VoxEU Column International trade Labour Markets Poverty and Income Inequality

Does the winner take it all? Wage inequality and exports

Is international trade a source of widening wage inequality in industrial countries? This column shows that export activity in Germany contributes to wage inequality among workers with different levels of skill, but diminishes wage gaps in German manufacturing between men and women and between German citizens and non-citizens.

The theoretical case for the potential effect of trade on the distribution of income has a long and distinguished history. It starts with the first musings of David Ricardo and has advanced to now include models with heterogeneous firms, heterogeneous workers, and labour market imperfections, which have shown the consequences of trade for income distribution across different sets of individuals (e.g. Helpman et al. 2009, Egger and Kreickemeier 2009).

The practical relevance of these insights, however, continues to be controversial. In the 1990s, there was a heated debate about the possible contributions of trade to income inequality, with some eventual consensus among trade and labour economists that rising inequality was more likely a reflection of technological change rather than the growth of trade.

The ongoing increase in inequality, however, has brought the question back to the top of policy agenda stoked by the continuing expansion of exports from low-wage countries. Perhaps most notably, Paul Krugman has shifted his view from one contending that trade was too small to influence wages significantly (Krugman 1995) to one arguing for an important role for the contribution of trade to inequality due to the increasing role of China and other rapidly industrialising countries (Krugman 2007, 2008).

The sense that there could be a renewed and empirically important link between trade and inequality, along with recent developments in the availability and means to analyse large matched employer-employee datasets, has renewed research on this topic.

  • Munch and Skaksen (2008) find that wages are higher in Danish firms with high export intensity and highly educated workers but lower in high-export-intensity Danish firms with workers who have lower levels of education.
  • Schank et al. (2007) estimate separate regressions for blue-collar and white-collar German manufacturing workers while controlling for a range of individual characteristics including age, gender, level of education, and nationality. In contrast with much of the other literature, they find a higher export wage premium for blue-collar workers than for white-collar workers.

Both of these studies use longitudinal data sets that match workers with firms, enabling the researchers to control for characteristics of both establishments and individuals. This is important because it goes a long way towards distinguishing between the role of exporting and the role of other, possibly confounding factors like firm size or the skill or education level of particular workers.

The skill-structure of the export wage premium

In a very recent contribution to this literature, we investigate the skill, gender, and nationality structure of the wage export premium or discount over the period 1993 – 2007 for workers employed by western German manufacturing plants (Klein et al. 2010).

Our study is based on the linked employer-employee LIAB data set, which provides detailed information on workers and plants (this is the same data set used by Schank et al. 2007). We construct four skill categories for workers based on their individual levels of educational attainment, occupation, and classification status in the German social security system.

We find that there is a significant export wage premium for workers in the two highest skill categories and evidence of an export wage discount for lower-skilled workers. The export wage premium for higher-skilled workers combined with the wage discount for lower-skilled workers implies an increase in manufacturing wage disparities with an expansion in the number of plants that export, or with an increase in the share of exports relative to total manufacturing output.

While the use of four constructed skill categories simplifies the presentation of our results, we find very similar results when estimating the export wage premium or discount across 340 occupations defined in the data set. We present some of these occupation-based results here to offer an impression of the differential wage effects of exporting.

Tables 1 and 2 report the ten occupations with the highest export wage premium and the ten occupations with the largest export wage discount (among those occupations that have at least 20,000 observations in the population to ensure economic significance of these effects). The tables also report the predominant skill group for each of these occupations.

Table 1 shows that the occupations with the highest estimated export wage premium include several engineering disciplines, business and management, and qualified technicians – all among the two highest skill categories. Noticeably, the conditional wage difference of working in a firm that exports all its production rather than in a firm that exports nothing may be up to 20% for particular professions. Perhaps most comforting to the readers of this report, results in Table 1 show that economic and social scientists gain from trade (which could have the unfortunate consequence of raising suspicions that economists’ arguments for the gains from trade are motivated by self-interest rather than dispassionate analysis).

