Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff
Author(s)
Angrist, Joshua
Rokkanen, Miikka Aatto Tapio
Date Issued
January 2016
Journal
Journal of the American Statistical Association
Publisher
Informa UK Limited
Citation
Angrist, Joshua D. and Rokkanen, Miikka. “Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff.” Journal of the American Statistical Association 110, 512 (October 2015): 1331–1344 © 2015 American Statistical Association
Version
Original manuscript
Abstract
In regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification and estimation away from the cutoff. Our identification strategy exploits the availability of dependent variable predictors other than the running variable. Conditional on these predictors, the running variable is assumed to be ignorable. This identification strategy is used to study effects of Boston exam schools for inframarginal applicants. Identification based on the conditional independence assumptions imposed in our framework yields reasonably precise and surprisingly robust estimates of the effects of exam school attendance on inframarginal applicants. These estimates suggest that the causal effects of exam school attendance for 9th grade applicants with running variable values well away from admissions cutoffs differ little from those for applicants with values that put them on the margin of acceptance. An extension to fuzzy designs is shown to identify causal effects for compliers away from the cutoff. Supplementary materials for this article are available online. Keywords: causal inference; conditional independence assumption; instrumental variables; treatment effects
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/01621459.2015.1012259
