Small Area Estimation of Poverty Indicators

Monica Pratesi (), Caterina Giusti () and Stefano Marchetti ()
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Monica Pratesi: University of Pisa (Italy), Department of Statistics and Mathematics Applied to Economics
Caterina Giusti: University of Pisa (Italy), Department of Statistics and Mathematics Applied to Economics
Stefano Marchetti: University of Pisa (Italy), Department of Statistics and Mathematics Applied to Economics

A chapter in Survey Data Collection and Integration, 2013, pp 89-101 from Springer

Abstract: Abstract The estimation of poverty, inequality and life condition indicators all over the European Union has become one topic of primary interest. A very common target is the core set of indicators on poverty and social exclusion agreed by the Laeken European Council in December $$2001$$ and called Laeken indicators. They include measures of the incidence of poverty, such as the Head Count Ratio (also known as at-risk-of-poverty-rate) and of the intensity of poverty, as the Poverty Gap. Unfortunately, these indicators cannot be directly estimated from EU-SILC survey data when the objective is to investigate poverty at sub-regional level. As local sample sizes are small, the estimation must be done using the small area estimation approach. Limits and potentialities of the estimators of Laeken indicators obtained under EBLUP and M-quantile small area estimation approaches are discussed here, as well as their application to EU-SILC Italian data. The case study is limited to the estimation of poverty indicators for the Tuscany region. However, additional results are available and downloadable from the web site of the SAMPLE project, funded under the 7FP ( http://www.sample-project.eu ).

Keywords: Root Mean Square Error; Small Area Estimation; Poverty Indicator; Equivalised Household Income; Tuscany Region (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-21308-3_6

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