this extent, I treat microregions as local subeconomies subject to differential price shocks according to their initial profiles of industry specialization. By comparing worker groups in regions differentially exposed to commodity price shocks, the reduced-form exercise sheds light on the adjustment pattern of wages and employment following sectoral labor demand shifts. In the exercise, I consider a balanced panel 9

10 of 518 microregions in the years of 1991, 2000 and Heterogeneity in Exposure to Commodity Price Shocks To capture heterogeneous exposure to fluctuations in international commodity prices, I construct a Bartik-Instrument for each microregion and group. Specifically, the shock exposure corresponds to the interaction between the commodity participation in the group-microregion wage bill on the initial year and the change in the international commodity price on the period. Precisely, the exposure of group g in microregion r to the commodity price shock is given by Z g,r,t = j J C φ g,r (j) ln p t (j) (1) where ln p t (j) is the log-change in the international price of product j between years t 1 and t; and φ g,r (j) is the share of industry j in total labor payments of the commodity sector to individuals of group g in microregion r on the initial year of Intuitively, expression (1) entails a stronger response in the regional demand for worker groups specialized in the production of commodities experiencing stronger international price gains. 10 Consequently, Z g,r,t embeds two dimensions of exposure heterogeneity. First, cross-regional variation in initial industry composition creates exposure heterogeneity for individuals of the same group that reside in different microregions. Second, cross-industry variation in skill intensity generates exposure heterogeneity between groups in the same region. As shown in Table A4, these two variation sources are important in Brazil, giving rise to great exposure heterogeneity across both regions and groups. Figure 3 exhibits the pattern of spatial exposure to the commodity price shock among microregions for HSG (left panel) and HSD (right panel). Notice that shock exposure differs significantly for the two groups with a cross-region correlation of only.493. Moreover, Figure 3 indicates that HSG tend to have lower shock exposure than HSD specifically, the median exposure is 4.7 log-points for HSG and 6.3 log-points for HSD. 9 I restrict the sample to only include those microregions with positive employment in the commodity sector for all years and groups. As a result, the final sample contains 518 microregions that represented 98.4% of the country s population on As discussed in Appendix A, there are two reasons to exclude the period from the baseline sample. First, changing municipality borders only allows the construction of a subset of the microregions included in the Censuses. Second, the period was marked by severe economic turbulences in Brazil that have the potential to hinder the connection between international and domestic relative commodity prices these events include hyperinflationary sprouts, suspension of foreign currency convertibility, and the adoption of restrictive internal controls on prices and wages. Nevertheless, Appendix B attests that similar results are obtained with a restricted sample of microregions spanning the entire period. 10 The initial industry composition is indicative, as in Costinot (2009), of the region s comparative advantage in production, reflecting local availability of natural resources like soil fertility and oil reserves. Accordingly, the intuition behind the labor demand response in expression (1) follows directly from the comparative static exercises in Costinot and Vogel (2010, 2014). Alternatively, Kovak (2013) connects production specialization to comparative advantage implied by the local endowment of industry-specific factors, deriving an expression similar to (1) for the response of regional labor demand to small price shocks. Recently, reduced-form strategies based on related measures of local exposure to trade shocks have become popular in the literature; see e.g. Topalova (2010), Kovak (2013), and Autor, Dorn, and Hanson (2013). 10

