There is an old adage that the rich get richer while the poor get poorer. Recent media reports have focused on the disproportionate wealth held by the top one percent of the population. Middle class individuals are being squeezed by the current economic situation and income tax structure. Increasing numbers of less fortunate persons receive some form of income support including unemployment benefits or food stamps. In several countries, fiscal and debt crises have resulted in economic and fiscal policies involving austerity measures that adversely affect the poor and middle class members of society.
Commentators on inequality of income and wealth have attempted to quantify this inequality, by measuring its trend over a period of years and making comparisons between countries or regions. Evidence-based analysis of inequality requires careful definitions of what is to be measured and consideration of various structural factors that may affect the incidence and trends of different measures of inequality. Clear and consistent definitions of exactly what comprises wealth or income are essential for meaningful analysis. Different results for the assessment of the relative distribution of income inequality arise from measuring pre-tax or post-tax income.
Statisticians quantify inequality by means of a Gini Coefficient. This is a statistical measure on a scale from zero to one that is generally converted to a percentile scale, where zero represents complete equality and a score close to one hundred on the Gini scale represents extreme inequality. Understanding the composition of the Gini Coefficient and interpreting its meaning in a range of 25 to 75 is important to an objective assessment and discussion of inequality.
The Gini Coefficient is named for an Italian statistician Corrado Gini who derived the measure from a mathematical 2012concept known as a Lorenz curve. Gini presented this coefficient in a 1912 paper on Variability and Mutability. Although the Gini Coefficient is currently widely used in connection with income and wealth inequality, it is equally applicable for measuring inequality in other areas such as agriculture, chemistry, ecology, economics, engineering, health science, and sociology. A Lorenz curve tracks the proportion of a population that is associated with the cumulative distribution of a specified variable; for example with respect to the distribution of incomes, the Lorenz curve compares the percentage of total income of the population attributable to the bottom 5%, 10%, 15%, etcetera. The shape of the Lorenz curve and its deviation from a straight line in a simple plot diagram provides the data on which the Gini Coefficient is based. From this diagram, the Gini Coefficient is quantified by the extent of the Lorenz curve plot that falls below the straight line representing cumulative equal values of the percentage of population and the percentage of total income.
The computation of Gini Coefficients for different countries at a specific point in time or for a single country measured sequentially over time will provide valuable insights into the incidence and trends of income inequality. Gini Coefficients may influence public policy on economic and social issues including employment, taxation, and economic stimulus strategies. However, caution is required in interpreting the significance of a Gini Coefficient, particularly in comparisons between countries or over a period of time. It is essential to consider the effect of certain economic and demographic variables, such as the workforce participation rate and age distribution when comparing between countries. The trends in these variables may have significant effects on the values of Gini Coefficients measured over an extended period of years.
A moderate level of income inequality might be measured at around 25 on the Gini percentile scale, whereas a level of around 75 would indicate an extreme incidence of income inequality. The Gini Index on a percentile scale for income inequality has been estimated for the entire world population as between 61 and 68 by various United Nations agencies and the World Bank. The Organization for Economic Cooperation and Development and the US Census Bureau publish various statistical measures of income inequality on both a pre-tax and post-tax basis. Taxation and social policy spending typically reduce income inequality as measured by the Gini percentile index. Some illustrative pre-tax and post-tax indices are: United States (49 and 38), United Kingdom (51 and 34), Greece (44 and 31), and Italy (53 and 34). The corresponding numbers measured fifteen years earlier are: United States (44 and 34), United Kingdom (41 and 31), Greece (43 and 34), and Italy (42 and 31). Statistical estimates of long-term trends indicate that the Gini Index for worldwide income inequality increased from about 43 to 71 during the nineteenth and twentieth century. However, the International Monetary Fund has demonstrated from recent experience with development in emerging countries that economic policies aimed at reducing income inequality are a significant factor associated with achieving growth of the national economy.