Trade Liberalization, Productivity, Employment and Wages: A Difference-in- Difference Approach

Transcription

1 Trade Liberalization, Productivity, Employment and Wages: A Difference-in- Difference Approach Preliminary version August 2010 Abstract Adriana Peluffo* This paper analyzes the effects of increased competition resulting from the creation of the Southern Common Market (MERCOSUR) on productivity, employment and wages for the Uruguayan manufacturing sector at the plant level. We use impact evaluation techniques -which are not common in trade reform studies- namely matching and difference-in-differences estimation for the period The definition of an appropriate control group is a big challenge and it may be improved by the use of matching procedures. We define a set of tradable and non-tradable industries according to the level and stability of the import penetration and export orientation ratio of the industries. We further explore on the behavior of plants belonging to the export oriented and import competing sectors. One of the most robust findings is that increased trade liberalization seems to improve total factor productivity with a greater effect on plants belonging to the import competing sector. Furthermore, we find reductions in employment driven mainly by the decrease in blue collars, increases in wages and a reduction in the wage gap between white and blue collars as a result of increased trade exposure. Thus, the increase in productivity along with the unemployment of unskilled workers would indicate a room for training, labour and social policies in order to countervail the negative impact of trade liberalization on less qualified workers. JEL Classification: F13, O12, J2, J3 Keywords: trade policy, productivity, employment, wages. *Instituto de Economía, FCEA, Universidad de la República, Uruguay [email protected], [email protected] Acknowledgements: I am indebted to Prof. Germán Calfat and Prof. Ariel Barraud for their help and support. All remaining errors are on my own. 1

2 1. Introduction Recent models of international trade with firm heterogeneity (e.g. Melitz, 2003; Bernard et al, 2003; Bernard, Redding and Schott, 2007; Melitz and Ottaviano, 2008) predict than a movement from autarky to free trade leads to an increase in productivity and size led by expansion in high productivity firms, along with job creation and job destruction and increases in real wages. We analyze the effect of increased trade openness on firms productivity, employment and wages using matching and difference-in-difference techniques (MDID) which compare the effect of the increase in trade exposure on Uruguayan manufacturing plants performance before and after the creation of the Southern Common Market (MERCOSUR). The difference-in-difference approach has the advantage of removing the effects of common shocks providing so a more accurate analysis of the impact of trade openness. A contribution of this paper is the use of the difference-in-difference approach which is not common in trade empirical works 1 to analyse the impact of trade liberalization at the micro level for a small developing country -in particular the impact of MERCOSUR s creation-. To the best of our knowledge this is the first work to use this methodology for the MERCOSUR and in particular for Uruguay using micro level data to analyze the impact of increased openness on firms productivity, employment and wages. The impact of trade liberalization on economic performance and welfare gains is an important but controversial issue. Even though most of the cross-country empirical evidence finds that more open economies experience a faster growth, 2 some economists are sceptical to the robustness of this result (e.g. Rodriguez and Rodrik, 2000). Moreover, these studies do not identify the specific mechanisms by which trade openness may affect growth (Edwards, 1993; Hallak and Levinsohn, 2004). Traditional trade models predict that a movement towards free trade would increase efficiency by reallocating resources from comparative disadvantage industries to comparative advantage ones. These models assume representative firms, perfect competition and full employment. By assuming full employment the effect on labour markets is reduced to adjustment costs, though it is recognised that some workers can be better off and others worse off after trade liberalization. Gains in efficiency and long run growth are assumed to countervail the adverse effect on those who lose. In the neoclassical model a movement towards freer trade would lead to factor reallocation towards comparative 1 The difference-in-difference approach is commonly used in labour economics. As far as we know only 4 papers out of more than 95 use difference-in-difference methodology (Slaughter, 2001; Giavazzi and Tabellini, 2005; Girma, Greenaway and Kneller, 2008; Trefler, 2004) to analyse the effect of trade policies. 2 See for instance Dollar (1992), Sachs and Warner (1995), Edwards (1998), and Frankel and Romer (1999). For recent surveys see Lewer and Van den Berg (2003) and Lopez (2005). 2

3 advantage sectors, which are those intensive in relative abundant factors. In this model, the Stolper- Samuelson Theorem which links changes of price of production factors to changes in the prices of the goods produced states that opening to trade will raise the price of the abundant factor. Thus if developing economies are abundant in unskilled labour when opening to trade the relative wage of unskilled labour should rise, improving so income distribution. Up to the mid of the 90s trade models assume representative firms and usually perfect or monopolistic competition. Melitz (2003) was the first to elaborate a theoretical model introducing explicitly firm heterogeneity. He presents a model with one factor and one sector of production and constant markups, and shows that in the presence of firm s heterogeneity in productivity trade openness leads to significant within-industry reallocation effects from less to more productive firms. In Melitz s model exposure to trade generate within-industry reallocation effects which increases the average productivity and average size of firms while reduces its number. The new new models 3 of trade which incorporate firm heterogeneity, predict that trade liberalization could generate significant across and within-industry reallocation effects. In these models opening to trade and consequently increased trade exposure may not only generate the traditional resource reallocation effects from comparative disadvantage industries to comparative advantage ones, but also from less to more productive firms within industries. On the other hand, the new new trade models that introduce firm heterogeneity predict that trade reform will trigger job creation and job destruction in all sectors, as both net-exporting and net-importing sectors will be characterized by expanding high productivity firms while low-productivity firms shrink or close down. This implies that an important reshuffling of jobs takes place within sectors. Moreover, these new models predict an increase in real wages driven by the expansion of more productive firms. With respect to wage inequalities they have changed substantially over the past few decades, in developing as well as in developed countries. In some cases these changes are in line with the predictions of Heckscher-Ohlin theory: widening wages or unemployment gaps between skilled and unskilled workers in the North, and symmetrically narrowing gaps in parts of the South, particularly in East Asia in the 1960s and 1970s (Wood, 1994). In other cases the wage changes have diverged from these predictions. In particular in the South, wage inequalities rose in many countries in the 1980s and 1990s, most notably in middle-income countries (Robbins, 1996; Wood, 1998), but also in some low-income countries (UNCTAD, 1997). Several explanations have been put forth to explain these findings, some of them emphasise other forces than globalization (reform of labour markets, institutions or exogenous technical change), others suggesting alternative channels through which the effects of globalization might flow. 3 Recently, the examination of the new microeconomic evidence points out that exporting firms are more productive than non-exporting ones, and that increased exposure to international markets may increase productivity. This stylized fact gives raise to new models that incorporates firms heterogeneity. 3

4 Though the efficiency argument for trade liberalisation is usually accepted the main argument against trade reform in developing countries is that trade liberalization would increase income inequality and worsen the conditions of the poor. In particular concerns regarding higher unemployment among workers displaced by the contraction of import competing sectors, greater uncertainty and precariousness of job conditions, and the creation of new job opportunities only for the most qualified segments of the workforce. In this paper we examine the impact of increased exposure to trade on firms productivity, employment and wages. 4 We use a difference-in-difference methodology, also named treatment effect approach. We classify industries into two groups: tradable industries as those that received the treatment (which experienced an increased exposure to international markets) and non-tradable industries or control group (those that were not affected by trade liberalization). 5 We use matching propensity score methods (nearest neighbour and kernel techniques) and then difference-in differences estimates. Matching should improve the selection of the control group allowing a better insight on the effects of trade exposure on plants behaviour. Data come from two data sources. Data at the plant level was provided by the National Institute of Statistics (Instituto Nacional de Estadisticas del Uruguay, INE) for the period Data at the industry level on imports and gross product comes from UNIDO dataset (Nicita and Olarreaga, 2007). This last set of data was used to classify industries as tradable and non-tradable as explained below. This work structures as follows: after this introduction, we present briefly some theoretical issues on the links between trade liberalization, productivity, employment and wages. In the third section we present some empirical evidence. In the fourth section we present the empirical implementation and in the fifth the results and finally our preliminary conclusions. 2. Links between trade liberalization, productivity, employment and wages 2.1. Trade liberalization and productivity The allocative efficiency argument for free trade has been extensively debated by traditional trade theory in the context of perfectly competitive markets. These models predict that trade liberalization will induce the reallocation of resources across sectors from comparative disadvantage industries to comparative advantage ones. 4 In other related work we analysed the impact of trade on plants s mark-ups and sales. 5 This group is less affected by increased trade openness. 4

