IDEAS home Printed from https://ideas.repec.org/a/spr/demogr/v55y2018i5d10.1007_s13524-018-0698-z.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13524-018-0698-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13524-018-0698-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucia Zanotto & Vladimir Canudas-Romo & Stefano Mazzuco, 2021. "A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 1-27, March.
    2. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    3. Marco Bonetti & Ugofilippo Basellini, 2021. "Epilocal: A real-time tool for local epidemic monitoring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(12), pages 307-332.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    2. Lingbing Feng & Yanlin Shi, 2018. "Forecasting mortality rates: multivariate or univariate models?," Journal of Population Research, Springer, vol. 35(3), pages 289-318, September.
    3. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.
    4. Ainhoa-Elena Léger & Stefano Mazzuco, 2021. "What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 769-798, November.
    5. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    6. Carl Schmertmann, 2021. "D-splines: Estimating rate schedules using high-dimensional splines with empirical demographic penalties," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(45), pages 1085-1114.
    7. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    8. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    9. Niels Haldrup & Carsten P. T. Rosenskjold, 2019. "A Parametric Factor Model of the Term Structure of Mortality," Econometrics, MDPI, vol. 7(1), pages 1-22, March.
    10. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    11. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 485564, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.
    12. D’Amato, Valeria & Di Lorenzo, Emilia & Haberman, Steven & Sagoo, Pretty & Sibillo, Marilena, 2018. "De-risking strategy: Longevity spread buy-in," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 124-136.
    13. Hong Li & Johnny Siu-Hang Li, 2017. "Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1073-1095, June.
    14. Dowd, Kevin & Cairns, Andrew J.G. & Blake, David & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2010. "Evaluating the goodness of fit of stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 255-265, December.
    15. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    16. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    17. Myrskylä, Mikko & Elo, Irma T. & Kohler, Iliana V. & Martikainen, Pekka, 2014. "The association between advanced maternal and paternal ages and increased adult mortality is explained by early parental loss," Social Science & Medicine, Elsevier, vol. 119(C), pages 215-223.
    18. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    19. Feng, Lingbing & Shi, Yanlin & Chang, Le, 2021. "Forecasting mortality with a hyperbolic spatial temporal VAR model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 255-273.
    20. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:demogr:v:55:y:2018:i:5:d:10.1007_s13524-018-0698-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.