Bounding Program Benefits when Participation is Misreported

Abstract: Instrumental variables are commonly used to estimate treatment effects in case of noncompliance. However, program participation is often misreported in survey data, and standard techniques are not sufficient to point-identify and consistently estimate the effects of interest. In this paper, we first derive a new link between the true and mismeasured effect, which is mediated by a parameter of the misclassification probabilities. Second, we provide an instrumental variable method to partially identify the heterogeneous treatment effects when noncompliance and the misreporting of treatment status are present. Third, we formalize a strategy to combine external information about misclassification probabilities of treatment status to tighten the bounds or obtain a point estimate. Finally, we use our new Stata command, ivbounds, and obtain two novel results of the benefits of participating in the 401(k) pension plan on savings. Our method has several applications. First, it can be used as the leading identification strategy in any setting where the practitioner knows that the endogenous binary treatment is not well measured. Second, it can be used as the leading robustness check in case misreporting is only suspected. Third, it can assess the sensitivity of program benefits under different assumptions of the misclassification probabilities.

If you want to partake, please contact Frank Kleibergen via f.r.kleibergen@uva.nl