The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable

Zhuan Pei and Yi Shen ()
Additional contact information
Yi Shen: University of Waterloo

No 10320, IZA Discussion Papers from IZA Network @ LISER

Abstract: Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the first stage relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This paper provides sufficient conditions for identification when only the mismeasured assignment variable, the treatment status and the outcome variable are observed. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Keywords: measurement error; regression discontinuity design (search for similar items in EconPapers)
JEL-codes: C10 C18 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2016-10
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published - published in: Regression Discontinuity Designs (Advances in Econometrics, 38), Emerald Publishing Limited, Bingley, 2017, 455-502

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https://docs.iza.org/dp10320.pdf (application/pdf)

Related works:
Chapter: The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable (2017)
Working Paper: The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable (2016)
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