A significant amount of work time is lost each year due to worker absence, but evidence on the productivity losses from absenteeism remains scant due to difficulties with identification. We use uniquely detailed data on the timing, duration, and cause of absences among teachers to address many of the potential biases from the endogeneity of worker absence. Our analysis indicates that worker absences have large negative impacts: the expected loss in daily productivity from employing a temporary substitute is on par with replacing a regular worker of average productivity with one at the 10th–20th percentile of productivity.

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.