Decomposing Quantile Wage Gaps: A Conditional Likelihood Approach

Summary The paper develops a parametric variant of the Machado–Mata simulation methodology to examine quantile wage differences between groups of workers, with an application to the wage gap between native and foreign workers in Luxembourg. Relying on conditional-likelihood-based ‘parametric quantile regression’ in place of the standard linear quantile regression is parsimonious and cuts computing time drastically with no loss in the accuracy of marginal quantile simulations in our application. We find that the native worker advantage is a concave function of quantile: the advantage is small (possibly negative) for both low and high quantiles, but it is large for the middle half of the quantile range (between the 20th and 70th native wage percentiles).