Determinants of Inequality in Selected SEE Countries: Results from Shapley Value Decompositions
By: Leitner, Sebastian.
Contributor(s): Stehrer, Robert.
Material type: BookSeries: wiiw Balkan Observatory Working Papers: 84Publisher: Wien : Wiener Institut für Internationale Wirtschaftsvergleiche (wiiw), 2009Subject(s): Inequality decomposition | Gini | Shapley value | Western Balkan countriesCountries covered: SEEClassification: C20 | D63 Online resources: Click here to access online Summary: In this paper we provide a comparative analysis of inequality in household consumption per capita in four South-Eastern European countries, Albania, Bosnia & Herzegovina, Bulgaria, and Serbia. The analysis is based on a largely consistent dataset derived from the World Bank’s Living Standards Measurement Study (LSMS) providing data for at least two years for each of these countries and a comparable set of variables. We apply inequality decomposition methods based on regression analysis and variants of the Shapley value approach. We also present results from related methods like a decomposition of the explained variance using different approaches for comparisons. The results suggest that three groups of variables are particularly important for explaining patterns of inequality; these are socio-demographic variables, employment status and education. Regional aspects and nationality or ethnicity plays a less important role though there are some country differences.Item type | Current library | Call number | Status | Date due | Barcode | |
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Paper | WIIW Library | 84 (Browse shelf(Opens below)) | Available | 1000010003209 |
In this paper we provide a comparative analysis of inequality in household consumption per capita in four South-Eastern European countries, Albania, Bosnia & Herzegovina, Bulgaria, and Serbia. The analysis is based on a largely consistent dataset derived from the World Bank’s Living Standards Measurement Study (LSMS) providing data for at least two years for each of these countries and a comparable set of variables. We apply inequality decomposition methods based on regression analysis and variants of the Shapley value approach. We also present results from related methods like a decomposition of the explained variance using different approaches for comparisons. The results suggest that three groups of variables are particularly important for explaining patterns of inequality; these are socio-demographic variables, employment status and education. Regional aspects and nationality or ethnicity plays a less important role though there are some country differences.