The Association Between Income and Life Expectancy in the United States, 2001-2014

Abstract

Importance: The relationship between income and life expectancy is well established but remains poorly understood.

Objectives: To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.

Design and setting: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

Exposure: Pretax household earnings as a measure of income.

Main outcomes and measures: Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.

Results: The sample consisted of 1,408,287,218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61,175 per year). There were 4,114,380 deaths among men (mortality rate, 596.3 per 100,000) and 2,694,808 deaths among women (mortality rate, 375.1 per 100,000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = -0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001).

Conclusions and relevance: In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

Conflict of interest statement

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Cutler reported being on the academic and policy advisory board for Kyruss Inc and receiving a small amount of stock for doing so (Kyruss Inc provides software solutions to help provider organizations match patients to health care providers, improve the quality of referral and appointment scheduling processes, and optimize health care provider capacity); having nonpaid positions at the Institute of Medicine, Alliance for Aging Research, National Bureau of Economic Research, Aging, Health Care, Public Economics, and Productivity programs, Employee Benefit Research Institute, National Academy of Social Insurance, Institute for Research on Poverty, Center for American Progress, and Center for Healthcare Transparency; receiving grants from the National Institutes of Health; and receiving personal fees from Novartis Princeton, MedForce, Veterans Health Administration, International Monetary Fund, National Council and Community Behavioral Healthcare, Delaware Health Sciences Alliance, Dartmouth College, Healthcare Financial Management Association, New York City Health and Hospitals Corporation, Robert W. Baird & Co, Healthcare Billing and Management Association, Cadence Health, Pompeu Fabra University, Aon Hewitt, American Health Lawyers Association, Parenteral Drug Association, UBS, Aetna, Toshiba, Ernst and Young, Yale University, Bank of America Webinar, and New York University. No other disclosures were reported.

Figures

Figure 1. Gompertz Approximations and Empirical Survival Curves for Men at 5th and 95th Income Percentiles, 2001–2014

For panels A and B, the data for the scatter points were derived from cross-sectional mortality rates by age using income 2 years prior for men aged 40 to 62 years and cohort mortality rates by year using income observed at age 61 years for men aged 63 to 76 years. Empirical mortality rates were observed until the age of 76 years; therefore, empirical survival rates are observed until the age of 77 years. Solid lines show Gompertz extrapolations through the age of 90 years. In panel B, uniform mortality rates from the National Center for Health Statistics (NCHS) and the Social Security Administration (SSA) were used beyond the age of 90 years. Analogous results for women appear in eFigure 4 in the Supplement.

Figure 2. Race- and Ethnicity-Adjusted Life Expectancy for 40-Year-Olds by Household Income Percentile, 2001–2014

Life expectancies were calculated using survival curves analogous to those in Figure 1. The vertical height of each bar depicts the 95% confidence interval. The difference between expected age at death in the top and bottom income percentiles is 10.1 years (95% CI, 9.9–10.3 years) for women and 14.6 years (95% CI, 14.4–14.8 years) for men. To control for differences in life expectancies across racial and ethnic groups, race and ethnicity adjustments were calculated using data from the National Longitudinal Mortality Survey and estimates were reweighted so that each income percentile bin has the same fraction of black, Hispanic, and Asian adults.

Figure 3. Changes in Race- and Ethnicity-Adjusted Life Expectancy by Income Group, 2001–2014

Scatter points in the A panels show the race- and ethnicity-adjusted life expectancy estimates by year and household income quartile. Solid lines represent best fit lines estimated using ordinary least-squares regression. The B panels plot the slopes from analogous regressions estimated separately by income ventile (5 percentile point bins). Dashed lines show 95% confidence intervals.

Figure 4. Race- and Ethnicity-Adjusted Life Expectancy by Income Ventile in Selected Commuting Zones, 2001–2014

Estimates of race- and ethnicity-adjusted expected age at death for 40-year-olds computed by income ventile (5 percentile point bins).

Figure 5. Race- and Ethnicity-Adjusted Life Expectancy by Commuting Zone and Income Quartile, 2001–2014

Estimates of race- and ethnicity-adjusted expected age at death for 40-year-olds computed by commuting zone. The 595 commuting zones with populations above 25 000 are grouped into deciles and colored from dark to light as expected age at death increases. The second and third quartiles appear in eFigure 10 in the Supplement.

Figure 6. Maps of Annual Change in Life Expectancy by State for Bottom Income Quartile, 2001–2014

Annual changes estimated using ordinary least-squares regression of race- and ethnicity-adjusted expected age at death for 40-year-olds on calendar year by state. States are grouped into deciles and colored from red to turquoise as annual change in expected age at death increases.

Figure 7. Annual Change in Life Expectancy for Individuals in the Bottom Income Quartile Living in Selected Commuting Zones, 2001–2014

Solid lines indicate best linear fit, estimated using ordinary least-squares regression.

Figure 8. Correlations Between Life Expectancy in the Bottom Income Quartile and Local Area Characteristics, 2001–2014

Population-weighted univariate Pearson correlations estimated between local area characteristics and race- and ethnicity-adjusted expected age at death for 40-year-olds in the bottom income quartile. These correlations were computed at the commuting zone level after averaging life expectancy across sexes. The error bars indicate 95% confidence intervals with errors clustered by state. Definitions and sources of all variables appear in eTable 3 in the Supplement.

Figure 9. Correlations Between Life Expectancy in the Top Income Quartile and Local Area Characteristics, 2001–2014

Population-weighted univariate Pearson correlations estimated between local area characteristics and race- and ethnicity-adjusted expected age at death for 40-year-olds in the top income quartile. These correlations were computed at the commuting zone level after averaging life expectancy across sexes. The error bars indicate 95% confidence intervals with errors clustered by state. Definitions and sources of all variables appear in eTable 3 in the Supplement.