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The Gender Earnings Gap: Measurement and Analysis

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  • Esfandiar Maasoumi
  • Le Wang

Abstract

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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Paper provided by Department of Economics, Emory University (Atlanta) in its series Emory Economics with number 1305.

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Date of creation: Aug 2013
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Handle: RePEc:emo:wp2003:1305

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  1. Ximing Wu & Andreas Savvides & Thanasis Stengos, 2008. "The Global Joint Distribution of Income and Health," CESifo Working Paper Series 2367, CESifo Group Munich.
  2. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
  3. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
  4. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, 09.
  5. Maria Ana Lugo, 2008. "Multidimensional Poverty Measures from an Information Theory Perspective," OPHI Working Papers ophiwp011, Queen Elizabeth House, University of Oxford.
  6. Frölich, Markus, 2007. "Regression Discontinuity Design with Covariates," IZA Discussion Papers 3024, Institute for the Study of Labor (IZA).
  7. Heshmati, Almas & Maasoumi, Esfandiar, 1998. "Stochastic Dominance Amongst Swedish Income Distributions," Working Paper Series in Economics and Finance 279, Stockholm School of Economics.
  8. Jeff Biddle & Daniel S. Hamermesh, 2011. "Cycles of Wage Discrimination," NBER Working Papers 17326, National Bureau of Economic Research, Inc.
  9. Millimet Daniel L & Wang Le, 2006. "A Distributional Analysis of the Gender Earnings Gap in Urban China," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-50, February.
  10. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
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