Presented By: Social, Behavioral, and Experimental Economics (SBEE)
Social, Behavioral & Experimental Economics (SBEE): Interpreting Signals: Evidence from Medical Referrals
Heather Sarsons, Harvard University
Abstract
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 the referring physician views 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. After a bad experience with one female surgeon, physicians also become less likely to refer to new female surgeons 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 are beginning to refer to a surgeon. However, the empirical patterns are only consistent with Bayesian learning if physicians do not have rational expectations about the true distribution of surgeon ability.
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 the referring physician views 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. After a bad experience with one female surgeon, physicians also become less likely to refer to new female surgeons 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 are beginning to refer to a surgeon. However, the empirical patterns are only consistent with Bayesian learning if physicians do not have rational expectations about the true distribution of surgeon ability.
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