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Optimization of Gini Coefficient Affected by Imperfect Input Data

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  • Petr David

    (Mendel University in Brno, Czech Republic)

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.

Suggested Citation

  • Petr David, 2019. "Optimization of Gini Coefficient Affected by Imperfect Input Data," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 5(1), pages 21-29.
    • Handle: RePEc:men:journl:v:5:y:2019:i:1:p:21-29
    DOI: 10.11118/ejobsat.v5i1.160
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    References listed on IDEAS

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    More about this item

    Keywords

    Gini coefficient; Lorenz curve; distribution; income; taxation;
    All these keywords.

    JEL classification:

    • H22 - Public Economics - - Taxation, Subsidies, and Revenue - - - Incidence
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies

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