Chapter 1. How’s Life in 2017?

A key reason for measuring well-being is to understand whether, where and how life is getting better for people. This chapter provides an overview of OECD countries’ achievements across 11 dimensions of current well-being and four different “capital stocks” that help to sustain well-being over time. It features a diverse set of statistics, ranging from household wealth to time spent on leisure, and from air pollution to how safe people feel walking alone at night. Since the last 10 years have been a turbulent time in most OECD economies, the chapter has a particular focus on changes in people’s well-being. It seeks to address the simple question: is life today better or worse than it was in 2005, before the financial crisis took hold? The overview provided here is complemented by Chapter 2, which examines inequalities in current well-being outcomes, and Chapter 5, which provides profiles of each OECD country and 6 OECD partner countries.

    

Introduction: The OECD approach to measuring well-being

Many governments, charities and businesses make it their mission to improve people’s lives. But how can they know whether they are succeeding? The purpose of measuring well-being is to help understand whether life is getting better for people – so that, ultimately, we might better identify what drives positive and negative changes in people’s lives. Well-being is a concept that has gained increasing traction in the last 10 years, yet we still often hear that “well-being means different things to different people” – thus making it a very challenging target to assess. To have a meaningful impact, whether in public policy, business or the third sector, the concept of well-being must be made concrete and measurable. While there is now fairly widespread agreement that “better lives” means more than just higher Gross Domestic Product (GDP), how much more has remained a topic of fierce debate. How well-being outcomes are distributed in society is also a critical issue – since we need to know not just whether life is getting better on average, but also for whom.

Figure 1.1. The OECD well-being framework
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Source : OECD (2015), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en.

The OECD framework for measuring well-being (Figure 1.1) encompasses a range of different individual, household and societal-level outcomes, as well as the stocks of resources that are important for sustaining those outcomes over time. This framework was first presented in 2011, and has provided the backbone for all past editions of How’s Life? The framework does not specify the combination of outcomes necessary for achieving a “good life”, but instead focuses on some of the key ingredients that all people should have access to. It builds on a body of literature and a wide range of international examples, which together suggest an emerging consensus on several of the outcomes that contribute towards people’s well-being (Box 1.1). These include income, jobs, housing, health status, skills, the environment, governance and personal safety. The importance of more experiential elements of life, such as social connections, work-life balance and subjective well-being, is also increasingly recognised across these approaches.

Box 1.1. The OECD approach to measuring well-being

The OECD framework for measuring well-being was introduced in How’s Life? 2011. It builds on a variety of national and international initiatives for measuring the progress of societies, as well as on the recommendations of the Stiglitz, Sen and Fitoussi Report (2009) and the input provided by the National Statistical Offices (NSOs) represented in the OECD Committee on Statistics and Statistical Policy. Conceptually, the framework reflects elements of the capabilities approach (Sen, 1985; Alkire and Sarwar, 2009; Anand, Durand and Heckman, 2011), with many dimensions addressing the factors that can expand people’s choices and opportunities to live the lives that they value – including health, education and income (see OECD, 2013a).

This approach to measuring current well-being has several important features:

  • It puts people (individuals and households) at the centre of the assessment, focusing on their life circumstances and their experiences of well-being.

  • It focuses on well-being outcomes – aspects of life that are directly and intrinsically important to people – rather than the inputs and outputs that might be used to deliver those outcomes. For example, in the education dimension, measures focus on the skills and competencies achieved, rather than on the money spent on schools or the number of teachers trained.

  • It includes outcomes that are both objective (i.e. observable by a third party) and intrinsically subjective (i.e. those where only the person concerned can report on their inner feelings and states), recognising that objective evidence about people’s life circumstances can be usefully complemented by information about how people experience their lives.

  • It considers the distribution of well-being outcomes across the population as an important feature shaping the well-being of societies, including disparities associated with age, gender, education and income. This is because national averages disguise a great deal of variation in people’s experiences within countries – and it is important to understand whether life is getting better, not just on average, but across all groups in society.

The OECD approach to assessing the resources for future well-being focuses on the broader natural, economic, human and social systems that embed and sustain individual well-being over time. These systems are underpinned by stocks of “capital” or resources. While the term “capital” is used to denote a store of future value, this value is not necessarily measured in monetary terms: in the majority of cases it is the physical stocks, rather than any monetary value attached to them, that are assessed in the illustrative indicator set shown in this report. Taking these stocks as the primary measurement focus is in line with the recommendations of the Stiglitz, Sen and Fitoussi Report (2009) as well as several other recent measurement initiatives, including the UNECE-Eurostat-OECD Task Force on Measuring Sustainable Development (United Nations, 2009), the UNU-IDHP and UNEP’s Inclusive Wealth Report (2012), the Conference of European Statisticians’ Recommendations on Measuring Sustainable Development (UNECE, 2014) and several country initiatives (e.g. FSO, 2015; Statistics New Zealand, 2011). A key feature in several of these frameworks is the distinction made between well-being “here and now” and the stocks of resources that can affect the well-being of future generations “later”. Several of these approaches go beyond simply measuring levels of stocks to consider how these are managed, maintained or threatened (see also Box 1.2).

Source : OECD (2015), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en.

Advances in measuring well-being have been closely intertwined with concepts of sustainable development. This was particularly the case in the focus of the “Rio+20” Conference on Sustainable Development on The Future We Want (UN Department of Economic and Social Affairs, 2012). Flowing from Rio+20, in 2015 all UN member states adopted a set of universal Sustainable Development Goals (SDGs). These goals put the concepts of well-being and sustainable development into practice through a series of internationally-agreed policy commitments. They set an ambitious agenda of 17 goals to be reached by the year 2030, backed by 169 targets and 232 indicators proposed by an Inter-Agency and Expert Group (UN Statistics Division, 2017). As described in the new OECD study, Measuring Distance to the SDG Targets (OECD, 2017a), there is a strong overlap between the SDGs and the OECD’s well-being framework. There are, however, also some differences in terms of both the intent and the measurement approach adopted (Box 1.2).

The data presented in How’s Life? 2017 offer an international perspective on well-being. As well as describing international trends and common experiences, they provide OECD and partner countries with insights about areas of comparative strength and weakness, relative to their peers. The requirement for internationally comparable data necessarily limits the indicators that can be used, and despite recent progress, important measurement gaps remain. However, the OECD’s work in this area seeks to complement both the more detailed information that countries collect on well-being at the national and subnational levels and the richer and more qualitative evidence available at a more grass-roots and community level.

This chapter of How’s Life? provides an overview of well-being in OECD countries, including what we know about whether life has been getting better since 2005. It summarises the latest data for current well-being, resources for future well-being, and changes over time in both. This is followed by a brief account of the statistical agenda ahead. The current chapter’s focus on average levels of well-being achieved across OECD countries is complemented by Chapter 2, which investigates well-being inequalities – i.e. the distribution of outcomes within OECD countries. Chapter 3 then explores the experiences of one important minority group in many OECD countries, by describing well-being outcomes for migrants. Chapter 4 examines issues of governance and well-being, focusing in particular on people’s experiences of and interactions with public institutions. The fifth and final chapter presents a series of well-being profiles for each OECD country, as well as three countries on the accession track to join the OECD (Colombia, Costa Rica and Lithuania) and three partner countries featured in the OECD’s Better Life Index (Brazil, the Russian Federation and South Africa).1 Focusing on average levels of achievement for each country, the profiles summarise comparative strengths and weaknesses in current well-being and resources for future well-being, as well as how these have changed since 2005.

Box 1.2. The OECD well-being framework and the UN Sustainable Development Goals

The OECD well-being framework is an analytic and diagnostic tool to assess the conditions of people and communities, whereas the 2030 Agenda is a list of policy commitments agreed by world leaders. Nonetheless, the 2030 Agenda touches on practically all the dimensions considered in the OECD well-being framework. As shown by Figure 1.2 below:

  • Eight of the 17 SDGs map onto 9 of the 11 dimensions of the OECD framework for current well-being. In most cases, the mapping is one-to-one – e.g. SDG 3 on health maps to the OECD dimensions of “health status”. Sometimes, however, more than one SDG is relevant for a single OECD well-being dimension – e.g. various aspects of SDGs 1 and 2, on poverty and food respectively, map to the OECD dimension of “income and wealth”. In other cases, a single SDG maps to several OECD dimensions – e.g. the decent work aspects of SDG 8 map to two OECD dimensions, “jobs and earnings” and “work-life balance”.

  • Three of the 17 SDGs relate strongly to the cross-cutting “inequality” aspect of the OECD well-being framework. The relation is direct in the case of SDG 10 on reducing inequalities. But SDG 1 on poverty also addresses inequality, while SDG 5 on gender equality concerns the inequalities experienced by a specific population group. More generally, the SDGs’ emphasis on “leaving no one behind” underscores the importance of looking at outcomes across a range of population characteristics such as age, gender, disability and socio-economic status.

  • The four types of “capital stocks” that provide resources for future well-being in the OECD framework are clearly reflected in 11 of the 17 SDGs. Natural capital features in SDGs 12 on sustainable production, 13 on climate, 14 on oceans and 15 on biodiversity. Economic capital is recognised in SDGs 7 on energy, 8 on decent work and the economy and 9 on infrastructure. Human capital is the focus of SDGs 3 on health and 4 on education, while social capital is addressed by SDG 16 on institutions. In some cases, the same SDG may be relevant for both current well-being and sustainability: for example, SDG 3 on health aims at lowering mortality and morbidity now, while supporting vaccine development for the future.

Only two dimensions of the OECD’s current well-being framework are not featured in the SDGs: “social connections” and “subjective well-being” (although “promoting well-being for all” is part of SDG 3 on health). Conversely, two aspects of the 2030 Agenda do not feature in the OECD well-being framework. The first is SDG 17 (means of implementation); this reflects the choice in How’s Life? to focus on universally-valued outcomes, rather than on the country-specific policies needed to attain them. The second is the 2030 Agenda’s focus on the “shared responsibility” of all countries in delivering global public goods and avoiding negative global impacts. Conceptually, this is a key element of resources for future well-being (OECD, 2015a; OECD, 2013a) yet the focus of How’s Life? to date has been on the conditions prevailing in each OECD country, rather than on the interrelationships among countries and their well-being achievements. The renewed attention given to global public goods in the 2030 Agenda, and on domestic policies and consumption patterns that can affect them, is a welcome feature, giving expression to the “elsewhere” dimension stressed in the recommendations by the Conference of European Statisticians’ Recommendations on Measuring Sustainable Development (UNECE, 2014).

Figure 1.2. Comparison of the OECD well-being framework and the 2030 Agenda
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Source : OECD (2017a), Measuring Distance to the SDG Targets: An Assessment of Where OECD Countries Stand, OECD Publishing, Paris, www.oecd.org/std/measuring-distance-to-the-sdgs-targets.htm.

Current well-being: How’s Life in 2017?

According to the latest available data, the average OECD resident2 has a net adjusted disposable income of just under 31 000 USD, lives in a household with an average net wealth of just over 330 000 USD, and (if aged between 15 and 64) has a 67% chance of having a job. Those who are employed collect, on average, gross annual earnings of around 44 000 USD. Over one-third of OECD workers experience “job strain” – where work demands (e.g. physical demands, work intensity, inflexibility of working hours) exceed the job resources available to them (e.g. task discretion and autonomy, training and learning opportunities, and opportunities for career advancement). In 2016, 2% of the OECD labour force had been unemployed for a year or more. The average OECD home has 1.8 rooms per person, but 2.1% of people live in dwellings that lack basic sanitary facilities (access to an indoor flushing toilet for the sole use of their household). On average, OECD households spend 19% of their disposable income on housing rent and maintenance, excluding the interest and principal repayment on their mortgages.

One in every 8 employees in the OECD regularly works 50 hours or more per week, and the average time devoted to leisure and personal care (including sleep) for full-time employees is just under 15 hours per day. In terms of health status, the average new-born in OECD countries can now expect to live until they are just over 80 years old, but only 69% of people report feeling in good health. Nearly three-quarters of people have attained at least an upper secondary education. When it comes to social support, almost 89% of people report having a friend or relative whom they can count on for help in case of need. While two-thirds of registered voters cast a ballot in their most recent national elections, only one-third of OECD residents feel that they have a say in what the government does in their country. People living in OECD countries are, on average, exposed to outdoor air pollution by fine particulate matter (PM2.5) at a level that is around 40% higher than the WHO recommended threshold of 10 micrograms per cubic metre. Around 80% of OECD residents are satisfied with the quality of their local water supply. The homicide rate is currently 3.6 per 100 000 people in the OECD on average, and just over two-thirds of people report that they feel safe when walking alone at night in the area where they live. Finally, when asked to rate their satisfaction with life on a 0 to 10 scale, the average OECD resident gives a response of 7.3.

Yet, as this volume shows, there are wide variations in people’s experiences of well-being, both within OECD countries (Chapter 2) and between them (Chapter 5). For ease of presentation in the analysis that follows, the headline indicator set for current well-being is divided into the “material conditions” and “quality-of-life” domains shown in Figure 1.1. Tables 1.1 and 1.2 summarise countries’ comparative strengths and weaknesses, based on a simple ranking of whether the country falls within the top (1), middle (2) or bottom (3) third of the OECD.3 For partner countries (shown in Tables 1.4 and 1.5), the “OECD-equivalent” rank is shown – i.e. their level of achievement is benchmarked against the top, middle and bottom third of OECD countries. Thus, a (1) indicates that the partner country has a level of achievement that is on a par with the top third of all OECD countries, a (2) indicates achievement on a par with the middle third of all OECD countries, and a (3) indicates achievement on a par with the bottom third of all OECD countries.

When it comes to current levels of material conditions (Table 1.1), some OECD countries do better than others, but few countries perform universally well (or badly) across all 10 indicators. Canada, Norway and the United States have comparative strengths in at least four-fifths of the indicators covering income and wealth, jobs and earnings, and housing. In addition, while they do have some areas of mid-ranging performance, Australia, Austria, Canada, Germany, Luxembourg, Norway and the United States have no areas of comparative weakness on the available indicators.

Table 1.1. Comparative strengths and weaknesses in material conditions, OECD countries

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Note : Based on a simple ranking of whether the country falls within the top (1), middle (2) or bottom (3) third of the OECD. Indicator definitions are available in Table 5.1, Chapter 5. All source data are provided in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

Table 1.2. Comparative strengths and weaknesses in quality of life, OECD countries

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* SWB indicates subjective well-being.

Based on a simple ranking of whether the country falls within the top (1), middle (2) or bottom (3) third of the OECD. Indicator definitions are available in Table 5.1, Chapter 5. All source data are provided in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

Among the outcomes addressing people’s quality of life (which spans the dimensions of work-life balance, health, education, social connections, civic engagement and governance, environmental quality, personal safety and subjective well-being), there are similarly no countries that have strengths in all 15 indicators (Table 1.2). Norway, Switzerland, Finland, Iceland and Sweden are top-performers on at least two-thirds of quality-of-life outcomes. When it comes to weaknesses, Denmark, Norway and Sweden are the only three countries with no outcomes ranked in the bottom third of the OECD.

For OECD partner countries, the available data set for assessing current well-being is much more limited. Table 1.3 shows OECD-equivalent strengths and weaknesses – i.e. given their level of achievement, would partner countries fall within the top, middle or bottom third of OECD countries? Perhaps the most notable finding is that all partner countries perform well on housing affordability (with a comparatively low share of household disposable income spent on housing costs), while all have comparative weaknesses in terms of life expectancy, the homicide rate and feelings of safety. Partner countries’ performance is most mixed in relation to long-term unemployment, working hours, educational attainment, voter turnout and air quality – where some countries are performing on a par with the top third of the OECD, while others are in line with the bottom third.

Table 1.3. OECD-equivalent comparative strengths and weaknesses on current well-being, partner countries

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* SWB indicates subjective well-being.

The rankings shown in the table represent the “OECD-equivalent” rank – meaning that levels of achievement in partner countries are benchmarked against the top (1), middle (2) and bottom third (3) of OECD countries. Indicator definitions are available in Table 5.1, Chapter 5. All source data are provided in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

There is generally a strong relationship between comparative performance on material conditions and quality-of-life outcomes (Figure 1.3). Norway, Sweden, Canada and Switzerland have many comparative strengths across both the material conditions and quality-of-life domains (top right). Conversely, Chile, Turkey, Hungary, Mexico and Latvia (bottom left) have few comparative strengths in either material conditions or quality of life. Countries above the blue diagonal line generally perform better on quality-of-life outcomes, relative to material conditions; the converse is true for those below the diagonal. Finland and Denmark, for example, have very high scores on quality of life, relative to their mid-ranking position on material conditions. By contrast, the United States, Australia, Luxembourg, the United Kingdom and Germany have a high number of comparative strengths on material conditions, compared to their relative position on quality of life indicators. Nevertheless, the top left and bottom right quadrants of Figure 1.3 are sparsely populated: no OECD country does well on quality of life without achieving a moderate level of material conditions, and vice versa.

