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The Impact of Selection into the Labor Force on the Gender Wage Gap

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  • Francine D. Blau
  • Lawrence M. Kahn
  • Nikolai Boboshko
  • Matthew L. Comey

Abstract

We study the impact of selection bias on estimates of the gender pay gap, focusing on whether the gender pay gap has fallen since 1981. Previous research has found divergent results across techniques, identification strategies, data sets, and time periods. Using Michigan Panel Study of Income Dynamics data and a number of different identification strategies, we find robust evidence that, after controlling for selection, there were large declines in the raw and the unexplained gender wage gaps over the 1981-2015 period. Under our preferred method of accounting for selection, we find that the raw median wage gap declined by 0.378 log points, while the median unexplained gap declined by a more modest but still substantial 0.204 log points. These declines are larger than estimates that do not account for selection. Our results suggest that women’s relative wage offers have increased over this period, even after controlling for their measured covariates, including education and actual labor market experience. However, we note that substantial gender wage gaps remain. In 2015, at the median, the selectivity-corrected gaps were 0.242 log points (raw gap) and 0.206 log points (unexplained gap).

Suggested Citation

  • Francine D. Blau & Lawrence M. Kahn & Nikolai Boboshko & Matthew L. Comey, 2021. "The Impact of Selection into the Labor Force on the Gender Wage Gap," NBER Working Papers 28855, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28855
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    Cited by:

    1. Iv'an Fern'andez-Val & Franco Peracchi & Aico van Vuuren & Francis Vella, 2018. "Selection and the Distribution of Female Hourly Wages in the U.S," Papers 1901.00419, arXiv.org, revised Jan 2022.
    2. Borjas, George J. & Edo, Anthony, 2023. "Monopsony, Efficiency, and the Regularization of Undocumented Immigrants," IZA Discussion Papers 16297, Institute of Labor Economics (IZA).
    3. Richard Blundell & James P. Ziliak & Hugo Lopez, 2023. "Labour market inequality and the changing life cycle profile of male and female wages," IFS Working Papers W23/16, Institute for Fiscal Studies.
    4. Iv'an Fern'andez-Val & Aico van Vuuren & Francis Vella, 2023. "Marital Sorting, Household Inequality and Selection," Papers 2310.07839, arXiv.org.
    5. Ahrsjö, Ulrika & Niknami, Susan & Palme, Mårten, 2021. "Wage Inequality, Selection and the Evolution of the Gender Earnings Gap in Sweden," Research Papers in Economics 2021:3, Stockholm University, Department of Economics.
    6. Joyce Jacobsen & Melanie Khamis & Mutlu Yuksel, 2024. "Demography, Human Capital Investment, and Lifetime Earnings for Women and Men," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 50(3), pages 259-277, June.
    7. Iván Fernández‐Val & Aico van Vuuren & Francis Vella & Franco Peracchi, 2023. "Selection and the distribution of female real hourly wages in the United States," Quantitative Economics, Econometric Society, vol. 14(2), pages 571-607, May.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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