Inequality Measures between Income Distributions with Applications

Econometrica ◽  
1980 ◽  
Vol 48 (7) ◽  
pp. 1791 ◽  
Author(s):  
Camilo Dagum
Keyword(s):  
Inequality Measures ◽  
Income Distributions

Entropy Based European Income Distributions and Inequality Measures

2017 ◽  
Author(s):  
Sofia Berto Villas-Boas ◽  
Quizi Fu ◽  
George Judge
Keyword(s):  
Inequality Measures ◽  
Income Distributions

scholarly journals Income Inequality in Pakistan: An Analysis of Existing Evidence

1984 ◽  
Vol 23 (2-3) ◽  
pp. 365-379 ◽  
Author(s):  
Zafar Mahmood
Keyword(s):  
Standard Deviation ◽  
Income Distribution ◽  
Gini Coefficient ◽  
Coefficient Of Variation ◽  
Single Measure ◽  
Lorenz Curves ◽  
Inequality Measures ◽  
Income Distributions ◽  
Income Inequalities ◽  
Rank Distributions

To study the consequences of an economic change on income distribution we rank distributions of income at different points in time and quantify the degree of income inequalities. Changes in income distribution can be ascertained either through drawing the Lorenz curves or through estimating different inequality indices, such as Gini Coefficient, coefficient of variation, standard deviation of logs of in• comes, Theil's Index and Atkinson's Index. Ranking the distributions of income through Lorenz curves is, of course, possible only as long as they do not intersect. Moreover, when Lorenz curves do not intersect each other, all inequality measures rank income distributions uniformly. However, if the Lorenz curves do intersect each other. different inequality measures may rank income distributions differently and thus the direction of change cannot be determined unambiguously. For this reason , the use of a single measure would be misleading. Accordingly , the use of a 'package' of inequality measures becomes essential.

scholarly journals Inequality Measures: The Kolkata Index in Comparison With Other Measures

2020 ◽  
Vol 8 ◽  
Author(s):  
Suchismita Banerjee ◽  
Bikas K. Chakrabarti ◽  
Manipushpak Mitra ◽  
Suresh Mutuswami
Keyword(s):  
Gini Coefficient ◽  
Gini Index ◽  
Empirical Studies ◽  
Nonlinear Function ◽  
Hirsch Index ◽  
Inequality Measures ◽  
Income Distributions ◽  
The Gini Coefficient ◽  
The Rich ◽  
Total Wealth

We provide a survey of the Kolkata index of social inequality, focusing in particular on income inequality. Based on the observation that inequality functions (such as the Lorenz function), giving the measures of income or wealth against that of the population, to be generally nonlinear, we show that the fixed point (like Kolkata index k) of such a nonlinear function (or related, like the complementary Lorenz function) offer better measure of inequality than the average quantities (like Gini index). Indeed the Kolkata index can be viewed as a generalized Hirsch index for a normalized inequality function and gives the fraction k of the total wealth possessed by the rich 1−k fraction of the population. We analyze the structures of the inequality indices for both continuous and discrete income distributions. We also compare the Kolkata index to some other measures like the Gini coefficient and the Pietra index. Lastly, we provide some empirical studies which illustrate the differences between the Kolkata index and the Gini coefficient.

scholarly journals Entropy Based China Income Distributions and Inequality Measures

2018 ◽  
Author(s):  
Sofia Berto Villas-Boas ◽  
Quizi Fu ◽  
George Judge
Keyword(s):  
Inequality Measures ◽  
Income Distributions

Entropy based European income distributions and inequality measures

2019 ◽  
Vol 514 ◽  
pp. 686-698 ◽  
Author(s):  
Sofia B. Villas-Boas ◽  
Qiuzi Fu ◽  
George Judge
Keyword(s):  
Inequality Measures ◽  
Income Distributions

scholarly journals Nierówności dochodowe gospodarstw domowych rolników na tle innych grup społeczno-ekonomicznych w Polsce w latach 2006-2014

2017 ◽  
Vol 17(32) (3) ◽  
pp. 166-176 ◽  
Author(s):  
Alina Jędrzejczak ◽  
Dorota Pekasiewicz
Keyword(s):  
Economic Policy ◽  
Point Of View ◽  
Inequality Measures ◽  
Income Distributions ◽  
Geographical Regions ◽  
Family Types ◽  
Central Statistical ◽  
Central Statistical Office ◽  
Dispersion Ratio ◽  
Over Time

Income distribution analyses can be conducted from the point of view of the comparisons between different geographical regions, family types or socio-economic groups as well as to assess the effects of an economic policy over time. In the study, the results of a research on income distributions in Poland by socio-economic group, with a special attention paid to the families of farmers in the years 2006-2014, are presented. They allowed to formulate several conclusions concerning the changes of income inequality, poverty and wealth for farmers as compared with different household groups distinguished by the Central Statistical Office of Poland. In the analysis we utilized Gini and Zenga inequality measures, quintile dispersion ratio, extremal groups dispersion ratio and the coefficient of maximum equalisation.

scholarly journals Entropy-based China income distributions and inequality measures

2019 ◽  
Vol 12 (3) ◽  
pp. 352-368
Author(s):  
Qiuzi Fu ◽  
Sofia B. Villas-Boas ◽  
George Judge
Keyword(s):  
Inequality Measures ◽  
Income Distributions

On Sharpness of Bounds for Some Inequality Measures

2014 ◽  
Author(s):  
DDsirr KKdagni
Keyword(s):  
Inequality Measures
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