Economics Research Seminar Series: Akanksha Negi

This paper studies identification and estimation of the average treatment effect on the treated (ATT) in difference-in-difference designs when the variable that classifies individuals into treatment and control groups (treatment status, D) is differentially (or endogenously) mis-measured. We show that misclassification in D hampers consistent estimation of ATT because 1) it restricts us from identifying the truly treated from those misclassified as being treated and 2) differential misclassification in counterfactual trends may result in parallel trends being violated with D even when they hold with the true but unobserved D*. We propose a two-step estimator to correct for this problem using a flexible parametric specification which allows for considerable heterogeneity in treatment effects. The solution uses a single exclusion restriction embedded in a partial observability probit to point-identify the true ATT. Subsequently, we derive the asymptotic properties of this estimator in panel and repeated cross-section settings. Finally, we apply this method to a large scale in-kind transfer program in India which is known to suffer from targeting errors.