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Sharp bounds for the Roy model

Author

Listed:
  • Ismael Mourifie
  • Marc Henry
  • Romuald Meango

Abstract

We analyze the empirical content of the Roy model, stripped down to its essential features, namely sector specific unobserved heterogeneity and self-selection on the basis of potential outcomes. We characterize sharp bounds on the joint distribution of potential outcomes and the identifying power of exclusion restrictions. The latter include variables that affect market conditions only in one sector and variables that affect sector selection only. Special emphasis is put on the case of binary outcomes, which has received little attention in the literature to date. For richer sets of outcomes, we emphasize the distinction between pointwise sharp bounds and functional sharp bounds, and its importance, when constructing sharp bounds on functional features, such as inequality measures. We analyze a Roy model of college major choice in Canada within this framework, and we take a new look at the under-representation of women in Science, Technology, Engineering or Mathematics (STEM).

Suggested Citation

  • Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds for the Roy model," Papers 1709.09284, arXiv.org.
  • Handle: RePEc:arx:papers:1709.09284
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    File URL: http://arxiv.org/pdf/1709.09284
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    References listed on IDEAS

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    11. Andrew Chesher & Adam M. Rosen & Konrad Smolinski, 2013. "An instrumental variable model of multiple discrete choice," Quantitative Economics, Econometric Society, vol. 4(2), pages 157-196, July.
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    Cited by:

    1. Vishal Kamat, 2018. "On the Identifying Content of Instrument Monotonicity," Papers 1807.01661, arXiv.org, revised Oct 2019.
    2. Andrew Chesher & Adam Rosen, 2018. "Generalized instrumental variable models, methods, and applications," CeMMAP working papers CWP43/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Vishal Kamat, 2017. "Identification with Latent Choice Sets," Papers 1711.02048, arXiv.org, revised Aug 2019.
    4. Casey B. Mulligan, 2018. "Quantifier Elimination for Deduction in Econometrics," NBER Working Papers 24601, National Bureau of Economic Research, Inc.

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