Why do we measure income inequality?
Economic inequality has been growing in the United States since the 1970s.(1) This trend has taken on a geographic dimension in the form of growing economic segregation – people who are economically privileged tend to reside in communities that are almost exclusively wealthy, while those who are deprived tend to reside in communities that are almost exclusively poor.(2) Research suggests that – due to many different factors in their social and physical environments – wealthier communities benefit the health of their residents, whereas poorer communities pose risks to their residents’ health.(3)
How do we measure income inequality?
The measure of income inequality that we use is called the Index of Concentration at the Extremes (ICE). For a given congressional district, it compares the number of households in the bottom 20% of national household income to the number of households that fall in the top 20% of national household income. This number then describes the mix of household incomes in the area, ranging from -100 (all of the households are in the disadvantaged category) to +100 (all of the households are in the advantaged category), with 0 signifying that both income groups are present in equal numbers or that all of the households fall somewhere in the middle.(4)
Check out our City Health Dashboard blog for more information on how to interpret this metric.
The ICE measure describes both the size and direction (whether shifted toward the less or more privileged) of income inequality in an area.(3)
The Index of Concentration at the Extremes is a robust measure for both small and large geographic areas, making it more versatile than related measures such as the Gini Index.(4)
The ICE measure is not in as widespread use as other measures of inequality, like the Gini Index or the 20:20 Ratio.(5)
The metric reflects the relative income distribution in a region, rather than serves as an absolute measure of disparity.
The ICE metric does not provide information on the causes of inequality.
The count of households at or below the 20th percentile in income distribution in a geographic area is subtracted from the count of households at or above the 80th percentile in income distribution in the same geographic area.(4) The resulting value is divided by the total number of households in the area for which income is reported. This value is then multiplied by 100 to provide a score that ranges from –100 to +100, with –100 indicating that all households are in the lowest category of income and +100 indicating that all households are in the wealthiest income category. A value of 0 suggests that both economic groups are present in equal numbers or that no households fall into either extreme group.
This metric was calculated by aggregating estimates from smaller geographies to the congressional district level. For more information on the calculation, please refer to the Congressional District Health Dashboard Technical Document.
Estimates for this metric are drawn from the American Community Survey five-year estimate data using the B19001 table.
Saez E, Zucman G. Wealth inequality in the United States since 1913: Evidence from capitalized income tax data. The Quarterly Journal of Economics. 2016;131(2):519-78..
Massey DS. The age of extremes: Concentrated affluence and poverty in the twenty-first century. Demography. 1996;33(4):395-412.
Kramer MR. Residential Segregation and Health. In Duncan, D (ed.). Neighborhoods and Health. 2018, p321-356.
Krieger N, Waterman PD, Spasojevic J, Li W, Maduro G, Van Wye G. Public Health Monitoring of Privilege and Deprivation With the Index of Concentration at the Extremes. American Journal of Public Health. 2016;106(2):256-263.
De Maio FG. Income inequality measures. J Epidemiol Community Health. 2007;61(10):849-852.
Last updated: January 24, 2023