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Regression Discontinuity Marginal Threshold Treatment Effects

Author

Listed:
  • Yingying Dong

    (Department of Economics, University of California-Irvine)

  • Arthur Lewbel

    (Department of Economics, Boston College)

Abstract

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.

Suggested Citation

  • Yingying Dong & Arthur Lewbel, 2011. "Regression Discontinuity Marginal Threshold Treatment Effects," Working Papers 111205, University of California-Irvine, Department of Economics.
    • Handle: RePEc:irv:wpaper:111205
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    References listed on IDEAS

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    1. 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 2209-2226, December.
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    Cited by:

    1. Dong, Yingying, 2010. "Jumpy or Kinky? Regression Discontinuity without the Discontinuity," MPRA Paper 25461, University Library of Munich, Germany.
    2. Yingying Dong, 2012. "Regression Discontinuity Applications with Rounding Errors in the Running Variable," Working Papers 111206, University of California-Irvine, Department of Economics.
    3. Joshua Angrist & Miikka Rokkanen, 2012. "Wanna Get Away? RD Identification Away from the Cutoff," NBER Working Papers 18662, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Regression discontinuity; Sharp design; Fuzzy design; Treatment effects; Program evaluation; Threshold; Running variable; Forcing variable;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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