Statistical Insights: Large inequalities in longevity by gender and education in OECD countries
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.
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:
- 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;
- 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: www.oecd.org/std/Inequalities-in-longevity-by-education-in-OECD-countries.xlsx
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 .
Economic complexity, institutions and income inequality
Is a country’s ability to generate and distribute income determined by its productive structure? Decades ago Simon Kuznets proposed an inverted-u-shaped relationship describing the connection between a country’s average level of income and its level of income inequality. Kuznets’ curve suggested that income inequality would first rise and then fall as countries’ income moved from low to high. Yet, the curve has proven difficult to verify empirically. The inverted-u-shaped relationship fails to hold when several Latin American countries are removed from the sample, and in recent decades, the upward side of the Kuznets curve has vanished as inequality in many low-income countries has increased. Moreover, several East-Asian economies have grown from low to middle incomes while reducing income inequality.
Together, these findings undermine the empirical robustness of Kuznets’ curve, and indicate that GDP per capita is a measure of economic development that is insufficient to explain variations in income inequality. This agrees with recent work arguing that inequality depends not only on a country’s rate or stage of growth, but also on its type of growth and institutions. Hence, we should expect that more nuanced measures of economic development, such as those focused on the types of products a country exports, should provide information on the connection between economic development and inequality that transcends the limitations of aggregate output measures such as GDP.
Scholars have argued that income inequality depends on a variety of factors, from an economy’s factor endowments, geography, and institutions, to its historical trajectories, changes in technology, and returns to capital. The combination of these factors should be expressed in the mix of products that a country makes. For example, colonial economies that specialised in a narrow set of agricultural or mineral products tend to have more unequal distributions of political power, human capital, and wealth. Conversely, sophisticated products, like medical imaging devices or electronic components, are typically produced in diversified economies that require more inclusive institutions. Complex industries and complex economies thrive when workers are able to contribute their creative input to the activities of firms.
This suggests a model of heterogeneous industries in which firms survive only when they are able to adopt or discover the institutions and human capital that work best in that industry. According to this model, the composition of products that a country exports should tell us about a country’s institutions and about the quality of its human capital. This model would also suggest that a country’s mix of products should provide information that explains inequality and that might escape aggregate measures of development such as GDP, average years of schooling, or survey-based measures of formal and informal institutions.
With our colleagues from the MIT Media Lab, we used the Economic Complexity Index (ECI) to capture information about an economy’s level of development which is different from that captured in measures of income. Economic complexity is a measure of the knowledge in a society that gets translated into the products it makes. The most complex products are sophisticated chemicals and machinery, whereas the least complex products are raw materials or simple agricultural products. The economic complexity of a country depends on the complexity of the products it exports. A country is considered complex if it exports not only highly complex products but also a large number of different products. To calculate the economic complexity of a country, we measure the average ubiquity of the products it exports, then the average diversity of the countries that make those products, and so forth.
For example, in 2012, Chile’s average income per capita and years of schooling ($21,044 at PPP in current 2012 US$ and 9.8 mean years of schooling) were comparable to Malaysia’s income per capita and schooling ($22,314 and 9.5), even though Malaysia ranked 24th in the ECI ranking while Chile ranked 72nd. The rankings reflect differences in these countries’ export structure: Chile largely exports natural resources, while Malaysia exports a diverse range of electronics and machinery (see illustration here). Moreover, these differences in the ECI ranking also point more accurately to differences in these countries’ level of income inequality. Chile’s inequality as measured through the Gini coefficient (0.49) is significantly higher than that of Malaysia (0.39)
We separated the correlation between economic complexity and income inequality from the correlation between income inequality and average income, population, human capital (measured by average years of schooling), export concentration, and formal institutions. Our results document a strong and robust correlation between the economic complexity index and income inequality. This relationship is robust even after controlling for measures of income, education, and institutions, and the relationship has remained strong over the last fifty years. Results also show that increases in economic complexity tend to be accompanied by decreases in income inequality.
Our findings do not mean that productive structures solely determine a country’s level of income inequality. On the contrary, a more likely explanation is that productive structures represent a high-resolution expression of a number of factors, from institutions to education, that co-evolve with the mix of products that a country exports and with the inclusiveness of its economy. Still, because of this co-evolution, our findings emphasize that productive structures are not only associated with income and economic growth, but also with how income is distributed.
We advance methods that enable a more fine-grained perspective on the relationship between productive structures and income inequality. The method is based on introducing the Product Gini Index or PGI, which estimates the expected level of inequality for the countries exporting a given product. Overlaying PGI values on the network of related products allows us to create maps that can be used to anticipate how changes in a country’s productive structure will affect its level of income inequality. These maps provide means for researchers and policy-makers to explore and compare the complex co-evolution of productive structures, institutions and income inequality for hundreds of economies.
