caa a22 4500 60619648X CHVBK 20210128100924.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10260-014-0293-4 doi (NATIONALLICENCE)springer-10.1007/s10260-014-0293-4 On the Gini coefficient normalization when attributes with negative values are considered [Elektronische Daten] [Emanuela Raffinetti, Elena Siletti, Achille Vernizzi] 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. Springer-Verlag Berlin Heidelberg, 2014 Negative income nationallicence Negative attribute nationallicence Gini coefficient nationallicence Normalization term nationallicence Pigou-Dalton transfers principle nationallicence Raffinetti Emanuela Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut Siletti Elena Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut Vernizzi Achille Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut Statistical Methods & Applications Springer Berlin Heidelberg 24/3(2015-09-01), 507-521 1618-2510 24:3<507 2015 24 10260 https://doi.org/10.1007/s10260-014-0293-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-014-0293-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Raffinetti Emanuela Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut NATIONALLICENCE 700 1- Siletti Elena Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut NATIONALLICENCE 700 1- Vernizzi Achille Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, Italy aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/3(2015-09-01), 507-521 1618-2510 24:3<507 2015 24 10260