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Measuring segregation on small units: A partial identification analysis

Author

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  • Xavier D'Haultfœuille
  • Roland Rathelot

Abstract

We consider the issue of measuring segregation in a population of small units, considering establishments in our application. Each establishment may have a different probability of hiring an individual from the minority group. We define segregation indices as inequality indices on these unobserved, random probabilities. Because these probabilities are measured with error by proportions, standard estimators are inconsistent. We model this problem as a nonparametric binomial mixture. Under this testable assumption and conditions satisfied by standard segregation indices, such indices are partially identified and sharp bounds can be easily obtained by an optimization over a low dimensional space. We also develop bootstrap confidence intervals and a test of the binomial mixture model. Finally, we apply our method to measure the segregation of foreigners in small French firms.

Suggested Citation

  • Xavier D'Haultfœuille & Roland Rathelot, 2017. "Measuring segregation on small units: A partial identification analysis," Quantitative Economics, Econometric Society, vol. 8(1), pages 39-73, March.
    • Handle: RePEc:wly:quante:v:8:y:2017:i:1:p:39-73
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    Cited by:

    1. David Card & Fabrizio Colella & Rafael Lalive, 2025. "Gender Preferences in Job Vacancies and Workplace Gender Diversity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(4), pages 2437-2471.
    2. Pierre Courtioux & Tristan-Pierre Maury & Johan Seux, 2023. "The Geographies of Segregation in French Universities from 2006 to 2016," Post-Print halshs-04118941, HAL.
    3. Renan Xavier Cortes & Sergio Rey & Elijah Knaap & Levi John Wolf, 2020. "An open-source framework for non-spatial and spatial segregation measures: the PySAL segregation module," Journal of Computational Social Science, Springer, vol. 3(1), pages 135-166, April.
    4. Laurent Davezies & Xavier D'Haultf{oe}uille & Louise Laage, 2021. "Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models," Papers 2105.00879, arXiv.org, revised Dec 2024.
    5. Bo E Honoré & Áureo de Paula, 2021. "Identification in simple binary outcome panel data models," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 78-93.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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