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What Levels the Association Between Income and Mortality in Later Life: Age or Health Decline?

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  • Johan Rehnberg
  • Jessica Kelley

Abstract

ObjectivesResearchers frequently use the “age-as-leveler” hypothesis to explain decreasing inequality and a weakened relationship between socioeconomic position and health in old age. This study examined whether health status can explain the age pattern in the association between income and mortality as predicted by the age-as-leveler hypothesis.MethodThis study used longitudinal (1991–2002) data from the SWEOLD and LNU surveys. The analytical sample consisted of 2,619 people aged 54–92 in 2003. Mortality (2003–2014) and income (1991–2000) was collected from Swedish national registers. Poisson regression was used to estimate associations between mortality, income, age, and health status. Average marginal effects were used to visualize interaction effects between income and age.ResultsThe association between income and mortality weakened in those aged 84 and older. However, health status explained a large part of the effect that age had on the association between income and mortality. Analyses done after stratifying the sample by health status showed that the association between income and mortality was strong in people who reported good health and weak or nonexistent in those who reported poor health.DiscussionAge leveled the income–mortality association; however, health status, not age, explained most of the leveling.

Suggested Citation

  • Johan Rehnberg & Jessica Kelley, 2020. "What Levels the Association Between Income and Mortality in Later Life: Age or Health Decline?," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 75(2), pages 426-435.
    • Handle: RePEc:oup:geronb:v:75:y:2020:i:2:p:426-435.
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    File URL: http://hdl.handle.net/10.1093/geronb/gbz082
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    References listed on IDEAS

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    1. Anna Zajacova & Noreen Goldman & German Rodriguez, 2009. "Unobserved Heterogeneity Can Confound the Effect of Education on Mortality," Mathematical Population Studies, Taylor & Francis Journals, vol. 16(2), pages 153-173.
    2. Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(1), pages 63-82, January.
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    1. Rehnberg, Johan & Östergren, Olof & Esser, Ingrid & Lundberg, Olle, 2021. "Interdependent pathways between socioeconomic position and health: A Swedish longitudinal register-based study," Social Science & Medicine, Elsevier, vol. 280(C).
    2. Hyunju Kim & Younkyoung Kim, 2020. "Factors influencing the use of health services by trauma patients according to insurance type and injury severity score in South Korea: Based on Andersen’s behavioral model," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. David Mhlanga & Rufaro Garidzirai, 2020. "The Influence of Racial Differences in the Demand for Healthcare in South Africa: A Case of Public Healthcare," IJERPH, MDPI, vol. 17(14), pages 1-10, July.
    4. Mengling Cheng & Nicolas Sommet & Daniela S. Jopp & Dario Spini, 2023. "Evolution of the income-related gap in health with old age: evidence from 20 countries in European and Chinese panel datasets," European Journal of Ageing, Springer, vol. 20(1), pages 1-11, December.

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