11 Figure 3: Heterogeneity in Exposure to Commodity Price Shock, Reduced-Form Evidence In order to investigate the effect of exposure to the commodity price shock on labor market outcomes, I consider the following reduced-form specification: Y g,r,t = β g Z g,r,t + X g,r,t γ g + v g,r,t (2) where Y g,r,t is the change in a labor market outcome for individuals of group g in microregion r between years t 1 and t; and X g,r,t is a control vector of group-microregion characteristics potentially correlated with the exposure measure. In the baseline specification, the control vector includes macroregion-period dummies, the initial share of group labor income in the commodity sector, and the initial share of workers earnings at most the federal minimum wage. Also, microregions are weighted by their share in the national population of 1991 and standard errors are clustered by microregion to account for serially correlated errors. Conditional on the initial share of group labor income in the commodity sector, specification (2) relies exclusively on the variation in relative product prices within the commodity sector. Accordingly, the causal interpretation of model (2) requires that shocks in Brazilian regions are not large enough to affect the world price of basic commodities. A requirement especially plausible given the strong growth in Chinese imports of agriculture and mining products during the period; arguably, an exoge- 11

12 nous demand shock to relative prices of raw materials. 11 Sectoral Wages and Employment. Columns (1)-(3) of Table 1 estimate equation (2) with the dependent variable being the commodity sector employment share of HSG in Panel A and HSD in Panel B. The positive significant coefficients in column (1) indicate that, for both HSG and HSD, higher commodity prices induce workers to reallocate from the non-commodity to the commodity sector. However, estimates suggest very limited between-sector mobility: a 10% increase in commodity prices cause the commodity sector employment share to increase by.31 p.p. for HSG and.77 p.p. for HSD. Compared to a region in the 10th percentile of shock exposure, these estimates imply that a region in the 90th percentile had a differential commodity sector expansion of 0.6 p.p. for HSG and 1.0 p.p. for HSD. To test robustness and potentially eliminate confounding effects, I augment the model with period dummies interacted with a set of initial labor market conditions in column (2) and a quadratic polynomial of the initial commodity sector size in column (3). These controls represent period-specific effects projected on initial region characteristics, capturing for example effects related to the introduction of cash transfer programs and secular differences in sector productivity growth. Although these additional controls absorb a large part of the cross-section variation in sector employment change, they only strengthen estimated coefficients. Turning to the impact of commodity prices on sectoral wages, columns (4)-(6) estimate model (2) with the commodity sector average wage premium as dependent variable. In column (4), the two groups exhibit different qualitative responses. The price shock triggers a significant positive response Table 1: Exposure to Commodity Price Shocks and Sector Employment and Wages Dependent Variable: A. High-School Graduates Change in Commodity Sector Change in Commodity Sector Employment Share Average Log Wage Premium (1) (2) (3) (4) (5) (6) Commodity price shock 0.031** 0.038** 0.038** 0.298** 0.437** 0.416** (0.009) (0.010) (0.010) (0.083) (0.095) (0.098) R B. High-School Dropouts Commodity price shock 0.077* 0.101** 0.099** (0.031) (0.029) (0.029) (0.144) (0.161) (0.161) R Baseline Controls Initial labor market conditions x period dummies No Yes Yes No Yes Yes Initial commodity sector size controls x period dummies No No Yes No No Yes Note. Stacked sample of 518 microregions in and All regressions are weighted by the microregion share in national population on All models include ten macroregion-period dummies and the following baseline controls on 1991: share of group labor income in the commodity sector and share of workers earnings at most the federal minimum wage. Labor market conditions: cubic of per-capita income, quadratic of share of white individuals, share of employed individuals, share of individuals in urban areas, share of workers earnings at most the federal minimum wage, share of social security dependents (only HSD). Commodity sector size controls: quadratic polynomial of commodity sector share in group labor income. Standard Errors clustered by microregion. ** p<0.01, * p< Between 1992 and 2010, the average annual growth rate of Chinese imports was 17.2% for all products, 16.2% for Agriculture, and 28.3% for Mining. Over the period, Hanson (2012) provides a careful discussion of the transformation in the profile of international trade of emerging economies and, in special, China. To the extent that this transformation was mainly driven by internal changes in the production structure of China, this large demand shock represented an exogenous impulse to world commodity prices in the period. In Table B4 of Appendix B, I investigate the sensitivity of results when the exposure to commodity price shocks is instrumented with the exposure to Chinese commodity imports growth. 12

