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Bounding Program Benefits When Participation Is Misreported

Author

Listed:
  • Tommasi, Denni

    (University of Bologna)

  • Zhang, Lina

    (University of Amsterdam)

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.

Suggested Citation

  • Tommasi, Denni & Zhang, Lina, 2020. "Bounding Program Benefits When Participation Is Misreported," IZA Discussion Papers 13430, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp13430
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    Cited by:

    1. Akanksha Negi & Digvijay S. Negi, 2025. "Difference‐in‐Differences With a Misclassified Treatment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(4), pages 411-423, June.
    2. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    3. Denni Tommasi & Lina Zhang, 2024. "Identifying program benefits when participation is misreported," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1123-1148, September.
    4. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    5. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Local Average and Marginal Treatment Effects with a Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Sep 2024.
    6. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    7. Ha Trong Nguyen & Le, Huong Thu & Blyth, Christopher & Connelly, Luke & Mitrou, Francis, 2024. "Identifying the effects of health insurance coverage on health care use when coverage is misreported and endogenous," GLO Discussion Paper Series 1432, Global Labor Organization (GLO).
    8. Didier Nibbering & Matthijs Oosterveen, 2023. "Instrument-based estimation of full treatment effects with movers," Papers 2306.07018, arXiv.org.
    9. Evan S. Totty & Thor Watson, 2024. "Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let's Find Out!," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    10. Augustine Denteh & D'esir'e K'edagni, 2022. "Misclassification in Difference-in-differences Models," Papers 2207.11890, arXiv.org, revised Jul 2022.
    11. Lina Zhang & David T. Frazier & D.S. Poskitt & Xueyan Zhao, 2025. "Decomposing identification gains and evaluating instrument identification power for partially identified average treatment effects," Econometric Reviews, Taylor & Francis Journals, vol. 44(7), pages 915-938, August.
    12. Santiago Acerenza, 2024. "Partial Identification of Marginal Treatment Effects with Discrete Instruments and Misreported Treatment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 74-100, February.
    13. Vitor Possebom, 2021. "Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification," Papers 2106.00536, arXiv.org, revised Jul 2023.
    14. Akanksha Negi & Digvijay S. Negi, 2024. "Difference-in-Differences with a Misclassified Treatment," Working Papers 121, Ashoka University, Department of Economics.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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