Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/196703 
Year of Publication: 
2019
Series/Report no.: 
IZA Discussion Papers No. 12205
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, nonlottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores, grades, and interviews to rank applicants to screened schools, combined with lottery tie-breaking at unscreened schools. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity designs to allow for multiple treatments and multiple running variables, some of which are randomly assigned. Lotteries generate assignment risk at screened as well as unscreened schools. Centralized assignment also identifies screened school effects away from screened school cutoffs. These features of centralized assignment are used to assess the predictive value of New York City's school report cards. Grade A schools improve SAT math scores and increase the likelihood of graduating, though by less than OLS estimates suggest. Selection bias in OLS estimates is egregious for Grade A screened schools.
Subjects: 
causal inference
natural experiment
local propensity score
instrumental variables
unified enrollment
school report card
school value added
JEL: 
I21
C78
C13
C18
C21
C26
Document Type: 
Working Paper

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