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Jumpy or Kinky? Regression Discontinuity without the Discontinuity

  • Dong, Yingying

Regression Discontinuity (RD) models identify local treatment effects by associating a discrete change in the mean 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. The identification results are interpreted 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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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 25461.

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Date of creation: 01 Aug 2010
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Handle: RePEc:pra:mprapa:25461
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  1. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
  2. Helena Skyt Nielsen & Torben Sørensen & Christopher R. Taber, 2008. "Estimating the Effect of Student Aid on College Enrollment: Evidence from a Government Grant Policy Reform," NBER Working Papers 14535, National Bureau of Economic Research, Inc.
  3. Carneiro, Pedro & Heckman, James J. & Vytlacil, Edward, 2009. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," IZA Discussion Papers 4324, Institute for the Study of Labor (IZA).
  4. B. Douglas Bernheim & Jonathan Skinner & Steven Weinberg, 1997. "What Accounts for the Variation in Retirement Wealth Among U.S. Households?," NBER Working Papers 6227, National Bureau of Economic Research, Inc.
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  6. 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.
  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. Emma Aguila & Orazio P. Attanasio & Costas Meghir, 2008. "Changes in Consumption at Retirement," Working Papers 621, RAND Corporation Publications Department.
  9. Milligan, Kevin & Lemieux, Thomas, 2006. "Incentive Effects of Social Assistance: A Regression Discontinuity Approach," Analytical Studies Branch Research Paper Series 2006280e, Statistics Canada, Analytical Studies Branch.
  10. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, July.
  11. 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.
  12. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs In Economics," Working Papers 1118, Princeton University, Department of Economics, Industrial Relations Section..
  13. Lee, David S., 2008. "Randomized experiments from non-random selection in U.S. House elections," Journal of Econometrics, Elsevier, vol. 142(2), pages 675-697, February.
  14. Erich Battistin & Agar Brugiavini & Enrico Rettore & Guglielmo Weber, 2009. "The Retirement Consumption Puzzle: Evidence from a Regression Discontinuity Approach," American Economic Review, American Economic Association, vol. 99(5), pages 2209-26, December.
  15. 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.
  16. Jonathan Guryan, 2001. "Does Money Matter? Regression-Discontinuity Estimates from Education Finance Reform in Massachusetts," NBER Working Papers 8269, National Bureau of Economic Research, Inc.
  17. Michael Hurd & Susann Rohwedder, 2004. "The Retirement-Consumption Puzzle: Anticipated and Actual Declines in Spending at Retirement," Working Papers wp069, University of Michigan, Michigan Retirement Research Center.
  18. repec:oup:restud:v:79:y::i:3:p:933-959 is not listed on IDEAS
  19. Yingying Dong & Arthur Lewbel, 2011. "Regression Discontinuity Marginal Threshold Treatment Effects," Working Papers 111205, University of California-Irvine, Department of Economics.
  20. 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.
  21. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
  22. John Ameriks & Andrew Caplin & John Leahy, 2002. "Retirement Consumption: Insights from a Survey," NBER Working Papers 8735, National Bureau of Economic Research, Inc.
  23. Yingying Dong & Arthur Lewbel, 2010. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models," Boston College Working Papers in Economics 759, Boston College Department of Economics, revised 15 Dec 2012.
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