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LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India

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  • Denni Tommasi
  • Arthur Lewbel
  • Rossella Calvi

Abstract

We show that a local average treatment effect (LATE) can sometimes be point identified and consistently estimated when treatment is mismeasured, or when treatment is estimated using a possibly misspecified structural model. Our associated estimator, which we call Mismeasurement Robust LATE (MR-LATE), is based on differencing two mismeasures of treatment. In our empirical application, treatment is women’s empowerment: whether a wife has significant control of household resources. Due to measurement difficulties and sharing of goods within a household, this treatment cannot be directly observed without error, and so must be estimated. Our outcomes are health indicators of family members. We first estimate a structural model to obtain the otherwise unobserved treatment indicator. Then, using changes in inheritance laws in India as an instrument, we apply our new MR-LATE estimator. We find that women’s empowerment substantially decreases their probability of being anemic or underweight, and children’s likelihood to suffer from cough, fever or diarrhea. We find no significant positive or negative effects on men’s health.

Suggested Citation

  • Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/251754
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    3. Haider, Steven J. & Stephens Jr., Melvin, 2020. "Correcting for Misclassied Binary Regressors Using Instrumental Variables," IZA Discussion Papers 13593, Institute of Labor Economics (IZA).
    4. Kasahara, Hiroyuki & Shimotsu, Katsumi, 2022. "Identification Of Regression Models With A Misclassified And Endogenous Binary Regressor," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1117-1139, December.
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    6. Calvi, Rossella & Keskar, Ajinkya, 2021. "'Til Dowry Do Us Part: Bargaining and Violence in Indian Families," CEPR Discussion Papers 15696, C.E.P.R. Discussion Papers.
    7. Orville Mondal & Rui Wang, 2024. "Partial Identification of Binary Choice Models with Misreported Outcomes," Papers 2401.17137, arXiv.org.
    8. Sofia Amaral, 2015. "Do Improved Property Rights Decrease Violence Against Women in India?," Discussion Papers 15-10, Department of Economics, University of Birmingham.
    9. Tommasi, Denni, 2019. "Control of resources, bargaining power and the demand of food: Evidence from PROGRESA," Journal of Economic Behavior & Organization, Elsevier, vol. 161(C), pages 265-286.
    10. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
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    More about this item

    Keywords

    causality; LATE; structural model; collective model; resource shares; bargaining power; 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

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