IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/959.html
   My bibliography  Save this paper

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"
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/EC-P/wp959.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    3. 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.
    4. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    5. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    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. 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.
    8. 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.
    9. Lina Zhang, 2020. "Spillovers of Program Benefits with Missing Network Links," Papers 2009.09614, arXiv.org, revised Apr 2023.
    10. Hiller, Victor & Touré, Nouhoum, 2021. "Endogenous gender power: The two facets of empowerment," Journal of Development Economics, Elsevier, vol. 149(C).

    More about this item

    Keywords

    LATE; missing treatment; measurement error; misclassification; collective model; resource shares; health;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:bocoec:959. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/debocus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.