Figure 1.3. Comparative performance on material conditions (x-axis) and quality of life (y-axis)
OECD countries, latest available data
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Note : Material conditions encompasses 10 indicators across 3 dimensions: income and wealth, jobs and earnings, and housing. Quality of life is measured through 15 indicators spanning 8 dimensions: work-life balance, health status, education and skills, social connections, civic engagement and governance, environmental quality, personal safety and subjective well-being. For each indicator, countries are “scored” according to their comparative performance (0 = bottom third of the OECD, 5 = middle third of the OECD, 10 = top third of the OECD). Scores are then averaged within dimensions (applying equal weights to each indicator), before then being averaged across dimensions (applying equal weights to each dimension in the material conditions and quality-of-life categories). Missing data points are excluded from each country’s score, and thus scores may be heavily under- or over-estimated in the case of large data gaps. The blue diagonal line indicates where countries would fall if there were perfect correspondence in their performance on material conditions and quality of life.

 StatLink http://dx.doi.org/10.1787/888933595451

Current well-being and inequality

Chapter 2 provides an overview of inequalities in well-being across OECD countries. Since there are many different ways to understand the question of “who gets what”, the chapter offers several different approaches to measuring inequalities. These include “vertical inequalities”, which focus on the size of the gap between people at the top and people at the bottom (for example, the average score of the top 20% on life satisfaction compared to the average score of those in the bottom 20%); “horizontal inequalities”, which focus on gaps in average performance between specific population groups (such as men and women, or young and old); and “deprivations”, which consider the share of people falling below a basic threshold of attainment.

Is there a relationship between average levels of performance on current well-being, and the dispersion of performance across the population? To explore this, we consider the measures of “vertical inequalities” developed in Chapter 2, since these summarise the overall dispersion of well-being scores (i.e. the size of the gap between the people at the top of the distribution and the people at the bottom). There are nine current well-being outcomes, listed in Table 1.4, for which it is possible to examine these “vertical inequalities”. In general, inequalities across these indicators are highest in the United States, Israel and Mexico, and lowest in Sweden, Norway and Finland (see Chapter 2 for further details).

Table 1.4. Current well-being outcomes for which both average performance and inequalities can be measured

Dimension

Outcome

Level indicator

Inequality indicator (vertical inequality)

Income and wealth

Household disposable income

Household net adjusted disposable income

S80/S20 household disposable income

Household net wealth

Household net wealth

S90 household net wealth

Jobs and earnings

Earnings

Average annual gross earnings per full-time employee

P90/P10 gross earnings

Work-life balance

Working hours

Percentage of employees who usually work 50 hours or more per week

S80/S20 hours worked

Health status

Life expectancy

Life expectancy at birth

Standard deviation of age at death

Education and skills

Adult skills

PIAAC mean proficiency in literacy and numeracy

P90/P10 mean proficiency in literacy and numeracy

Cognitive skills at age 15

PISA mean score in reading, science and maths

P90/P10 mean score in reading, science and maths

Civic engagement and governance

Having a say in government

Percentage of people aged 16-65 who feel that they have a say in what the government does

S80/S20 having a say in government

Subjective well-being

Life satisfaction

Mean value, 0-10 scale

S80/S20 life satisfaction

Note : Further details on the construction of the inequality indicators are provided in Chapter 2. More information about the definitions and units of measurement for the headline indicators is given in Chapter 5. S80/S20 refers to the ratio of the average outcome attained by the top 20% of the distribution, compared to the average outcome for the bottom 20%. The P90/P10 refers to the ratio between the outcome attained at the 90th percentile, and the outcome attained at the 10th percentile.

Figure 1.4 summarises the relationship between the average performance levels that countries achieve on these nine indicators and the distribution of those outcomes across the population. As with other analyses presented in this chapter, average performance is based on whether a given country falls within the top, middle or bottom third of the OECD on each indicator, while inequalities are understood as countries falling in the highest, middle and lowest third of the OECD.4

Figure 1.4. The relationship between average performance and inequalities for a selection of 9 current well-being indicators
Average performance (x-axis) on a 0-10 scale, plotted against average inequalities (y-axis) on a 1-3 scale
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Note : For each of the 9 indicators, countries are “scored” for both their level of equality (1 = bottom third of the OECD, 2 = middle third of the OECD, 3 = top third of the OECD) and their level of average performance (0 = bottom third of the OECD, 5 = middle third, 10 = top third of the OECD). In dimensions with more than one indicator, indicators were summed using equal weights, and then overall results were calculated taking the simple average score across dimensions. The blue diagonal line indicates where countries would fall if there were perfect correspondence between their average level of performance and their average level of equality.

