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Decomposing quantile wage gaps: a conditional likelihood approach

机译:分解分位数工资差距:一种条件似然法

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摘要

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).
机译:本文开发了一种Machado-Mata模拟方法的参数化变体,以检查工人群体之间的分位数工资差异,并将其应用于卢森堡本地工人与外国工人之间的工资差距。依靠基于条件可能性的“参数分位数回归”来代替标准线性分位数回归是简约的,并且在不减少我们应用中边际分位数模拟的准确性的情况下,大大节省了计算时间。我们发现本地工人的优势是分位数的凹函数:低分位数和高分位数的优势很小(可能为负),但分位数范围的中间一半(在第20至70位本地工资百分比之间)则优势很大)。

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