Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools
通过一项随机实验，向学校校长提供教师绩效的客观估计，检验了雇主如何在不完美信息下通过贝叶斯学习更新对员工能力的认知，并发现该信息影响了教师离职率和学生成绩。
We examine how employers learn about worker productivity in a randomized pilot experiment which provided objective estimates of teacher performance to school principals. We test several hypotheses that support a simple Bayesian learning model with imperfect information. First, the correlation between performance estimates and prior beliefs rises with more precise objective estimates and more precise subjective priors. Second, new information exerts greater influence on posterior beliefs when it is more precise and when priors are less precise. Employer learning affects job separation and productivity in schools, increasing turnover for teachers with low performance estimates and producing small test score improvements.