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Jumpy or Kinky? Regression Discontinuity without the Discontinuity

  • Dong, Yingying
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    Regression Discontinuity (RD) models identify local treatment effects by associating a discrete change in an outcome with a corresponding discrete change in the probability of treatment at a known threshold of a running variable. This paper shows that it is possible to identify RD model treatment effects without a discontinuity. The intuition is that identification can come from a slope change (a kink) instead of a discrete level change (a jump) in the treatment probability. Formally this can be shown using L'hopital's rule. I also interpret the identification results intuitively using instrumental variable models. Estimators are proposed that can be applied in the presence or absence of a discontinuity, by exploiting either a jump or a kink.

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    File URL: http://mpra.ub.uni-muenchen.de/25427/1/MPRA_paper_25427.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 25427.

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    Date of creation: Aug 2010
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    Handle: RePEc:pra:mprapa:25427
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    1. Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc.
    2. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs in Economics," NBER Working Papers 14723, National Bureau of Economic Research, Inc.
    3. David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Review, American Economic Association, vol. 98(5), pages 2242-58, December.
    4. Pedro Carneiro & James Heckman & Edward Vytlacil, 2009. "Evaluating marginal policy changes and the average effect of treatment for individuals at the margin," CeMMAP working papers CWP21/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
    6. Guido Imbens & Thomas Lemieux, 2007. "Regression Discontinuity Designs: A Guide to Practice," NBER Working Papers 13039, National Bureau of Economic Research, Inc.
    7. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-98, June.
    8. Imbens, Guido W. & Kalyanaraman, Karthik, 2009. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," IZA Discussion Papers 3995, Institute for the Study of Labor (IZA).
    9. Brian A. Jacob & Lars Lefgren, 2004. "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 226-244, February.
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