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.
Gender Pay Gap Segregation Sample Selection Bias Propensity Score Matching IPWRA USA