The Experts below are selected from a list of 21444 Experts worldwide ranked by ideXlab platform
Liu Wei-jiang - One of the best experts on this subject based on the ideXlab platform.
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Study on Gini Coefficient Method of Total Pollutant Load Allocation for Water Bodies
Research of Environmental Sciences, 2006Co-Authors: Liu Wei-jiangAbstract:According to the concept of Gini Coefficient which is an index to estimate equity of the income,a specific method of Gini Coefficient is established to evaluate the scenario of total pollutant load allocation among river basins.This method can be used to assess the rationality of total pollutant load allocation in river basins.Seven river basins of China is studied with this method,and correlative factors of the Gini Coefficient for total pollutant load allocation are analyzed in detail,such as population,GDP,water resources,the ability of incorporate contamination and so on,and the rationality is evaluated by the Lorenz curves of correlative factors.The results indicated that the Lorenz curves of water resource can focus on the key areas of water pollution in the main river basins of China correctly and also provide the basis for gross water pollutant reduction.
Fan Hua - One of the best experts on this subject based on the ideXlab platform.
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Application of Gini Coefficient Method on Total pollutant Load Allocation for Water Bodies
Journal of Anhui Agricultural Sciences, 2009Co-Authors: Fan HuaAbstract:According to the basic concept of Gini Coefficient which was an index to estimate equity of the income,a specific method of Gini Coefficient was established to evaluate the scenario of total pollutant load allocation among river basins.This method can be used to assess the rationality of total pollutant load allocation in river basins.Five river basins of Jiangxi Province used as an example,and correlative factors of the Gini Coefficient for total pollutant load allocation were analyzed with this method in detail,such as population,GDP,water resources and so on,and the rationality was evaluated by the Lorenz curves of correlative factors and corresponding reduction scheme were put forward.The results showed that the scheme was more fair and reasonable through evaluating and adjusting the scheme of total pollutant load allocation of five river basins in Jiangxi Province by Gini Coefficient method.
James B Davies - One of the best experts on this subject based on the ideXlab platform.
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The Gini Coefficient and Personal Inequality Measurement
2016Co-Authors: James B DaviesAbstract:The Gini Coefficient is based on the sum of pairwise income differences. For an individual, differences vis-a-vis poorer people represent advantage, and those versus richer people deprivation. Any weighted average of deprivation and advantage generates a “Gini admissible” personal inequality index. The mean value of such an index across individuals equals the Gini Coefficient. Properties of the personal indexes explain the differing sensitivity of the Gini Coefficient to inequality in various ranges of the income distribution. Applications to the Kuznets transformation in developing countries, to polarization in advanced countries and to broad increases or decreases in income dispersion are explored.
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The Gini Coefficient and Personal Inequality Measurement
2016Co-Authors: James B DaviesAbstract:The Gini Coefficient is the most popular inequality index. It is based on the sum of pairwise absolute income differences, which can be viewed as taking a separate sum for each individual of the differences between his/her income and others’, and then adding up those separate sums. The differences vis-a-vis people with lower income can be used to construct an individual’s advantage, while the differences with respect to people with higher incomes generate the individual’s deprivation. Deprivation and advantage can be weighted differently, producing a whole family of “Gini admissible” personal inequality indexes. The population average of any one of the latter equals the Gini Coefficient. The properties of the personal inequality indexes explain the sensitivity of the Gini Coefficient to transfers in different ranges of the income distribution. They also throw light on individual views of secular changes in income distribution interesting for their own sake. For example, throughout the change from a traditional to a modern economy that gives rise to the Kuznets curve, those in the traditional sector believe that inequality is constantly increasing while those in the modern sector believe the opposite. Personal views about polarization and rising inequality, as seen in most high income countries in recent decades, are also illuminated.
John P A Ioannidis - One of the best experts on this subject based on the ideXlab platform.
