Drivers of Inequality and Poverty in the CEE and other EU Member States
By: Leitner, Sebastian.
Contributor(s): Stehrer, Robert
.
Material type: 
BookSeries: wiiw Research Reports: 398Publisher: Wien : Wiener Institut für Internationale Wirtschaftsvergleiche (wiiw), 2014Description: 31 S., 8 Tables and 8 Figures, 30cm.Subject(s): inequality decomposition | multidimensional inequality | povertyCountries covered: European Unionwiiw Research Areas: Labour, Migration and Income DistributionClassification: A13 | D31 | I32 Online resources: Click here to access online Summary: Inequality is a multidimensional phenomenon though it is often discussed along a single dimension such as income. This is also the case for the various decomposition approaches of inequality indices by recipients or income sources. In this paper we study one- and multidimensional indices on inequality on data for CEE EU Member States in comparison to other EU countries including four dimensions in our measure of multidimensional inequality: income, health, education, and housing, and apply various decomposition methods to these one- and multidimensional indices and also to a poverty index. In doing so, we apply standard decomposition techniques to the Mean logarithmic deviation index (I₀) and decompositions based on regression analysis in conjunction with the Shapley value approach to Gini indices.
| Item type | Current library | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|
| Paper | WIIW Library | 5.600/398 (Browse shelf(Opens below)) | Available | 1000010003431 |
Inequality is a multidimensional phenomenon though it is often discussed along a single dimension such as income. This is also the case for the various decomposition approaches of inequality indices by recipients or income sources. In this paper we study one- and multidimensional indices on inequality on data for CEE EU Member States in comparison to other EU countries including four dimensions in our measure of multidimensional inequality: income, health, education, and housing, and apply various decomposition methods to these one- and multidimensional indices and also to a poverty index. In doing so, we apply standard decomposition techniques to the Mean logarithmic deviation index (I₀) and decompositions based on regression analysis in conjunction with the Shapley value approach to Gini indices.
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