Discrimination in Health Care
Friday, Jan. 4, 2019 8:00 AM - 10:00 AM
- Chair: Joanna Lahey, Texas A&M University and NBER
Interpreting Signals in the Labor Market: Evidence from Medical Referrals
AbstractThis 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.
Gender Homophily in Referral Networks: Consequences for the Medicare Physician Earnings Gap
AbstractIn this paper, I assess the extent to which the gender gap in physician earnings may be driven by physicians’ preference for working with specialists of the same gender. By analyzing administrative data on 100 million Medicare patient referrals, I provide robust evidence that doctors refer more to specialists of their same gender, a tendency known as homophily. I propose a new measure of homophily that is invariant to differences between the genders in the propensity to refer or receive referrals. I show that biased referrals are predominantly driven by physicians’ decisions rather than by endogenous sorting of physicians or patients or by gender differences in the labor supply. As 75% of doctors are men, estimates suggest biased referrals generate a 5% lower demand for female relative to male specialists, pointing to a positive externality for increased female participation in medicine.
Portland State University
Tel Aviv University and IZA
- I1 - Health
- J1 - Demographic Economics