Abstract
Empirical work must pay careful attention to how it measures the relative skill abundance of countries and the relative skill intensity embodied in trade flows. This paper compiles a new data set, using income levels, average education, manufacturing wages, and an index of these three variables, to classify countries and trade flows as relatively high skill or low skill. Then, in order to show the importance of skill classification, it uses a reduced-form fixed-effects model to estimate the relationship between trade flows and wage inequality. This specification not only controls for any time-invariant omitted variables, but also permits the inclusion of a large number of diverse countries. When more accurate skill rankings are utilized, results suggest that, in high-skill abundant countries, increased trade with lower-skill countries is correlated with an increase in wage inequality. This relationship is significant and highly robust and is driven by the negative relationship between trade and low-skill wages (instead of a positive relationship between trade and high-skill wages.) Results, however, are highly dependent on the skill classification utilized.
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