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Gini coefficients are simple, and this simplicity can lead to oversights and can confuse the comparison of different populations; for example, while both Bangladesh (per capita income of $1,693) and the Netherlands (per capita income of $42,183) had an income Gini coefficient of 0.31 in 2010, [72] the quality of life, economic opportunity and ...
A complete handout about the Lorenz curve including various applications, including an Excel spreadsheet graphing Lorenz curves and calculating Gini coefficients as well as coefficients of variation. LORENZ 3.0 is a Mathematica notebook which draw sample Lorenz curves and calculates Gini coefficients and Lorenz asymmetry coefficients from data ...
Online calculator computes the Gini Coefficient, plots the Lorenz curve, and computes many other measures of concentration for any dataset Online calculator: Online (example for processing data from Table HINC-06 [ permanent dead link ] , U.S. Census Bureau, 2007: Income Distribution to $250,000 or More for Households) and downloadable ...
The Gini coefficient for a continuous probability distribution takes the form: = where is the CDF of the distribution and is the expected value. For the log-logistic distribution, the formula for the Gini coefficient becomes:
This is a list of countries and territories by income inequality metrics, as calculated by the World Bank, UNU-WIDER, OCDE, and World Inequality Database, based on different indicators, like Gini coefficient and specific income ratios.
Gini: Higher Gini coefficients signify greater inequality in wealth distribution. A Gini coefficient of 0 reflects perfect wealth equality, where all wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal wealth inequality, a situation where a single individual has all the wealth while all others have none.
One relative measurement would be to compare the total wealth of the poorest one-third of the population with the total wealth of the richest 1% of the population. In this case, the number of people counted as poor could increase while their income rises. There are several different income inequality metrics; one example is the Gini coefficient.
The Gini coefficient for a continuous probability distribution takes the form: G = 1 μ ∫ 0 ∞ F ( 1 − F ) d x {\displaystyle G={1 \over {\mu }}\int _{0}^{\infty }F(1-F)dx} where F {\displaystyle F} is the CDF of the distribution and μ {\displaystyle \mu } is the expected value.