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The Italian statistician Corrado Gini developed the Gini coefficient and published it in his 1912 paper Variabilità e mutabilità (English: variability and mutability). [16] [17] Building on the work of American economist Max Lorenz, Gini proposed using the difference between the hypothetical straight line depicting perfect equality and the actual line depicting people's incomes as a measure ...
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 ...
The Gini coefficient is a number between 0 and 1 or 100, where 0 represents perfect equality (everyone has the same income). Meanwhile, an index of 1 or 100 implies perfect inequality (one person has all the income, and everyone else has no income).
The 1918 household Gini coefficient (excluding capital gains) was 40.8. A brief but sharp depression in 1920-1921 reduced incomes. Income inequality rose from 1913 to peaks in 1926 (1928 Gini 48.9, 1936 Gini 45.5) and 1941 (Gini 43.1), after which war-time measures of the Roosevelt administration began to equalize the income distribution. [20]
A score of "0" on the Gini coefficient represents complete equality, i.e. every person has the same income. A score of 1 would represent the case in which one person would have all the income and others would have none. Therefore, a lower Gini score is roughly associated with a more equal distribution of income and vice versa.
The Wealth Gini coefficients from 2008 are based on a working paper published by the National Bureau of Economic Research. [5] The Wealth Gini numbers for 2018, 2019, and 2021 come from the Global Wealth Databook by Credit Suisse. [6] [7] [8] * indicates "Wealth inequality in COUNTRY or TERRITORY" or "Income inequality in COUNTRY or TERRITORY ...
English: Gini coefficient diagram, based on the version by Bluemoose/BenFrantzDale. You can think of the horizontal axis as percent of people and the vertical axis as the percent of income those people receive.
Compared to the Gini coefficient in practice, CV puts higher weight on the right tail of the scale, making it sensitive to the rich. Coefficient of variation may be a suitable choice of measure if the goal of a study is to analyze the wealth concentration at the top of the distribution. [17] [18]