 StatLink http://dx.doi.org/10.1787/888933595470

The pattern of results in Figure 1.4 suggests that a higher average performance is generally associated with lower inequalities. Countries that combine a high level of average well-being with a low level of vertical inequality across these nine indicators include Norway, Sweden and Finland (top right). Conversely, countries with both low levels of average well-being and comparatively high levels of vertical inequality include Mexico, Hungary, Latvia and Portugal (bottom left). Countries above the blue diagonal line generally have more strengths in terms of equality than they do in terms of average performance: for example, Japan, Slovenia and Italy fall around the OECD midpoint in terms of overall average performance, but fare slightly better in terms of inequalities. By contrast, countries falling below the diagonal do better on average performance than on inequalities: for example, the United States and Ireland combine mid-ranging levels of average performance with relatively high levels of inequality. The results of this analysis are, however, sensitive to the share of missing values, which is relatively high in the cases of Iceland, Mexico, Switzerland, Turkey and Japan.

Change in current well-being: Is life getting better for people?

In the last 10 years, several OECD countries have experienced major economic and political shocks. What has happened to people’s well-being during this time? The analysis that follows examines recent changes in the headline indicators of current well-being, with a particular focus on whether life now is better or worse than it was in 2005, before the financial crisis took hold. Assessing changes over time in current well-being for the OECD as a whole is complicated by a number of factors, including infrequent data collections and methodological breaks that interrupt the time series data. Box 1.3 (below) describes the general approach adopted.

Box 1.3. Assessing changes in current well-being

Change over time can be assessed for all 10 material conditions indicators1, and for 11 out of the 15 quality-of-life indicators. Nevertheless, limited country coverage, methodological breaks and incomplete time series mean that the OECD average often refers to a reduced set of countries. In the figures and tables below, the number of countries covered by the OECD average is indicated in brackets in the legend of the figures (e.g. OECD 33), and is typically population-weighted (as indicated in the figure notes). This procedure gives more weight to countries with a larger population, relative to those with a smaller population, and is applied in order to describe the experience of the “average OECD person” (rather than focusing on the “average OECD country”). Due to large amounts of missing data, changes in OECD partner countries’ current well-being are not considered below. However, the country profiles in Chapter 5 provide detailed information on changes in average well-being for all 35 OECD countries and 6 partner countries.

The years covered typically range from 2005 to 2015/16 whenever possible. For measures that are collected on an infrequent basis in most countries (e.g. household net wealth, rooms per person, basic sanitation) or that capture phenomena that occur infrequently (e.g. voter turnout), the OECD average is computed over a multi-year period (such as 3 or 5 years) to maximise the number of countries included in the calculation. In the case of data sourced from the Gallup World Poll, a 3-year average is used so as to increase the sample size (typically limited to 1 000 people per country, per year) and reduce short-run volatility in the data. Similarly, exposure to outdoor air pollution by fine particulate matter (PM2.5), used to assess air quality, is computed as a 3-year rolling average in line with the approach adopted in the OECD’s Green Growth Indicators (OECD, 2017b).

Complete information about the time series for the OECD average and individual countries is detailed in the Online Data Annex: Current Well-Being that accompanies this publication (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en). Where the time series available for a given country spans fewer than 9 years, this country is excluded from the analyses shown in Figures 1.9 and 1.16, and counted as “missing” in Figures 1.10,  1.17and 1.18. The two exceptions to this are household net wealth, where only two time-points are available in all countries; and upper secondary educational attainment, where only the 3 most recent years are considered, due to a major break in the series that affects most OECD countries.

In the summary figures that describe results across countries (Figures 1.9,  1.10,  1.16.  1.17 and 1.18), changes are calculated as the simple difference between 2005 and 2015 (or the closest years available). The categories “improving”, “little or no change” and “worsening” are defined based on the thresholds detailed in the figure notes, and discussed in Annex 5.A. of Chapter 5. In a small number of indicators (most notably, access to basic sanitation, long-term unemployment, the incidence of long working hours, and the homicide rate) the very top-performing OECD countries have relatively little room for improvement. This can obviously therefore impact on the total number of improvements observed in those countries (e.g. Figures 1.10,  1.17and 1.18)

1. The OECD average change in household net wealth is difficult to characterise due to the scarcity of data, both within countries and over time. However, estimates are available for two years during the period 2008-16 for 16 countries, with the results summarised in Figure 1.9.

Change in material conditions

The decade between 2005 and 2015 has been a turbulent time for several aspects of material conditions in OECD countries – and particularly those relating to jobs. The OECD average household net adjusted disposable income was 8% higher, in real terms, in 2015 than in 2005, and average annual gross earnings per full-time employee were (in 2016) 7% higher (Figure 1.5). However, to put these findings in context, this represents only around half the cumulative growth rate observed between 1995 and 2005: while it would have taken around 40 years for OECD average income to double if it had grown at the rate observed in 1995-2005, it would now take 85 years if income kept rising at the rate recorded over the 2005-15 period.5

Figure 1.5. OECD average household income and earnings, since 2005
USD at 2010 PPPs, per capita, OECD 28 (left), and USD at 2016 PPPs, OECD 34 (right)
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Note : The OECD average for household net adjusted disposable income is population-weighted, and excludes Chile, Iceland, Israel, Korea, Luxembourg, New Zealand and Turkey, due to incomplete time series. For earnings, the OECD average is weighted by the number of employees in each country, and excludes Turkey.

Source : For household income: OECD calculations based on OECD National Accounts Statistics Database, http://dx.doi.org/10.1787/na-data-en. For average earnings: OECD Average annual wages Database, http://stats.oecd.org/Index.aspx?DataSetCode=AV_AN_WAGE.

 StatLink http://dx.doi.org/10.1787/888933595489

The employment rate took a heavy hit in the early years of the crisis, and recovery has been relatively slow. It took 10 years for employment to return to 2005 levels, and the 2007 peak was only exceeded in 2016. When it comes to job quality, rather than quantity, the share of European OECD employees experiencing job strain rose from 42% in 2005 to 43% in 2010, before falling to 38% in 2015 (Figure 1.6).