5 Since the late 1970s the new trade theory has shown that the gains from trade originating from specialisation according to comparative advantages are only part of the story, since in the presence of imperfectly competitive markets trade liberalisation can bring additional gains by reducing the dead weight losses created by domestic firm s market power. In particular it has been argued that trade liberalisation by increasing competition, forces firm s to lower cost-margins and hence move down their average cost curves, therefore raising firm size and scale efficiency. The New Trade Theory, based on models of monopolistic competition assumes the existence of a continuum of identical firms, each one producing a different variety of a product. Since firms have symmetric technologies, opening to trade induces all firms to export to all countries. Head and Ries (1999) use the Helpman and Krugman (1985) model of monopolistic competition to show that a tariff reduction leads to a decrease in the number of plants but has no effect on the output per plant. Thus, opening to trade would reduce the number of plants but would not affect the average size. Since the mid of the 90s several papers show that exporting firms are more productive than nonexporting ones. Further, they are bigger, pay higher wages and are more capital intensive. The studies by Bernard and Jensen (1995, 1999); Aw et al. (2000); Isgut (2001); Alvarez and Lopez (2005); Clerides et al. (1998) are some studies of this empirical literature. This new empirical evidence gives raise to theoretical models which introduce firm heterogeneity. These models show that in the presence of within industry firm heterogeneity, trade liberalisation causes more productive firms to expand at the expense of less efficient firms (which either shrink or die), thereby inducing additional efficiency gains also named rationalization effect. Melitz (2003) was the first widely cited work to introduce firm heterogeneity. In his model there is a continuum of firms, each one producing a different variety of a good. Preferences are of Dixit-Stiglitz type with a constant elasticity of substitution and fixed mark-ups. Labour is the only factor of production and productivity is randomly assigned to firms. Firms have to make an initial investment before they enter to the market and then they learn their productivity level and decide whether to produce or exit. Since not all firms can afford the entry cost to the export markets, only the more productive firms export. In addition, more firms, the ones with higher productivity draws, start producing the good. The expansion of exporters and the entry of new firms increase the demand for labour, which bids up the real wage and forces the least productive to exit. This reallocation of resources increases average productivity and average output per firm. Melitz and Ottaviano (2008) extend the Melitz s model by allowing for firms heterogeneity as well as endogenous mark-ups across firms. This model predicts that opening to trade reduces mark-ups, highlighting the potential pro-competitive effects of trade liberalization. In this model opening to trade 5

6 increases productivity and firms size while reduces its number. Further, aggregate productivity and average mark-ups respond to both the size of a market and the extent of its integration through trade (larger, more integrated markets exhibit higher productivity and lower mark-ups). Bernard, Redding and Schott (2007) introduce an additional industry and factor of production into Melitz s model with constant mark-ups. In this model comparative advantage plays an important role in determining the pattern of trade. This model can explain why some countries export more in certain industries, while at the same intra-industry trade exists. A movement to free trade increases firms productivity and size while decrease the number of firms. Average firm output increases more in comparative advantage industries while at the same time the largest decrease in the number of firms occurs in these industries. The selection on productivity drives the entry and exit process and it is more intense in comparative advantage industries because of the greater probability of export opportunities, therefore low productivity firms are less likely to survive trade openness in comparative advantage industries. Bernard et al. (2003) introduce a different type of trade model with heterogeneous firms. They assume stochastic firm productivity in the multi-country Ricardian model developed by Eaton and Kortum (2002). Domestic and foreign firms compete to produce the same variety, which generates an endogenous distribution of mark-ups. The model and the counterfactual experiment they develop predict that a decrease in trade barriers increases average productivity, reduce the number of firms but do not affect mark-ups. Aggregate productivity rises as employment shifts from low productivity plants driven out by import competition to high productivity plants turning towards export markets. The simulations also show that a 5 percent decrease in trade barriers decreases the number of firms by 3.3 percent while employment in the industry falls by 1.3 percent. Since employment falls less than the number of firms average employment per firm goes up. The degree of uncertainty with respect to technologies does not seem to affect the main predictions of these models. While Melitz (2003) assumes that technologies are randomly assigned to firms, Yeaple (2005) assumes that firms can choose between two technologies, a low-cost and a high-cost technology. These technologies use workers with different skills, which are completely observed by the firms. In Yeaple s model there are two industries: a differentiated manufactured good and a homogenous good. A reduction in trade costs increases average productivity and reduces the number of varieties produced in each country. Since each variety is produced by a single firm, the number of firm decreases, and sales concentration increases, increasing so firms size. Moreover, trade and investment liberalisation may promote technological progress and productivity growth in developing countries through several channels, such as technological progress embodied in 6

7 imported capital goods and intermediate inputs, technology transfers accompanying FDI and learning by exporting effects. As we stated above though the efficiency argument for trade liberalisation is usually accepted the main argument against trade reform in developing countries is that trade liberalisation would increase income inequality and worsen the conditions of the poor. In particular concerns regarding higher unemployment among workers displaced by the contraction of import competing sectors, greater uncertainty and precariousness of job conditions, and the creation of new job opportunities only for the most qualified segments of the workforce. We turn to this point in the section below Trade liberalization and its links with employment and wages Trade policies can have a significant impact on the level and structure of employment, on wages and wage differentials, and on labour market institutions and policies. Nevertheless labour and social policies also influence the outcomes of trade policies in terms of growth of output, employment and the distribution of income. Trade liberalization is associated with both job destruction and job creation. The net employment effect in the short run depends mainly on country specific factors such as the functioning of the labour market. In the long run, the efficiency gains due to trade liberalization are expected to generate positive employment effects, either in terms of quantity or quality of jobs or a combination of both. The theoretical literature provides insights into the process of job destruction and job creation following trade liberalization and illustrates how different country characteristics can affect temporary and permanent employment at the sectoral or country level (Lee and Vivarelli, 2006). The classical link between trade and income inequality is based on the Stolper-Samuelson Theorem developed in a model that assumed full employment. According to this theorem inequality is most likely to increase in industrialized countries as a consequence of trade with developing countries because the former are well endowed with skilled labour. While in developing countries is expected to observe a decline in inequality. This would happen because developing countries are typically well endowed with low skill labour relative to developed countries. With a move to free trade, developing countries will be more competitive in low skill intensive sectors which will expand. The increased demand for low skilled workers, who typically belong to the poorer segments of the population, will lead to an increase in their wages relative to the wages of skilled workers. 7

8 As we have mentioned above, traditional trade models assume full employment, though some workers may be better or worse off in the long run due to changes in wages. It is assumed that on average, individuals would be better off as a result of overall efficiency gains triggered by trade liberalization. However, many economies are not characterized by full employment. In this case trade liberalization would reduce demand for workers mainly in import competing sectors and unemployment would increase. Recent trade models point out that adjustment processes may not only be observed between sectors but also within sectors. The new-new trade models that introduce firm heterogeneity and fixed-market entry costs predict that trade reform will trigger job creation and job destruction in all sectors, as both net-exporting and net-importing sectors will be characterized by expanding high-productivity firms and low-productivity firms that shrink or close down. This implies that an important reshuffling of jobs takes place within sectors. On the other hand, on the question of the integration of countries with different level of development, some of the new geography models predict catastrophic agglomeration in developed countries and deindustrialization in less developed ones. Nevertheless, these effects would depend on the way integration is managed, the intensity and speed of the process, and the extent to which specific policies aimed at reducing the negative effects are implemented. 6 Policies such as those aimed at promoting technological spillovers would reduce the negative impacts on the less developed countries (Baldwin et al, 2003). With respect to wages, the theoretical literature predicts that trade liberalization raises average income levels, and some contributions to the theoretical growth literature suggest that trade also stimulates growth. A large number of multi-country case studies and econometric studies using cross-country datasets have tested the empirical validity of the trade-growth relationship but there is no full agreement among economists concerning the precise nature of this relationship. 7 The reasons for this disagreement may lay in differences in the quality of the data as well as on econometric issues of the studies. As we mention above the classical link between trade and income inequality is based on the Stolper- Samuelson Theorem developed in a model that assumed full employment. According to this theorem 6 Roughly speaking an increase in the mobility in goods and labor will foster agglomeration in the more developed countries while mobility of capital and knowledge will foster dispersion, i.e. convergence among economies. On the other hand, policies aimed at promoting the diffusion of technology spillovers and at reducing congestion would be first best policies (Baldwin et al, 2003). 7 See for instance Baldwin (2003), Rodríguez and Rodrik (2001), Dollar and Kraay (2004), Loayaza, Fajnzylber and Calderon (2005) and Wacziarg and Welch (2003). 8

9 inequality is most likely to increase in industrialized countries as a consequence of trade with developing countries because the former are well endowed with skilled labour. While in developing countries is expected to observe a decline in inequality. This would happen because developing countries are typically well endowed with low skill labour relative to developed countries. With a move to free trade, developing countries will be more competitive in low skill intensive sectors which will expand. The increased demand for low skilled workers, who typically belong to the poorer segments of the population, will lead to an increase in their wages relative to the wages of skilled workers. It is worth to note that the majority of trade in industrialized countries is intra-industry trade, i.e. trade with other industrial countries. Thus, the changes in relative demand for different factors of productions predicted by Stolper-Samuelson are not likely to hold. In this regard Manasse and Turrini (2001) analysed whether intra-industry trade have an impact on the demand for high-skilled and lowskilled labour and conclude that intra-industry trade can raise wage inequality within countries and within sectors. Duranton (1999) comes to a similar conclusion in a model that combines intra-industry trade with technological change. In his model trade and technological progress lead to increase wage inequality. 3. Evidence on the Effects of Trade Liberalisation on Firm Performance 3.1. Evidence on productivity gains Most studies that use plant or firm level data to analyse the impact of trade liberalisation on productivity show mixed results. Among these works are those by Tybout, de Melo and Corbo (1991) and Pavcnik (2002) for Chile, Tybout and Westbrook (1996) for Mexico, Harrison (1994) for Cote d Ivoire, Muendler (2002) for Brazil, Lopez-Cordova and Mesquita (2002) for Mexico and Brazil, Fernandes (2007) for Colombia and Krishna and Mitra (1997) and Topalova (2004) for India. Tybout, de Melo and Corbo (1991) find little evidence of productivity growth in manufacturing after trade reform in Chile. Tybout and Westbrook (1996) use plant-level data to study the efficiency gains induced by the Mexico trade liberalisation. They find modest increases in productivity due to output share reallocations among firms with different productivity levels, and even slighter from scale efficient effects. Most of the average increase in productivity comes from the catch-all residual effect. This implies that most of the estimated overall efficiency gains are indeed left unexplained. 9

