On the estimation of treatment effects with endogenous misreporting

Pierre Nguimkeu, Augustine Denteh and Rusty Tchernis

Journal of Econometrics, 2019, vol. 208, issue 2, 487-506

Abstract: Participation in social programs is often misreported in survey data, complicating the estimation of treatment effects. We propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. 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 regarding participation and misreporting. We establish the consistency and asymptotic normality of our proposed estimator and assess its small sample performance through Monte Carlo simulations. An empirical example illustrates the proposed method.

Keywords: Treatment effect; Misclassification; Endogeneity; Binary regressor; Partial observability; Bias (search for similar items in EconPapers)
JEL-codes: C35 C51 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (20)

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Related works:
Working Paper: On the Estimation of Treatment Effects with Endogenous Misreporting (2018)
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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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:208:y:2019:i:2:p:487-506

DOI: 10.1016/j.jeconom.2018.10.005

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