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LATE with Missing or Mismeasured Treatment

Author

Listed:
  • Rossella Calvi

    (Rice University)

  • Arthur Lewbel

    (Boston College)

  • Denni Tommasi

    (Monash University)

Abstract

We provide a new estimator, MR-LATE, that consistently estimates local average treatment effects when treatment is missing for some observations, not at random. If instead treatment is mismeasured for some observations, MR-LATE usually has less bias than the standard LATE estimator. We discuss potential applications where an endogenous binary treatment may be unobserved or mismeasured. We apply MR-LATE to study the impact of women’s control over household resources on health outcomes in Indian families. This application illustrates the use of MR-LATE when treatment is estimated rather than observed. In these situations, treatment mismeasurement may arise from model misspecification and estimation errors.

Suggested Citation

  • Rossella Calvi & Arthur Lewbel & Denni Tommasi, 2018. "LATE with Missing or Mismeasured Treatment," Boston College Working Papers in Economics 959, Boston College Department of Economics, revised 15 Aug 2021.
    • Handle: RePEc:boc:bocoec:959
    Note: previously circulated as "Women’s Empowerment and Family Health: Estimating LATE with Mismeasured Treatment"
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    Cited by:

    1. José L. Casco, 2024. "Intra-household resource shares under poverty transfers: evidence from Ecuador," Review of Economics of the Household, Springer, vol. 22(3), pages 1243-1274, September.
    2. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    3. 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.
    4. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    5. Hiller, Victor & Touré, Nouhoum, 2021. "Endogenous gender power: The two facets of empowerment," Journal of Development Economics, Elsevier, vol. 149(C).
    6. Zhao, Chuanmin & Qu, Xi, 2024. "Place-based policies, rural employment, and intra-household resources allocation: Evidence from China’s economic zones," Journal of Development Economics, Elsevier, vol. 167(C).
    7. Sung Jae Jun & Sokbae Lee, 2023. "Identifying the Effect of Persuasion," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 2032-2058.
    8. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    9. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    10. Klein, Matthew J. & Barham, Bradford L., 2018. "Point Estimates of Household Bargaining Power Using Outside Options," Staff Paper Series 590, University of Wisconsin, Agricultural and Applied Economics.
    11. 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.
    12. Akanksha Negi & Digvijay S. Negi, 2024. "Difference-in-Differences with a Misclassified Treatment," Working Papers 121, Ashoka University, Department of Economics.
    13. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    14. 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.
    15. Lewbel, Arthur & Lin, Xirong, 2022. "Identification of semiparametric model coefficients, with an application to collective households," Journal of Econometrics, Elsevier, vol. 226(2), pages 205-223.
    16. Lina Zhang, 2020. "Spillovers of Program Benefits with Missing Network Links," Papers 2009.09614, arXiv.org, revised Aug 2024.

    More about this item

    Keywords

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    JEL classification:

    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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