Table 1. And the winners are…
Largest export wage discounts by occupation
Wage premium (in percent)
Predominant skill-group
Entrepreneurs, managing directors
Management consultants
Foreman, master mechanics
Other engineers
Other manufacturing engineers
Economic and social scientists
Electrical engineering technicians
Data processing specialists
Electrical engineers
Commercial agents, travellers
Note: Based on results from Table 5, Panel C, plant-individual fixed effects, at least 20,000 employees per occupation; all reported coefficients on wage premia/discounts are significant at least at the 95% confidence level; for more details see Klein, Moser and Urban (2010).

Table 2 identifies potential losers from increasing export activity. Those occupations with the highest estimated export wage discount include several types of manual workers and service personal – all among the two lowest skill categories. Thus, an expansion of export activity would tend to widen skill-based income inequality in German manufacturing since it benefits higher-skilled workers and detracts from wages of lower-skilled workers. Yet, as we show in the next section, exporting firms reduces wage inequality along the dimensions of gender and citizenship.

Table 2. And the losers are…
Largest export wage discounts by occupation
Wage discount
(in percent)
Wood preparers
Household cleaners
Office auxiliary workers
Machine attendants
Packagers, good receivers
Plastic processors
Stores, transport workers
Transportation equipment drivers
Motor vehicle repairers
Note: Based on results from Table 5, Panel C, plant-individual fixed effects, at least 20,000 employees per occupation; all reported coefficients on wage premia/discounts are significant at least at the 95% confidence level; for more details see Klein, Moser and Urban (2010).
Gender, nationality, and export wage premia

Another set of results presented in our research shows that an increase in exports diminishes manufacturing wage gaps due to gender or nationality. Higher-skilled women, who are paid less than men with comparable personal characteristics in comparable plants, enjoy a higher export wage premium than men, and there is no evidence of an export wage discount for medium-skilled and lower-skilled women. Likewise, higher-skilled manufacturing workers who are not German citizens enjoy an export wage premium and there is not a significant export wage discount for these workers either. One conjecture is that exporting firms exhibit less wage discrimination than non-exporting firms because they face stiffer competition, which would be consistent with Becker (1957).


Our research shows that the links between trade and inequality are subtle. An increase in the average export share of the German economy raises wage inequality along the dimension of skill. But this same shift in the economic profile of the economy lowers wage inequality along the dimensions of gender and citizenship. These effects point out the potentially complex role of increasing globalisation on wage inequality.


Becker, Gary S (1957), The Economics of Discrimination, 1st Edition, University of Chicago Press.

Bernard, A B, JB Jensen (1995), “Exporters, Jobs, and Wages in U.S. Manufacturing: 1976–1987”, Brookings Papers on Economic Activity. Microeconomics:67–119.

Egger, H and U Kreickemeier (2009), “Firm Heterogeneity and the Labour Market Effects of Trade Liberalisation”, International Economic Review, 50:187-216.

Helpman, E, O Itskhoki, and S Redding (2010), “Unequal Effects of Trade on Workers with Different Abilities”, Journal of the European Economic Association, Papers and Proceedings, 8:456-466.

Klein, M, C Moser, and D Urban (2010), “The Contribution of Trade to Wage Inequality: The Role of Skill, Gender, and Nationality,” NBER Working Paper 15985, May.

Krugman, P (1995), "Growing World Trade: Causes and Consequences", Brookings Papers on Economic Activity, 26:327-377.

Krugman, P (2007), “Trade and inequality, revisited”, VoxEU.org, 15 June.

Krugman, P (2008), “Trade and Wages, Reconsidered”, Brookings Papers on Economic Activity, Spring:103-154.

Munch, JR, JR Skaksen (2008), “Human capital and Wages in Exporting Firms", Journal of International Economics, 75:363-372.

Schank, T, Schnabel, C, J Wagner (2007), “Do Exporters Really Pay Higher Wages? First Evidence from German Linked Employer–employee Data“, Journal of International Economics, 72:52-74.

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