Income Inequality
Index (-100 to +100) reflecting households with income at the extremes of the national income distribution (the top or bottom 20%)
Why do we measure income inequality?
Income inequality matters to community health. Economic inequality in the U.S. has been growing since the 1970s,1 taking 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 with few economic resources tend to reside in communities that are almost exclusively poor.2 Research suggests that, due to social and physical environmental factors, living in a wealthier community benefits health, whereas living in a poorer community poses risks to 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 city or census tract, it compares the number of households in the bottom 20th percentile of national household income to the number of households that fall in the top 20th percentile 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 underprivileged category) to +100 (all of the households are in the privileged category), with 0 signifying that both economic groups are present in equal numbers, or that all households fall into either extreme group.4 Check out our blog for more information!
Strengths and Limitations
Strengths of Metric | Limitations of 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 • In small geographic areas (like census tracts), the Index of Concentration at the Extremes is a more robust measure 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. It is not an absolute measure of disparity. • The ICE metric does not provide information on the causes of inequality. |
Calculation
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 for 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 +100.0 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 all households fall into either extreme group. For more information on the calculation, please refer to the City Health Dashboard Technical Document.
Data Source
Estimates for this metric are drawn from American Community Survey five-year estimate data using the B19001 table. Multi-year data are available for this metric. For more information, please refer to Using Multi-Year Data: Tips and Cautions.
Years of Collection
Calculated by the Dashboard Team using data from 2022, 5 year estimate.
References
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: July 26, 2023