This article is based on Linking Economic Complexity, Institutions and Income Inequality, by D. Hartmann, M.R. Guevara, C. Jara-Figueroa, M. Aristarán, C.A. Hidalgo.
The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning; 29/09 afternoon; 30/09 morning
A bright digital future for all: global cooperation to make the best of the digital economy
Ministers, the business community, civil society, labour and the Internet technical community will gather in Cancún, Mexico on 21-23 June for an OECD Ministerial Meeting on the Digital Economy: Innovation, Growth and Social Prosperity. Today’s post is by Andrus Ansip, Vice-President of the European Commission, leading the Project Team Digital Single Market.
Slowly but surely, digitisation has transformed the world’s economy and people’s daily lives and our habits as consumers: how we work, travel, shop and are entertained.
The internet is an amazingly diverse source of innovation and creativity. Nearly three billion internet users are both creators of information as well as consumers.
Digital technologies and the internet offer remarkable development potential. With the OECD’s Ministerial Meeting on the Digital Economy about to be held in Cancún, Mexico, it is clear that digital issues are firmly on the global political agenda.
Although ICT is the fastest growing sector in the world, many millions of people are losing out on the opportunities offered by the digital age simply because they do not have access to digital technologies.
In fact, more than half the world’s people are offline. They cannot download anything, they cannot view a website, surf the internet or send an e-mail. The other half of the world which is online takes all this for granted.
Europe is not immune to this problem.
One hundred million Europeans are digitally excluded, and the percentage of people who have never used the internet is still very high.
There are huge differences in internet usage between young and old people, and similar differences between those with high and low levels of education.
This has an impact not only on individual lives – income, health, education – but also on families, communities, on political process, democracy, public services.
We are tackling these in our plan to build a Digital Single Market for Europe.
This aims to remove barriers that are today preventing people and businesses from getting the most, and best, out of the opportunities offered by the digital age.
It will allow every European to enjoy digital content and services – wherever they are in the EU- for their work, leisure and education.
It is the digital equivalent to the right to non-discrimination.
In Japan in April, G7 ministers agreed a plan for 1.5 billion more people to have internet access by 2020.
This is a good start towards getting rid of digital divides and exclusion around the world. But ultimate success will depend on at least a couple of factors: connectivity and skills.
Firstly and most obviously, we can only achieve this goal if more people have online access – preferably with a high-speed connection.
At the moment, only 15% of the world’s population can afford one.
This is often caused by a lack of competition in many markets, where expensively-priced services are only available to the few who can pay for them.
Secondly, people also need to have the skills to use digital technologies and be able to apply them in a working environment. Digital transformation is structurally changing labour markets around the world; in fact, the very nature of work as we know it today.
At the OECD meeting in Cancún, I will be chairing a panel of experts who will address these and other issues, including the need to raise digital skills and how best to go about it.
I have high hopes for Cancún in general, for it to reach good levels of understanding on a range of digital issues that call for international cooperation and discussion: internet governance, the free flow of data and its protection, net neutrality – just to name a few.
The internet should be a dynamic source of growth in the digital economy, for everyone to benefit. Everyone who is involved, or who has an interest, should have a say in how it is governed. The digital economy depends on a properly functioning, fair and open internet.
As such, all countries need to work together so that the global digital economy fulfils its potential for enhancing fairness and social inclusion – for everyone and everywhere.
I look forward to seeing many of you at the OECD meeting in Mexico.
Boris Cournède and Oliver Denk, OECD Economics Department
Finance is the lifeblood of modern economies, but too much of the wrong type of finance can hamper economic prosperity and social cohesion. We have taken a holistic approach to study the consequences of finance for the inclusiveness of growth, in the spirit of the OECD initiative New Approaches to Economic Challenges.
The UN’s Sustainable Development Goals are looking at finance in a similar way. They specify the target of better financial regulation under Goal 10, “Reduced Inequalities” and thereby directly recognise the importance of finance for inequality. Our research thus provides an empirical foundation for the SDGs’ target to improve the regulation of financial markets and institutions to attain greater economic prosperity and income equality.
Credit intermediation and stock markets have seen a spectacular expansion over the past half-century. Since the 1960s, credit by financial institutions to households and businesses has grown three times as fast as economic activity. Stock markets too have expanded enormously. These secular changes to the financial landscape have taken place amidst a global economy in which growth has declined and inequalities have widened. They have therefore raised deep questions about the role of finance: What are the effects of changes in the size and structure of finance on economic growth? How do financial developments influence income inequality? Which policies can improve the contribution of finance to people’s well-being?
