Gini Index of Inequality: Now on PolicyMap

Gini Index of Inequality on PolicyMap


Gini Index of Income Inequality



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  • Incomes & Spending
    • Income Inequality
      • Gini Index

From presidential debates to Federal Reserve Bank initiatives to street protests, talk about income inequality is everywhere these days. Measuring this phenomenon is not a simple task, but the Gini Index of Income Inequality is one of the most broadly used measures to try and do so. You may be most familiar with the Gini Index as a way of measuring income inequality across nations; however, the calculation can be done to examine inequality at any geographic level. Courtesy of the Census Bureau, it is now available for PolicyMap users at various geography levels from nation to census tract.

So what is the Gini Index? The Gini Index assigns income inequality a value ranging from 0 to 1, which reflects the nature of income distribution in a given region. A value of zero indicates perfect equality, indicating that all households in an area have the same income, while a value of one indicates perfect inequality, denoting that only one household earns all the income in an area, with all other households having no income. The lower the value, the more evenly distributed incomes are, and the higher the value, the less evenly distributed income is. The index is derived from a graph of the share of income plotted against cumulative population percentiles (commonly known as Lorenz Curve) and its deviation from a line of perfect income equality.

Credit: Wikimedia Commons

The Gini Index value for the nation is 0.48. You can explore the Gini values in your local area of interest using the map below:

Existing research on inequality offers contrasting views on its effects on social outcomes. While some economists maintain that income inequality is a positive sign of a highly functioning capitalist economy, other economic experts and policy makers argue that high levels of income inequality have an incremental effect on social problems such as lack of social cohesion and trust, disinvestment in public goods, and increased violent crime. Some also say it can cause an accentuation of existing disparities in educational attainment, job security, or income generation.

The effects of income inequality can be interpreted in a variety of ways across geographies. Income inequality at the neighborhood or county level can be effectively viewed as economic diversity, which is frequently associated with improvement in social outcomes such as health and education. The benefits of mixed income housing are based on the belief that lower income individuals may benefit from close proximity to higher income individuals, particularly via access to higher quality services due to a larger tax base. Some studies have suggested that higher income inequality at the county or census tract level is associated with lower risk of obesity, and better mental health.

A report by the Census Bureau sought to identify the determinants of inequality at the state and tract level, and found the following demographics and housing indicators to have the strongest correlations with the Gini index:

  • Percent of population living in assisted households (receiving Supplemental Security Income, cash public assistance, or Food Stamps)
  • Percent of households with no workers
  • Percent of those 16 or older with earnings
  • Percent of households with two or more workers
  • Percent of housing units that are single-family
  • Percent of housing units that are owner-occupied

The Census report notes, however, that these relationships differ considerably when income deprivation is taken into account, thus underscoring one of the limitations of the Gini Index. The very simplicity that makes it easy to understand also considerably limits its explanatory powers. For example, if a region has an index of 0.6, it is difficult to tell if it is because of a concentration of high income groups, or possibly because of lower income groups extending the tail of the distribution.

After giving it a lot of thought, one of the conclusions we came to was that income inequality, as measured by the Gini Index, is not inherently a “good” or “bad” thing, but instead should be treated as one more data point that can help inform a fuller picture of a location. To make it easier for users to get deeper insights into the income distribution of an area, we have provided additional information in the bubble that appears when you click on the map, including the distribution of households across various income brackets along with median household income. Below, you can see how Fairfield County, CT, with a reputation as one of the most unequal counties in the country, has a Gini Index value of 0.54, as compared with the lower 0.33 value in the more evenly distributed Sublette County, WY.

Particularly when used along with additional income and poverty data, the Gini Index is an important measure to help understand the economic diversity of an area. We also encourage anyone interested in reading more on the strength and limitations of various income inequality measures to check out this article from the Journal of Epidemiology and Community Health. Despite its limitations, the Gini Index remains a very insightful measure of income inequality as long as users know what to look for, and we hope that this blog makes it easier to make the most of this latest addition to PolicyMap.