Skip to main content

The gender pay gap in the US: a matching study.

Meara, K., Pastore, F. and Webster, A., 2020. The gender pay gap in the US: a matching study. Journal of population economics, 33 (1), 271-305.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
Meara2020_Article_TheGenderPayGapInTheUSAAMatchi.pdf - Published Version
Available under License Creative Commons Attribution.

501kB
[img]
Preview
PDF
Meara Pastore Webster_edits revised AW.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

616kB

DOI: 10.1007/s00148-019-00743-8

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 wage decompositions that divide the gender wage gap into “explained” and “unexplained” components. One of the problems with this approach is 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, a number of other factors, such as parenthood, gender segregation, part-time working and unionization, contribute to the gender wage gap. 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 influences. The literature has noted the existence of these interactions, but precise or systematic estimates of such effects remain 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.

Item Type:Article
ISSN:0933-1433
Uncontrolled Keywords:gender wage gap; part-time work; gender segregation; unionization; sample selection bias; matching; inverse probability weighted regression adjustment estimator
Group:Faculty of Management
ID Code:32565
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:23 Jul 2019 13:10
Last Modified:05 Sep 2020 01:08

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -