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Small area estimation of equivalized income for local labour systems in Italy via M-quantile area-level models

Author

Listed:
  • Stefano Marchetti

    (University of Pisa)

  • Nicola Salvati

    (University of Pisa)

  • Enrico Fabrizi

    (Università Cattolica del S. Cuore)

  • Nikos Tzavidis

    (University of Southampton, Highfield Campus)

Abstract

Small area estimators based on area-level random effect models are popular. When the normality assumption fails for random effects, the properties of the estimators deteriorate. In these cases, robust versions of small area predictors are useful. As an alternative to robust empirical best linear unbiased predictors, we propose an extension of M-quantile small-area methods to area-level models. We apply our methodology to estimate the mean equivalized income for local labour systems in Italy via data from the EU-SILC survey. The advantages of the proposed technique are demonstrated in the application and in a simulation exercise.

Suggested Citation

  • Stefano Marchetti & Nicola Salvati & Enrico Fabrizi & Nikos Tzavidis, 2025. "Small area estimation of equivalized income for local labour systems in Italy via M-quantile area-level models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(3), pages 449-470, July.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:3:d:10.1007_s10260-025-00791-3
    DOI: 10.1007/s10260-025-00791-3
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    References listed on IDEAS

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    1. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2018. "Bayesian small area estimation for skewed business survey variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 861-879, August.
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    3. Annamaria Bianchi & Enrico Fabrizi & Nicola Salvati & Nikos Tzavidis, 2018. "Estimation and Testing in M‐quantile Regression with Applications to Small Area Estimation," International Statistical Review, International Statistical Institute, vol. 86(3), pages 541-570, December.
    4. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
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    6. Xueying Tang & Malay Ghosh & Neung Soo Ha & Joseph Sedransk, 2018. "Modeling Random Effects Using Global–Local Shrinkage Priors in Small Area Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1476-1489, October.
    7. Ray Chambers & Hukum Chandra & Nicola Salvati & Nikos Tzavidis, 2014. "Outlier robust small area estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 47-69, January.
    8. Martin Biewen & Stephen P. Jenkins, 2006. "Variance Estimation for Generalized Entropy and Atkinson Inequality Indices: the Complex Survey Data Case," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(3), pages 371-383, June.
    9. Tim Goedemé, 2013. "How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 89-110, January.
    10. Annamaria Bianchi & Nicola Salvati, 2015. "Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(11), pages 2416-2429, June.
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