Decomposing Quantile Wage Gaps: A Conditional Likelihood Approach
本文提出一种基于条件似然的参数化分位数回归方法，用于分解不同工人群体间的工资差距，并以卢森堡本地与外国工人的工资差距为例，发现本地工人优势呈凹函数形态，在中部分位数区间最大。
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).