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
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)
New Economics Papers: this item is included in nep-ecm
Note: ED HE LS TWP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22) Track citations by RSS feed
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.
Downloads: (external link)
Journal Article: HEAPING-INDUCED BIAS IN REGRESSION-DISCONTINUITY DESIGNS (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:17408
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().