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Demographic Perspectives on Predicting Individual-level Mortality

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  • Breen, Casey
  • Seltzer, Nathan

    (University of California, Berkeley)

Abstract

There are striking disparities in longevity across sociodemographic groups in the United States. Yet, can sociodemographic characteristics meaningfully explain individual-level variation in longevity? Here, we leverage machine-learning algorithms and large-scale administrative data to predict individual-level mortality using an array of social, economic, and demographic predictors measured in early adulthood. We conduct two distinct analyses: a cohort analysis, which predicts the exact age of death for individuals in the same birth cohort, and a period analysis, which predicts whether individuals age 54–95 will die within the next 10 years. We are not able to make accurate predictions in either our cohort analysis (R2= 0.014) or our period analysis (R2= 0.166).Together, these analyses demonstrate that later life longevity is unpredictable using sociodemographic characteristics alone, and underscore the crucial need to account for stochastic processes in demographic theory

Suggested Citation

  • Breen, Casey & Seltzer, Nathan, 2023. "Demographic Perspectives on Predicting Individual-level Mortality," SocArXiv znsqg_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:znsqg_v1
    DOI: 10.31219/osf.io/znsqg_v1
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    References listed on IDEAS

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