Mis-measurement of inequality: a critical reflection and new insights

Clementi, Fabio; Gallegati, Mauro; Gianmoena, Lisa; Landini, Simone; Stiglitz, Joseph E.

doi:10.1007/s11403-019-00257-2

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…When applying the new concept to analyze party systems in 18 European countries (158 election cases), we found that most of them had a concentration of inequality close to the Pareto principle and a left-hand asymmetry PC, which distinguishes significantly the PC of party systems from the PC of income and urban settlement systems, which featured both righthand and left-hand asymmetry (Clementi et al, 2019;Grachev, 2022).…”

Section: Discussionmentioning

confidence: 99%

Analysis of party systems by measures of concentration of inequality and asymmetry of the Pareto curve

Grachev

2022

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Section: Discussionmentioning

confidence: 99%

Analysis of party systems by measures of concentration of inequality and asymmetry of the Pareto curve

Grachev

2022

Preprint

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“…These previous attempts are similar to the approach presented in this paper, but for the calculation of the novel measures the weights are attached directly to the n-1 distances between L F and the 45-degree line, not to individual incomes or their ranks. It has been noted before, that the most useful part of the Lorenz curve is its distance from the 45-degree line of perfect equality (Clementi et al, 2019).…”

Section: Inequality Measures With Different Focusmentioning

confidence: 99%

“…This is inconsistent with the notion of an inequality index that is sensitive to those distances that are of more social concern since distributional asymmetry is regularly driven by remarkably low or high incomes at the tails of the distribution. Several authors have suggested the use of indices that are sensitive to the asymmetry of the distribution (Bowden(2016), Clementi et al(2019)). I argue that measures of statistical dispersion that attach a higher weight to income differences at the tails of the distribution compared to the Gini can be just as useful to measure inequality more appropriately.…”

Section: Introductionmentioning

confidence: 99%

Unbottling the Gini: New Tools from an Old Concept

Schlemmer¹

2021

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The Gini index signals only the dispersion of the distribution and is not very sensitive to income differences at the tails of the distribution, where it would matter most. In the current work, novel inequality measures are proposed that address these limitations. In addition, two related measures of skewness are established. They all are based on a pair of functions that is obtained by attaching simple weights to the distances between the Lorenz curve and the 45-degree line, both in ascending and descending order, resulting in a pair of alternative inequality curves. The inequality measures derived from these two alternative inequality curves either complement the information about distributional dispersion measured by the Gini coefficient with information about distributional asymmetry, or are more sensitive to income differences at both tails of the distribution. The novel tools measure inequality appropriately and their Lorenz-based graphical representations give them intuitive appeal. Beyond socioeconomics, they can be applied in other disciplines of science.

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“…The main disadvantage of G is that it sums up PCs of different shapes in the same way (Voitchovsky, 2005;Osberg, 2017;Clementi et al, 2019).…”

Section: Gini Coefficientmentioning

confidence: 99%

Size Distribution of States, Counties, and Cities in the USA: New Inequality Form Information

Grachev

2020

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In this article, we propose a new approach for studying the patterns of size distribution in settlement systems, based on the analysis of the shape of the Pareto curve (PC). To study the shape of the PC, we used the Gini coefficient, the asymmetry coefficient, and, by analogy with the physics of phase transitions, critical exponent — the index of the PC degree in the neighborhood of zero. An empirical analysis of the PC of various levels of aggregation in the US settlement system has been performed. The form of size distribution of states was studied by decades from 1790 to 2010. The spatial analysis of the PC shape for counties and cities was performed for 2010. The results of an empirical study showed that the PC of the states had left-hand asymmetry over 220 years. The PC of districts and cities had both right-hand and left-hand asymmetries. The obtained results explain in which cases the Pareto distribution having a PC with right-hand asymmetry, and the lognormal distribution with a symmetric PC may not correspond objectively to real settlement systems. As an alternative to power-series distribution and lognormal distribution, we considered an analytically simple two-parameter model with a wide range of PC asymmetry that combines the properties of power-series distribution and lognormal distribution. Verification of the model showed that it adequately described the size of settlements in homogeneous settlement systems.

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Mis-measurement of inequality: a critical reflection and new insights

Cited by 18 publications

References 21 publications

Analysis of party systems by measures of concentration of inequality and asymmetry of the Pareto curve

Analysis of party systems by measures of concentration of inequality and asymmetry of the Pareto curve

Unbottling the Gini: New Tools from an Old Concept

Size Distribution of States, Counties, and Cities in the USA: New Inequality Form Information

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