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Wanna Get Away? RD Identification Away from the Cutoff

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Author Info

  • Angrist, Joshua

    ()
    (MIT)

  • Rokkanen, Miikka

    ()
    (MIT)

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 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 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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 7429.

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Length: 52 pages
Date of creation: May 2013
Date of revision:
Handle: RePEc:iza:izadps:dp7429

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Keywords: regression discontinuity; human capital; school quality;

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  1. Guido Imbens & Karthik Kalyanaraman, 2010. "Optimal bandwidth choice for the regression discontinuity estimator," CeMMAP working papers CWP05/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  8. C. Kirabo Jackson, 2010. "Do Students Benefit from Attending Better Schools? Evidence from Rule-based Student Assignments in Trinidad and Tobago," Economic Journal, Royal Economic Society, vol. 120(549), pages 1399-1429, December.
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  10. Frandsen, Brigham R. & Frölich, Markus & Melly, Blaise, 2012. "Quantile treatment effects in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 168(2), pages 382-395.
  11. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
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  14. Battistin, Erich & Rettore, Enrico, 2008. "Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs," Journal of Econometrics, Elsevier, vol. 142(2), pages 715-730, February.
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Cited by:
  1. Alexander Hijzen & Leopoldo Mondauto & Stefano Scarpetta, 2013. "The Perverse Effects of Job-security Provisions on Job Security in Italy: Results from a Regression Discontinuity Design," OECD Social, Employment and Migration Working Papers 151, OECD Publishing.
  2. Coviello, Decio & Mariniello, Mario, 2014. "Publicity requirements in public procurement: Evidence from a regression discontinuity design," Journal of Public Economics, Elsevier, vol. 109(C), pages 76-100.
  3. Sander Gerritsen & Karen van der Wiel & Erik Plug (UVA), 2013. "Up or out? How individual research grants affect academic careers in the Netherlands," CPB Discussion Paper 249, CPB Netherlands Bureau for Economic Policy Analysis.
  4. Clark, Damon & Del Bono, Emilia, 2014. "The long-run effects of attending an elite school: evidence from the UK," ISER Working Paper Series 2014-05, Institute for Social and Economic Research.

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