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Sae estimation of related labor market indicators for different overlapping areas

Author

Listed:
  • Michele D’Alò

    (ISTAT: Istituto Nazionale di Statistica)

  • Danila Filipponi

    (ISTAT: Istituto Nazionale di Statistica)

  • Silvia Loriga

    (ISTAT: Istituto Nazionale di Statistica)

Abstract

The aim of this study is to provide a comprehensive description of the statistical methodology used to produce estimates for various labor market variables at both the City and FUA levels, along with an analysis of the results obtained. To achieve this goal, small area estimates were computed using a unit-level multivariate model. This model was specifically designed to enable coherent estimation of the variables of interest collected by the Labour Force Survey, exploiting information derived from administrative data and statistical Registers. The use of such administrative data at the unit-level represents a novel approach to estimation based on Italian Labour Force Survey data. The estimator used in this work is based on a multivariate model implemented through the Mind R package, which was developed by Istat. The method presented in this study represents an extended multivariate version of the conventional linear mixed model at the unit level. To ensure consistency across different domains, a single cross-classification model was employed, encompassing all relevant domains of interest. The outcomes of this analysis reveal significant improvements in efficiency compared to direct estimates. This is particularly noteworthy in the estimation of unemployed individuals (both total and by gender), where direct estimates are prone to relatively high sampling errors.

Suggested Citation

  • Michele D’Alò & Danila Filipponi & Silvia Loriga, 2024. "Sae estimation of related labor market indicators for different overlapping areas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1027-1049, September.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:4:d:10.1007_s10260-024-00753-1
    DOI: 10.1007/s10260-024-00753-1
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    References listed on IDEAS

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    1. M. Giovanna Ranalli & Giorgio E. Montanari & Cecilia Vicarelli, 2018. "Estimation of small area counts with the benchmarking property," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 349-378, December.
    2. Isabel Molina & Ayoub Saei & M. José Lombardía, 2007. "Small area estimates of labour force participation under a multinomial logit mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 975-1000, October.
    3. Esther López-Vizcaíno & María José Lombardía & Domingo Morales, 2015. "Small area estimation of labour force indicators under a multinomial model with correlated time and area effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 535-565, June.
    4. Ray Chambers & Nicola Salvati & Nikos Tzavidis, 2016. "Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 453-479, February.
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