Estimator

Gender wage gap (adjusted for individual characteristics) as a phenomenon means that women are paid unjustifiably less than men, i.e. below their productivity. Meanwhile, efficiency wages as a phenomenon mean that a group of workers is paid in excess of productivity. However, productivity is typically unobservable, hence it is proxied by some observable characteristics. If efficiency wages are effective only in selected occupations and/or industries, and these happen to be dominated by men, measures of adjusted gender wage gaps will confound (possibly) below productivity compensating of women with above productivity efficiency wage prevalence. We propose to utilize endogenous switching models to estimate adjusted gender wage gaps. We find that without correction for the prevalence of efficiency wages, the estimates of the adjusted gender wage gaps tend to be substantially inflated.

It appears plausible that efficiency wages may be more relevant for men — whereas women may value more some other attributes of work both as incentive to avoid shirking. This would imply that a part of wage differential typically unexplained by observable characteristics could actually reflect wages of men in excess of marginal productivity (efficiency wages) and not discriminatory pricing of women’s work.

While this hypothesis is by no means new, it is challenging to address empirically. Typically, efficiency wages are not identified directly. In standard wage datasets, such as linked employer-employee data or labor force survey, prevalence of efficiency wages may be confirmed or rejected, but usually not attributed to respective workers. Indeed, individual productivity is rarely observed, thus making it impossible to judge if wage exceeds it. Moreover, clearly, if there is sorting, even identifying productivities is not going to help much due to endogeneity. A class of full information maximum likelihood estimators with endogenous switching provides consistent estimators of returns to individual characteristics, accounting simultaneously for selection and wage determination, but these models require that the data comprises assignment between the markets, e.g. unionized vs non-unionized workers, public vs. private sector, etc.

We propose an estimator of gender wage gap, which accounts for bias stemming from a separation between a privileged and standard labor markets, when this separation is endogenous and a priori unknown (unobservable). We analyze estimates of the gender wage gaps in European countries using linked employer-employee data for the European countries (EU SES). Thus, we address an important concern implicit in the previous literature that the estimates of adjusted gender wage gap are inflated by the incidence of efficiency wages.

Derivation

Matlab code