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Cited by 2,812Health disparities and outcomesIncome, Poverty, and InequalityInsurance, Mortality, Demography, Risk Management

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

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Chetty, Raj, Stepner, Michael, Abraham, Sarah, Lin, Shelby, Scuderi, Benjamin, Turner, Nicholas, Bergeron, Augustin, Cutler, David·2016·JAMA
DOI 10.1001/jama.2016.4226PMC PMC4866586
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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.

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  • Non-biological social and physical environmental factors mediate the link between race and health, as illustrated by health equity problems among US racial and ethnic minority groups.

    “The connection between race and health is also mediated by non-biological factors that encompass the social and physical environment, as illustrated by the health equity challenges faced by racial and ethnic minority groups in the United States.3,4”

    From Culturally Informed Digital Behavioral Intervention Development for Inflammatory Bowel Disease: A Qualitative Study (2025) · cites this paper as [3]

    The Methods section adjusts income–life-expectancy estimates for racial and ethnic composition, while the Results and Discussion examine environmental factors in relation to income-based longevity differences. Our copy does not address whether social or physical environments mediate the relationship between race and health or explain health inequities among US racial and ethnic minority groups (pages not provided). (LLM, conf=0.74)

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  • A blanket ban on all PFAS substances could have serious economic, industrial, and environmental consequences, and public health implications.

    “A blanket ban on all substances could have serious economic, industrial, and environmental consequences, and, paradoxically, public health implications [5,6].”

    From The EU’s Per- and Polyfluoroalkyl Substances (PFAS) Ban: A Case of Policy over Science (2023) · cites this paper as [5]

    The Results section discusses income, life expectancy, and environmental factors, but not PFAS or bans on substances. Searches for a blanket PFAS ban and for the economic, industrial, environmental, and public-health effects of chemical bans found no passage addressing the claim. Page numbers were unavailable in our copy. (LLM, conf=0.78)

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  • The gap in age at death between the richest and poorest 1% of individuals in the US between 2000-2014 was about 15 years in males and 10 years in females.

    “The gap in age at death between the richest and poorest 1% of individuals in the US between 2000–2014 was about 15 years in males and 10 years in females.65”

    From Global health 2050: the path to halving premature death by mid-century (2024) · cites this paper as [65]

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Research works printed but not linked: 4

  1. [5]Berkman L Kawachi I Glymour M Social Epidemiology 2014 2nd Oxford, England Oxford University Press
  2. [12]National Academy of Science Engineering and Medicine The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses 2015 Washington, DC National Academies Press 26468563
  3. [17]Coile C Gruber J The effect of Social Security on retirement in the United States. Social Security Programs and Retirement Around the World: Micro-estimation 2004 Chicago, IL University of Chicago Press 691 730
  4. [43]Skinner J Pauly M McGuire T Barros P Causes and consequences of regional variations in health care Handbook of Health Economics 2012 Amsterdam, the Netherlands Elsevier

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Authors
Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., & Cutler, D.
Affiliations
  • Stanford University (US)
  • Massachusetts Institute of Technology (US)
  • McKinsey & Company (United States) (US)
  • Harvard University (US)
  • United States Department of the Treasury (US)
ORCID
Venue
JAMA
Publisher
American Medical Association (AMA)
Published
2016-04-26
Volume
315
Issue
16
Pages
1750
ISSN
  • 0002-9955 (ISSN-L)
  • 0098-7484
DOI
10.1001/jama.2016.4226
PMC
PMC4866586
OpenAlex
W2332787986
Cited by
2,812
References
67
Citations by year
2026: 180 · 2025: 303 · 2024: 284 · 2023: 262 · 2022: 305 · 2021: 483 · 2020: 268 · 2019: 241 · 2018: 186 · 2017: 208 · 2016: 87 · 2014: 1
FWCI
317.97
Type
journal article
Language
en
Open access
Locations
  • JAMA (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute on Aging (P01AG005842)
Topics
  • Social Sciences › Health › Health disparities and outcomes
  • Social Sciences › Sociology and Political Science › Income, Poverty, and Inequality
  • Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
Keywords
income inequality, household earnings, regional variation, small area variation, racial and ethnic disparities, sex differences, mortality trends, low-income populations, United States, tax registers, health disparities, socioeconomic status, health care access, health insurance coverage, health care costs
MeSH terms
Age Factors, Aged, Educational Status, Employment, Environment, Female, Health Services Accessibility, Humans, Income, Life Expectancy, Longevity, Male, Middle Aged, Mortality, Poverty, Sex Factors, Sex Ratio, Socioeconomic Factors, Time Factors, United States, Emigrants and Immigrants

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