Replication package for: "Sources of Wage Inequality: Decomposing the Conditional Gini Coefficient"

This paper introduces a new econometric method to identify factors influencing the disparities within the distribution of a positive random variable, focusing on US wages between 1986 and 2015. I relate the conditional Lorenz curve to the conditional quantile function to additively decompose the conditional Gini index. Moreover, this paper presents a technique to disentangle temporal changes in the distribution. The analysis shows that despite reduced impacts of race and gender on wages, persistent disparities require ongoing intervention, while higher education, especially college degrees, significantly reduces wage inequality

本文提出一种全新的计量经济学方法，用于识别影响正随机变量分布内部差异的因素，研究聚焦于1986年至2015年的美国薪资水平。本文将条件洛伦兹曲线（conditional Lorenz curve）与条件分位数函数（conditional quantile function）相结合，实现条件基尼系数的可加分解。此外，本文还提出一种可用于拆解分布随时间变化特征的分析技术。分析结果表明，尽管种族与性别对薪资的影响有所减弱，但持续存在的薪资差异仍需持续介入干预；与此同时，高等教育——尤其是大学学历——能够显著降低薪资不平等水平。