Figure 1.6. OECD average employment rate and job strain, since 2005
Employed people aged 15-64 as a percentage of the population (left), share of employees experiencing job strain (right)
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Note : The OECD average for employment is population-weighted, and excludes Chile, Germany, New Zealand, Norway, Portugal and Switzerland, due to breaks in the time series. The OECD average for job strain is population-weighted and excludes Australia, Canada, Chile, Iceland, Israel, Japan, Korea, Mexico, New Zealand, Switzerland and the United States, due to an incomplete time series.

Source : For employment: “Labour Force Statistics”, OECD Employment and Labour Market Statistics Database, http://dx.doi.org/10.1787/lfs-lfs-data-en. For job strain: provisional (September 2017) estimates prepared for the OECD Job quality Database, http://dotstat.oecd.org/Index.aspx?DataSetCode=JOBQ.

 StatLink http://dx.doi.org/10.1787/888933595508

The OECD average labour market insecurity due to unemployment quadrupled between 2007 and 2009, fell sharply in 2010, and then gradually fell further in recent years – although in 2015 it was still around one-third higher than in 2007. The OECD average long-term unemployment rate fell from 2.1% to 1.5% between 2005 and 2008, then more than doubled to peak at 2.8% in 2013; it has since fallen back to 2005 levels, but not yet reached the pre-crisis low of 2008 (Figure 1.7).

Figure 1.7. OECD average labour market insecurity and long-term unemployment, since 2005
Percentage of the labour force unemployed for one year or more (left), average expected monetary loss associated with becoming and staying unemployed, as a share of previous earnings (right)
picture

Note : For the long-term unemployment rate, the OECD average is population-weighted and excludes Chile, Germany, Israel, Luxembourg, New Zealand, Norway, Portugal, Sweden and Switzerland, due to incomplete time series. For labour market insecurity, the OECD average is population-weighted and excludes Chile, Korea, Latvia, Portugal, the Slovak Republic and Sweden, due to incomplete time series.

Source : For long-term unemployment: “Labour Force Statistics”, OECD Employment and Labour Market Statistics Database, http://dx.doi.org/10.1787/lfs-lfs-data-en. For labour market insecurity: OECD Job Quality Database, http://dotstat.oecd.org/Index.aspx?DataSetCode=JOBQ.

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Housing outcomes have improved for the average OECD resident, with the biggest gains made in access to basic sanitation: the average share of people living in a dwelling that lacks an indoor flushing toilet for the sole use of the household has fallen by around one-third.6 The share of income spent on housing costs has fallen by around half a percentage point since 2005 (Figure 1.8), and in the countries where changes be assessed, the number of rooms per person in the average OECD home has also increased marginally, from 1.8 to 1.9.

Figure 1.8. OECD average housing affordability, since 2005
Average expenditure on housing, as a percentage of household gross adjusted disposable income
picture

Note : The OECD average is population-weighted and excludes Chile, Iceland, Israel, Italy, Luxembourg, New Zealand, Norway, Switzerland and Turkey, due to incomplete time series for these countries.

Source : OECD calculations based on OECD National Accounts Statistics Database, http://dx.doi.org/10.1787/na-data-en.

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The OECD average, however, masks the diversity of country experiences (Figure 1.9). More than half of all OECD countries have improved in terms of average earnings, household income and employment rates since 2005, but a significant share have seen little or no change in these measures, or are worse-off. For example, household income has fallen substantially relative to 2005 in Spain (by 6%), Italy (10%) and Greece (27%). Half of all OECD countries now perform worse on housing affordability and long-term unemployment than they did in 2005. The share of employees experiencing job strain has improved for around one-third of countries, but worsened in Switzerland, Greece, New Zealand, the United Kingdom and Australia. And while labour market insecurity in 2015 (when it was last measured) is generally higher than in 2007 (when it was first measured), it has improved for a very small group of countries. Overall, the countries where material conditions improved the most include Germany, Estonia, the Slovak Republic, Latvia and the Czech Republic (Figure 1.10).

Figure 1.9. Changes in material conditions indicators, relative to 2005
Share of OECD countries
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Note : Countries with fewer than 9 years’ time series are excluded from this analysis, with the exception of net household wealth where only two observations are available for all countries. Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and are defined as values greater than or equal to the following thresholds: earnings +/- 1 000 USD; household income +/- 1 000 USD; employment +/- 1.0%; rooms per person +/- 0.1; job strain +/- 3.0%; basic sanitation +/- 0.4%; housing affordability +/- 0.4%; net wealth +/- 9 000 USD; long-term unemployment rate +/- 0.2; and labour market insecurity +/- 0.3. For further information, see Annex 5.A, in Chapter 5. Further information can be found in the country profiles of Chapter 5, and full-time series information is available in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595565

Figure 1.10. Countries’ changes in selected material conditions outcomes, relative to 2005
Share of indicators (out of 10 indicators in total)
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Note : Change is shown as “missing” for countries with fewer than 9 years’ time series, with the exception of net household wealth where only two observations are available for all countries. In a small number of indicators (most notably, access to basic sanitation, and long-term unemployment) the very top-performing OECD countries have relatively little room for substantial improvement. This can obviously therefore impact on the total number of improvements observed in these countries. Further information can be found in the country profiles of Chapter 5, and full-time series information is available in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595584

Change in quality of life

The OECD average share of employees working very long hours (50 or more per week) fell by nearly 1 percentage point between 2005 and 2009, and has remained relatively stable since then (Figure 1.11).

