Wanna Get Away? Rd Identification Away from the Cutoff

49 Pages Posted: 3 Jan 2013 Last revised: 3 Jul 2018

See all articles by Joshua D. Angrist

Joshua D. Angrist

Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Miikka Rokkanen

Massachusetts Institute of Technology (MIT) - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: December 2012

Abstract

In the canonical regression discontinuity (RD) design for applicants who face an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The impact of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than are required for identification at the cutoff. This paper discusses RD identification 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 illustrated with data on applicants to Boston exam schools. Functional-form-based extrapolation generates unsatisfying results in this context, either noisy or not very robust. By contrast, identification based on RD-specific conditional independence assumptions produces 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.

Suggested Citation

Angrist, Joshua and Rokkanen, Miikka, Wanna Get Away? Rd Identification Away from the Cutoff (December 2012). NBER Working Paper No. w18662. Available at SSRN: https://ssrn.com/abstract=2195849

Joshua Angrist (Contact Author)

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Miikka Rokkanen

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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