Bounding Program Benefits When Participation is Misreported
Denni Tommasi and
Lina Zhang ()
No 24/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
In empirical research, measuring correctly the benefits of welfare interventions is incredibly relevant for policymakers as well as academic researchers. Unfortunately, the endogenous program participation is often misreported in survey data and standard instrumental variable techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we focus on the weighted average of local average treatment effects (LATE) and (i) derive a simple relationship between the causal and the identifiable parameter that can be recovered from the observed data, (ii) provide an instrumental variable method to partially identify the heterogeneous treatment effects, (iii) formalize a strategy to combine administrative data on the misclassification probabilities of treated individuals to further tighten the bounds. Finally, we use our method to reassess the benefits of participating to the 401(k) pension plan on savings.
Keywords: heterogenous treatment effects; causality; binary treatment; endogenous measurement error; discrete or multiple instruments; weighted average of LATEs; endogeneity; program evaluation (search for similar items in EconPapers)
JEL-codes: C14 C21 C26 C35 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
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Working Paper: Bounding Program Benefits When Participation Is Misreported (2020)
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