This study examines the gender wage gap between male and female workers in the US using a cross-section from the Current Population Survey (CPS) It shows that the extent of gender segregation by both industry and occupation is significantly greater than previously supposed. For the wage gap this creates problems of sample selection bias, of non-comparability between male and female employment. To address these problems the study uses a matching approach, which we also extend to a more recent methodological version with a yet stronger statistical foundation – Inverse Probability Weighted Regression Adjustment (IPWRA) – not previously used in related studies.
Despite this, doubts remain about even these well founded and appropriate techniques in the presence of such strong gender segregation. To secure even greater precision we repeat the matching analysis for a small number of industries and occupations, each carefully selected for employing similar numbers of men and women. This is an approach that has not previously been explored in the relevant literature. The findings for the full sample are replicated at the level of industry and occupation, where comparability is more reliable. The study supports the view of the existing literature that the gender wage gap varies by factors such as age and parenthood. But it also finds that, even when these and other important "control" variables such as part-time working, industry and occupation are taken into account, a statistically significant gender wage gap remains.
We use cookies to provide you with an optimal website experience. This includes cookies that are necessary for the operation of the site as well as cookies that are only used for anonymous statistical purposes, for comfort settings or to display personalized content. You can decide for yourself which categories you want to allow. Please note that based on your settings, you may not be able to use all of the site's functions.
Cookie settings
These necessary cookies are required to activate the core functionality of the website. An opt-out from these technologies is not available.
In order to further improve our offer and our website, we collect anonymous data for statistics and analyses. With the help of these cookies we can, for example, determine the number of visitors and the effect of certain pages on our website and optimize our content.