Skill classi cation does matter: estimating the relationship between trade ows and wage inequality

نویسنده

  • Kristin J. Forbes
چکیده

Empirical work must pay careful attention to how it measures the relative skill abundance of countries and the relative skill intensity embodied in trade  ows. 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  ows as relatively high skill or low skill. Then, in order to show the importance of skill classiŽ cation, it uses a reduced-form Ž xed-effects model to estimate the relationship between trade  ows and wage inequality. This speciŽ cation 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 signiŽ cant 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 classiŽ cation utilized.

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تاریخ انتشار 2001