10 Harrison (1994) working with a panel of firms for Cote d Ivoire finds significant productivity gain after trade liberalisation. Mulaga and Weiss (1996) working at the firm level for the period find that the reduction in protection increases total factor productivity in Malawi. Nevertheless, when productivity estimates were adjusted for changes in capacity utilisation the association between productivity growth and the fall in protection vanishes. Lopez-Cordova and Mesquita (2003) estimate firm level productivity for Brazil and Mexico and test its causal links with trade and foreign direct investment variables. The results suggest strong trade related gains, with import discipline emerging as the dominant effect. Topalova (2004) analyses the effects of India s trade reform and finds that the reduction in trade protectionism leads to higher levels and growth of firms productivity, with a stronger effect for private firms compared to state companies. All these studies were conducted on firm or plant level panel data. Muendler (2002) using data on Brazilian manufacturing finds that the shutdown probability of inefficient firms rises with competition from abroad, thus contributing positively to aggregate productivity. Krishna and Mitra (1997) working with a panel of firms for the period obtained some weak evidence of an increase in the rate of growth in productivity after trade reform. Pavcnik (2002) and Fernandes (2007) observe productivity gains for Chile and Colombia respectively. Pavcnik (2002) compares plants productivity growth in the export oriented and import competing sectors with that of firms in the non traded sector. Her results suggest a dramatic productivity growth differential in favour of plants exposed to international competition with respect to inward-oriented plants. They also suggest that, in the case of a unilateral trade liberalization (such as the one experienced by Chile) trade-induced productivity gains can be higher for import competing plants relative to export oriented plants. Fernandes (2007) analyze whether increased exposure to foreign competition generates productivity gains for Colombia manufacturing plants. She uses a two step approach as well as a production function equation that includes trade policy measures as a regressor (direct approach). She finds a strong positive impact of liberalization on plant productivity, even after controlling for plant and industry heterogeneity, real exchange rates and cyclical effects. 10

11 Pavcnik (2002) and Fernandes (2007) find that the reallocation of resources in favour of more productive firms is a critical determinant of productivity growth and that, consistent with Melitz s (2003) model, this effect can be largely due to trade liberalization. Trefler (2004) using a difference-in-difference approach finds evidence that the Canada-US Free Trade Agreement has led to substantial increases in plants labour productivity as well as contraction of plants in the import competing and an expansion of plants in the export oriented industries. Casacuberta et al. (2003) analysing the Uruguayan Manufacturing sector find increases in total factor productivity especially in sectors where tariff reductions were larger and unions were not present. The main methodological problems involved in estimating the productivity gains from trade reform are the identification of the trade effects, and simultaneity and self-selection bias in the estimation of productivity Evidence of the effect of trade liberalization on employment and wages Regarding to the impact of trade liberalization on the labour market the literature has flourished in the last decades. The economic literature has produced a large number of empirical studies analysing the effects of trade on labour market outcomes. Nevertheless, so far no clear message emerges from the literature. The only general conclusion that may be justified is that employment effects depend on a large number of country-specific factors. One shortcoming of the studies is that they fail to distinguish the different possible causes of employment changes. Labour market policies, macroeconomic policies, technological changes or movements along the business cycle are only a few examples of factors that may affect an economy s employment level. In this regard, the work by Gaston and Trefler (1997) on the Canada-US Free Trade Agreement, make a distinction between the employment effects of the trade agreement and those of a general recession affecting both trading partners in the same period. Gaston and Trefler (1997) find that tariffs cuts contributed to reduce employment during the years following the agreement but that they also contributed to important productivity increases leading to long run efficiency gains. However, after controlling for recession, it appears that the FTA accounted for only 9-14 per cent of the jobs lost over the period. Trefler (2001) analysing the Canada-US free trade agreement finds 8 The simultaneity/endogeneity bias arises since more productive plants are willing to hire more inputs so the error term is positively correlated with factor inputs. The self-selection bias is induced by plant closing down. 11

12 instead a bigger role for the tariff cuts in the employment declines. According to his estimates nearly 30 per cent of the observed employment losses in manufacturing were a result of the FTA tariff cuts. His work shows that the adjustment process took seven years and during this process many workers moved to high-end manufacturing jobs along with dramatic productivity growth. Both, aspects reflect important long run efficiency gains from trade. Trefler also finds increases in workers annual earning and these increases are significantly higher in those industries that cut tariff rates most. Rama (2003) points out that in many developing countries temporary increases in unemployment following trade reform have been observed, though he finds that unemployment rates do not appear to be systematically higher in more open economies. Milner and Wright (1998) analyzed labour market responses to trade liberalization in Mauritius. They show that manufacturing employment increased significantly in the period following the 1983 trade liberalization. Though employment increases in the long run exceeded those that occurred immediately after the reform, the short-run impacts on employment were significant and positive. Rama (1994), in contrast, finds a negative effect of trade liberalization on employment in his analysis of trade policy reform in Uruguay in the late 1970s and early 1980s. Further evidence on developing countries is given by Harrison and Revenga (1995). They find evidence of increases in manufacturing employment following trade liberalization periods in Costa Rica, Peru and Uruguay. Instead, in a number of transitional economies (Czechoslovakia, Poland and Romania), employment fell during the transition period. As the authors note, however, those countries were undergoing significant other reforms that went well beyond trade liberalization. There are some cross-country studies that provide insights into the income effects of trade reform for subgroups in the population. The study by Rama (2003) explicitly looks at the effects of trade reform on wages and finds that wages grow faster in economies that integrate with the rest of the world. The author finds that trade can have a negative impact on wages in the short run, but finds that it only takes a few years for this effect to change sign. Lopez (2004) distinguishes between the short and long run effect of trade policies. He finds that trade openness raises inequality and stimulates growth at the same time and refers to trade liberalization as a win-lose policy. Improvements in infrastructure and in education on the other hand reduce inequality and increase growth at the same time, so does inflation reduction. Most empirical works for Latin America suggest that trade liberalization has led to and increase in both income and wage inequality and a skill bias of labour demand (Slaughter, 2000; Wood, 1997; Robbins,1996; Attanasio et al. 2004; Feenstra and Hanson, 1997; Perry and Olarreaga, 2006; Barraud, 2008). Dollar and Kraay (2002) find that trade openness affects income distribution positively. A 12

13 similar result is obtained by Behrman, Birdsall and Szely (2003) for a set of Latin American countries. However, Sanchez-Paramo and Schady (2003) find the opposite result in six Latin American countries, where trade volumes would negatively affect inequality. Spilimbergo et al. (1999) also find that trade openness would be associated with higher inequality, whereas Edwards (1998) does not find any significant effect of trade on income distribution. This literature does not appear to allow for any general conclusion on the link between trade liberalization and income distribution and the impression arises that this link is country and situation specific. Barraud (2008) analysing the effect of trade liberalization on wages for Argentina using difference in differences and matching techniques finds that labour market and poverty indicators deteriorated in the liberalization period in Argentina. Galiani and Porto (2006) find a negative effect of tariff reforms on the wage levels in Argentina. 9 For the Uruguayan case Casacuberta and Vaillant (2002) find higher reductions in employment and wages in those industries with higher tariff reductions. Hence, so far empirical research into the link between trade liberalization and market labour outcomes has produced mixed results. While the evidence for Asia seems to confirm a reduction in inequality following trade liberalization in Latin America inequality seems to show an increase. Several explanations have been put forward for the differences in outcome between East Asia and Latin America. One line of argument focuses on the fact that Latin America opened its markets later than the East Asian economies (Wood, 1997). As a result, the entry of China and other large lowincome Asian countries into the world market for labour-intensive manufactures in the 1980s shifted the comparative advantage of middle-income Latin American countries into goods of medium skills intensity. Increased openness in middle income countries thus reduced the relative demand for unskilled workers by causing sectors of low-skill intensity to contract. This would explain why relative wages of unskilled workers decreased. Moreover, it has also been argued that most Latin American economies are abundant in natural resources rather than low-skilled labour. This would also explain why wage inequality did not decrease in Latin America. Another explanation for rising inequality in some developing countries is that liberalization introduces new skill intensive activities into developing countries. For example, the work by Feenstra and Hanson (1997) shows that there is evidence that liberalization in Mexico induced larger FDI inflows from the 9 Winters et al. (2004) and Hertel and Reimer (2004) surveyed the effects of trade on income levels. 13

