The Gender Gap between Earnings Distributions

• 成果类型: Article
• 署名作者: Maasoumi, Esfandiar; Wang, Le
• 署名单位: Emory University; University of Oklahoma System; University of Oklahoma - Norman
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/701788
• 发表日期: 2019
• 页码: 2438-2504
• 关键词: wage inequality; quantile regression; semiparametric estimation; labor-market; match bias; models; selection; TRENDS; DECOMPOSITION; inference

摘要

We advocate a different approach to measure the gender gap, summarizing each distribution by suitable evaluative functions and computing the difference between the evaluations. Unlike the conventional approach, ours does not assume rank invariance. We discuss the decision-theoretic framework behind different functions and introduce measures based on entropy functions. We further adopt quantile-copula approaches to account for selection into full-time employment and discuss how to take into account nonmarket values in measuring the gap. The evolution of the gender gap depends on the measure of it and whether nonmarket values are incorporated. We further assess and challenge a variety of assumptions, hypotheses, and findings in the literature.