Bayesian Learning and Gender Segregation
用贝叶斯学习模型解释职业性别隔离为何持续存在，以及各国隔离程度为何不同。个体对职业成功概率信息不完全，基于先验信念做选择，信念随贝叶斯规则更新，导致过去性别偏好差异影响当前选择，即使偏好相同，职业选择仍不同。
We present an explanation for the persistence of gender segregation in occupations and for the observed cross-country differences in its extent. Agents have imperfect information about their probability of success in different occupations and base their career choices on prior beliefs about these probabilities. Beliefs are updated according to Bayes's rule, implying that past differences in preferences over occupations across genders affect the beliefs of the current generation. Consequently, even when men and women become identical in their preferences, their career choices differ. Moreover, the way in which preferences change is shown to affect the degree of segregation.