Abstract
We propose an extension of the univariate Lorenz curve and of the Gini coefficient to the multivariate case, i.e., to simultaneously measure inequality in more than one variable. Our extensions are based on copulas and measure inequality stemming from inequality in each single variable as well as inequality stemming from the dependence structure of the variables. We derive simple nonparametric estimators for both instruments and exemplary apply them to data of individual income and wealth for various countries.
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Data Availability
Python computer code for the paper is public available in the following repository: https://github.com/FabianKaechele/Multivariate_Extension_Lorenz_Gini.git Raw datasets analysed in Section ?? are available from (SOEP 2019) and (Luxembourg Wealth Study (LWS) Database 2020) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Information on how to obtain it and reproduce the analysis is available from the corresponding author on request.
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Grothe, O., Kächele, F. & Schmid, F. A multivariate extension of the Lorenz curve based on copulas and a related multivariate Gini coefficient. J Econ Inequal 20, 727–748 (2022). https://doi.org/10.1007/s10888-022-09533-x
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DOI: https://doi.org/10.1007/s10888-022-09533-x