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The Gender Pay Gap in the US: A Matching Study

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  • Meara, Katie
  • Pastore, Francesco
  • Webster, Allan

Abstract

This study examines the gender wage gap in the US using two separate cross-sections from the Current Population Survey (CPS). The extensive literature on this subject includes papers which use wage decompositions to divide gender wag gaps into “explained” and “unexplained” components. Problems with this approach include the heterogeneity of the sample data. In order to address the difficulties of comparing like with like this study uses a number of different matching techniques to obtain estimates of the gap. By controlling for a wide range of other influences, in effect, we estimate the direct effect of simply being female on wages. However, to form a complete picture, one should consider that gender wages are affected by a number of other factors such as parenthood, gender segregation, part-time working and unionization. This means that it is not just the core “like for like” comparison between male and female wages that matters but also how gender wage differences interact with other relevant risk factors which are more common for women. That these interactions exist has already been discussed in the literature but evidence that precisely or systematically estimates such effects remains scarce. The most innovative contribution of this study is to do that. Our findings imply that the idea of a single uniform gender pay gap is perhaps less useful than an understanding of how gender wages are shaped by multiple different forces.

Suggested Citation

  • Meara, Katie & Pastore, Francesco & Webster, Allan, 2019. "The Gender Pay Gap in the US: A Matching Study," GLO Discussion Paper Series 363, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:363
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    Cited by:

    1. Francine D. Blau & Lawrence Kahn & Nikolai Boboshko & Matthew Comey, 2021. "Th Impact of Selection into the Labor Force on the Gender Wage Gap," CESifo Working Paper Series 9103, CESifo.
    2. Daniel Graeber & Alexander S. Kritikos & Johannes Seebauer, 2020. "COVID-19: A Crisis of the Female Self-Employed," Discussion Papers of DIW Berlin 1903, DIW Berlin, German Institute for Economic Research.
    3. Doruk, Ömer Tuğsal & Pastore, Francesco, 2020. "Gender Wage Gap - A Matching Analysis for Three MENA Countries: Egypt, Jordan and Turkey," IZA Discussion Papers 13934, Institute of Labor Economics (IZA).
    4. Anthony Strittmatter & Conny Wunsch, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Papers 2102.09207, arXiv.org, revised Feb 2021.
    5. Chen, Jie & Pastore, Francesco, 2021. ""Study hard and make progress every day": Updates on returns to education in China," GLO Discussion Paper Series 787, Global Labor Organization (GLO).
    6. Emilio M. Santandreu & Joaquín López Pascual & Salvador Cruz Rambaud, 2020. "Determinants of Repayment among Male and Female Microcredit Clients in the USA. An Approach Based on Managers’ Perceptions," Sustainability, MDPI, Open Access Journal, vol. 12(5), pages 1-17, February.
    7. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," IZA Discussion Papers 14128, Institute of Labor Economics (IZA).
    8. Anthony Strittmatter & Conny Wunsch, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CESifo Working Paper Series 8912, CESifo.
    9. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Working papers 2021/05, Faculty of Business and Economics - University of Basel.

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    More about this item

    Keywords

    gender pay; Current Population Survey; part-time working; gender segregation; unionization; sample selection bias; matching; IPWRA; USA;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • K38 - Law and Economics - - Other Substantive Areas of Law - - - Human Rights Law; Gender Law

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