14 United States. These FDI inflows reflect the shift of low-skill intensive activities from the US to Mexico. Nevertheless, Mexico s relative demand for skilled workers within industries in manufacturing rose along with FDI inflows into those industries, which led to increases in Mexican wage inequality. One possible explanation for this finding is that jobs which were low-skill intensive in the United States were relatively skill-intensive in a country like Mexico. The tariff schedule in place before trade liberalization may also affect the impact of trade on wage inequality. If protection was higher in the low-skill intensive sectors, then trade liberalization may actually lead to shrinkage of these sectors. As a consequence, wage inequality would increase. It has been suggested in the literature that this phenomenon has been observed in Mexico and Morocco. Attanasio et al. (2003) find evidence for a link between trade, skill-biased technological change and increases in wage inequality in line with the theoretical literature discussed before. They analysed the effects of the drastic tariff reductions in the 1980s and 1990s in Colombia on the wage distribution. They identify three main channels through which the wage distribution was affected: increasing returns to college education, changes in industry wages that hurt sectors with initially lower wages and a higher fraction of unskilled workers, and shifts of the labour force towards the informal economy that typically pays lower wages and offers no benefits. Their results suggest that trade policy affected each of the three channels. The increase in the skill premium was primarily driven by skilled-biased technological change. However, the authors suggest that this change may have been in part motivated by the increased foreign competition to which the trade reform exposed domestic producers. They also find that wage premiums decreased by more in sectors that experience larger tariff cuts. Finally, they find some evidence that the increased size of the informal economy was related to increased foreign competition as sectors with larger tariff cuts and higher trade exposure experienced a greater increase in informality, though this effect was concentrated in the years prior to the labour market reform. The authors conclude that increasing returns to education and changes in industry premiums and informality alone cannot fully explain the increase in wage inequality observed over the period, suggesting that overall the effect of the trade reforms on the wage distribution may have been small. As we have already mentioned we should keep in mind the difficulty of isolating the effects of trade from other policies implemented simultaneously with trade reform. In most studies, the identification of trade effects relies on the comparison before and after a policy change. As a consequence, this approach attributes changes originating from other sources to trade policy. Most studies use data covering only a short time period after the reform which implies that the results can be heavily affected by the cyclical behaviour of the economy. The difference-in-difference methodology should eliminate the effects of common shocks and provides more precise description of the impact of trade policy as we explain in Section 4. 14

15 4. Empirical implementation This paper use a difference-in-differences methodology which allows to study the impact of increased trade exposure (the treatment) on the liberalizing group (the treated) relative to firms in industries that did not increase their exposure to foreign competition (the control group). The effect of trade liberalization is the estimated difference-in-difference of the outcome variable (productivity, employment, wages and relative wages between white and blue collars) between the treated and the control groups. The difference-in-difference methodology is implemented matching plants with similar propensity scores and Difference-in-Differences estimation Matching firms with similar propensity scores and difference-in-difference estimation Our aim is to evaluate the impact of increased trade exposure on a set of outcome variables (Y), where Y represents productivity, employment and wages. Y is referred to as the outcome in the evaluation literature. 10 Let TL it { 0,1} be an indicator (dummy variable) of whether plant i was exposed to greater foreign competition after MERCOSUR s creation, i.e. after time period t=1991, and 1 Y i, t + s the outcome of the 0 treated at t+s, i.e. after the creation of the MERCOSUR. Also denote by Y i,t+s the outcome of plant i had it not experienced a greater trade exposure (control). The causal effect of trade openness for plant i 1 0 at period (t+s) is defined as: Y i, t + s Y i, t + s. 0 The fundamental problem of causal inference is that the quantity Y i, t + s is unobservable. Thus the analysis can be viewed as confronting a missing data problem. In common to most of the microeconomic evaluation literature (cf. Heckman et al., 1997) we define the average effect of trade openness as: { Y 1, 1} {, 1} {, 1}, Y TL i t = = E Y TL i t = E Y TL i = E i t + s i, t + s i, t + s i, t + s t Eq.(2) Causal inference relies on the construction of the counterfactual for the last term in equation (2) E { Y 0, 1}, TL i = i t + s t which is the outcome that plants would have experienced on average had they not been exposed to greater trade competition after the creation of the Southern Common Market. The counterfactual is estimated by the corresponding average value of plants that belong to non-tradable 10 Blundell and Costa Dias (2000) present a review of the microeconomic evaluation literature. 15

16 industries, and therefore almost unaffected due to increased trade exposure E { Y 0, 0}, TL i = i t + s t important issue in the construction of the counterfactual is the selection of a valid control group. We assume that all the difference in the outcome (Y) between firms affected by increased trade openness and the appropriately selected control group is captured by a vector of observables X it and the level of the outcome variable Y i,t-1, before MERCOSUR s creation. The basic idea of matching is to select from the group of plants belonging to the non-tradable industries (non-treated or control group) those plants in which the distribution of the variables affecting the outcome is as similar as possible to the distribution to the firms belonging to the tradable industries (treated group). Nevertheless, some assumptions have to hold for this to be a valid comparison group. One of these assumptions is conditional independence that states that the treatment status is random, conditional on some set of attributes, X it, and independent of the potential outcomes 1 0 ( Y it, Yit ). This assumption implies that given a set of observable characteristics the outcome of a carefully defined group of individuals unaffected by the policy can be used as a counterfactual of the outcome levels of the treated had them not be treated. The matching procedure consists in linking each treated individual with the same values of the X it. To solve for the difficulties that arises when X it is multidimensional, the results of Rosenbaum and Rubin (1983) show that if the conditional independence assumption holds, it will also hold conditional on a single index that captures the information from the X it in the so called propensity score, i.e. ( Y 1, 0 it Y it ) TL it P( X it ). Thus, we adopt the propensity score matching method of Rosenbaum and Rubin (1983). To this end, we first identify the probability of being a plant affected by increased trade openness (the propensity score ) for all plants, irrespective if they belong to tradable or non-tradable sectors by means of a logit or probit model. For instance for the probit model: P ( TLi, t = 1) = F( X it ) where F is the normal cumulative distribution for the probit model, or the logistic for the logit model and X stands for full set of control variables.. An Let P it denote the predicted probability of being affected by trade openness at time t for plant i (which is actually or potentially affected by increased trade exposure). A plant k belonging to the non-tradable industries, which is closest in terms of its propensity score to a plant belonging to the tradable industries, is then selected as a match for the former. More formally, at each point in time and for each firm exposed to increased foreign competition i, a non-tradable plant j is selected such that 11 P it P { } { Pit Pkt } = kt = k Lib i, t 0 min. 11 A firm unaffected by increased trade openness can be match to more than one firm that experienced the effect of increased trade exposure. 16

17 This type of matching procedure is preferable to randomly or arbitrarily choosing the comparison group because it is less likely to suffer from selection bias by picking plants with markedly different characteristics. There are several matching techniques, and in this work we use the nearest-neighbour matching method and we also try the kernel matching method to check the robustness of the results. 12 Once selected the comparison group, we adopt a difference-in-difference methodology 13 to isolate the role of increased trade exposure on plants dynamics. As Blundell and Costa Dias (2004) point out, a combination of matching and difference-in-difference is likely to improve the quality of non-experimental evaluation studies. The difference-in-difference approach is a two step procedure. Firstly, the difference between the average output variable before and after MERCOSUR s creation is estimated for firms belonging to the tradable sectors, conditional on a set of covariates. However, this difference can not be attributed only to increased trade exposure since after the creation of MERCOSUR the output variables might be affected by other macroeconomic factors, such as policies aimed to stabilization of the economy. To cater for this the difference obtained at the first stage is further differenced with respect to the before and after difference for the control group of non-tradable plants. The difference-in-difference estimator therefore removes effects of common shocks and provides a more accurate description of the impact of trade openness. According to the literature the independent variables to include in the logit/probit regression should be correlated to the outcome variable and to participation in the policy, but they should not be potentially changed by the policy itself. Thus, the choice of variables priorizes the use of time invariant variables which poses another challenge to the analysis. To tackle the issue we construct some categorical variables such as size, high value added defined as a dummy that takes the value of one if the plant has a value added higher than the median- high gross output, high capital intensity (capital labour ratio) and export status as we explained below Variables As outcome variables we consider total factor productivity (TFP), employment and wages at the plant level. TLit is the trade liberalization variable (the intervention on the treated). TLit takes the value of one after MERCOSUR creation and zero otherwise. It is constructed by interacting plants belonging to 12 The matching is performed using the command psmacht2 in Stata, version 9 as described in Sianesi (2001). Additionally, we tested the balancing properties with the command pscore. 13 See Meyer (1994) for a review of this approach. 17

18 the tradable industries ( Libit, where tradables=1 and non-tradables=0) with a time dummy that takes the value of one from 1992 onwards (a year after the creation of the MERCOSUR). 14 Total Factor Productivity estimates were provided by Casacuberta et al. (2004). TFP was estimated using the Levinshon and Petrin s methodology which allows more accurate estimates since this method reduces the simultaneity problem present in most works when estimating total factor productivity at the micro level. As is usual in the empirical literature the estimation of a production function uses the real value of output rather than physical units of output produced by the plant as a measure of output. A measure of productivity based on the real value of output might not reflect the ranking of plants on their productivity if plants charge different mark-ups. Discriminating between the true productivity and the specific mark-up is quite difficult in the productivity literature. In order to distinguish the true efficiency from the plant specific mark-ups we would need plant level price data which is not available for Uruguay. This caveat should be considered when interpreting the results. 15 We measure employment as the number of workers per plant and we also discriminate between white collar and blue collars. Wages are measure as total wages per plant, wages per employee, wages per white collar and wages per blue collar. We also analyse the effect of increased trade exposure on gross output per plant. Wages are deflated by a wage index while gross output is deflated by the wholesales price index. To construct the liberalization variable ( TLit ), we define the treated group as those establishments belonging to the tradable industries ( Libit ) after MERCOSUR s creation. Our control group is integrated by firms belonging to the non-tradable industries, which are likely to be less affected by trade openness. In so doing we follow a similar definition to the one adopted by Pavnick (2002) 16 who defined as tradable industries those with an import penetration ratio (IP) equal or greater than 0.20 and/or and exports to output (EXP_O) ratio greater than 0.20 and non-tradable otherwise. 17 We checked that these variables (IP and EXP_O) were greater than 0.20 for the whole period ( ) taking into account the level as well as the stability of the import penetration and export-output ratio. 14 The Asuncion Treaty, signed on the 26th March of 1991 is a regional integration agreement to create the Southern Common Market. It was signed by Argentina, Brazil, Paraguay and Uruguay. 15 Harrison is one of the few studies that explicitly models plants mark-ups, assuming that plants are Cournot competitors and allows the mark-ups to vary over time and across industries, but not across plants within and industry. This is the same as assuming that all firms within an industry have the same market share. If one does not have plant level price data we need to impose some assumptions on the joint distribution of productivity and mark-ups. Bernard, Eaton, Jensen and Kortum (2000) provide an example. They set up a model in which they show that on average, a more efficient plant charges a higher mark-up. 16 Pavnick (2002) takes an IP and EXP_O of 15 % and carries out a sensitivity analysis for 10 % and 20 % of these indexes finding that results are robust to these levels of IP and EXP_O. 17 Import penetration is defined as total imports in the industry over total output, while exports to output as total exports over total output at the industry level. To construct these variables we use data from Nicita and Olarreaga (2007). 18