13 of the commodity sector wage differential for HSG; in contrast, there is a nonsignificant response for HSD. These conclusions are robust to the inclusion of additional controls in columns (5) and (6). In light of the results in Table 1, I state the following conclusion. Fact 2. Exposure to commodity price shocks is positively related to the commodity sector employment of both HSG and HSD. In addition, shock exposure is ambiguously related to the commodity sector wage premium, exhibiting a positive relation for HSG and a nonsignificant relation for HSD. Group Wages. Table 2 investigates the consequences of the commodity price shock to the average log wage of HSG in Panel A and HSD in Panel B. Columns (1)-(3) indicate that exposure to higher commodity prices is associated with higher average wages for the two worker groups. In columns (4)-(6), I access the robustness of this relation to the use of an alternative exposure measure based on the interaction of Z g,r,t with the initial commodity sector size. As in Autor, Dorn, and Hanson (2013), this alternative measure explicitly incorporates differences in the overall sectoral allocation of workers across microregions and groups. 12 Again, estimates indicate a positive response of average wages to higher commodity prices. This result suggests that, despite the mixed wage response at the sector level, the commodity price shock has a positive impact on the microregion s labor demand, putting upward pressure on wages of HSG and HSD. Results in Table 2 imply a sizeable effect of the commodity price shock on the behavior of region Dependent Variable: A. High-School Graduates Table 2: Exposure to Commodity Price Shocks and Group Wages Commodity price shock 0.296** 0.283** 0.288** (0.069) (0.066) (0.064) Change in Average Log Wage (1) (2) (3) (4) (5) (6) Commodity sector size x Commodity price shock 0.694* 0.510* 0.636* (0.342) (0.260) (0.292) R B. High-School Dropouts Commodity price shock 0.271** 0.280** 0.268** (0.103) (0.097) (0.099) Commodity sector size x Commodity price shock 0.628** 0.557** 0.558* (0.204) (0.208) (0.221) R Baseline Controls Initial labor market conditions x period dummies No Yes Yes No Yes Yes Initial commodity sector size controls x period dummies No No Yes No No Yes Note. Stacked sample of 518 microregions in and All regressions are weighted by the microregion share in national population on 1991 and include ten macroregion-period dummies. Baseline controls as in Table 1. Standard Errors clustered by microregion. ** p<0.01, * p< In this case, exposure to the price shock of each commodity is proportional to its overall participation in the regional wage bill of the worker group. In the model presented below, the response of group average wage is proportional to the overall sector allocation of group workers, being more closely related to the alternative exposure measure. In contrast, the model predicts that sectoral wages and employment within each group respond to sectoral demand shocks, being more closely related to Z g,r,t. In Table B6, I attest that qualitative conclusions of the sector-level analysis remain valid if the alternative exposure measure is considered. 13

14 and education wage gaps in Brazil. To see this, consider the differential wage growth of a microregion in the 90th percentile of shock exposure relative to another in the 10th percentile. Estimates in column (4) predict that this differential growth is 1.9 p.p. for HSG and 4.2 p.p. for HSD. Higher relative wage gains for HSD follow from their higher exposure to commodity price shocks and, in this case, translate into a 2.2 p.p. reduction in the high-school wage premium. Fact 3. Exposure to commodity price shocks is positively related to the average wage of both HSG and HSD. Estimated coefficients suggest a sizeable impact of commodity prices on educational and regional wage differentials. Sensitivity Analysis. Having established Facts 2 and 3, Appendix B turns to an empirical investigation of the robustness of these results. In particular, I obtain similar conclusions if the baseline specification is extended to include additional sector composition controls, microregion-specific time trends, additional periods and additional worker groups (Tables B1-B3). To address concerns regarding the effect of supply conditions in Brazil on world commodity prices, I also attest that similar results are obtained when the exposure to commodity price shocks is instrumented with the exposure to Chinese commodity imports growth in (Table B4). Lastly, I show that exposure to commodity price shocks is not related to the change in the labor supply of both native and migrant workers (Table B5). Following an increase in the world price of commodities, Facts 2 and 3 indicate that the adjustment pattern of wages and employment depends on the shock exposure across regions and groups. To be more precise, regional economies more exposed to the price shock experience a stronger employment expansion in the commodity sector for both HSG and HSD. Although shock exposure is related an increase in the average wage at the group level, there are mixed within-group responses in sector average wages. In the next section, I propose a Two-Sector Roy Economy that, following commodity price shocks, generates such adjustment pattern through the self-selection of heterogeneous individuals to sectors. This structural framework delivers a methodology to evaluate the impact of commodity price shocks on the wage distribution, allowing a quantitative investigation of the aggregate relation between the Brazilian wage structure and the international commodity prices emphasized in Fact 1. 