Figure 1.11. OECD average employees working very long hours, since 2005
Percentage of employees who usually work 50 hours or more per week
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Note : The OECD average is population-weighted; it excludes Chile, Germany, Iceland, Israel, Japan, Korea, New Zealand, Norway, Portugal, Switzerland and Turkey, due to an incomplete time series and/or breaks in the data for these countries.

Source : OECD calculations based on “Labour Force Statistics”, OECD Employment and Labour Market Statistics (database), http://dx.doi.org/10.1787/lfs-lfs-data-en.

 StatLink http://dx.doi.org/10.1787/888933595603

For the average OECD citizen, life expectancy at birth gained almost 2 years between 2005 and 2014. This gain stalled, however, in 2015, both for the OECD (population-weighted) average and in over half of all OECD countries. A slight increase in perceived health between 2005 and 2008 has failed to gain traction since then (Figure 1.12).

Figure 1.12. OECD average life expectancy and perceived health, since 2005
Years (left) and percentage of adults reporting “good” or “very good” health (right)
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Note : For life expectancy, the OECD average is population-weighted and excludes Belgium, Canada, Hungary, Israel, Luxembourg, Poland, Slovenia and Turkey, due to incomplete time series for these countries. For perceived health, the OECD average time series has been estimated by interpolating missing data points in the time series for some countries. For each country, missing data have been replaced by the average of the closest preceding and following year. Countries have been included in the OECD average only if the times series contains at least 3 data points, and at least one of them refers to 2014 or 2015. The OECD average is population-weighted and excludes Chile and Switzerland (due to a break in the time series) and Mexico (for which only two data points are available).

Source : For life expectancy: “Health status”, OECD Health Statistics database, http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_STAT. For perceived health: OECD calculations based on “Health status”, OECD Health Statistics database, http://dx.doi.org/10.1787/data-00540-en, and INEC calculations based on the National Health Survey for Costa Rica.

 StatLink http://dx.doi.org/10.1787/888933595622

The 10-year change in educational attainment cannot be assessed due to a recent break in the data that affects most OECD countries. However, between 2013 and 2016, the percentage of adults with at least an upper secondary education increased by just over 1 percentage point, from 73.5 to 74.6. Social support (measured as the share of people who report having a friend or relative whom they can count on in times of trouble) fell from 92% in 2005-07 to 88% in 2014-16. Over the same period, the OECD average voter turnout rate (as a share of those registered to vote) also fell from 72% to 69% (Figure 1.13).

Figure 1.13. OECD average social support and voter turnout, since 2005
Percentage of people who have relatives or friends whom they can count on to help in case of need (left), percentage of votes cast among the population registered to vote (right)
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Note : For social support, the OECD average for is population-weighted and excludes Iceland and Luxembourg, due to an incomplete time series. For voter turnout, the OECD average has been calculated across four-year periods. This required excluding Austria, Finland, Italy, Luxembourg and Mexico. Chile is also excluded since compulsory voting was dropped in 2012, introducing a break in the series.

Source : For social support: OECD calculations based on Gallup World Poll, www.gallup.com/services/170945/world-poll.aspx. For voter turnout: International Institute for Democracy and Electoral Assistance (IDEA) (2017), www.idea.int, the register of the Supreme Electoral Tribunal for Costa Rica, and the Federal Statistical Office (FSO) of Switzerland.

 StatLink http://dx.doi.org/10.1787/888933595641

Both the OECD average exposure to air pollution and satisfaction with water quality improved in the first half of the decade, but worsened thereafter, eventually returning to near-2005 levels (Figure 1.14).

Figure 1.14. OECD average air pollution and satisfaction with water quality, since 2005
Population-weighted exposure to PM2.5 concentrations, micrograms per cubic metre, 3-year moving average (left), percentage of satisfied people in the overall population, (right)
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Note : For air pollution, values are 3-year moving averages. 2013 values are interpolated from 2012, 2013 and 2015, as estimates for 2014 are not available. The OECD average is population-weighted. For satisfaction with water quality, the OECD average is population-weighted and excludes Iceland and Luxembourg, due to incomplete time series for these countries.

Source : For air pollution: OECD calculations based on the OECD Exposure to air pollution Database, http://dotstat.oecd.org/Index.aspx?DataSetCode=EXP_PM2_5. For satisfaction with water quality: OECD calculations based on the Gallup World Poll, www.gallup.com/services/170945/world-poll.aspx.

 StatLink http://dx.doi.org/10.1787/888933595660

In the case of personal security, the share of people who feel safe when walking alone at night in the area where they live increased from 66% in 2005-07 to 69% in 2014-16 (Figure 1.15). However, the OECD average rate of deaths due to assault also increased from 3.4 to 3.9 per 100 000 people.7 Since 2005, life satisfaction has declined slightly, with the average score (on a scale from 0 = “not at all satisfied” to 10 = “completely satisfied”) falling from 6.7 in 2005-07 to 6.5 in 2014-16 (Figure 1.15).

Figure 1.15. OECD average feelings of safety and life satisfaction, since 2005
Percentage of the population declaring feeling safe when walking alone at night in the city or area where they live (left), mean values of life satisfaction, on a 0-10 scale (right)
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Note : The OECD averages are population-weighted and exclude Iceland and Luxembourg, due to incomplete time series.

Source : OECD calculations based on the Gallup World Poll, www.gallup.com/services/170945/world poll.aspx.