19 We should note that this definition of the tradable and non-tradable groups is not free of criticism: on one hand it may be sensitive to the level of aggregation used. Moreover, usually the non-tradable industries except for the work of Pavcnik (2002) - are defined as the service sector (construction, communication, transport, and financial services). Lack of plant level data on services for the period analysed prevented us from checking the sensitivity of the result using services as a control group. Besides, Barraud and Calfat (2008) analysing the effect of trade liberalization on wages for Argentina find evidence of significant impacts of trade liberalization on several non-tradable sectors as well as an important shift of manufacturing workers to services, which would indicate that the service sector is also likely to be affected by liberalization. Furthermore, in the Uruguayan case services are liberalized and consumed mainly by not residents namely tourism, transportation and financial services- except for the public services provided to domestic residents by public companies (electricity, fuels and telecommunications). As we have mentioned above, we further classify tradable industries into different groups according to its trade orientation: export oriented industries, import competing industries and intra-trade industries, since trade liberalization may have a different impact on plants belonging to the tradable industries according to the trade orientation of the various sub-groups. We define export oriented industries as those industries (EXPO) with an export/output ratio equal or greater than 0.20 and an import penetration ratio lower than 0.20 during the whole period under study. The import competing industries (IMPC) are defined as those with an import penetration ratio equal or greater than 0.20 but an export to output ratio lower than 0.20 during the whole period. Intra-industry trade industries (IIT) are defined as those industries with an import penetration and export ratio equal or greater than 0.20 for the whole period. 18 In Appendix 1 we present the import penetration and export-output ratio for the 26 industries considered in this work. We exclude industries 353 and 354 due to missing data and also those industries that experienced important changes in its trade orientation after MERCOSUR s creation. In a first step we compare tradable versus non-tradable industries, while afterwards we take a closer look into the tradable group according to the trade orientation of the various groups of industries that composed it. We also examine if the impact of trade liberalization differs within the tradable industries depending on the trade orientation of the various sub-sectors that integrates it (EXPO and IMPC) The advantage of these openness measures is that they are specific to the manufacturing industries while cross-country comparisons use aggregate measures that avoid having a better insight on industry and plants dynamics. Nevertheless, as we note previously, even though the difference-in-difference methodology should eliminate common shocks, we should be cautious in interpreting the results since under the period analyzed Uruguay did not only liberalized trade but also pursue other set of 18 The average plant export propensity of the tradable group is 0.15, while for the export oriented this figure is 0.25 and 0.05 for import competing plants, according to this definition. 19

20 macroeconomic policies aimed to the stabilization of the economy. One of these policies was the exchange rate policy which was pegged to the dollar and domestic currency appreciated in order to control inflation. 19 Thus, we checked that the output variables analysed in the treated and control group have a common trend, otherwise results will we flawed unless we use inverse probability weighting techniques Data Sources We use two data sources. Data at the plant level comes from the National Institute of Statistics (INE) for the period Estimates of TFP were provided by Casacuberta et al. (2004). Data at the industry level on imports and gross product comes from UNIDO dataset (Nicita and Olarreaga, 2007). This last set of data was used to classify industries as tradable and non-tradable as explained above. The micro level data provided by the National Institute of Statistic, Uruguay (INE) contain information on gross product, value added, expenditures on intermediates and materials, energy, employment, wages, sales, exports, capital and age at the plant level for the period In 1988, the starting year of our sample, the Second National Economic Census was conducted. The rest of the data comes from annual surveys. The surveys report information from manufacturing plants with five or more employees. All the plants with more than 100 employees are compulsorily included in the sample. A random sampling process is conducted on plants with less than 100 employees and has to satisfy the requirement that the total employment of all the selected establishments must account at least for 60 % of the total employment of the sector according to the economic Census of These selection criteria biased the database towards big plants. Each year the INE revises the sample coverage, and if necessary, due to the closure of firms, includes new ones. Once a firm enters the survey, it is followed until its death. Therefore, when we have no more data for a particular establishment this is interpreted as a plant closure (exit or death). Gross output, value added, sales and exports were deflated by the wholesale index with base year Capital was deflated by specific industry price deflators for capital, as well as employment, wages and energy. The deflators were provided by the Department of Economics, School of Social Science, elaborated by the INE. In the period there was an important reduction in the number of plants, which along with missing observations left us with and important reduction in the number of observations. Actually in the period there is exit as well entry of new firms which is quite difficult to analyze due to the sampling methodology followed by the INE. The INE periodically includes new establishments, but these do not 19 In future work we will also try the methodologies of Inverse Probability Weighting which allows for variations in the trends between treated and control groups. 20

21 necessarily belong to newborn firms. Thus we can not identify newborn firms in the data. In Table 1 we present the number of plants per year. In Table 2 we present some descriptive statistics on employment, wages, gross output, value added, labour productivity, total factor productivity, and export propensity for firms belonging to the tradable and non-tradable industries as well for the pooled data (whole sample). Total factor productivity (TFP) and labour productivity (LP) per plant increase for the whole sample in the period. When we discriminate between tradable and non tradable groups, we find that TFP and LP are higher for plants belonging to the non tradable group than for those belonging to the tradable one. Both variables increase in the period for the non tradable as well as for the tradable group. Total employment per plant shows a decrease in the period. In 1988 this variable is higher for plants belonging to the tradable group in relation to those in the non tradable one. Nevertheless, employment decrease for the tradable group in the period and in 1995 average employment per plant is higher in the non tradable sector compare to the tradable one. Thus the reduction in employment in the sample is led by the decrease in employment in the tradable group. When we split total employment in white collars and blue collars we observe that the number of white collars per plant is higher in the non tradable group than in the tradable one. Moreover there is an increase in white collars in the non tradable group and a very slight decrease in the tradable one. On the other hand the number of blue collars decrease in the period for the whole sample. This variable is higher for the tradable group compared to the non tradable one, and it decreases in the period for plants belonging to the tradable group while remains constant in the non tradable one. Regarding to wages, total wages per plant increases for the whole sample in the period. We observe that wages per plant are higher for the non tradable vs. the tradable group. Moreover it increases in the period for both groups with a higher increase for the tradable one. Wages per worker increase in the period for the whole sample as well as for the non tradable and the tradable group. The increase is slightly higher in the non tradable group compare to the tradable one. Wages per white collar increase in the period for the whole sample. In 1988 wages per white collar are slightly higher for the tradable group compared to the non tradable one, while in 1995 this variable is higher for the non tradable one. For both groups there is an increase in wages per blue collar in the period. On the other hand wages per blue collar increases for the whole sample in the period. This variable is lower for the tradable group in relation to the non tradable one in both years, though it shows an increase for both groups of plants in the period. Finally, gross output per plants increases for the whole sample in the period. This variable is higher for the non tradable group than for the tradable group in both years, with a higher difference in 1995, i.e. gross output per plant increases more in the non tradable group than in the tradable one. 21

22 Thus, in 1988, average values of gross output, wages, value added, total factor productivity and labour productivity are higher for plants belonging to non-tradable industries, and lower for the tradable ones (except for wages per white collar which are slightly higher for the tradable plants). Total employment per plant and the number of blue collars is higher for tradable plants compared to non tradable ones. Export propensity is higher for plants belonging to tradable industries and lower for non-tradable ones as was expected by the definition used. Except for employment that shows a decrease in the period wages, gross output, value added, labour productivity and total factor productivity increases in the period. In 1995 except for total employment per plant and the number of blue collars that are lower for non tradable plants, all the variables present a similar behaviour to that in In Table 3 we present the same variables discriminating the firms belonging to the tradable industries into export oriented and import competing as defined above. Total factor productivity (TFP) and labour productivity (LP) per plant increase for the tradable group in the period. When we discriminate between export oriented and import competing sectors, we find that TFP and LP are higher for plants belonging to the import competing sector than for those export oriented one. Both variables increase in the period for the export oriented as well as for the import competing sector. Total employment per plant shows a decrease in the period. This variable is higher for plants belonging to the export oriented sector in relation to those in the import competing one. Nevertheless, employment decrease for both sectors. When we split total employment in white collars and blue collars we observe that the number of white collars per plant is higher in the export oriented group than in the import competing one. There is a very slight decrease in white collars in both sectors in the period. On the other hand the number of blue collars decrease in the period for the tradable group as well as for the export oriented and import competing sectors. This variable is higher for the export oriented sector compared to the import competing one. Regarding to wages, total wages per plant increases in the tradable group, as well as in the export oriented and import competing sectors in the period. We observe that wages per plant are higher for the export oriented vs. the import competing sectors. Moreover it increases in the period for both sectors. Wages per worker increase in the period for the tradable group as well as for the export oriented and import competing sectors. The increase is slightly higher in the import competing sector compared to the export oriented one. Wages per white collar increase in the period for the tradable, export oriented and import competing sectors. Wages per white collar are slightly higher for the export oriented sector compared to the import competing one. 22

