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Heaping-Induced Bias in Regression-Discontinuity Designs

Author

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

Abstract

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 are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the magnitude and direction of the bias varies with bandwidth choice and the location of the data heaps relative to the treatment threshold. Finally, we discuss approaches to correcting for this type of problem before considering these issues in several non-simulated environments.

Suggested Citation

  • Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2011. "Heaping-Induced Bias in Regression-Discontinuity Designs," NBER Working Papers 17408, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:17408
    Note: ED EH LS TWP
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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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