On the Estimation of Treatment Effects with Endogenous Misreporting

Pierre Nguimkeu, Augustine Denteh and Rusty Tchernis

No 11426, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Participation in social programs is often misreported in survey data, complicating the estimation of the effects of those programs. In this paper, we propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. We show that failure to account for endogenous misreporting can result in the estimate of the treatment effect having an opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when researchers have access to information related to both participation and misreporting. We establish the consistency and asymptotic normality of our estimator and assess its small sample performance through Monte Carlo simulations. An empirical example is given to illustrate the proposed method.

Keywords: partial observability; binary regressor; treatment effect; misclassification; endogeneity; bias (search for similar items in EconPapers)
JEL-codes: C35 C51 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2018-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

Published - published in: Journal of Econometrics, 2019, 208 (2), 487 - 506

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Related works:
Journal Article: On the estimation of treatment effects with endogenous misreporting (2019)
Working Paper: On the Estimation of Treatment Effects with Endogenous Misreporting (2018)
Working Paper: On the Estimation of Treatment Effects with Endogenous Misreporting (2017)
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