23 On the other hand wages per blue collar increases for the tradable group in the period. This variable is lower for the export oriented sector in relation to the import competing one in both years, though it shows an increase for both sectors in the period. Finally, gross output per plant shows increases in the period for export oriented and import competing sectors, with a higher increase for the export oriented one. Thus, in 1988 observe that export oriented plants show higher total employment, white collars, blue collars, total wages, gross output, value added and labour productivity than import competing plants. On the other hand plants belonging to the import competing sector have higher wages per worker, higher wages per white collar and blue collar and total factor productivity than the plants belonging to the export oriented sector. In 1995 plants belonging to the export oriented sector have in average a higher employments, total wages per plant, gross output, wages per white collar, value added, but lower wages per worker, wages per blue collar, labour productivity and total factor productivity than plants belonging to the import competing industries. Thus, in the period wages per white collar increase for plants in the export oriented sector along with labour productivity increased. 5. Results In Table 4.1 we present the results of the difference-in-difference estimation using as matching method the nearest-neighbor (with 3 and 5 neighbors with replacement), and the kernel with two weighting functions the Epanechnikov and the Gaussian). As we mentioned before there are several matching techniques that differ on the selection and weighting of the observations in the control group. Each treated individual can be compared with a single control unit, or with the whole comparison group using nearest neighbour matching or kernel functions respectively and an appropriate weight function. The most commonly used functions include the unity (identical) weight(s) to the nearest observation(s) and zero to the rest, or kernel weights which penalize distant observations according to their propensity score. Usually increasing the neighbourhood to create the counterfactual will reduce the variance and increase the bias resulting from using more and distant matches. According to the theoretical literature, the independent variables to include in this regression should be correlated to the outcome variables and to participation in the policy, but they should not potentially be changed by the policy itself. Thus, this is not a simple task in this study since most of the variables are continuous ones, hence we choose to construct categorical variables. We choose as covariates those that satisfied the balancing properties for the three sets of plants analysed: tradable, export 23

24 oriented and import competing plants. After analysing the balancing tests, 20 we retain as covariates the export status of the firm (dummy that takes the value of one for exporting firms and zero otherwise), a dummy equal one for those plants with a gross output higher than the median of the whole sample and a dummy for plants with value added higher than the median for the whole sample and zero otherwise Tradable vs. Non-Tradable Results are presented in Table for firms belonging to the tradable sector. We found that total factor productivity increases significantly for all the matching procedures tried 22. The magnitude of the estimated effect is of 12 % in most of the estimations. We find that average gross output per plant shows an increase but it is not significantly different from zero for the five estimations performed. This result is similar to Trefler s finding for Canada. Regarding to employment we find a significant decrease in the number of total workers per plant ranging from 20 up to 24 less workers per plant. When we discriminate between white collar and blue collar we find that the reduction in white collars is not significant (2-3 blue collars less per plant) while the reduction in blue collars is negative and significant (17-18 blue collars less per plant). Hence, the reduction in total workers is driven by the reduction in blue collars. Total wages per plant shows an increase but this is not significant. On the other hand wages per employee shows a positive and significant increase of 500 up to 526 constant pesos. Furthermore when we discriminate between wages per white collar and wages per blue collar (in terms of number of workers in each category) we find significant increases for both. Finally, the ratio of wages per white collar/wages per blue collar is negative and significant showing a decrease in the gap between the wages for both types of workers in the period analysed. The reduction in the gap is approximately of Summing up, the results show an increase in productivity, decrease in employment and increases in real wages, with a reduction in the gap between white and blue collars. The reduction in employment along with increases in productivity may be explained by the technological modernization in the early 90s, namely a substitution of unskilled labour by capital 23 (Casacuberta et al., 2004; Noya and Lorenzo,1998; Llambí and López, 1997). This modernization process takes place due to a higher competition as well as the reduction in the real price of the capital in the period. On the other hand, the 20 We analysed the balancing test with two Stata s programs: the psmatch2 and the pscore option. 22 A T-stat equal or higher than 1.67 is significant at the 10 % level. 23 We observe increases in capital per worker in the period. These statistics are available upon request. 24

25 reduction in the relative wage of white to blue collar may be explained due to the decrease in employment, which was mainly driven by blue collars. It could be argued that the blue collars that lost employment are likely to be the less skilled ones in this category of workers. Furthermore, the period is characterized by an important subcontracting of work by the firms, and particularly of less skill intensive activities. We would need more accurate information on the skill levels of workers which is not available in the Industrial Surveys such as years of schooling- in order to have a sound explanation for this result.. In Table we present for each period under study the estimates of the propensity score of a binary variable that equals one if the plant belong to the tradable industries after MERCOSUR s creation and zero if it belongs to the non-tradable or control group. In Table the balancing tests for the kernel matching procedure Export Oriented vs. Non-Tradable En Table we present the difference-in-difference estimates for plants belonging to the export oriented industries, while in Table and we present the propensity scores and the balancing tests for the kernel method respectively. Total factor productivity shows an increase of 6 % but it is not statistically different from zero in the five estimations tried. On the other hand gross output show increases for all the estimations performed and the increases are higher than for the whole set of tradable firms but they are not statistically significant. Thus, for the group of export oriented firms we find no significant increases in productivity and in size. Regarding to employment we find a significant decrease in the number of total workers ranging from 28 up to 32 less workers per plant. When we discriminate between white collar and blue collar we find that the reduction in white collars is not significant (3 white collars less per plant) while the reduction in blue collars is negative and significant (20-22 blue collars less per plants). Hence, similarly to the whole set of tradable plants, the reduction in total workers is driven by the reduction in blue collars. Total wages per plant shows an increase but it is not significant. On the other hand wages per employee shows a positive and significant increase of 378 up to 393 constant pesos. Furthermore when we discriminate between wages per white collar and wages per blue collar (in terms of number of workers) we find significant increases for both. Finally, the ratio of wages per white collar/wages 25

26 per blue collar is negative and significant showing a decrease in the gap between wages for both types of workers in the period analysed. The reduction ranges from 0.17 up to Import Competing vs. Non-Tradables We present the results of the difference-in-difference estimation in Table 4.3.1, while in Table we present the propensity scores and in Table the balancing tests. We find significant increases in total factor productivity for all the matching techniques and these increases are higher than for the plants belonging to the tradable and export oriented industries. The average increase is approximately of 22 %. 24 On the other hand gross output per plant shows a reduction for all the estimations performed but it is not statistically significant. Total employment per plant shows a not significant reduction. When we discriminate between white collar and blue collar we find that the reduction in white collars is not significant (4 blue collars less per plant). Moreover, the reduction in blue collars is negative and not significant (8 blue collars less per plant). Hence, in this subgroup it seem that the adjustment is driven by the reduction in the number of plants, and those plants which remain in the market were the most productive ones. It should be note that the average number of workers in both categories- in this subgroup was smaller than for export oriented plants. Total wages per plant shows not significant reductions for five neighbours and Epanechnikov kernel and not significant increases for three neighbours and the normal kernel. 25 On the other hand wages per worker shows a positive and significant increase of 518 up to 547 constant pesos. Furthermore when we discriminate between wages per white collar and wages per blue collar (in terms of number of workers) we find significant increases for both. Finally, the ratio of wages per white collar/wages per blue collar is negative and significant showing a decrease in the gap between the wages for both types of workers in the period analysed. The reduction ranges from 0.22 up to Thus productivity shows a significant increase for the plants in the tradable and import competing sectors, with a higher increase in import competing sectors and not significant increase in the plants belonging to the export oriented sectors. This is not unexpected since the effect of greater trade 24 We should note that this sub-group is the one that have experienced a greater decrease in the number of plants. 25 The nearest neighbours with five neighbours and the kernel Epanechnikov are probably more reliable estimates than the nearest neighbour with 3 neighbours (lower bias but higher variance) and the epanechnikov distribution is probably more appropriatte than the normal according to the kdensity. 26

