Regression Discontinuity Design with Continuous Measurement Error in the Running Variable

Thomas Le Barbanchon and Laurent Davezies

No 11775, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming nondifferential measurement error, we propose a consistent nonparametric estimator of the LATE when the discrepancy between the true running variable and its noisy measure is observed in an auxiliary sample of treated individuals, and when there are treated individuals at any value of the true running variable - two-sided fuzzy designs. We apply our method to estimate the effect of receiving unemployment benefits.

Keywords: Regression discontinuity design; Measurement error; Unemployment insurance (search for similar items in EconPapers)
JEL-codes: C14 C21 C51 (search for similar items in EconPapers)
Date: 2017-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (28)

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Related works:
Journal Article: Regression discontinuity design with continuous measurement error in the running variable (2017)
Working Paper: Regression Discontinuity Design with Continuous Measurement Error in the Running Variable (2017)
Working Paper: Regression Discontinuity Design with Continuous Measurement Error in the Running Variable (2014)
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