We study the returns to experience in teaching, estimated using evaluation ratings from classroom observations. We describe the assumptions required to interpret changes in observation scores over time as the causal effect of experience on performance. We compare two difference-in-differences style estimation strategies: the within-teacher estimator common in the literature, and an alternative which avoids potential biases in the common approach. Using data from Tennessee and Washington, DC, we show empirical tests relevant to assessing the identifying assumptions and substantive threats—e.g., leniency bias, manipulation, changes in incentives or job assignments—and find our estimates are robust to several threats.
I study the effects of a labor-replacing computer technology on the productivity of classroom teachers. Focusing on one occupation—and a setting where both workers and their job responsibilities remain fixed—provides an opportunity to examine the heterogeneity of effects on individual productivity. In a series of field-experiments, teachers were provided computer-aided instruction (CAI) software for use in their classrooms; CAI provides individualized tutoring and practice to students one-on-one with the computer acting as the teacher. In math classes, CAI reduces by one-fifth the variance of teacher productivity, as measured by student test score gains. The smaller variance comes both from productivity improvements for otherwise low-performing teachers, but also losses among high-performers. The change in productivity partly reflects changes in teachers’ level of work effort and teachers’ decisions about how to allocate class time. How computers affect teacher decisions and productivity is immediately relevant to both ongoing education policy debates about teaching quality and the day-to-day management of a large workforce.