An Alternative Estimator for Industrial Gender Wage Gaps: A Normalized Regression Approach

Using normalized regression equations, we propose an alternative estimator of industrial gender wage gaps which is identified in the sense that it is invariant to the choice of an unobserved non-discriminatory wage structure, and to the choice of the reference groups of any categorical variables. The proposed estimator measures the pure impact of industry on gender wage gaps after netting out wage differentials due to differences in characteristics and their coefficients between men and women. Furthermore, the proposed estimator is easy to implement, including hypothesis tests. We compare the proposed estimator with existing estimators using samples from 1998 Current Population Survey of US.