Heaping-Induced Bias in Regression-Discontinuity Designs

Alan Barreca, Jason Lindo and Glen Waddell ()

No 17408, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C14 C21 I12 (search for similar items in EconPapers)
Date: 2011-09
New Economics Papers: this item is included in nep-ecm
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Published as 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, 01.

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Journal Article: HEAPING-INDUCED BIAS IN REGRESSION-DISCONTINUITY DESIGNS (2016)
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