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Title: Sociodemographic characteristics alone cannot predict individual-level longevity
Citation Type: Miscellaneous
Publication Year: 2023
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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 a large- scale administrative dataset (N = 122,651) to predict individual-level longevity using an array of social, economic, and demographic predictors. Our top-performing model explains only 1.4% of the variation in age of death, demonstrating that human longevity is highly unpredictable using sociodemographic characteristics alone. These results un- derscore the limitations of using machine learning to predict major life outcomes and emphasize the need to better account for stochastic processes in demographic theory.
Url: https://osf.io/preprints/socarxiv/znsqg/download
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Authors: Breen, Casey F.; Seltzer, Nathan
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Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Methodology and Data Collection, Population Data Science
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