The development of credit markets boosts economic growth when it starts from a low base, and many developing countries have a lot to gain from further financial expansion. Nevertheless, looking at the data over the last 50 years, our empirical analysis shows that credit expansion has reduced economic prosperity on average across OECD countries. An increase in credit by financial institutions by 10% of GDP has been associated with a 0.3 percentage point reduction in long-term growth (figure 1). At the levels now reached in most OECD countries, further credit accumulation is therefore likely to lower long-term growth. On the other hand, further expansions in equity finance are found to promote economic growth (figure 1).
We identify three main channels linking the long-term expansion of credit with lower growth:
Excessive financial deregulation. OECD countries relaxed financial regulation in the 40 years preceding the global financial crisis, and this initially benefited economic activity. Relaxation of regulation however went too far and resulted in too much credit.
The structure of credit. Our research decomposes credit by lending and borrowing sectors. These breakdowns show that, on the lender side, bank loans have been linked with lower growth than bonds (figure 1). On the borrower side, credit has dragged down growth more when it went to households rather than businesses (figure 1).
Too-big-to-fail guarantees. Our findings of excessive financial deregulation and over-reliance on bank credit suggest that too-big-to-fail guarantees to banks have been one channel encouraging too much credit. This is further supported by evidence that the link between credit and growth is not as negative in OECD countries where creditors incurred losses due to bank failures as in those where they incurred no such losses.
Finance may also exacerbate inequalities, a concern that comes out very strongly in the formulation of the SDGs. Our work finds that this has indeed been the case. Expansions in bank credit and stock markets are both linked with a more unequal distribution of income. We suggest three underlying mechanisms:
The high concentration of workers in finance at the top of the earnings distribution. There are few financial sector employees in low-income brackets and many higher up in the income distribution (figure 2). The strong presence of financial sector workers among top earners is justified as long as very high productivity underpins their earnings. However, our detailed econometric investigations show that financial firms pay wages well above what employees with similar profiles earn in other sectors. The premium is especially large for top earners.
Unequal bank lending. Banks generally concentrate their lending on higher-income borrowers. Credit is twice as unequally distributed as household income in the euro area (figure 3). This may reduce credit risk, but it also means that well-off people have greater opportunities than the poor to borrow money and fund profitable projects. In this way, lenders are likely to amplify inequalities in income, consumption and opportunities.
Unequal distribution of stock market wealth. Stock market wealth is concentrated among high-income households who thus get most of the income and capital gains generated through capital markets.
A better architecture for the financial system
The evidence base from our research therefore suggests that the SDGs’ target of reforming finance is likely to contribute to greater economic prosperity and income equality. Reforms should involve avoiding credit overexpansion and improving the structure of finance:
Avoiding credit overexpansion. Macro-prudential instruments can provide tools to keep credit growth in check. Caps on debt-service-to-income ratios have been identified as effective in this regard. Strong capital requirements on banks and other lenders help limit the extent to which financial institutions can fund lending through liabilities that benefit from public support. Further reforms are necessary to reduce explicit and implicit subsidies to too-big-to-fail financial institutions and level the playing field for competition between large and small banks. This could be achieved through break-ups, structural separation, capital surcharges or credible resolution plans. In the short term, however, measures to avoid credit overexpansion may temporarily hurt economic activity.
Improving the structure of finance. Tax systems in most OECD countries currently encourage corporate funding through loans rather than equity. Tax reforms can improve the structure of finance, by reducing this so-called debt bias, which leads to too much debt, and not enough equity. They would help make finance more favourable to long-term economic growth. Measures to encourage broad-based participation in stock holdings, for instance a wider application of nudging in pension plans, can allow for a better sharing of the benefits from stock market expansion
Cournède, B. and O. Denk (2015), “Finance and Economic Growth in OECD and G20 Countries”, OECD Economics Department Working Papers, No. 1223, OECD Publishing, Paris.
Cournède, B., O. Denk and P. Hoeller (2015), “Finance and Inclusive Growth”, OECD Economic Policy Papers, No. 14, OECD Publishing, Paris.
Denk, O. (2015), “Financial Sector Pay and Labour Income Inequality: Evidence from Europe”, OECD Economics Department Working Papers, No. 1225, OECD Publishing, Paris.
Denk, O. and A. Cazenave-Lacroutz (2015), “Household Finance and Income Inequality in the Euro Area”, OECD Economics Department Working Papers, No. 1226, OECD Publishing, Paris.
Denk, O. and B. Cournède (2015), “Finance and Income Inequality in OECD Countries”, OECD Economics Department Working Papers, No. 1224, OECD Publishing, Paris.