27 exposure might be more important for plant belonging to import competing sectors than for export oriented plants already used to compete in foreign markets. Furthermore, this result is in line with Pavcnik s study, who finds higher increases in TFP for plants belonging to import competing sectors. We observe a significant reduction in employment for plants belonging to the tradable and export oriented while the decrease is not significant for plants belonging to the import competing sector. The reduction in employment along with increases in productivity may be explained by the technological modernization in the early 90s, namely a substitution of labour by capital, 26 as me have mentioned above. This modernization process takes place due to a higher competition as well as the reduction in the real price of the capital in the period. Furthermore, as we comment above we observe that the reduction in employment is driven mainly by the decrease in blue collars, and may be the case that those blue collars that lost employment are the less skilled among this category of workers, and may be easily substitute by subcontracting of activities. The lack of significance of changes in employment in the import competing sector may be due to the fact that the adjustment was mainly through the number of plants, i.e. a reduction in its number. This issue will be further analyzed in future work. Moreover average employment in this subgroup was already smaller than for the export oriented and the tradable group. Regarding to wages we find and increase in real wages and a reduction in the gap between wages of white collars and blue collars in the three groups considered. The increase in wages along with the reduction in employment, once again leads us to think that it is pretty likely that those that lost employment were the less skilled ones. Finally, gross output per plant increases for the tradable and export oriented group and decreases for the import competing one, but in the three cases these changes are not significant. 27 Preliminary Conclusions Since the return to the democratic regime in 1985, the Uruguayan economy underwent considerable policy reforms. Among them, one of the most salient and stable of these reforms was trade liberalisation and the increasing integration of the country with the region and the world economy. This increased trade liberalisation raised voices of concern regarding the likelihood of a negative impact on the Manufacturing Uruguayan industry, which has been developed in a framework of high protection. In this regard our work contributes to the debate to improve our understanding of the 26 We observe increases in capital per worker in the period. This statistics are available upon request. 27 Trefler (2004) find a significant contraction in import competing sectors and an expansion in exported oriented ones. 27

28 effects of increased liberalization on manufacturing productivity and labour market outcomes at the micro level for a small developing country. In order to analyse the impact of increased trade exposure on plants productivity and labour market outcomes we use matching procedures and difference-in-difference approach which is not very common for evaluating trade reform. One of the most robust findings is that trade liberalization seems to increase total factor productivity, decreases employment namely for unskilled workers, increases wages and reduces the gap between white and blue collar wages. In Table 5 we present a summary of the results. The results for gross output show a not significant increase for plants belonging to the tradable and export oriented sectors. Further, there is some evidence of a reduction in gross output for those plants belonging to the import competing one, but it is not significant. Thus productivity shows a significant increase for the plants in the tradable and import competing sectors, with a higher increase in import competing sectors and not significant increase in the plants belonging to the export oriented sectors. This is not unexpected since the effect of greater trade exposure might be more important for plant belonging to import competing sectors than for export oriented plants already used to compete in foreign markets. We observe a significant reduction in employment for plants belonging to the tradable and export oriented sectors while it is not significant in import competing sectors. The reduction in employment along with increases in productivity may be explained by the technological modernization in the early 90s, namely a substitution of labour by capital, 28 as me mention above. This modernization process takes place due to a higher competition as well as the reduction in the real price of the capital in the period (Casacuberta et al., 2004; Llambí and López, 1997). On the other hand, the reduction in the relative wage of white to blue collar may be explained due to the decrease in employment, which was mainly driven by blue collars. It could be argued that the blue collars that lost employment are likely to be the less skilled ones in this category of workers. Furthermore, the period is characterized by an important subcontracting of work by the firms, and particularly of less skilled activities. We would need more accurate information on the skill levels of workers in order to test this possible explanation for this result. Thus, one of the most robust results that emerge from this work is increases in total factor productivity, reductions in employment, and increases in wages and a reduction in the wage gap between white and blue collars as a result of increased trade exposure. The reduction in employment 28 We observe increases in capital per worker in the period. This statistics are available upon request. 28

29 along with increases in productivity and real wages may be explained by the technological modernization in the early 90s, namely a substitution of labour by capital, 29 as me mention above. This modernization process takes place due to a higher competition as well as the reduction in the real price of the capital in the period. Furthermore, as we have already mentioned we observe that the reduction in employment is driven mainly by the decrease in blue collars, and may be the case that those blue collars that lost employment are the less skilled among this category of workers, and be easily substitute by subcontracting of activities. A deeper insight on this possible explanation would required more detailed data on qualification levels such as years of schooling. Nevertheless, the increase in productivity along with the unemployment of unskilled workers would indicate a room for training, labour and social policies in order to countervail the negative impact on less qualified workers. References Abadie, A., Drukker, Leber Herr, D. J., Imbens, G. W. (2001). Implementing Matching Estimators for Average Treatment Effects in Stata. The Stata Journal (2001), 1(1): Alvarez, R. and Lopez, R. A. (2008). Trade Liberalization and Industry Dynamics: A Difference and Difference Approach. Center for Applied Economics and Policy Research, CAEPR Working Paper Alvarez, R., and Lopez, R. A. (2005). Exporting and Performance: Evidence from Chilean Plants. Canadian Journal of Economics 38(4): Amjad, R. (1971). Profitability and industrial concentration in Pakistan. The Journal of Development Studies, 13: Attanasio, O., Godberg, P., and N. Pavcnik (2004). Trade Reforms and Wage Inequality in Colombia. Journal of Development Economics, 74: Aw, B. Y., Chung, S., and Roberts, M. J. (2000). Productivity and Turnover in the Export Market: Micro-Level Evidence from the Republic of Korea and Taiwan (China). World Bank Economic Review 14(1): Baier, Scott L., Gerald P. Dwyer Jr. and Tamura, R. (2002). Crescimento e Productividade no Brasil: o que nos diz o Registro de Longo Prazo. Seminarios DINAC N 52 IPEA, Rio de Janeiro. Baldwin, R. and F. Robert-Nicoud (2008). Trade and Growth with heterogenous firms, Journal of International Economics 74(1): 21-34, January Baldwin, R., Forslid, R. Martin, P., Ottaviano, G. and Robert-Nicoud, F. (2003). Economic Geography and Public Policy. Princeton University Press, Princeton, NJ. 29 We observe increases in capital per worker in the period. This statistics are available upon request. 29

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38 Table 1: Number of Establishment per year Year No. of plants

39 Table 2: Descriptive Statistic for firms belonging to the Tradable and Non-Tradable Industries and for the whole sample (average values per plant). Variable Sample Employment T (number of employees NT per plant) All White collars T (number of employees NT per plant) All Blue collars T (number of employees NT per plant) All Wages T 107, ,040 (constant pesos base NT 129, ,708 year=1988) All 110, ,396 Wages per employee T 963 1,529 (constant pesos base NT 999 1,933 year=1988) All 967 1,580 Wages per white collar T 1,593 2,345 (constant pesos base NT 1,569 2,932 year=1988) All 1,590 2,419 Wages per blue collar T 831 1,299 (constant pesos base NT 913 1,664 year=1988) All 842 1,344 Gross Output T 1,041,503 1,356,696 (constant pesos base NT 1,090,459 1,971,452 year=1988) All 1,047,679 1,434,395 Value Added T 445, ,323 (constant pesos base NT 721,933 1,221,311 year=1988) All 481, ,058 Labour Productivity T 4,695 5,660 (constant pesos base NT 5,201 9,425 year=1988) All 4,761 6,135 Total Factor Productivity T (% in relation to the NT industry average TFP) All Export Propensity T (%) NT All T: Tradable Industries, NT: Non-Tradable Industries, ALL: the whole sample. 39

40 Table 3: Descriptive Statistic discriminating between export oriented and import competing firms in the Tradable Industries Variable Sub-sample Employment EXPO (number of employees IMPC per plant) T White collars EXPO (number of employees IMPC 11 9 per plant) T Blue collars EXPO (number of employees IMPC per plant) T Wages EXPO 127, ,216 (constant pesos base IMPC 62,755 73,078 year=1988) T 107, ,040 Wages per employee EXPO 858 1,347 (constant pesos base IMPC 960 1,506 year=1988) T 963 1,529 Wages per white collar EXPO 1,553 2,241 (constant pesos base IMPC 1,523 2,093 year=1988) T 1,593 2,345 Wages per blue collar EXPO 764 1,194 (constant pesos base IMPC 916 1,396 year=1988) T 831 1,299 Gross Output EXPO 1,354,873 1,867,483 (constant pesos base IMPC 499, ,520 year=1988) T 1,041,503 1,356,696 Value Added EXPO 493, ,579 (constant pesos base IMPC 297, ,904 year=1988) T 445, ,323 Labour Productivity EXPO 4,819 4,959 (constant pesos base IMPC 4,443 6,478 year=1988) T 4,695 5,660 Total Factor Productivity EXPO (% in relation to the IMPC industry average TFP) T Export Propensity EXPO (%) IMPC T EXPO: Export oriented firms, IMPC: Import Competing firms, T: Tradables. 40

41 Table 4.1.1: Difference-in-Differences using Matching Methods, plants belonging to the Tradable vs. Non-Tradable industries Matching Procedure Output Variable Treated Controls Difference* S.E. T-stat No. treated No. controls No. total Nearest Neighbor=3 Total Factor Productivity Gross output (constant pesos) 1,356,696 1,212, , , ,615 Total Number of Workers ,121 1,749 White collar (number) ,121 1,749 Blue collars (in number) ,121 1,749 Total wages (constant pesos) 167, ,696 18,927 17, ,508 Wages/worker 1, , ,508 Wages/worker (white collar) 2, , ,508 Wages/worker (blue collar) 1, ,508 Wages wc/wages bc ,615 Nearest Neighbor=5 Total Factor Productivity Gross output (constant pesos) 1,356,696 1,210, , , ,615 Total Number of Workers ,121 1,749 White collar (number) ,121 1,749 Blue collars (in number) ,121 1,749 Total wages (constant pesos) 167, ,245 20,379 17, ,508 Wages/worker 1, , ,508 Wages/worker (white collar) 2, , ,508 Wages/worker (blue collar) 1, ,508 Wages wc/wages bc ,615 Kernel Total Factor Productivity (Epanechnikov) Gross output (constant pesos) 1,356,696 1,187, , , ,615 Total Number of Workers ,121 1,749 White collar (number) ,121 1,749 Blue collars (in number) ,121 1,749 Total wages (constant pesos) 167, ,548 22,075 17, ,508 Wages/worker 1, , ,508 Wages/worker (white collar) 2, , ,508 Wages/worker (blue collar) 1, ,508 Wages wc/wages bc ,615 Kernel Total Factor Productivity (Gaussian) Gross output (constant pesos) 1,356,696 1,154, , , ,615 Total Number of Workers ,121 1,749 White collar (number) ,121 1,749 Blue collars (in number) ,121 1,749 Total wages (constant pesos) 167, ,659 29,965 17, ,508 Wages/worker 1, , ,508 Wages/worker (white collar) 2, , ,508 Wages/worker (blue collar) 1, ,508 Wages wc/wages bc ,615 * ATT: average treatment effect on the treated 41

