Introduction
Increased longevity poses great challenges to the welfare state, including the sustainability of pension systems. In response to these challenges, several countries introduced changes in the pension system and increased the retirement age [
1,
2]. According to the OECD, around two-thirds of reforms automatically linked future pensions to (projected) changes in life expectancy. Some countries adjust benefit levels to life expectancy (Germany, Finland, and Portugal), other countries link the number of years of contributions needed for a full pension to life expectancy (France), whereas again in other countries the pension age is linked to the increase in life expectancy (the Netherlands, Denmark, Estonia) [
2,
3]. For these and other purposes, national projections of future mortality are made periodically by statistical offices or actuarial societies in Europe. Most projections are based on extrapolative approaches, with the Lee-Carter method mostly used [
4‐
6]. The Lee-Carter model summarizes mortality by age and period for a single population as an overall time trend, an age profile, and the age-specific deviations of mortality change over the entire fitting period [
7]. Some recent national projections include mortality data from neighboring countries to increase the robustness of the projections using the Lee and Li approach [
5,
6,
8].
Linkage of future pensions to the life expectancy of the national population might have different consequences for different socioeconomic groups, because of differences in mortality within the national population. People with a lower level of education on average have higher mortality than people with a higher level of education [
9]. Inequalities in mortality translate into substantial inequalities in life expectancy. For example, in the Netherlands the gap in period life expectancy at birth between high and low educated is 6.3 years for men and 3.3 years for women [
10].
National projections of life expectancy may not provide a good expectation of the future trends in life expectancy of different social-economic groups. First, there is no guarantee that trends in mortality of different socioeconomic groups are parallel or converging to a common overall trend. Over the past two to four decades relative inequalities in mortality have increased in almost all European countries, whereas absolute inequalities in mortality trends have followed a more variable course [
9,
11‐
13]. Moreover, trends in equalities have been shown to differ depending on the mortality measures and inequality measures that are used [
9].
Second, even in the situation of equal trends in mortality rates of different socioeconomic groups, this may not translate into equal trends in life expectancy of these groups. Paradoxically, an increasing gap in life expectancy between socioeconomic groups may even arise in the situation of an identical drop in mortality rates for each group. Such an identical absolute drop in mortality rates increases life expectancy of the higher socioeconomic group more because of the higher ‘ex post survivability’, i.e., as compared to lower socioeconomic groups, higher socioeconomic groups have lower mortality rates at ages above the ages at which the drop occurred. Moreover, people from lower socioeconomic groups are less likely than those from higher socioeconomic groups to survive long enough to benefit from the reduction in mortality that occurs at older ages (‘ex ante survivability’) [
14]. This implies that an equal reduction in mortality of low and high educated may translate into a larger life expectancy increase of the higher socioeconomic group.
Third, when socioeconomic status is measured by education and different educational groups have identical trends in life expectancy, this common trend will not be equal to that of the national population. Because of educational expansion, i.e., a growing part of the population having a higher education and a reducing part having a lower education, the increase in life expectancy at the national level is partly due to changes in the educational composition. As a consequence, even in the unlikely situation of zero change in life expectancy of each educational subgroup, life expectancy of the national population will increase due to educational expansion. Similarly, identical non-zero trends in life expectancy of each educational subgroup yield larger increases in life expectancy of the national population than for each subgroup. Luy et al. [
15] estimated that the change in educational composition between around 1990 and 2010 accounted for approximately one year of the increase in life expectancy at age 30 in Italy and Denmark, and about 0.5 year in the United States. This corresponds to 19.1% of the total increase in that period for Italy, 19.9% for the US, and 24% for Denmark.
Because future trends in life expectancy for different socioeconomic subgroups cannot be assumed to be same as for the national population, there is an urgent need for mortality projections for different socioeconomic groups. To date, projections of mortality and of resulting life expectancy for different socioeconomic groups are scarce. Some exceptions are a recent projection of life expectancy at birth for different income groups for South Korea [
16], projections of remaining life expectancy for socioeconomic groups in Denmark based on an individual affluence index [
17], and a projection of life expectancy at age 65 for different education groups for the Netherlands, published five years ago [
18]. In addition, there are a few projections by deprivation or wealth index of small areas [
19,
20], a study that models mortality for socioeconomic groups but does not produce forecasts [
21], and a study that models and projects mortality for different socioeconomic groups but does not present forecasts of future life expectancies of these groups [
22].