4 Two-Sector Roy Economy 4.1 Environment Consider a segmented labor market populated by workers of multiple groups, g, that self-select into either the commodity sector (k = C) or the non-commodity sector (k = N). In sector k, production depends on the sum of sector-specific tasks performed by all sector employees of a particular worker group. Following a long tradition in Roy-like models, assume that workers can only be employed in a single sector. Employees in each sector are paid a salary in exchange for each completed unit of the sector-specific task assigned to their group. Denote w k g as the unit price in sector k of the task performed by workers of group g. This notation allows, but does not require, sector-specific task prices to differ 14

15 across groups. 13 Within each group, there is a continuum of heterogeneous individuals indexed by i I g. Individual i is endowed with a bivariate skill vector, (T C g (i), T N g (i)), that determines her effective supply of sector-specific tasks. Specifically, individual i if employed in sector k performs T k g(i) units of the task specific to sector k and earns a wage of w k gt k g(i). The analysis is simplified by working with a log-linear transformation of earnings so that the potential log-wage of individual i of group g in sector k is given by ω k g + ln T k g(i) where ω k g ln w k g. Assume that individuals choose to be employed in the sector that gives them the highest wage. 14 Accordingly, individual i self-selects into sector K g (i) where { K g (i) = arg max ωg C + ln Tg C (i); } ωg N + ln Tg N (i). (3) To characterize the structure of employment and wages in the Two-Sector Roy Economy, let us define individual i s comparative advantage as s g (i) ln(tg C (i)/tg N (i)), and absolute advantage as a g (i) ln Tg N (i). In a given group, suppose individuals independently draw their skill vector from a common bivariate distribution such that s g (i) F g (s) and {a g (i) s g (i) = s} H g (a s) (4) where α g (q) (F g ) 1 (q) is the quantile function of comparative advantage. The skill distribution in (4) implies that, in a particular quantile q [0, 1], there is a set of individuals in group g whose level of comparative advantage is α g (q). Among these individuals, there is a ( conditional distribution of absolute advantage, H g a αg (q) ), with average and variance respectively denoted by A g (q) E [ a g (i) s g (i) = α g (q) ] and V g (q) Var [ a g (i) s g (i) = α g (q) ]. 4.2 Sectoral Wages and Employment: Comparative and Absolute Advantage The most direct implication of the preference structure in (3) is the endogenous sorting of workers to sectors. Due to the heterogeneity in sector-specific task efficiency of workers, this endogenous sorting decision has strong implications for the pattern of employment and wages in the two sectors of the economy. To analyze such pattern, I consider a graphical representation of the economy in terms of quantiles of the comparative advantage distribution. Figure 4 exhibits the average potential log wage in each sector for individuals distributed across different comparative advantage quantiles. If employed in the non-commodity sector, workers in quantile q perform, on average, A g (q) units of the sector-specific task, receiving in return an average potential 13 This particular formulation follows closely the environment in the extensive literature inspired by the seminal work of Roy (1951); see e.g. Heckman and Sedlacek (1985), Heckman and Honore (1990), and, more recently, Ohnsorge and Trefler (2007), Lagakos and Waugh (2013) and Young (2014). In Appendix C, I provide a detailed description of the production and market structures that generate such sector-specific task prices in general equilibrium. 14 Equivalently, one could assume that individuals derive utility solely from the optimal consumption bundle acquired with their labor income. By introducing within-group heterogeneity entirely on sector-specific labor efficiency, the distributive impact of a trade shock is completely captured by the behavior of observable labor income. In this environment, identical homothetic preferences imply that changes in the welfare distribution are directly related to changes in the wage distribution. Notice that this preference structure abstracts from private employment benefits and mobility costs. In Appendix D.3, I explore an extension that incorporates such features into the model. 15