A Method for Disentangling Multiple Treatments from a Regression Discontinuity Design
提出一种利用进入与退出处理不对称性，从单一断点中识别不同处理成分的方法，并应用于纽约市班级规模上限政策，发现班级规模缩小提升学生成绩，但被新教师效应抵消。
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.