42 Propensity score estimation Variable Coef. Std. Err. z P>z Hvbp Hva Exp Constant Number of obs=999; Log likelihood= ; LR chi(3)=14.24; Prob>chi2=0.002, Pseudo R2=0.01 Results from: psmatch2 tl1 hvbp2 hva exp, kernel outcome(tfplp) common logit ties Table Balancing tests Mean % t-test Variable Sample Treated Control %bias Reduction t p>t Hvbp2 Unmatched Matched Hva Unmatched Matched Exp Unmatched Matched Results from the balancing tests after kernel matching with pstest (Leuven and Sianesi, 2003) 42

43 Table Difference in Difference estimation for plants belonging to Export Oriented vs Non-Tradable Industries Matching Procedure Output Variable Treated Controls Difference* S.E. T-stat No. treated No. controls No. total Nearest Neighbor=3 Total Factor Productivity Gross output (constant pesos) 1,867,483 1,478, , , Total Number of Workers White collars (number) Blue collars (in number) Total wages (constant pesos) 192, ,573 14,939 30, Wages/worker 1,383 1, Wages/worker (white collar) 2,226 1, Wages/worker (blue collar) 1, Wages wc/wages bc Nearest Neighbor=5 Total Factor Productivity Gross output (constant pesos) 1,867,483 1,467, , , Total Number of Workers White collars (number) Blue collars (in number) Total wages (constant pesos) 192, ,422 20,090 30, Wages/worker 1, Wages/worker (white collar) 2,226 1, Wages/worker (blue collar) 1, Wages wc/wages bc Total Factor Productivity Kernel Gross output (constant pesos) , (Epanechnikov) Total Number of Workers White collars (number) Blue collars (in number) Total wages (constant pesos) 192, ,997 9,515 29, Wages/worker 1,383 1, Wages/worker (white collar) 2,226 1, Wages/worker (blue collar) 1, Wages wc/wages bc Kernel Total Factor Productivity (Gaussian) Gross output (constant pesos) 1,867,483 1,452, , , Total Number of Workers White collars (number) Blue collars (in number) Total wages (constant pesos) 192, ,182 20,329 28, Wages/worker 1, Wages/worker (white collar) 2,226 1, Wages/worker (blue collar) Wages wc/wages bc * ATT: average treatment effect on the treated 43

44 Propensity score estimation Variable Coef. Std. Err. z P>z Hvbp Hva Exp Constant Number of obs=566; Log likelihood= ; LR chi(3)=13.49; Prob>chi2=0.00, Pseudo R2=0.02 Results from:. psmatch2 tl2 hvbp2 hva exp, kernel outcome(tfplp) common logit ties Table Balancing tests Mean % t-test Variable Sample Treated Control bias Reduction t p>t hvbp2 Unmatched Matched hva Unmatched Matched exp Unmatched Matched Results from the balancing tests after kernel matching with pstest (Leuven and Sianesi, 2003) 44

45 Table Difference in Difference estimation for plants belonging to Import Competing vs Non-Tradable Industries Matching Procedure Output Variable Treated Controls Difference* S.E. T-stat No. treated No. controls No. total Nearest Neighbor=3 Total Factor Productivity Gross output (constant pesos) 915, ,347-28, , Total Number of Workers White collar (number) Blue collars (in number) Total wages (constant pesos) 130, , Wages/worker 1, Wages/worker (white collar) 2, Wages/worker (blue collar) 1, Wages wc/wages bc Nearest Neighbor=5 Total Factor Productivity Gross output (constant pesos) 915, ,398-30, , Total Number of Workers White collar (number) Blue collars (in number) Total wages (constant pesos) 130, ,134-2,746 30, Wages/worker 1,741 1, Wages/worker (white collar) 2,245 1, Wages/worker (blue collar) 1,522 1, Wages wc/wages bc Kernel Total Factor Productivity (Epanechnikov) Gross output (constant pesos) 915, ,936-73, , Total Number of Workers White collar (number) Blue collars (in number) Total wages (constant pesos) 130, ,620-5,231 28, Wages/worker 1,741 1, Wages/worker (white collar) 2,245 1, Wages/worker (blue collar) 1,522 1, Wages wc/wages bc Kernel Total Factor Productivity (Gaussian) Gross output (constant pesos) 915, ,954 57, , Total Number of Workers White collar (number) Blue collars (in number) Total wages (constant pesos) 130, ,219 4,170 33, Wages/worker 1,741 1, Wages/worker (white collar) 2,245 1, Wages/worker (blue collar) 1,522 1, Wages wc/wages bc * ATT: average treatment effect on the treated 45

46 Propensity score estimation Variable Coef. Std. Err. z P>z Hvbp Hva Exp Constant Number of obs=297; Log likelihood= ; LR chi(3)=9.68; Prob>chi2=0.02, Pseudo R2=0.03 Results from:. psmatch2 tl5 hvbp2 hva exp, kernel outcome(tfplp) common logit ties Table Balancing tests Mean % t-test Variable Sample Treated Control bias Reduction t p>t Hvbp2 Unmatched Matched Hva Unmatched Matched Exp Unmatched Matched Results from the balancing tests after kernel matching with pstest (Leuven and Sianesi, 2003) 46

47 Table 5: Summary of the results Variable Sample Treatment Effect, 1995 vs TRADABLE Positive Significant TFP EXPO Positive Not Significant IMPC Positive Significant TRADABLE Positive Not Significant Gross Output EXPO Positive Not Significant IMPC Negative Not Significant TRADABLE Negative Significant Employment (total number of EXPO Negative Significant workers) IMPC Negative Not Significant TRADABLE Negative Not Significant Number of White Collars EXPO Negative Not Significant IMPC Negative Not Significant TRADABLE Negative Significant Number of Blue Collars EXPO Negative Significant IMPC Negative Not Significant TRADABLE Positive Not Significant Total wages EXPO Positive Significant IMPC Not significant TRADABLE Positive Significant Wages per worker EXPO Positive Significant IMPC Positive Significant TRADABLE Positive Significant Wages per white collar EXPO Positive Significant IMPC Positive Significant TRADABLE Positive Significant Wages per blue collar EXPO Positive Significant IMPC Positive Significant TRADABLE Negative Significant Relative wages* EXPO Negative Significant IMPC Negative Significant * Wages per white collar/wages per blue collar. 47

48 Appendix 1.1. Import Penetration ISIC code Average Import Penetration: Imports/Gross Output at the industry level. 48

49 Appendix 1.2. Export Propensity ISIC code Average Average EO: export oriented industry, IC: import competing industry 49

50 1.3. Openness index (defined as imports plus exports over output at the industry level) ISIC code Avg Avg Avg Average

51 1.5. Tradable and Non-Tradable Industries Tradable Industries Trade Orientation ISIC code No. Obs.* EXPO 311 1,921 EXPO 321 1,313 EXPO EXPO EXPO IMPC IMPC IMPC IMPC IIT IIT IIT IIT NS NS NS NS NS NS ,954 EXPO: export oriented, IMPC: import competing ITT: intra-industry trade. NS: Not Specialized, i.e. tradable not classified as import competing or export Oriented or intra-industry group. *Number of observations for the whole period Non-Tradable Industries ISIC code No. Obs.* *Number of observations for the whole period 51

52 Append ix 2: Industry Classification ISIC code Industry Description 311 Meat products. 313 Beverage industries. 314 Tobacco manufactures. 321 Manufacture of textiles. 322 Manufacture of wearing apparel, except footwear. 323 Manufacture of leather and products of leather, leather substitutes and fur, except footwear and wearing apparel. 324 Manufacture of footwear, except vulcanized or moulded rubber or plastic footwear. 331 Manufacture of wood and wood and cork products, except furniture. 332 Manufacture of furniture and fixtures, except primarily of metal. 341 Manufacture of paper and paper products. 342 Printing, publishing and I allied products. 351 Manufacture of industrial chemicals. 352 Manufacture of other chemical products. 355 Manufacture of rubber products. 356 Manufacture of plastic products not elsewhere classified. 361 Manufacture of pottery, china and earthenware. 362 Manufacture of glass and glass products. 369 Manufacture of other non methalic mineral products. 371 Iron and steel basic industries. 372 Non-ferrous metal basic-industries. 381 Manufactures of fabricated metal products, except machinary and equipment. 382 Manufacture of machinery except electrical. 383 Manufacture of electrical machinery apparatus, appliances and supplies. 384 Manufacture of transport equipment. 385 Manufacture of professional and scientific, and measuring and controlling equipment not elsewhere classified, and photographics and optical tools. 390 Other manufacturing industries. 52