Statistical Insights: Large inequalities in longevity by gender and education in OECD countries

OECD Statistics Directorate

Click for more Statistical Insights

While differences in average longevity, or life expectancy, between countries are well-documented, inequalities in longevity within countries are less well-understood and are not fully comparable beyond a handful of European countries. A recent OECD working paper (Murtin et al., 2017) fills this gap by analysing inequalities in longevity by education and gender in 23 OECD countries in 2011.

Measures of inequalities in longevity show that, on average, the gap in life expectancy between high and low-educated people is equal to 8 years for men and 5 years for women at the age of 25 years; and  3.5 years for men and 2.5 years for women at the age of 65. Cardio-vascular diseases, the primary cause of death for the over 65s, are the primary cause of mortality inequality between the high and low-education elderly.

Key findings

Figure 1 shows the longevity gaps between high and low-educated people at the age of 25 and 65. At age 25, life expectancy is 48.9 years for men with low education, 52.6 years for those with medium education and 56.6 years for those with high education. The corresponding figures for women are 55.5 years, 58.3 years and 60.1 years respectively.

Longevity gaps differ markedly across countries. High-educated 25 year-old men for example can expect to live more than 11 years longer than their low-educated counterparts in Latvia, Poland, the Czech Republic and Hungary, while the gap is  less than  5 years in Portugal, Turkey, Italy, New Zealand and Mexico. In the case of women, inequalities in life expectancy are relatively small in Austria, Israel, Portugal and Italy, but amount to over 6 years in Latvia, Poland, Belgium and Chile.

Large inequalities in longevity by education persist even at older ages. At 65 years, life expectancy for men, on average in the OECD, is 15.8 years for those with low education, 17.1 for those with medium education and 19.2 years for those with high education. The corresponding figures for women are 19.6, 20.8 and 21.9 years. In relative terms, i.e. expressed as a share of the remaining lifespan, gaps in longevity are larger at 65 than at 25.

While differences in average life span (i.e. longevity or life expectancy) between groups of education and gender are large, this masks wider differences in life span within groups when other factors, such as genetics and exposure to risk factors are taken into account. Indeed, combined, education and gender, only account for around 10% of the total variation in lifespan.

Breaking down mortality rates (measured as the probability of death in a given year) of people aged between 65 and 89 years by causes of death (circulatory causes such as heart failure, neoplasms or cancer, external causes such as accidents, and other causes) reveals that circulatory problems are the leading cause of death for both gender and education groups (Figure 2). Indeed they account for about 40% of total mortality, with neoplasms and other causes of death accounting for between 25% and 30%. Circulatory problems are slightly more prevalent among the low-educated, for both men and women.

Focusing on low-educated older men, circulatory problems are the most frequent cause of death in high-mortality countries such as Latvia, the Czech Republic, Poland and Hungary, where they account for around half of all deaths, as compared to around one third of deaths in Canada (28%), the United Kingdom (30%), Norway (37%) and Turkey (31%). Conversely, other causes of death are relatively more prevalent in low-mortality countries.

Circulatory problems are also the main factor explaining the mortality gap between education groups at older age. For elderly people, circulatory diseases contribute to 41% of the difference in mortality rates between low and high-educated men and 49% between low and high educated women.

Addressing the risk factors underlying circulatory diseases, in particular smoking, seems as an efficient way of reducing both average mortality rates and inequalities in longevity across education groups. According to Mackenbach (2016), smoking accounts for up to half of the observed inequalities in mortality rates in some European countries; also, while its contribution to inequalities in longevity has decreased in most countries for men, it has increased among women.

 The measure explained

Longevity is statistically defined as the average number of years remaining at a given age. It is calculated as the mean length of life of a hypothetical cohort assumed to be exposed since starting age until death of all their members to the mortality rates observed at a given year. Mortality rate is a measure of the number of deaths in a particular population, scaled to the size of that population, per unit of time.

Estimates of life expectancy by education are drawn from data compiled in different ways in different countries. Two main approaches (study design) can be distinguished:

  1. A “cross-sectional’ (unlinked) design implies that information on the socio-economic characteristic of the deceased is drawn directly from death certificates, as reported by relatives or public officials, while population numbers for the same population categories (the denominator of mortality rates) are drawn from the most recent population censuses. An obvious drawback of this “unlinked” design, which is still used by most countries reviewed in the accompanying OECD working paper, is that it can only be implemented when death certificates include information on the occupation and education of the deceased. In addition, even when this information is included in death certificates, it may be affected by (large) recording errors;
  2. A “linked design” implies that socio-economic information on the deceased is retrieved by individual data linkage to the most recent population census or administrative register records. While both types of data are used in this study the “linked approach” is generally associated with higher quality data. Beyond these differences, a number of data treatments are implemented to correct for statistical biases and anomalies that may arise when calculating mortality rates based on a small number of deceased with specific age, gender and education characteristics.

The measure of inequalities in longevity described above have two specific features that may affect cross-country comparisons: first, life expectancy is disproportionately affected by mortality rates at very young ages compared to mortality rates at older ages; second, measures of life expectancy by education are also affected by the fact that distributions of education vary across countries and time. Alternative measures of inequalities in longevity have been examined, and they all show very large correlations across countries. In other terms, accounting for differences in the size of the various educational groups or using average mortality rates rather than life expectancy measures, does not change the assessment of countries’ rankings significantly. There are also significant cross-country differences between these measures of inequality in longevity and more traditional measures of inequality in, for example, income: longevity inequality are lowest in Italy, where income inequality is relatively high, and highest in many Eastern European countries, where income inequality is relatively low.

Where to find the underlying data

The underlying data can be found online at the following address:

Useful links

Mackenbach, J.P. (2016), Health Inequalities in Europe, Erasmus University Publishing, Rotterdam

Murtin, F., Mackenbach, J.P., Jasilionis, D. and M. Mira d’Ercole  (2017), “Inequalities in Longevity by Education in OECD Countries: Insights from New OECD Estimates”, OECD Statistics Working Papers, 2017/2, OECD Publishing, Paris .

Guest author

Has one comment to “Statistical Insights: Large inequalities in longevity by gender and education in OECD countries”

You can leave a reply or Trackback this post.

Leave a Reply