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How Has the Lower Boundary of Human Mortality Evolved, and Has It Already Stopped Decreasing?

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  • Marcus Ebeling

    (University of Rostock
    Max Planck Institute for Demographic Research)

Abstract

In contrast to the upper boundary of mortality, the lower boundary has so far largely been neglected. Based on the three key features—location, sex-specific difference, and level—I analyze past and present trends in the lower boundary of human mortality. The analysis is based on cohort mortality data for 38 countries, covering all the cohorts born between 1900 and 1993. Minimum mortality is analyzed using observed as well as smoothed estimates. The results show that the ages at which minimum mortality is reached have shifted to lower ages. Although the differences have become almost negligible over time, males are showing higher levels of minimum mortality than females. The level of minimum mortality was halved more than five times over the analyzed time horizon. The results also suggest that even after more than 150 years of mortality improvements, minimum mortality has not yet reached a lowest limit and is likely to decrease further in the near future. Trends in the three key features also raise questions about the importance of evolutionary, social, and biological determinants for the recent and future development of minimum mortality.

Suggested Citation

  • Marcus Ebeling, 2018. "How Has the Lower Boundary of Human Mortality Evolved, and Has It Already Stopped Decreasing?," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1887-1903, October.
  • Handle: RePEc:spr:demogr:v:55:y:2018:i:5:d:10.1007_s13524-018-0698-z
    DOI: 10.1007/s13524-018-0698-z
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    References listed on IDEAS

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    1. Modin, Bitte, 2002. "Birth order and mortality: a life-long follow-up of 14,200 boys and girls born in early 20th century Sweden," Social Science & Medicine, Elsevier, vol. 54(7), pages 1051-1064, April.
    2. David Cutler & Angus Deaton & Adriana Lleras-Muney, 2006. "The Determinants of Mortality," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 97-120, Summer.
    3. Djeundje, V. A. B. & Currie, I. D., 2011. "Smoothing dispersed counts with applications to mortality data," Annals of Actuarial Science, Cambridge University Press, vol. 5(1), pages 33-52, March.
    4. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    5. Chu, C.Y. Cyrus & Chien, Hung-Ken & Lee, Ronald D., 2008. "Explaining the optimality of U-shaped age-specific mortality," Theoretical Population Biology, Elsevier, vol. 73(2), pages 171-180.
    6. Camarda, Carlo G., 2012. "MortalitySmooth: An R Package for Smoothing Poisson Counts with P-Splines," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i01).
    7. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
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    Cited by:

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