Identifying Program Benefits When Participation Is Misreported

Denni Tommasi and Lina Zhang ()
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Lina Zhang: University of Amsterdam

No 15427, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In cases of non-compliance with a prescribed treatment, estimates of causal effects typically rely on instrumental variables. However, when participation is also misreported, this approach can be severely biased. We provide an instrumental variable method that researchers can use to identify the true heterogeneous treatment effects in data that include both non-compliance and misclassification of treatment status. Our method can be used regardless of whether the treatment is misclassified because it is missing at random, missing not at random, or was generally mismeasured. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.

Keywords: weighted average of LATEs; non-differential misclassification; causality; treatment effect; endogeneity; program evaluation (search for similar items in EconPapers)
JEL-codes: C14 C21 C26 C35 C51 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2022-07
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

Published - published online in: Journal of Applied Econometrics, 29 June 2024

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