A simple way to calculate the Gini coefficient, and some implications

1997 ◽  
Vol 56 (1) ◽  
pp. 45-49 ◽  
Author(s):  
Branko Milanovic
2021 ◽  
pp. 1-6
Author(s):  
Constantin Kaplaner ◽  
Yves Steinebach

Abstract Punctuated Equilibrium Theory posits that policy-making is generally characterized by long periods of stability that are interrupted by short periods of fundamental policy change. The literature converged on the measure of kurtosis and L-kurtosis to assess these change patterns. In this letter, we critically discuss these measures and propose the Gini coefficient as a (1) comparable, but (2) more intuitive, and (3) more precise measure of “punctuated” change patterns.


2014 ◽  
Vol 152 ◽  
pp. 214-223 ◽  
Author(s):  
Juan Gabriel Rodríguez ◽  
Rafael Salas

2009 ◽  
Vol 36 (12) ◽  
pp. 3240-3246 ◽  
Author(s):  
Tammy Drezner ◽  
Zvi Drezner ◽  
Jeffery Guyse

2019 ◽  
Vol 6 (1) ◽  
pp. 147-173
Author(s):  
Rudolf Schuessler

Abstract What impact should sufficientarianism have on the measurement of inequality? Like other theories of justice, sufficientarianism influences how economic inequality is conceived. For the purpose of measurement, its standards of justice can be approximated by income-based thresholds of sufficiency. At which income level could a threshold of having enough be pegged in OECD countries? What would it imply for standard indicators of inequality, such as decile comparisons of cumulated income, income spreads, or the Gini coefficient? This paper suggests some answers to these questions, showing that sufficientarian ideas could make a difference with respect to the measurement of inequality in a society.


2017 ◽  
Vol 40 (2) ◽  
pp. 205-221 ◽  
Author(s):  
Shahryar Mirzaei ◽  
Gholam Reza Mohtashami Borzadaran ◽  
Mohammad Amini

In this paper, we consider two well-known methods for analysis of the Gini index, which are U-statistics and linearization for some incomedistributions. In addition, we evaluate two different methods for some properties of their proposed estimators. Also, we compare two methods with resampling techniques in approximating some properties of the Gini index. A simulation study shows that the linearization method performs 'well' compared to the Gini estimator based on U-statistics. A brief study on real data supports our findings.


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