[1]Weili Ding: Estimating Context-Independent Treatment Effects in Education Experiments In this study, we first document that the magnitude of the estimated treatment effect in Project STAR is substantially larger in schools where fewer students were assigned to small classes. The differences in student performance across schools cannot be explained by failure in randomization, other observed school level characteristics or differences in selective test taking. Further, we show that these achievement gains are driven by students in small classes from schools where fewer students were in small classes. The results are suggestive that there was a proportionate change in motivation or effort by teachers who teach small classes but not in regular classes. [2]Steven F. Lehrer: Targeting Policies: Multiple Testing and Distributional Treatment Effects Economic theory often predicts that treatment responses may depend on individuals' characteristics and location on the outcome distribution. Policymakers need to account for such treatment effect heterogeneity in order to efficiently allocate resources to subgroups that can successfully be targeted by a policy. However, when interpreting treatment effects across subgroups and the outcome distribution, inference has to be adjusted for multiple hypothesis testing to avoid an overestimation of positive treatment effects. |
