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 considered these factors. We focus on both women and men, using longitudinal data from the PSID and the BHPS. We show that country of origin, country of residence, and gender are all important determinants of the earnings differential. For instance, a large 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 suggesting that immigrant men in the U.S. and the U.K. earn lower wages, but the most significant results are found for British workers emigrating 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 understanding immigrant wage effects.
immigrants earnings quantile regression panel data