Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method.
IntroductionLife expectancy (LE) has been increasing in most Western countries and is expected to continue to do so (Tuljapurkar et al. 2000;Oeppen and Vaupel 2002; White 2002;Bongaarts 2004;Christensen et al. 2009). It is a phenomenon with important implications for society because larger numbers of elderly people pose additional burdens on the healthcare and pension systems (Bongaarts 2004;Christensen et al. 2009). In many countries it has led to political debates about the statutory retirement age, and about how to finance the growing healthcare expenditures with public funds. Some Western European countries have explicitly linked their retirement age to the increase in LE (Ageing Working Group 2012), and the prospect of a continuing rise is reflected in the higher premiums now charged by the life insurance and annuity industry (Pitacco et al. 2009;De Waegenaere et al. 2010). But what are ignored by such measures are the great differences in length of life by socio-economic status (SES) (Mackenbach et al. 2008;Van Kippersluis et al. 2010). Those with fewer years of education have much shorter lives, and a growing number of studies report a widening of inequalities in LE between SES groups. In consequence the trend in the LE of the population as a whole becomes ever less informative about the life expectancies of different subgroups (Mackenbach et al. 2003). This means that forecasts of the life expectancies for these groups are needed if the political debate is to be adequately informed.From about the 1980s, a growing number of techniques for forecasting LE became available (Booth and Tickle 2008). Although there have been exceptions, most approaches are based on time-series extrapolation models such as the Lee-Carter model (Lee 2000). Lee-Carter-based methods decompose time series of age-specific mortality rates into a latent time trend and an associated interaction with different age categories. The latent time trend is then forecast using ARIMA modelling (basically a random walk with drift) and serves as a basis for the derivation of future age profiles of mortality rates a...