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LATE With Missing or Mismeasured Treatment

Author

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  • Rossella Calvi
  • Arthur Lewbel
  • Denni Tommasi

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, then 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, 2022. "LATE With Missing or Mismeasured Treatment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1701-1717, October.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:4:p:1701-1717
    DOI: 10.1080/07350015.2021.1970573
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    Cited by:

    1. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    2. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    3. Hiller, Victor & Touré, Nouhoum, 2021. "Endogenous gender power: The two facets of empowerment," Journal of Development Economics, Elsevier, vol. 149(C).
    4. 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).
    5. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    6. 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.
    7. 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.
    8. Lina Zhang, 2020. "Spillovers of Program Benefits with Missing Network Links," Papers 2009.09614, arXiv.org, revised Aug 2024.
    9. 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.
    10. 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.
    11. 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.

    More about this item

    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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