1,746,254 research outputs found
Linking Yitzhaki’s and Dagum’s Gini Decompositions
Full text linkIn this article we show that the Gini coefficient is simultaneously decomposable both by sources of income and by populations of income receivers for non-overlapping income distributions: the so-called first-best Gini multi-decomposition. We show that this multidimensional decomposition is useful for many reasons: (i) it is related to the degree of inequality aversion of the decision maker, (ii) it is especially well suited to study inequalities between poor and non-poor people, (iii) it enables one to measure within- and between-group Gini elasticities, which gauge the impact of global transfers on within- and between-group inequalities, respectively.
An elementary characterization of the Gini index
Full text linkThe Gini coefficient or index is perhaps one of the most used indicators of social and economic conditions. In this paper we characterize the Gini index as the unique function that satisfies the properties of scale invariance, symmetry, proportionality and convexity in similar rankings. Furthermore, we discuss a simpler way to compute it.Gini index, income inequality, axiomatization
A Note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini Coefficient
No full textThe Gini coefficient has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimate is crucial. We show how to use jackknife and linearization to estimate the variance of the Gini coefficient, allowing for the effect of the sampling design. The aim is to show the asymptotic equivalence (or consistency) of the generalised jackknife estimator and the Taylor linearization estimator for the variance of the Gini coefficient. A brief simulation study supports our findings
The Gini coefficient reveals more.
Full text linkWe revisit the well-known decomposition of the Gini coefficient into betweengroups, within-groups and overlap terms in the context of two groups in which the incomes in one group may be scaled and that group’s population weight modified. In this more general setting than usual, we focus on the properties of the overlap term, proving inter alia that overlap unambiguously reduces as a result of a within-group progressive transfer, and is increased by scaling up the incomes in the group with the lower mean, reaching a maximum when the two means become the same. In the case of a socially heterogeneous population and equivalized incomes, the effect on the Gini overlap of changing the income unit is determined, along with that of adjusting the equivalence scale deflator in case the income unit is the equivalent adult (such adjustment simultaneously changing the weighting of income units).
The Gini Coefficient Reveals More
Full text linkWe revisit the well-known decomposition of the Gini coefficient into betweengroups, within-groups and overlap terms in the context of two groups in which the incomes in one group may be scaled and that group’s population weight modified. In this more general setting than usual, we focus on the properties of the overlap term, proving inter alia that overlap unambiguously reduces as a result of a within-group progressive transfer, and is increased by scaling up the incomes in the group with the lower mean, reaching a maximum when the two means become the same. In the case of a socially heterogeneous population and equivalized incomes, the effect on the Gini overlap of changing the income unit is determined, along with that of adjusting the equivalence scale deflator in case the income unit is the equivalent adult (such adjustment simultaneously changing the weighting of income units).
Decomposition of Gini and the generalized entropy inequality measures
Full text linkIn this article we provide an overview of the Gini decomposition and the generalized entropy inequality measures, a free access to their computation, an application on French wages, and a different way than Dagum to demonstrate that the Gini index is a more convenient measure than those issued from entropy: Theil, Hirschman-Herfindahl and Bourguignon.
A Rethink on Measuring Health Inequalities Using the Gini Coefficient
Full text linkObjective- We show that a standardized Gini coefficient that takes into account the feasible range of health inequality for a given health attribute is a better instrument than the normal Gini coefficient for quantifying inter-individual health inequality. Methods- The standardized Gini coefficient is equal to the normal Gini coefficient divided by the maximal attainable Gini coefficient, which is computed based on the maximal level of a health attribute an individual could achieve. Both the old and new coefficients are used to estimate the lifespan inequality of 185 countries for year 1990, 2000 and 2006, respectively. The results are then compared both across countries and over time. Findings- Firstly, the standardized Gini coefficient can still be related to the Lorenz curve. Secondly, changes in standardized Gini coefficients can be decomposed into respectively the change in the distribution of health outcomes and the change in the average health outcomes. Thirdly, the standardized Gini coefficient provides richer information and often gives different conclusions regarding health inequality in individual countries as well as country ranking, as compared to the normal Gini coefficient. Conclusion- Accounting for the maximal level of health attribute an individual could achieve is important when measuring health inequality. The proposed standardized Gini coefficient can provide more accurate information regarding the actual level of health inequality in a society than the normal Gini coefficient
On Dagum’s Decomposition of the Gini Coefficient
Full text linkTo measure the contributions to inequality from population subgroups, the Gini coefficient is often decomposed into inequality within groups, inequality between groups and a residual term arising from the overlapping of income distributions from different groups. In this paper we show that two decompositions presented separately in the literature, a traditional decomposition and a decomposition suggested by Dagum (1997), are identical, a fact not previously acknowledged in the literaturepopulation subgroups; between inequality; within inequality
On the Decomposition of the Gini Coefficient: an Exact Approach, with an Illustration Using Cameroonian Data
Full text linkDecomposing inequality indices across household groups or income sources is useful in estimating the contribution of each component to total inequality. This can help policy makers draw efficient policies to reduce disparities in the distribution of incomes using targeting tools. Decomposing relative inequality indices, such as the Gini coefficient, is not a simple procedure since, in many cases, the functional form of inequality indices is not additively separable in incomes. More importantly, for some of the indices on which this decomposition can be performed, the interpretation of the decomposition components is often not well founded. In this paper, we use the Shapley value as well as analytical approaches to perform the decomposition of the Gini coefficient and generalize it, in some cases, to the decomposition of other inequality indices. For the analytical approach, our aim is to extend the same interpretation, attributed to the Gini coefficient, to that of the contribution components.Equity, Inequality, Decomposition, Shapley value
Measuring and explaining economic inequality: An extension of the Gini coefficient.
Full text linkThis paper proposes a new class of inequality indices based on the Gini’s coefficient. The properties of the indices are studied and in particular they are found to be regular, relative and satisfy the Pigou-Dalton transfer principle. A subgroup decomposition is performed and the method is found to be similar to the one used by Dagum when decomposing the Gini index. The theoretical results are illustrated by case studies, using Cameroonian data.Measuring inequality; Generalisation of the Gini index; Pigou-Dalton’s transfer; Subgroup decomposition.
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