Gender Reveals in the Labor Market

Abstract We study gender discrimination in an online health care market. Statistical discrimination implies that the impact of gender on prices should decline, and the impact of reviews increase, as reviews accumulate. However, in our context these implications do not hold, because doctors choose how strongly to signal gender. We develop a new test for the implications of statistical discrimination based on this choice. We find evidence consistent with statistical discrimination against female doctors in male-dominated fields, and vice versa. For example, female doctors mask gender more strongly initially in male-dominated fields, and the gender gap in signaling declines as reviews accumulate.