In many settings, a policy discontinuity comprises several treatments that cannot be separately identified using a standard regression discontinuity design. I propose a method for identifying distinct treatment components from a single discontinuity by exploiting the asymmetry between entities entering versus exiting treatment. Using data from New York City for 2009–13, I apply my strategy to the discontinuity associated with the introduction of class size caps—a widespread approach for reducing class sizes. I find that class size reductions increase student achievement, although these gains are counteracted by a newly hired teacher effect. The method has broad potential applicability.

We show that teachers’ value added (VA) depends on the VA of the teachers who preceded them. To do so, we use administrative data from North Carolina and find that a one-standard deviation increase in last grade’s mean teacher VA causes a 0.08σ decrease in teacher VA. Controlling for prior teacher assignment eliminates this bias. Under a benchmark policy that releases teachers in the bottom 5% of the VA distribution, 32% of teachers are incorrectly released using traditional techniques. Our results highlight the importance of incorporating dynamic features of education production into the estimation of teacher quality.

An administrative rule in North Carolina allowed students who failed an exam to retake it approximately two weeks later, triggering a brief yet intense test preparation period. We develop a structural model that accounts for selection and find that these students score much higher on the retest. Using a regression discontinuity design, we find substantial fade-out of the test score gains after one year but some persistence thereafter. Unlike other interventions that produce similar initial increases in performance, we do not observe benefits to long-term outcomes. Our findings highlight that persistence should be accounted for when comparing educational interventions.