Decomposition of differences in distribution using quantile regression

Over the last twenty years, many researchers have attempted to explain the determinants of wage inequality. I propose a flexible, intuitive and semiparametric estimator of distribution functions in the presence of covariates. The conditional wage distribution is estimated by quantile regressions. Then, the conditional distribution is integrated over the range of the covariates to obtain an estimate of the unconditional distribution. Counterfactual distributions can be estimated, allowing the decomposition of changes in distribution into three factors: changes in regression coefficients, changes in the distribution of covariates and changes in residuals. I use the proposed approach to re-assess the sources of changes in the distribution of wages in the United States between 1973 and 2001. Unlike most others, I find that residuals plays only a minor role in the overall growth in wage inequality. This suggests that there was no or only a small increase in the price of unmeasured skills. The reason of this difference between my results and those obtained with others methodologies is that quantile regressions account for heteroscedasticity. Indeed, the variance of the residuals expands with education and experience. Therefore, the fact that the population is getting older and more educated put more weight on groups with higher residual variances.