computes the Gini coefficient adjusted for negative (even weighted) data.
a vector of attributes containing even negative elements
a vector containing the weights associated with the elements of the attribute vector
Gini_RSV(y,w) is the Gini coefficient for negative income data proposed by Raffinetti, Siletti and Vernizzi (2015) and based on a new
definition of the "polarized" scenario, where the total negative attribute amount is assigned to one unit, the total positive amount to another unit, while all
the other units have a zero amount of attribute. It provides a value always included into the close range [0,1].
the value of the Gini coefficient adjusted for negative attributes.
If the vector
w contains unitary elements, compute
Emanuela Raffinetti, Fabio Aimar
E. Raffinetti, E. Siletti, A. Vernizzi (2014), Inequality measures and the issue of negative income. Italian Statistical Society Conference (SIS), Book of Short Papers: "SIS2014. 47th Scientific Meeting of the Italian Statistical Society", CUEC (Cooperativa Universitaria Editrice Cagliaritana), 11-13 June 2014
E. Raffinetti, E. Siletti, A. Vernizzi (2015), On the Gini coefficient normalization when incomes with negative values are considered, Statistical Methods & Applications, 24(3), 507-521
E. Raffinetti, E. Siletti, A. Vernizzi (2016), Analyzing the effects of negative and non-negative values on income inequality. Evidence from the Survey of Household Income and Wealth of the Bank of Italy (2012), Social Indicators Research (published on line http://link.springer.com/article/10.1007
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# generate the vector of attributes with even negative elements y<-c(-7,-15,11,-10,2,4,40) # generate the vector of weights w<-c(2.5,1.1,3.6,4.4,0.8,2.7,1.9) # compute the Gini coefficient adjusted for negative values Gini_RSV(y,w) data(BI2012) # define the vector of weights w<-BI2012$weight # select the vector of incomes (e.g., the incomes from transfers YTA) y<-BI2012$YTA # compute the Gini coefficient adjusted for negative values Gini_RSV(y,w) # select the vector of incomes (e.g., the incomes from financial capital gain YCF) y<-BI2012$YCF # compute the Gini coefficient adjusted for negative values Gini_RSV(y,w)
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