Seventh IZA Annual Migration Meeting, 3-5 June 2010, Bonn, Germany
June 1, 2010
In this paper we use a relatively new panel data quantile regression technique to examine native-immigrant earnings differentials 1) throughout the conditional wage distribution, and 2) controlling for individual heterogeneity. No previous papers have simultaneously consider these factors. We focus on both women and men, using longitudinal data from the PSID and the BHPS. We show that failing to control for individual heterogeneity does indeed generate biased estimates. Country of origin, country of residence, and gender are all important determinants of the differential. For instance, the largest wage penalty occurs in the U.S. among female immigrants from non-English speaking countries, and the penalty is most negative among the lowest (conditional) wages. On the other hand, women in Britain experience hardly any immigrant-native wage differential. We find evidence that suggests that immigrant men in the U.S. and the U.K. earn lower wages, but the most significant results are found for British workers immigrating from non-English speaking countries. The various differentials we report in this paper reveal the value of combining quantile regression with controls for individual heterogeneity in better understand immigrant wage effects.