Academic journal article Demographic Research

Impact of Different Mortality Forecasting Methods and Explicit Assumptions on Projected Future Life Expectancy: The Case of the Netherlands

Academic journal article Demographic Research

Impact of Different Mortality Forecasting Methods and Explicit Assumptions on Projected Future Life Expectancy: The Case of the Netherlands

Article excerpt

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.

(ProQuest: ... denotes formulae omitted.)

1. Introduction

With the rapid aging of the population, mortality forecasts have become more important. Recent reforms in the pension systems in Europe-which were necessary to ensure that pensions remain sustainable-have made the link between pensions and changes in life expectancy more apparent than ever. In general, monthly pension payments are based on remaining life expectancy when people retire. But whereas in some countries benefit levels are linked to life expectancy (Germany, Finland, and Portugal), in others the pension age is set to rise with increasing life expectancy (Denmark, the Netherlands), or the contribution period for pensions is set to be extended as people live longer (France) (OECD 2007). The accurate modelling and projection of mortality rates and life expectancy are therefore of growing interest to researchers.

As mortality forecasts have become increasingly important, numerous models for mortality modelling and forecasting have been developed (for reviews see Pollard 1987; Tabeau 2001; Wong-Fupuy and Haberman 2004; Booth and Tickle 2008). The various methods for mortality forecasting can be divided into three approaches: extrapolation, explanation, and expectation (Booth and Tickle 2008). Extrapolative methods make use of the regularity typically found in both age patterns and trends in time. The explanation approach makes use of structural or epidemiological models of mortality from certain causes of death for which the key exogenous variables are known and can be measured. The expectation approach is based on the subjective opinions of experts involving varying degrees of formality. It should be noted that some mortality forecasting methods include aspects of one or more approaches.

In the past, most methods were relatively simple and were largely based on subjectivity (Pollard 1987). Over time, however, more sophisticated methods that make increasing use of standard statistical methods have been developed and applied (Booth and Tickle 2008). …

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