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the Gini Coefficient as a measure for understanding accrual inequalities in multicenter clinical studies
Journal of Clinical Epidemiology, 2004Co-Authors: Annabettina Haidich, John P A IoannidisAbstract:Abstract Objective Clinical sites participating in multicenter trials may have unequal performance in recruiting subjects. We propose using the Gini Coefficient as a quantitative measure of site accrual inequalities. Study design and setting We evaluated the relationship of this metric to other study characteristics across 166 clinical studies (27,865 subjects) conducted by the AIDS Clinical Trials Group between 1986 and 1999. Results Overall there was a modest recruitment inequality among clinical centers (mean Gini = 0.33). In multivariate modeling, site accrual inequalities were higher when there was more protracted enrollment, and a larger number of sites and were lower in antiretroviral studies than other studies. In long-term studies, the site accrual inequality increased significantly over time (P = 0.004). In efficacy trials, a higher Gini Coefficient was associated with higher likelihood of the study results being statistically significant (P = 0.010). Conclusion The Gini Coefficient may be easily and routinely incorporated in the description of the characteristics of a clinical study and may provide insights about its enrollment pattern.
Achille Vernizzi - One of the best experts on this subject based on the ideXlab platform.
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The Gini Coefficient and the case of negative values
Electronic Journal of Applied Statistical Analysis, 2019Co-Authors: Francesca De Battisti, Francesco Porro, Achille VernizziAbstract:When calculating the Gini Coefficient for distributions which include negative values, the Gini Coefficient can be greater than one, which does not make evident its interpretation. In order to avoid this awkward result, common practice is either replacing the negative values with zeros, or simply dropping out units with negative values. We show how these practices can neglect significant variability shares and make comparisons unreliable. The literature also presents some corrections or normalizations which restrict the modified Gini Coefficient into the range [0-1]: unluckily these solutions are not free of deficiencies. When negative values are included, the Gini Coefficient is no longer a concentration index, and it has to be interpreted just as relative measure of variability, taking account of its maximum inside each particular situation. Our findings and suggestions are illustrated by an empirical analysis, based on the Bank of Italy SHIW.
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on the Gini Coefficient normalization when attributes with negative values are considered
Statistical Methods and Applications, 2015Co-Authors: Emanuela Raffinetti, Elena Siletti, Achille VernizziAbstract:Typically, inequality indices appear both as basic concepts in the analysis of welfare economics and as technical tools applied to income or other transferable attributes. Several findings in such research fields are provided by the standard Gini Coefficient, traditionally introduced for incomes taking non-negative values. Even if negative income can appear as an unfamiliar concept, it can arise in real surveys, especially when assessing families’ financial assets. The main troubles associated with the treatment of negative income regards the violation of the normalization principle. The inclusion of income taking negative values can yield for the standard Gini Coefficient achieving values \(>\)1. The Gini Coefficient then has to be adjusted in order to ensure that its range is bounded between 0 and 1. In this paper, a reformulation of the Gini Coefficient with respect to that proposed in the literature is presented and discussed in light of the negative income issue. In particular, a new definition of the Gini Coefficient normalization term, revealing more coherence with the classical situation of maximum inequality, is provided. Finally, an empirical application based on the Survey of Household Income and Wealth data of the Bank of Italy (2012) further validates the actual attitude of the new Gini Coefficient in catching inequality in the distribution of the attribute.
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On the Gini Coefficient Normalization When Attributes With Negative Values are Considered
Statistical Methods & Applications, 2014Co-Authors: Emanuela Raffinetti, Elena Siletti, Achille VernizziAbstract:Typically, inequality indices appear both as basic concepts in the analysis of welfare economics and as technical tools applied to income or other transferable attributes. Several findings in such research fields are provided by the standard Gini Coefficient, traditionally introduced for incomes taking non-negative values. Even if negative income can appear as an unfamiliar concept, it can arise in real surveys, especially when assessing families’ financial assets. The main troubles associated with the treatment of negative income regards the violation of the normalization principle. The inclusion of income taking negative values can yield for the standard Gini Coefficient achieving values $$>$$ > 1. The Gini Coefficient then has to be adjusted in order to ensure that its range is bounded between 0 and 1. In this paper, a reformulation of the Gini Coefficient with respect to that proposed in the literature is presented and discussed in light of the negative income issue. In particular, a new definition of the Gini Coefficient normalization term, revealing more coherence with the classical situation of maximum inequality, is provided. Finally, an empirical application based on the Survey of Household Income and Wealth data of the Bank of Italy ( 2012 ) further validates the actual attitude of the new Gini Coefficient in catching inequality in the distribution of the attribute. Copyright Springer-Verlag Berlin Heidelberg 2015
