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Gender Wage Disparities among the Highly Educated

Open access
Black, Dan A., Haviland, Amelia M., Sanders, Seth G., Taylor, Lowell J.·2008·Journal of Human Resources

Abstract

In the U.S. college-educated women earn approximately 30 percent less than their non-Hispanic white male counterparts. We conduct an empirical examination of this wage disparity for four groups of women-non-Hispanic white, black, Hispanic, and Asian-using the National Survey of College Graduates, a large data set that provides unusually detailed information on higher-level education. Nonparametric matching analysis indicates that among men and women who speak English at home, between 44 and 73 percent of the gender wage gaps are accounted for by such pre-market factors as highest degree and major. When we restrict attention further to women who have "high labor force attachment" (i.e., work experience that is similar to male comparables) we account for 54 to 99 percent of gender wage gaps. Our nonparametric approach differs from familiar regression-based decompositions, so for the sake of comparison we conduct parametric analyses as well. Inferences drawn from these latter decompositions can be quite misleading.

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Metadata

Authors
Black, D. A., Haviland, A. M., Sanders, S. G., & Taylor, L. J.
Venue
Journal of Human Resources
Published
2008
Volume
43
Issue
3
Pages
630-659
ISSN
  • 0022-166X (ISSN-L)
  • 1548-8004
OpenAlex
W2158117696
Cited by
228
References
4
FWCI
20.48
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Gender Wage Disparities among the Highly Educated. https://app.cochat.ai/papers/10.1353/jhr.2008.0030
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Black, D. A., Haviland, A. M., Sanders, S. G., & Taylor, L. J. (2008). Gender Wage Disparities among the Highly Educated. Journal of Human Resources. https://doi.org/10.1353/jhr.2008.0030

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.