Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands
Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy Stoeldraijer, L.; van Duin, C.; van Wissen, Leonardus; Janssen, Fanny 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
... IVE 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 Stoeldraijer et al.: Impact on projected future life expectancy of different mortality forecasting methods 324 http://www.demographic-research.org 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 ) 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.