Full Citation

Title: Women's Potential Earnings Distributions

Citation Type: Book, Section

Publication Year: 2022

ISBN: 978-1-80262-065-8

ISSN: 0731-9053

DOI: 10.1108/S0731-90532021000043B010

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Abstract: Building on recent advantages in inverse probability weighted identification and estimation of counterfactual distributions, the authors examine the history of wage earnings for women and their potential wage distributions in the United States. These potentials are two counterfactuals, what if women received men's market "rewards" for their own "skills" and what if they received the women's rewards but for men's characteristics? Using the Current Population Survey data from 1976 to 2013, the authors analyze the entire counterfactual distributions to separate the "structure" and human capital "composition" effect. In contrast to Maasoumi and Wang (2019), the reference outcome in these decompositions is women's observed earnings distribution, and inverse probability methods are employed, rather than the conditional quantile approaches. The authors provide decision theoretic measures of the distance between two distributions, to complement assessments based on mean, median, or particular quantiles. We assess uniform rankings of alternate distributions by tests of stochastic dominance in order to identify evaluations robust to subjective measures. Traditional moment-based measures severely underestimate the declining trend of the structure effect. Nevertheless, dominance rankings suggest that the structure ("discrimination"?) effect is bigger than human capital characteristics.

Url: https://ideas.repec.org/h/eme/aecozz/s0731-90532021000043b010.html

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Authors: Maasoumi, Esfandiar; Wang, Le

Editors:

Pages: 229-252

Volume Title: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology

Publisher: Emerald Publishing

Publisher Location: Bingley

Volume: 43B

Edition:

Data Collections: IPUMS CPS

Topics: Gender, Labor Force and Occupational Structure

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