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Heaping-Induced Bias in Regression-Discontinuity Designs

Author: Alan Barreca; Jason M Lindo; Glen R Waddell
Publisher: Cambridge, Mass. National Bureau of Economic Research 2011.
Series: Working paper series (National Bureau of Economic Research), no. w17408.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alan Barreca; Jason M Lindo; Glen R Waddell
OCLC Number: 1027304633
Notes: September 2011.
Description: 1 online resource.
Details: Mode of access: World Wide Web.
Series Title: Working paper series (National Bureau of Economic Research), no. w17408.
Responsibility: 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.

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