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Regression Discontinuity Marginal Threshold Treatment Effects

  • Yingying Dong

    ()

    (Department of Economics, University of California-Irvine)

  • Arthur Lewbel

    ()

    (Department of Economics, Boston College)

In regression discontinuity models, where the probability of treatment jumps discretely when a running variable crosses a threshold, an average treatment effect can be nonparametrically identified. We show that the derivative of this treatment effect with respect to the threshold is also nonparametrically identified and easily estimated, in both sharp and fuzzy designs. This marginal threshold treatment effect (MTTE) may be used to estimate the impact on treatment effects of small changes in the threshold. We use it to show how raising the age of Medicare eligibility would change the probability of take up of various types of health insurance.

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File URL: http://www.economics.uci.edu/files/docs/workingpapers/2011-2012/dong-05.pdf
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Paper provided by University of California-Irvine, Department of Economics in its series Working Papers with number 111205.

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Length: 31 pages
Date of creation: Nov 2011
Date of revision:
Handle: RePEc:irv:wpaper:111205
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Web page: http://www.economics.uci.edu/

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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.
  2. Kenneth Y. Chay & Michael Greenstone, 1998. "Does Air Quality Matter? Evidence from the Housing Market," NBER Working Papers 6826, National Bureau of Economic Research, Inc.
  3. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, 01.
  4. James J. Heckman, 2010. "Building Bridges Between Structural and Program Evaluation Approaches to Evaluating Policy," NBER Working Papers 16110, National Bureau of Economic Research, Inc.
  5. 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.
  6. Agar Brugiavini & Erich Battistin, & Enrico Rettore & Guglielmo Weber, 2007. "The Retirement Consumption Puzzle: Evidence from a Regression Discontinuity Approach," Working Papers 2007_27, Department of Economics, University of Venice "Ca' Foscari".
  7. Angus Deaton, 2009. "Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development," Working Papers 1122, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  8. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs in Economics," NBER Working Papers 14723, National Bureau of Economic Research, Inc.
  9. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
  10. 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.
  11. Dong, Yingying, 2010. "Jumpy or Kinky? Regression Discontinuity without the Discontinuity," MPRA Paper 25461, University Library of Munich, Germany.
  12. James J. Heckman & Sergio Urzua, 2009. "Comparing IV With Structural Models: What Simple IV Can and Cannot Identify," NBER Working Papers 14706, National Bureau of Economic Research, Inc.
  13. repec:oup:restud:v:79:y::i:3:p:933-959 is not listed on IDEAS
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