Gender Reveals in the Labor Market: Evidence on Gender Signaling and Statistical Discrimination in an Online Health Care Market

A recent approach to testing for customer statistical discrimination involves studying price gaps between sellers from different gender, race, or ethnic groups and how they evolve as buyers obtain more information about seller quality. We consider a similar setting, testing for statistical discrimination against female doctors in an online health care market. But we show that this kind of analysis does not provide evidence on statistical discrimination in this setting because doctors have a choice about how strongly to signal gender. We develop a new approach to identifying statistical discrimination using doctors’ choices about signaling their gender. We find evidence of statistical discrimination against female doctors in male-dominated fields, and against male doctors in female-dominated fields. In particular, female doctors mask gender more strongly initially in male-dominated fields, and male doctors do the same in female-dominated fields. But in both female- and male-dominated fields the gender gap in signaling decreases with number of customer reviews of doctors. More generally, our evidence indicates how, in some markets, sellers may be able to reduce statistical discrimination by masking their group membership.