Gini Coefficients - Income inequality

The Gini coefficient is a measure of statistical dispersion most prominently used as a measure of inequality of income distribution or inequality of wealth distribution.

It is defined as a ratio with values between 0 and 1: A low Gini coefficient indicates more equal income or wealth distribution, while a high Gini coefficient indicates more unequal distribution. 0 corresponds to perfect equality (everyone having exactly the same income) and 1 corresponds to perfect inequality (where one person has all the income, while everyone else has zero income). The Gini coefficient requires that no one have a negative net income or wealth.

The Gini coefficient was developed by the Italian statistician Corrado Gini and published in his 1912 paper "Variability and Mutability".

Gini coefficient is measured as the area above the Lorenz Curve and below the diagonal. Because the Lorenz curve is unknown, different approach to approximate the curve produces slightly different Gini coefficients.

Using the Gini can help quantify differences in welfare and compensation policies and philosophies. However it should be borne in mind that the Gini coefficient can be misleading when used to make political comparisons between large and small countries.

Worldwide, Gini coefficients range from approximately 0.249 in Japan to 0.707 in Namibia. While most developed European nations tend to have Gini indices between 24 and 36, the United States' and Mexico's Gini indices are both above 40, indicating that the United States and Mexico have greater inequality.

Poor countries (those with low per-capita GDP) have Gini indices that fall over the whole range from low (25) to high (71), while rich countries generally have intermediate Gini indices (under 40). The lowest Gini coefficients can be found in Japan, Scandinavian countries, and in many recently ex-socialist countries of Eastern Europe. Note that in many of the former socialist countries, the sizeable underground economy hides income for many. In such a case, earning/wealth statistics over-represent certain income ranges (i.e., in lower-income regions), and may decrease the Gini coefficient even in the presence of real inequality.