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Sharp Bounds in the Binary Roy Model

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  • Marc Henry
  • Ismael Mourifie

Abstract

We derive the empirical content of an instrumental variables model of sectoral choice with discrete outcomes. The partial identification results extend existing work on sharp bounds in binary choice threshold crossing models in allowing sector specific unobserved heterogeneity. Assumptions on selection include the simple, extended and generalized Roy models. The derived bounds are nonparametric intersection bounds and are simple enough to lend themselves to existing inference methods. Identification implications of exclusion restrictions are also derived. The derived bounds are applied to the analysis of the effect of Swan-Ganz catheterization and the robustness of previous findings to the introduction of procedure-specific unobserved heterogeneity is examined.

Suggested Citation

  • Marc Henry & Ismael Mourifie, 2014. "Sharp Bounds in the Binary Roy Model," Working Papers tecipa-506, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-506
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    References listed on IDEAS

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    10. Qi Li & Jeffrey S. Racine & Jeffrey M. Wooldridge, 2008. "Estimating Average Treatment Effects with Continuous and Discrete Covariates: The Case of Swan-Ganz Catheterization," American Economic Review, American Economic Association, vol. 98(2), pages 357-362, May.
    11. Bayer, Patrick & Khan, Shakeeb & Timmins, Christopher, 2011. "Nonparametric Identification and Estimation in a Roy Model With Common Nonpecuniary Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 201-215.
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    21. Jay Bhattacharya & Azeem M. Shaikh & Edward Vytlacil, 2008. "Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization," American Economic Review, American Economic Association, vol. 98(2), pages 351-356, May.
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    Cited by:

    1. Ismael Mourifié & Yuanyuan Wan, 2017. "Testing Local Average Treatment Effect Assumptions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 305-313, May.
    2. Laurent Gobillon & Carine Milcent, 2016. "Evaluating the Effect of Ownership Status on Hospital Quality: The Key Role of Innovative Procedures," Annals of Economics and Statistics, GENES, issue 121-122, pages 161-186.
    3. Andrews, Donald W.K. & Shi, Xiaoxia, 2014. "Nonparametric inference based on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 179(1), pages 31-45.
    4. Kamat, Vishal, 2019. "Identification with Latent Choice Sets," TSE Working Papers 19-1031, Toulouse School of Economics (TSE).
    5. Vishal Kamat, 2017. "Identifying the Effects of a Program Offer with an Application to Head Start," Papers 1711.02048, arXiv.org, revised Aug 2023.
    6. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.
    7. Lee, Ji Hyung & Park, Byoung G., 2023. "Nonparametric identification and estimation of the extended Roy model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1087-1113.

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    More about this item

    Keywords

    sectorial choice; treatment specific unobservables; partial identification; intersection bounds;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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