Trade and income distribution…who benefits, who loses

by Dan Crawford (Rdan)

Voxeu carries a post on research into increasing global trade, technology, and wages patterns:

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.