The Gender Earnings Gap: Measurement and Analysis
Esfandiar Maasoumi () and
Le Wang ()
Emory Economics from Department of Economics, Emory University (Atlanta)
This paper presents a set of complementary tools for measurement and analysis of the gender gap that move beyond the simple moment-based comparison of the earnings distributions. In particular, we propose a new measure of the gender gap based on the the distance between two whole distributions, instead of their specific parts. We also introduce tests based on stochastic dominance to allow for robust welfare comparisons of the earnings distributions between men and women. Using the Current Population Survey data, we first construct a new series on the gender gap from 1976 to 2011 in the United States. We find that traditional moment-based measures severely underestimate the declining trend of the gender gap during this period. More important, these traditional measures do not necessarily reflect the cyclicality of the gender differentials in earnings distributions, and thus may even lead to a false conclusion about how labor market conditions are related to the gender gap at the aggregate level. Second, we find that stochastic dominance (or a clear ranking of the earnings distributions) is rare, and that instances in which we do find stochastic dominance appear to be disproportionately concentrated in the prewelfare reform period and related to economic recessions. Finally, our counterfactual analysis show that in most cases neither changing earnings structure nor changing human capital characteristics would necessarily improve women's well-being uniformly in the society.
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Persistent link: https://EconPapers.repec.org/RePEc:emo:wp2003:1305
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