Subgroup and Shapley Value Decompositions of Multidimensional Inequality – An Application to Southeast European Countries
By: Leitner, Sebastian.
Contributor(s): Stehrer, Robert
.
Material type: 
BookSeries: wiiw Working Papers: 74Publisher: Wien : Wiener Institut für Internationale Wirtschaftsvergleiche (wiiw), 2011Description: 37 S., 19 Tables and 1 Figure, 30cm.Subject(s): multidimensional inequality | inequality decomposition | Shapley valueCountries covered: Bulgaria | Romania | Serbiawiiw Research Areas: Labour, Migration and Income DistributionClassification: C20 | D63 Online resources: Click here to access online Summary: Inequality is a multidimensional phenomenon though it is often discussed along a single dimension like income. This is also the case for the various decomposition approaches of inequality indices. In this paper we study one- and multidimensional indices on inequality on data for three large Southeast European countries, Bulgaria, Romania and Serbia. We include four dimensions in our measure of multidimensional inequality: income, health, education and housing. We apply various decomposition methods to these one- and multidimensional indices. In doing so, we apply standard decomposition techniques of the mean logarithmic deviation index (I0) and decompositions based on regression analysis in conjunction with the Shapley value approach.
| Item type | Current library | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|
| Paper | WIIW Library | 5.700/74 (Browse shelf(Opens below)) | Available | 1000010001834 |
Inequality is a multidimensional phenomenon though it is often discussed along a single dimension like income. This is also the case for the various decomposition approaches of inequality indices. In this paper we study one- and multidimensional indices on inequality on data for three large Southeast European countries, Bulgaria, Romania and Serbia. We include four dimensions in our measure of multidimensional inequality: income, health, education and housing. We apply various decomposition methods to these one- and multidimensional indices. In doing so, we apply standard decomposition techniques of the mean logarithmic deviation index (I0) and decompositions based on regression analysis in conjunction with the Shapley value approach.
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