Bounding Moments, the Gini Index and Lorenz Curve From Grouped Data for Unimodal Density Functions

1979 ◽  
Vol 74 (366) ◽  
pp. 375 ◽  
Author(s):  
Abba M. Krieger
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
Density Functions ◽  
Grouped Data ◽  
Unimodal Density

The Interpolation of the Lorenz Curve and Gini Index from Grouped Data

Econometrica ◽  
1976 ◽  
Vol 44 (3) ◽  
pp. 479 ◽  
Author(s):  
Joseph L. Gastwirth ◽  
Marcia Glauberman
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
Grouped Data ◽  
The Lorenz Curve

scholarly journals The Advantages of Using Group Means in Estimating the Lorenz Curve and Gini Index From Grouped Data

2016 ◽  
Vol 70 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Merritt Lyon ◽  
Li C. Cheung ◽  
Joseph L. Gastwirth
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
Grouped Data ◽  
The Lorenz Curve

Comparisons Between Poorest and Richest to Measure Inequality

2018 ◽  
Vol 49 (2) ◽  
pp. 526-561 ◽  
Author(s):  
Youri Davydov ◽  
Francesca Greselin
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Lorenz Curve ◽  
Gini Index ◽  
Specific Information ◽  
Grouped Data ◽  
Inequality Measure ◽  
Policy Makers ◽  
The One ◽  
Measures Of Inequality

The observed increase in economic inequality, where the major concern is relative to the huge growth of the highest incomes, motivates to revisit classical measures of inequality and to offer new ways to synthesize the variability of the entire income distribution. The idea is to provide policy makers a way to contrast the economic position of the group of the poorer [Formula: see text] percent of the population and to compare their mean income to the one owned by the [Formula: see text] percent of the richest. The new measure is still a Lorenz-based one, but the significant focus is based here in equally sized and opposite parts of the population whose difference is so remarkable nowadays. We then highlight the specific information given by the new inequality measure and curve, by comparing it to the widely employed Lorenz curve and Gini index and the more recent Zenga approach, and provide an application to Italian data on household income, wealth, and consumption along the years 1980–2012. The effects of estimating inequality indices and curves from grouped data are also discussed.

Lorenz Curve and Gini Index

1990 ◽  
Vol 40 (1-4) ◽  
pp. 307-324
Author(s):  
T. S. K. Moothathu
Keyword(s):  
Lorenz Curve ◽  
Gini Index

The Contest for Olympic Success as a Public Good

2013 ◽  
Author(s):  
Loek Groot
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
World Population ◽  
Level Playing Field ◽  
Actual Distribution ◽  
Playing Field ◽  
Inequality Measures ◽  
The Rich ◽  
The Lorenz Curve ◽  
Rich Countries

In this study it is demonstrated that standard income inequality measures, such as the Lorenz curve and the Gini index, can successfully be applied to the distribution of Olympic success. Olympic success is distributed very unevenly, with the rich countries capturing a disproportionately higher share compared to their world population share, which suggests that the Olympic Games do not provide a level playing field. The actual distribution of Olympic success is compared with alternative hypothetical distributions, among which are chosen the distribution according to population shares, the welfare optimal distribution under the assumption of zero government expenditures, and the non-cooperating Nash-Cournot distribution. By way of conclusion, a device is proposed to make the distribution of Olympic success more equitable.

Lorenz Curve Interpolation and the Gini Coefficient

2010 ◽  
Author(s):  
Nicholas Charles Rohde
Keyword(s):  
Gini Coefficient ◽  
Lorenz Curve ◽  
Polynomial Spline ◽  
The Philippines ◽  
The Body ◽  
Grouped Data ◽  
Parametric Methods ◽  
Lorenz Curves ◽  
Curve Interpolation ◽  
Income Decile

This article presents a simple non-polynomial spline that may be used to construct Lorenz curves from grouped data. The spline is naturally convex and works by determining a series of piecewise segments that may be joined to give a smooth and continuous Lorenz curve. The method is illustrated with an empirical example using income decile data from the Philippines from 1991-2003 where the proposed technique is used alongside other parametric and non-parametric methods. We also use the spline to approximate some known Lorenz curves and assess the technique by comparing the estimated Gini coefficient to the known Gini. Our findings suggest that the method is an attractive addition to the body of techniques used for developing Lorenz curves from grouped data.

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