2015
DOI: 10.1007/s11205-015-1193-1
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Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy

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 r… Show more

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Cited by 18 publications
(16 citation statements)
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References 23 publications
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“…Multivariate models with such densities have not been studied in SAE and are a topic for future work. Despite the issues regarding level 1 residuals, our small area estimates are in line with previous studies considering similar economic well‐being indicators (Moretti et al , ; Marchetti et al , ; Giusti et al , ).…”
Section: Applicationsupporting
confidence: 91%
“…Multivariate models with such densities have not been studied in SAE and are a topic for future work. Despite the issues regarding level 1 residuals, our small area estimates are in line with previous studies considering similar economic well‐being indicators (Moretti et al , ; Marchetti et al , ; Giusti et al , ).…”
Section: Applicationsupporting
confidence: 91%
“…The lowest point estimates of the latent economic well-being indicator are estimated for Carrara and Seravezza municipalities and the highest values for Firenze and Arezzo municipalities. Our results based on the EBLUPs of the factor scores in Figure 7 are more comparable with other SAE studies on welfare and poverty in Tuscany (Giusti et al 2015;Marchetti, Tzavidis, and Pratesi 2012) compared to the averages of a dashboard of EBLUPs in Figure Figure 8. Latent economic well-being indicator based on simple and weighted averages of single empirical best linear unbiased predictions (1 ¼ first quartile, 2 ¼ second quartile, 3 ¼ third quartile, 4 ¼ fourth quartile).…”
Section: Small Area Estimatessupporting
confidence: 85%
“…These EU-SILC data contain a sample of 1,448 households for Tuscany. EU-SILC is designed to deliver estimates at the national and also regional (NUTS-2) level (Giusti, Masserini, and Pratesi 2015). Therefore, this situation is typical of most survey situations in that EU-SILC cannot be used to derive usable income estimates at smaller subregional geographies such as municipalities due to low or zero survey sample sizes.…”
Section: Application To Small Area Income Estimation In Italian Municmentioning
confidence: 99%