Laboratory experiments involving a real effort task are conducted to examine the importance of gender differences in competition aversion for generating gender wage gaps. Cross-subject design treatment and control experiments suggest that gender differences in risk aversion play no significant...

Probit and logit models typically require a normalization on the error variance for model identification. This paper shows that in the context of sample mean probability decompositions, error variance normalizations preclude estimation of the effects of group differences in the latent variable...

Failure of participants to complete training programs is pervasive in existing active labor market programs both in developed and developing countries. The proportion of dropouts in prototypical programs ranges from 10 to 50 percent of all participants. From a policy perspective, it is of...

Laboratory experiments involving a real effort task are conducted to examine the importance of gender differences in competition aversion for generating gender wage gaps. Cross-subject design treatment and control experiments suggest that gender differences in risk aversion play no significant...

Failure of participants to complete training programs is pervasive in existing active labor market programs both in developed and developing countries. The proportion of dropouts in prototypical programs ranges from 10 to 50 percent of all participants. From a policy perspective, it is of...

In addition to discrimination, market power, and human capital, gender differences in risk preferences might also contribute to observed gender wage gaps. We conduct laboratory experiments in which subjects choose between a risky (in terms of exposure to unemployment) and a secure job after...

This paper analyzes wage decomposition methodology in the context of panel data sample selection embedded in a correlated random effects setting. Identification issues unique to panel data are examined for their implications for wage decompositions. As an empirical example, we apply our...