Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/180444 
Year of Publication: 
2018
Series/Report no.: 
IZA Discussion Papers No. 11426
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
endogeneity
misclassification
treatment effect
binary regressor
partial observability
bias
JEL: 
C35
C51
Document Type: 
Working Paper

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