
A new inequality measurement tool: The Vinci index
This paper presents a new inequality measurement tool that gives more we...
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Decomposing the Quantile Ratio Index with applications to Australian income and wealth data
The quantile ratio index introduced by Prendergast and Staudte 2017 is a...
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On a property of the inequality curve λ(p)
The Zenga (1984) inequality curve is constant in p for Type I Pareto dis...
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Extremal points of Lorenz curves and applications to inequality analysis
We find the set of extremal points of Lorenz curves with fixed Gini inde...
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Outliers and The Ostensibly Heavy Tails
The aim of the paper is to show that the presence of one possible type o...
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Use of the geometric mean as a statistic for the scale of the coupled Gaussian distributions
The geometric mean is shown to be an appropriate statistic for the scale...
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Inequality in Education: A Comparison of Australian Indigenous and Nonindigenous Populations
Educational achievement distributions for Australian indigenous and noni...
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Beware the Gini Index! A New Inequality Measure
The Gini index underestimates inequality for heavytailed distributions: for example, a Pareto distribution with exponent 1.5 (which has infinite variance) has the same Gini index as any exponential distribution (a mere 0.5). This is because the Gini index is relatively robust to extreme observations: while a statistic's robustness to extremes is desirable for data potentially distorted by outliers, it is misleading for heavytailed distributions, which inherently exhibit extremes. We propose an alternative inequality index: the variance normalized by the second moment. This ratio is more stable (hence more reliable) for large samples from an infinitevariance distribution than the Gini index paradoxically. Moreover, the new index satisfies the normative axioms of inequality measurement; notably, it is decomposable into inequality within and between subgroups, unlike the Gini index.
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