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<p>Research on dissimilarity indexes has received limited attention in the Small Area Estimation literature. Motivated by the high variability of direct estimators, this paper proposes a new statistical methodology for estimating Duncan Segregation Indexes in small areas. Empirical best, simplified, and plug-in predictors are developed based on a unit-level logit mixed model. The mean squared error of the predictors is estimated using parametric bootstrap procedures, with and without bias correction. Simulation experiments are conducted to evaluate the additional variability arising from the estimation of auxiliary information and to assess the performance of the proposed predictors and their mean squared error estimators. The results are compared with those obtained from direct estimators and from models fitted to area-level data. The methodology is applied to the Spanish Labour Force Survey to assess sex segregation in the workplace.</p>

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Empirical best prediction of segregation indexes based on unit-level models

  • 期刊论文

  • 发表日期：

    2026年6月28日

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  • María Bugallo,
  • María José Lombardía,
  • Domingo Morales

摘要

Research on dissimilarity indexes has received limited attention in the Small Area Estimation literature. Motivated by the high variability of direct estimators, this paper proposes a new statistical methodology for estimating Duncan Segregation Indexes in small areas. Empirical best, simplified, and plug-in predictors are developed based on a unit-level logit mixed model. The mean squared error of the predictors is estimated using parametric bootstrap procedures, with and without bias correction. Simulation experiments are conducted to evaluate the additional variability arising from the estimation of auxiliary information and to assess the performance of the proposed predictors and their mean squared error estimators. The results are compared with those obtained from direct estimators and from models fitted to area-level data. The methodology is applied to the Spanish Labour Force Survey to assess sex segregation in the workplace.