Bounding Program Benefits When Participation Is Misreported

Denni Tommasi and Lina Zhang

No 13430, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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)
Pages: 90 pages
Date: 2020-06
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (8)

Published - published in: Journal of Econometrics 2024, 238, (1), 105556

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Related works:
Journal Article: Bounding program benefits when participation is misreported (2024)
Working Paper: Bounding Program Benefits When Participation is Misreported (2020)
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