Gender, learning, and earnings estimate accuracy

Abstract

We analyze the underlying source of gender differences in earnings estimates on a crowdsourcing platform, Estimize, to understand the mechanisms driving analyst ability. Estimates made by females are more accurate than those made by males. This outperformance is not consistent with explanations based on females’ innate ability to process information, females utilizing more up-to-date information, superior stock selection among females, copycat estimates, gender bias, or survivorship bias. Instead, our evidence is consistent with females learning more quickly through making estimates, leading to their outperformance.