Publication Date: March 2019
Revision Date: December 2020
Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over-subscribed schools using randomly assigned lottery numbers, non-lottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at \screened” schools, combined with lottery tie-breaking at unscreened \lottery” schools. We show how to identify causal eﬀects of school attendance in such settings. Our approach generalizes regression discontinuity methods to allow for multiple treatments and multiple running variables, some of which are randomly assigned. The key to this generalization is a local propensity score that quantiﬁes the school assignment probabilities induced by lottery and non-lottery tie-breakers. The local propensity score is applied in an empirical assessment of the predictive value of New York City’s school report cards. Schools that receive a high grade indeed improve SAT math scores and increase graduation rates, though by much less than OLS estimates suggest. Selection bias in OLS estimates is egregious for screened schools.
Keywords: Causal Inference, Natural Experiment, Local Propensity Score, Instrumental Variables, Unified Enrollment, School Report Card, School Value AddedSee CFDP Version(s): CFDP 2170
See CFP: CFP 1766