The Sustainable Development Goals: A Duty and an Opportunity
Gabriela Ramos, Special Counsellor to the OECD Secretary-General, Chief of Staff and G20 Sherpa
The Sustainable Development Goals (SDGs) are universal, multi-dimensional, and ambitious. To achieve them we need an integrated framework that promotes a growth path that respects the environment, and whose benefits are shared by all, not only by the privileged few. The concept of sustainable development challenges us to rethink how we relate to the world around us and how we expect governments to make policies that support that world view.
First, there is the realisation that economic growth alone is not enough: the economic, social and environmental aspects of any action are interconnected. Considering only one of these at a time leads to errors in judgment and unsustainable outcomes. The growth accounting that we have relied on has fallen short, by not raising the alarm regarding the accumulated imbalances that brought the worst crisis in our lifetime in 2008, and regarding natural resource depletion and high inequalities of income and outcomes for people.
Next, the interconnected nature of sustainable development calls for going beyond geographical or institutional borders, in order to co-ordinate strategies and make good decisions. Problems are rarely easy to contain within predefined jurisdictions such as one government agency or a single neighbourhood, and intelligent solutions require co-operation as part of the decision-making process. Our policy decisions should keep in mind that our decisions and actions will have impacts elsewhere, will influence the future, and be bound by national circumstances, institutional settings, and the historical and cultural traits that define our societies.
Most of all, we need a growth path that puts people’s well-being at the core of policy efforts, and where GDP per capita and income are key elements of course, but not the only ones. In a highly interconnected global economy, the linkages between our economies, societies and environment should be central, and our policy choices should be informed by this high level of complexity.
The SDG’s therefore are a healthy reminder that, to deliver, we should change the way we operate and update the tools that we use to understand the world. Indeed, realize that GDP is a means to an end, and not an end in itself.
At the OECD we have been preparing for this in the last decade. We launched the New Approaches to Economic Challenges Initiative that makes a call to develop an agenda for sustainable and inclusive growth. We have also developed a hands-on agenda for green growth, and we have been working to address the slowdown of productivity growth with policy measures that will also have a positive impact on reducing inequalities of income and opportunities. That means changing the way we work, getting away from the “silo” approach, and trying to anticipate and shed light on the unintended consequences of the choices we make.
Our work on inclusive growth is a good illustration of this. Rising income inequality is often accompanied by greater polarisation in educational and health outcomes, perpetuating a vicious circle of exclusion and inequality. Moreover, inequalities impose costs on economic growth, particularly where inequality of opportunity locks in privilege and exclusion, undermining intergenerational social mobility. Accounting for the multidimensional nature of inequalities means evaluating the effects of policies on both income and non-income outcomes, as well as for different social groups.
Our analysis shows that “multidimensional living standards” – a measure that combines changes in household income, health and labour market outcomes – rose faster for more affluent social groups than for middle class or low-income households on average among OECD countries, and suggests that improvements in life expectancy and strong job creation during 1995-2007 did not compensate for widening income inequality.
A better understanding of the effects of policies on specific social groups allows policy makers to identify trade-offs and complementarities between growth and distributional objectives. For instance reducing regulatory barriers to domestic competition, trade and inward foreign direct investment can lift the incomes of the lower-middle class by more than it does GDP per capita. Conversely, a tightening of unemployment benefits for the long-term unemployed, if implemented without a strengthening of job-search support and other activation programmes, may lead to a decline in the income of the lower-middle class, even if it boosts average incomes.
These findings are reinforced by our work on the quality of jobs, defined as good pay, labour market security, and a decent working environment. There appear to be no major trade-offs between job quality and quantity but rather, potential synergies: countries that do relatively poorly with respect to job quality tend to have relatively low employment rates and vice versa.
In talking about jobs and equality, it is important to remember that the environment is not something you can think about later, once you have enough growth. Economic progress rests on ecological foundations. Natural capital – air, water, and other resources – is finite and has to be managed just as carefully as other forms of capital. More stringent environment policies, when well-designed, need not undermine productivity growth. Similarly, policies that make environmental sense can support economic growth and promote social inclusion too.
Designing a strategy to implement the SDGs comes down to answering three questions. What should economies be doing? How should they be doing it? And for whom? These questions are not new. Gro Brundtland’s answer in her 1987 report Our Common Future was economies promoting “growth that is forceful and at the same time socially and environmentally sustainable”. But after 20 years after Brundtland, we have still not managed to develop an integrated framework that combines the main objectives of well-being in a synergistic way. To do so we need to develop the best tools, but more importantly, to change habits –which is not easy- or to go against vested interests that benefit from the status quo. The political economy of reform is not going to be easy.
On the side of change, the SDGs give us not just the duty but the opportunity to advance our thinking. Let’s not waste it!