Income is linked to one’s ability to acquire resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. It is estimated that approximately 119,200 (5%) of the 2.4 million United States deaths in 2000 were attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers.
Many cross sectional, ecological studies have compared western industrialized countries, including the United States, along a gradient of a health outcome and the corresponding gradient of income inequality using the Gini coefficient, a measure of inequality of income and wealth. Studies using this index often show a linear relationship between increasing income inequality and poorer health outcomes such as life expectancy, infant mortality, obesity, mental illness, homicide, and other outcomes. Several, large longitudinal studies that followed healthy participants at baseline were combined to estimate the number of U.S. deaths in 2000 attributable to income inequality.
How to Analyze the Gini Coefficient (Gini)
Note to LHDs in California: The California Department of Public Health’s Health Communities Data and Indicators (HCI) project has collected, cleaned, and compiled the Gini coefficient for cities with greater than 20,000 residents, counties, and regional transportation planning districts in California, which can be found at http://www.cdph.ca.gov/programs/Pages/HealthyCommunityIndicators.aspx. Appendix D explains how to download and filter these data.
The Gini is the easiest measure to indicate the distribution of income or wealth across a geographic area. The Gini is a score between zero and one. A geography with a Gini value of zero signifies that every household in that geography owns an equal share of income or perfect income equality. Conversely, a Gini value of one signifies that one household owns all of the income or perfect inequality. Thus, a higher Gini means more inequality. The main drawback to the Gini is that the magnitude of the wealth or poverty is not measured, just the spread. Thus, if you had a very segregated high-income neighborhood, the Gini would be low. However, if you have a neighborhood that has mixed incomes, the Gini would be high. So it’s best to use the Gini at larger geographic regions, and best to compare across time rather than across geographies.
The normal geographic unit of analysis is the metropolitan area. These can be seen as commute sheds, where people may live in any part of the area and work in any part. For the Bay Area, the nine counties are considered the metro area. Another common geographic unit of analysis is the nation.
For a detailed explanation of how to access American Community Survey data, see Appendix B. The American Community Survey reports the Gini for every level of geography in indicator B19083. However, for the reasons explained above, BARHII does not recommend displaying maps of Census tracts with high Gini coefficients. Instead, BARHII recommends showing trends in the Gini coefficient at the county or regional level like the figure below. With caution, larger cities may also be used. The Bay Area nine-county region’s Gini increased steadily from 0.4014 in 1980 to 0.4714 in 2012.
Figure 15: Gini Coefficient, San Francisco Bay Area, 1980–2012
Source: 1980, 1990, and 2000 calculated by BARHII; 2012 from 1-year ACS estimates.
Alternatively and for older data, the Gini can be calculated manually. This is an elaborate process. The Gini is the ratio of two areas derived from the Lorenz curve. The cumulative share of population is on the x-axis (p in Figure 16) and the cumulative share of income is on the y-axis (L). The line of parity is where each household has the same income (solid blue line). The Lorenz curve shows the actual distribution (dotted blue line). As the Lorenz curve bows away from the line of parity, income distribution is becoming more unequal. The ratio of the area of A to the area of A plus B is the Gini. If the income is evenly distributed, the ratio would be zero, while a ratio of one would mean that all the income belongs to one household.
Figure 16: Making the Lorenz Curve and Calculating the Gini Coefficient Using Sample Data
Figures adapted from François Nielsen, http://www.unc.edu/~nielsen/special/s2/s2.htm
III. BAY AREA LOCAL HEALTH DEPARTMENT EXAMPLES
The Alameda County Public Health Department’s (ACPHD) Place Matters Economics Workgroup is leading a stakeholder process to explore ways that Alameda County can support low-income, underbanked residents to protect their income and assets and build long-term financial health. As envisioned by ACPHD Place Matters and its advisory partners, a healthy credit program would leverage existing county funds in order to expand credit and financial opportunities for low-income county residents, support small lenders in reaching a wider pool of underserved people, and reduce predatory lending and the associated financial and health consequences for low-income communities.
Contra Costa inserted a program into their Women, Infants, & Children (WIC) services to help WIC recipients understand the income tax process and apply for the Earned Income Tax Credit. Agency leaders understood that poverty is a major determinant of poor health, and that by helping support asset development and economic sustainability, the health department can advance the health of women and children in their community. So far, over 6,000 women have participated, and participants report feeling more confident about handling money and have an improved understanding of the impact of money on health.
Galea S, Tracy M, Hoggatt KJ, DiMaggio C, Karpati A. 2011. Estimated Deaths Attributable to Social Factors in the United States. American Journal of Public Health 101(8):1456-1465.
Parthasarathy P, Dailey DE, Young MED, Lam C, Pies C. 2014. Building Economic Security Today: Making the Health–Wealth Connection in Contra Costa County’s Maternal and Child Health Programs. Maternal and Child Health Journal 18(2):396-404.
Wilkinson R, Pickett K. 2009. The Spirit Level: Why Equal Societies Almost Always Do Better. London, Pilgrim Press.