Two reasons may explain the scarcity of mortality projections for different socioeconomic groups. First, the availability of time-series data of mortality by socioeconomic group, age and gender is limited, and if available time series are generally shorter than routinely used for mortality projections. Second, independent extrapolations of mortality for separate socioeconomic groups can lead to inconsistent results across the subgroups because it ignores common factors that may affect all subgroups. Mortality projections by subgroup require more complex approaches, such as the Lee and Li approach [
23], that account for those common factors and that produce coherent projections for the different subgroups. The original Li and Lee approach uses mortality data of several countries to create a broad empirical basis for the identification of the most likely long-term common trend combined with country-specific deviations from that common trend [
8,
24,
25]. It is currently used to project national mortality rates for Belgium and the Netherlands, using data from multiple countries [
6]. The combination of the multi-country approach and different socioeconomic groups that we develop in our study is a natural next step that allows us to maximally use available data to make stable projections for educational groups.
The objective of this study is to derive insight in future trends in life expectancies for low, mid and high educated men and women living in the Netherlands. To improve the robustness of the extrapolations and to include information on longer time trends of mortality than the relatively short time series by education in the Netherlands, we use a three-layered Lee and Li approach. As upper layer we use national mortality data by age and gender in the Netherlands and five other North-Western European countries for the period 1970–2018, as second layer we use education-specific mortality by age and gender from these countries per 5-year periods for the period 1990–2015, and as third layer we use mortality by education, age and gender per year for the Netherlands for the period 2006–2018.
Conclusion and implications
Based on our projections, we conclude that whereas all educational groups are expected to experience further increases in life expectancy between age 35 and 85 as well as in remaining life expectancy at age 35 and 65, low educated are not expected to catch up with higher educated peers. Trends for low educated women are expected to be the least favorable, particularly between ages 35 and 85. We further conclude that inequality in mortality and life expectancy between educational groups are persistent and may increase further in the future.
This persistence of inequalities in life expectancies between education groups combined with the declining share of lower educated and increasing share of higher educated groups in the population, has implications for the interpretation of future changes in period life expectancy of national populations and for their use in economic and social policy. First, part of the increase in period life expectancy does not reflect a reduction in mortality rates of the different groups, but an increase in the fraction of high educated in the population. Second and more importantly, national projections mask the large and persistent inequalities between the socioeconomic groups we found in our study. This is particularly relevant when national projections of life expectancy are used in pension policies, as is the case in many countries. In the Netherlands, for example, pension age is linked to projected changes in life expectancy of the national population [
60]. This causes equity concerns. To assess the viability of such policies, there is a need to address variations in level and future trends in life expectancy of different socioeconomic groups. In addition, variations in years lived in good health relative to the increased pension age [
38] are also important to consider in a pension reform, as poor health may push people to unemployment or disability benefits before they reach the retirement age. Differentiation of the statutory pension age by socio-economic position is a possible scenario to address both inequalities in life expectancy after retirement as well as inequalities in the expected number of years in good health before the retirement. However, practical implementation of a differentiated pension age is highly complicated and politically contentious. It involves dividing people in groups based on criteria that are partly subjective, while the financial consequences of the division can be substantial.
Acknowledgements
Results were based on calculations by Erasmus MC, Rotterdam, the Netherlands using non-public microdata from Statistics Netherlands. Under certain conditions, these microdata are accessible for statistical and scientific research. For further information: microdata@cbs.nl. In addition, aggregate data were used from 5 European countries. The underlying country-specific datasets are not publicly available but are accessible through the authors under certain conditions. Mortality data for other countries have been collected as part of the LIFEPATH project, which has received financial support from the European Commission (Horizon 2020 grant number 633666), and as part of the DEMETRIQ project, which also received support from the European Commission (grant number FP7-CP-FP grant no. 278511). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Henrik Brønnum-Hansen, Patrick Pekka Martikainen, Matthias Bopp, Patrick Deboosere and Heine Strand for providing data for their countries. The mortality data for Switzerland were obtained from the Swiss National Cohort, which is based on mortality and census data provided by the Federal Statistical Office and supported by the Swiss National Science Foundation (grant nos. 3347CO-108806, 33CS30_134273 and 33CS30_148415).
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