A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States

This paper uses longitudinal data from the PSID and the BHPS to examine native-immigrant earnings differentials throughout the conditional wage distribution, while controlling for individual heterogeneity. We employ quantile regression techniques to estimate conditional quantile functions for longitudinal data. We show that country of origin, country of residence, and gender are all important determinants of earnings differentials. A large wage penalty occurs in the USA 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 USA earn lower wages, while British workers emigrating from English-speaking countries earn higher wages. The various differentials we report in this paper reveal the value of employing panel data quantile regression in estimating and better understanding immigrant wage effects.