Interpreting Signals in the Labor Market: Evidence from Medical Referrals
This paper provides evidence that a person's gender influences the way others interpret information about his or her ability and documents the implications for gender inequality in labor markets. Using data on physicians' referrals to surgical specialists, I find that referring physicians view patient outcomes differently depending on the performing surgeon's gender. Physicians become more pessimistic about a female surgeon's ability than a male's after a patient death, indicated by a sharper drop in referrals to the female surgeon. However, physicians become more optimistic about a male surgeon's ability after a good patient outcome, indicated by a larger increase in the number of referrals the male surgeon receives. Physicians also change their behavior toward other female surgeons after a bad experience with one female surgeon, becoming less likely to refer to new women in the same specialty. There are no such spillovers to other men after a bad experience with one male surgeon. Consistent with learning models, physicians' reactions to events are strongest when they have just begun to refer to a surgeon. However, the empirical patterns are consistent with Bayesian learning only if physicians do not have rational expectations about the true distribution of surgeon ability.