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Jumpy or Kinky? Regression Discontinuity without the Discontinuity

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  • Dong, Yingying
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    Abstract

    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/
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    Bibliographic Info

    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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    Keywords: Regression Discontinuity; Fuzzy design; Average treatment effect; Identification; Jump; Kink; Threshold;

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    1. 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.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, Econometric Society, vol. 69(1), pages 201-09, January.
    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, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP21/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. 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.
    6. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, Elsevier, vol. 142(2), pages 615-635, February.
    7. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs in Economics," NBER Working Papers 14723, National Bureau of Economic Research, Inc.
    8. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    9. 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.
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    Cited by:
    1. Ganong, Peter & J├Ąger, Simon, 2014. "A Permutation Test and Estimation Alternatives for the Regression Kink Design," IZA Discussion Papers 8282, Institute for the Study of Labor (IZA).
    2. Landais, Camille, 2013. "Assessing the Welfare Effects of Unemployment Benefits Using the Regression Kink Design," IZA Discussion Papers 7589, Institute for the Study of Labor (IZA).

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