4 research outputs found
Difference-in-Differences with a Misclassified Treatment
This paper studies identification and estimation of the average treatment
effect on the treated (ATT) in difference-in-difference (DID) designs when the
variable that classifies individuals into treatment and control groups
(treatment status, D) is endogenously misclassified. 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 solution to correct for endogenous
one-sided misclassification in the context of a parametric DID regression which
allows for considerable heterogeneity in treatment effects and establish its
asymptotic properties in panel and repeated cross section settings.
Furthermore, we illustrate the method by using it to estimate the insurance
impact of a large-scale in-kind food transfer program in India which is known
to suffer from large targeting errors