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The Linear Link: Deriving Age-Specific Death Rates from Life Expectancy

Author

Listed:
  • Marius D. Pascariu

    (Biometric Risk Modelling Chapter, SCOR Global Life SE, 75795 Paris, France
    Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, 5000 Odense, Denmark)

  • Ugofilippo Basellini

    (Max Planck Institute for Demographic Research (MPIDR), 18057 Rostock, Germany
    Institut National D’études Démographiques (INED), 93300 Aubervilliers, France)

  • José Manuel Aburto

    (Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, 5000 Odense, Denmark
    Max Planck Institute for Demographic Research (MPIDR), 18057 Rostock, Germany
    Leverhulme Centre for Demographic Science, Department of Sociology and Nuffield College, University of Oxford, Oxford OX1 2BQ, UK)

  • Vladimir Canudas-Romo

    (School of Demography, The Australian National University, Canberra 2600, Australia)

Abstract

The prediction of human longevity levels in the future by direct forecasting of life expectancy offers numerous advantages, compared to methods based on extrapolation of age-specific death rates. However, the reconstruction of accurate life tables starting from a given level of life expectancy at birth, or any other age, is not straightforward. Model life tables have been extensively used for estimating age patterns of mortality in poor-data countries. We propose a new model inspired by indirect estimation techniques applied in demography, which can be used to estimate full life tables at any point in time, based on a given value of life expectancy at birth. Our model relies on the existing high correlations between levels of life expectancy and death rates across ages. The methods presented in this paper are implemented in a publicly available R package.

Suggested Citation

  • Marius D. Pascariu & Ugofilippo Basellini & José Manuel Aburto & Vladimir Canudas-Romo, 2020. "The Linear Link: Deriving Age-Specific Death Rates from Life Expectancy," Risks, MDPI, vol. 8(4), pages 1-18, October.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:4:p:109-:d:431746
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    References listed on IDEAS

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    1. John Bongaarts, 2005. "Long-range trends in adult mortality: Models and projection methods," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 23-49, February.
    2. Møller,Thomas & Steffensen,Mogens, 2007. "Market-Valuation Methods in Life and Pension Insurance," Cambridge Books, Cambridge University Press, number 9780521868778.
    3. Les Mayhew & David Smith, 2013. "A new method of projecting populations based on trends in life expectancy and survival," Population Studies, Taylor & Francis Journals, vol. 67(2), pages 157-170, July.
    4. Adrian E. Raftery & Nevena Lalic & Patrick Gerland, 2014. "Joint probabilistic projection of female and male life expectancy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(27), pages 795-822.
    5. John Wilmoth & Shiro Horiuchi, 1999. "Rectangularization revisited: Variability of age at death within human populations," Demography, Springer;Population Association of America (PAA), vol. 36(4), pages 475-495, November.
    6. John Wilmoth & Sarah Zureick & Vladimir Canudas-Romo & Mie Inoue & Cheryl Sawyer, 2012. "A flexible two-dimensional mortality model for use in indirect estimation," Population Studies, Taylor & Francis Journals, vol. 66(1), pages 1-28.
    7. Dickson,David C. M. & Hardy,Mary R. & Waters,Howard R., 2013. "Actuarial Mathematics for Life Contingent Risks," Cambridge Books, Cambridge University Press, number 9781107044074, October.
    8. Ediev, Dalkhat M., 2011. "Robust backward population projections made possible," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1241-1247, October.
    9. 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.
    10. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
    11. Dickson,David C. M. & Hardy,Mary R. & Waters,Howard R., 2013. "Solutions Manual for Actuarial Mathematics for Life Contingent Risks," Cambridge Books, Cambridge University Press, number 9781107620261, February.
    12. Pascariu, Marius D. & Canudas-Romo, Vladimir & Vaupel, James W., 2018. "The double-gap life expectancy forecasting model," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 339-350.
    13. Lenny Stoeldraijer & Coen van Duin & Leo van Wissen & Fanny Janssen, 2013. "Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(13), pages 323-354.
    14. Roland Rau & Eugeny Soroko & Domantas Jasilionis & James W. Vaupel, 2008. "Continued Reductions in Mortality at Advanced Ages," Population and Development Review, The Population Council, Inc., vol. 34(4), pages 747-768, December.
    15. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
    16. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    17. Torri, Tiziana & Vaupel, James W., 2012. "Forecasting life expectancy in an international context," International Journal of Forecasting, Elsevier, vol. 28(2), pages 519-531.
    18. Juha Alho, 2000. "“The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications“, Ronald Lee, January 2000," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 91-93.
    19. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.
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    Cited by:

    1. Andrea Nigri & Susanna Levantesi & Jose Manuel Aburto, 2022. "Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(8), pages 199-232.
    2. Jorge Miguel Bravo & Mercedes Ayuso & Robert Holzmann & Edward Palmer, 2021. "Intergenerational Actuarial Fairness when Longevity Increases: Amending the Retirement Age," CESifo Working Paper Series 9408, CESifo.
    3. 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.
    4. Bergeron-Boucher, Marie-Pier & Vázquez-Castillo, Paola & Missov, Trifon, 2022. "A modal age at death approach to forecasting mortality," SocArXiv 5zr2k, Center for Open Science.

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