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Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy

Author

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  • Caterina Giusti

    (University of Pisa)

  • Lucio Masserini

    (University of Pisa)

  • Monica Pratesi

    (University of Pisa)

Abstract

The aim of this paper is to highlight some key issues and challenges in the analysis of poverty at the local level using survey data. In the last years there was a worldwide increase in the demand for poverty and living conditions estimates at the local level, since these quantities can help in planning local policies aimed at decreasing poverty and social exclusion. In many countries various sample surveys on income and living conditions are currently conducted, but their sample size is not enough to obtain reliable estimates at local level. When this happens, small area estimation (SAE) methods can be used. In this paper, a SAE model is used to compute the mean household equivalised income and the head count ratio for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. The caveats of the analysis of poverty at the local level using small area methods are many, and some are still not so well explored in the literature, starting from the definition of the target indicators to the relevant dimensions of their measurement. We suggest in this paper that together with the universally recognized multidimensional, longitudinal and local dimensions of poverty, a new dimension must be considered: the price dimension, which should take into account local purchasing power parities to correctly compare the poverty indicators based on income measures.

Suggested Citation

  • Caterina Giusti & Lucio Masserini & Monica Pratesi, 2017. "Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 235-254, March.
    • Handle: RePEc:spr:soinre:v:131:y:2017:i:1:d:10.1007_s11205-015-1193-1
    DOI: 10.1007/s11205-015-1193-1
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    References listed on IDEAS

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    1. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
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    8. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
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    10. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    11. Enrico Fabrizi & Caterina Giusti & Nicola Salvati & Nikos Tzavidis, 2014. "Mapping average equivalized income using robust small area methods," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 685-701, August.
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    Cited by:

    1. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.
    2. Petr Jansk & Marek ediv, 2018. "How Do Regional Price Levels Affect Income Inequality? Household-level Evidence From 21 Countries," LIS Working papers 752, LIS Cross-National Data Center in Luxembourg.
    3. 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.
    4. Benedetti, Ilaria & Crescenzi, Federico, 2023. "The role of income poverty and inequality indicators at regional level: An evaluation for Italy and Germany," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    5. Simona Ballabio & Marianna Filandri & Lia Pacelli & Flavio Verrecchia, 2022. "Poverty of Young People: Context and Household Effects in North-Western Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 819-842, June.
    6. Monica Pratesi & Luciana Quattrociocchi & Gaia Bertarelli & Alessandro Gemignani & Caterina Giusti, 2021. "Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 563-586, August.
    7. Luca Secondi, 2021. "Estimating Household Consumption Expenditure at Local Level In Italy: The Potential of the Cokriging Spatial Predictor," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 651-674, January.

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