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Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands

Author

Listed:
  • Lenny Stoeldraijer

    (Centraal Bureau voor de Statistiek (CBS))

  • Coen van Duin

    (Centraal Bureau voor de Statistiek (CBS))

  • Leo van Wissen

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

  • Fanny Janssen

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

Abstract

Background: With the rapid aging of the population, mortality forecasting becomes increasingly important, especially for the insurance and pension industries. However, a wide variety of projection methods are in use, both between and within countries, that produce different outcomes. Objective: We review the different mortality forecasting methods and their assumptions in Europe, and assess their impact on projections of future life expectancy for the Netherlands. Methods: For the Netherlands, we assess the projections of life expectancy at birth (e0) and at age 65 (e65) up to 2050 resulting from different methods using similar explicit assumptions regarding the historical period and the jump-off rates. We compare direct linear extrapolation, the Lee-Carter model, the Li-Lee model, a cohort model, separate projections of smoking- and non-smoking-related mortality, and the official forecast. Results: In predicting mortality, statistical offices in Europe mostly use simple linear extrapolation methods. Countries with less linear trends employ other approaches or different assumptions. The approaches used in the Netherlands include explanatory models, the separate projection of smoking- and non-smoking-related mortality, and the projection of the age profile of mortality. There are clear differences in the explicit assumptions used, including assumptions regarding the historical period. The resulting e0 in 2050 varies by approximately six years. Using the same historical period (1970-2009) and the observed jump-off rates, the findings generated by different methods result in a range of 2.1 years for women and of 1.8 years for men. For e65, the range is 1.4 and 1.9 years, respectively. Conclusions: As the choice of the explicit assumptions proved to be more important than the choice of the forecasting method, the assumptions should be carefully considered when forecasting mortality.

Suggested Citation

  • 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.
    • Handle: RePEc:dem:demres:v:29:y:2013:i:13
    DOI: 10.4054/DemRes.2013.29.13
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    6. Malene Kallestrup‐Lamb & Søren Kjærgaard & Carsten P. T. Rosenskjold, 2020. "Insight into stagnating adult life expectancy: Analyzing cause of death patterns across socioeconomic groups," Health Economics, John Wiley & Sons, Ltd., vol. 29(12), pages 1728-1743, December.
    7. 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.
    8. 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.
    9. Jorge M. Uribe & Helena Chuliá & Montserrat Guillen, 2018. "Trends in the Quantiles of the Life Table Survivorship Function," European Journal of Population, Springer;European Association for Population Studies, vol. 34(5), pages 793-817, December.
    10. Jackie Li & Leonie Tickle & Nick Parr, 2016. "A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes," Journal of Population Research, Springer, vol. 33(4), pages 333-360, December.
    11. Apostolos Bozikas & Georgios Pitselis, 2018. "An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing," Risks, MDPI, vol. 6(2), pages 1-34, April.
    12. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    13. 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.
    14. F. Peters & J. P. Mackenbach & W. J. Nusselder, 2016. "Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 687-702, December.
    15. Ševčíková Hana & Raftery Adrian E., 2021. "Probabilistic Projection of Subnational Life Expectancy," Journal of Official Statistics, Sciendo, vol. 37(3), pages 591-610, September.
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    17. Malene Kallestrup-Lamb & Søren Kjærgaard & Carsten P. T. Rosenskjold, 2019. "Insight into Stagnating Life Expectancy: Analysing Cause of Death Patterns across Socio-economic Groups," CREATES Research Papers 2019-20, Department of Economics and Business Economics, Aarhus University.
    18. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
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    More about this item

    Keywords

    Europe; Netherlands; smoking; nonlinear models; extrapolation; Lee-Carter model;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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