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Measuring segregation on small units: A partial identification analysis

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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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    References listed on IDEAS

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    Cited by:

    1. 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.

    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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