 Optimization of Gini Coefficient Affected by Imperfect Input DataEuropean Journal of Business Science and Technology, 2019, vol. 5, issue 1, 21-29 Abstract: Most indicators used for determining the distributional effects of taxes as well as the inequality in the income distribution are based on the Gini coefficient and the Lorenz curve to a substantial extent, although the potential application of the Gini coefficient itself is much larger. However, the Lorenz curve and in particular the Gini coefficient need not present precise information on income or the distribution of wealth in a society. The Gini coefficient values may be affected by the form of the input data. We have ascertained that the level of Gini coefficient distortion depends on the number of households included in the research given that the income distribution in the sample is unequal. In addition, we define the form of the Gini coefficient in light of the form of the input data. Keywords: Gini coefficient; Lorenz curve; distribution; income; taxation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:men:journl:v:5:y:2019:i:1:p:21-29 DOI: 10.11118/ejobsat.v5i1.160 Access Statistics for this article European Journal of Business Science and Technology is currently edited by Svatopluk Kapounek More articles in European Journal of Business Science and Technology from Mendel University in Brno, Faculty of Business and Economics Contact information at EDIRC. |
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