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Small area estimation of the Gini concentration coefficient

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  • Fabrizi, Enrico
  • Trivisano, Carlo

Abstract

The Gini coefficient is a popular concentration measure often used in the analysis of economic inequality. Estimates of this index for small regions may be useful to properly represent inequalities within local communities. However, the small area estimation for the Gini coefficient has not been thoroughly investigated. A method based on area level models, thereby avoiding the assumption of the availability of Census data at the micro level, is proposed. A modified design based estimator for the coefficient with reduced small sample bias is suggested as input for the small area model, while a hierarchical Beta mixed regression model is introduced to combine survey data and auxiliary information. The methodology is illustrated by means of an example based on Italian data from the European Union Survey on Income and Living Conditions.

Suggested Citation

  • Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    • Handle: RePEc:eee:csdana:v:99:y:2016:i:c:p:223-234
    DOI: 10.1016/j.csda.2016.01.010
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    4. Francesco Giovinazzi & Daniela Cocchi, 2022. "Social Integration of Second Generation Students in the Italian School System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 287-307, February.
    5. Natalia Rojas‐Perilla & Sören Pannier & Timo Schmid & Nikos Tzavidis, 2020. "Data‐driven transformations in small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 121-148, January.
    6. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    7. Rojas-Perilla, Natalia & Pannier, Sören & Schmid, Timo & Tzavidis, Nikos, 2017. "Data-driven transformations in small area estimation," Discussion Papers 2017/30, Free University Berlin, School of Business & Economics.
    8. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    9. Natascha Hainbach & Christoph Halbmeier & Timo Schmid & Carsten Schröder, 2019. "A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)," SOEPpapers on Multidisciplinary Panel Data Research 1055, DIW Berlin, The German Socio-Economic Panel (SOEP).
    10. Paul Walter & Marcus Groß & Timo Schmid & Nikos Tzavidis, 2021. "Domain prediction with grouped income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1501-1523, October.
    11. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
    12. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2020. "A functional approach to small area estimation of the relative median poverty gap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1273-1291, June.
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