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Heaping-Induced Bias In Regression-Discontinuity Designs

Author

Listed:
  • Alan I. Barreca
  • Jason M. Lindo
  • Glen R. Waddell

Abstract

type="main" xml:id="ecin12225-abs-0001"> This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics may not be well suited to identifying this type of problem, we provide alternatives, and then discuss the usefulness of different approaches to addressing the bias. We then consider these issues in multiple non-simulated environments. (JEL C21, C14, I12)

Suggested Citation

  • Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2016. "Heaping-Induced Bias In Regression-Discontinuity Designs," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 268-293, January.
    • Handle: RePEc:bla:ecinqu:v:54:y:2016:i:1:p:268-293
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Justin McCrary & Heather Royer, 2011. "The Effect of Female Education on Fertility and Infant Health: Evidence from School Entry Policies Using Exact Date of Birth," American Economic Review, American Economic Association, vol. 101(1), pages 158-195, February.
    3. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    4. Dickert-Conlin, Stacy & Elder, Todd, 2010. "Suburban legend: School cutoff dates and the timing of births," Economics of Education Review, Elsevier, vol. 29(5), pages 826-841, October.
    5. Douglas Almond & Joseph J. Doyle & Amanda E. Kowalski & Heidi Williams, 2010. "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 591-634.
    6. 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.
    7. Buddelmeyer, Hielke & Skoufias, Emmanuel, 2003. "An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA," IZA Discussion Papers 827, Institute of Labor Economics (IZA).
    8. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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