 StatLink http://dx.doi.org/10.1787/888933595679

As in the case of material conditions, the pattern of change for quality of life outcomes varies across countries, however (Figure 1.16). Among all the headline indicators for current well-being, life expectancy at birth is the only outcome that is higher today than in 2005 for every OECD country with available data (notwithstanding the recent fall in in 2015). Educational attainment has also increased in three-quarters of all countries – although since the 10-year change cannot be assessed due to a major break, this analysis considers only the three most recent years. Around half of all OECD countries have experienced improvements in feelings of safety and in the homicide rate, while just under half have improved in terms of air quality and working hours since 2005. However, voter turnout is currently lower for just over half of OECD countries, and both life satisfaction and social support have each fallen in around one-quarter of countries. A small minority of countries (around 5) have experienced worsening air and water quality. Considered on a country-by-country basis (Figure 1.17), the Slovak Republic, Latvia, Estonia, Spain and Poland have experienced improvements in the largest number of quality of life indicators.

Figure 1.16. Changes in selected quality-of-life indicators, relative to 2005
Share of OECD countries
picture

Note : Countries with fewer than 9 years’ time series are excluded from this analysis, with the exception of educational attainment where only the 3 most recent years are considered, due to a break in the time series for the majority of countries. Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and are defined as values greater than or equal to the following thresholds: life expectancy +/- 0.5 years; educational attainment +/- 0.5%; working hours +/- 0.6%; homicides +/- 0.3 per 100 000; feeling safe at night +/- 3.0; voter turnout +/- 1.0%; life satisfaction according to 95% confidence intervals, roughly equating to a change of around 0.2 or 0.3 scale points on a 0 to 10 scale; water quality +/- 3.0%; perceived health +/- 3.5%; and social support +/- 3.0%. For further information, see Annex 5.A, in Chapter 5. Further information can be found in the country profiles of Chapter 5, and full-time series information is available in the Online Data Annex: Current Well-Being that accompanies this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595698

Figure 1.17. Countries’ changes in selected quality of life outcomes, relative to 2005
Share of indicators (out of 11 indicators in total)
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Note : Change is shown as “missing” for countries with fewer than 9 years’ time series, with the exception of educational attainment where only the 3 most recent years are considered, due to a break in the time series for the majority of countries. In a small number of indicators (most notably, the incidence of long working hours, and the homicide rate) the very top-performing OECD countries have relatively little room for substantial improvement. This can obviously therefore impact on the total number of improvements observed in those countries. Further information can be found in the country profiles of Chapter 5, and full-time series information is available in the Online Data Annex: Current Well-Being that accompanies this volume (ww.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595717

Looking at the combined improvements across all current well-being indicators, it becomes clear that while some countries have a balanced performance across both material conditions and quality of life, others have made more gains in one domain than in the other (Figure 1.18). For example, Estonia, the Slovak Republic and Latvia recorded a high number of improvements across both material conditions and quality of life. By contrast, in Luxembourg, Germany, France, Norway and the Czech Republic, improvements in material conditions outnumber those in quality of life by at least two-to-one. There are also countries where the inverse is true: in Italy, Spain, Denmark, Chile and Austria, at least two-thirds of all improvements have occurred among the quality-of-life indicators, rather than in material conditions. These patterns of change over time will, in part, reflect the different starting positions of different countries on these indicators, since in a limited number of indicators (e.g. access to basic sanitation) the best-performing countries have relatively little room to improve further.

Figure 1.18. Countries’ improvements in current well-being, relative to 2005
Number of indicators in which there have been net improvements since 2005
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Note : This figure shows the total number of indicators in which there have been improvements since 2005. Missing data are not taken into account. Countries with more than 2 missing indicators of material conditions are: Chile (7 indicators missing), Israel and Korea (5), Turkey (4), Canada, New Zealand and Switzerland (3). Countries with more than 2 missing indicators on quality of life are: Luxembourg and Iceland (5 indicators missing) and Japan (3).

 StatLink http://dx.doi.org/10.1787/888933595736

Resources for future well-being: Taking stock in 2017

Alongside measures of current well-being (which focus on the outcomes that affect people’s lives today), it is important to consider what is happening to the stocks of resources that will help to sustain well-being over time, for generations to come. How’s Life? 2015 introduced a new set of indicators to illustrate some of these stocks (described in terms of natural, human, economic and social capital), as well as a range of relevant flows (e.g. investments, depletions, emissions) and risk factors that may affect how these stocks evolve over time (Table 1.5). In this edition, these indicators are presented as a dashboard, featured on page 3 of each country profile in Chapter 5. The dashboards provide a country-level summary of whether a given indicator falls within the top, middle or bottom third of OECD countries, as well as (where possible) whether the level of each indicator has improved or worsened since 2005.

Table 1.5. Resources for future well-being indicators considered in this chapter

Type of capital

Indicators related to the “stock” of capital

Indicators related to flows (investment in, and depletion of, capital stocks)

Indicators related to risk factors

Natural capital

Exposure to PM2.5 air pollution*

Greenhouse gas emissions from domestic production

Forest area

CO2 emissions from domestic consumption

Renewable freshwater resources

Freshwater abstractions

Threatened mammals

Threatened birds

Threatened plants

Human capital

Young adults’ educational attainment (aged 25-34)

Educational expectancy

Long-term unemployment*

Cognitive skills at 15*

Smoking prevalence

Adult skills*

Obesity prevalence

Life expectancy at birth*

Economic capital

Produced fixed assets

Gross fixed capital formation

Financial net worth of the total economy

Intellectual property assets

Investment in R&D

Banking sector leverage

Household net wealth*

Household debt

Financial net worth of government

Social capital

Trust in others

Volunteering through organisations

Trust in the police

Voter turnout*

Trust in the national government

Government stakeholder engagement

Note : * denotes indicators also included in the current well-being indicator set, since it is relevant both for well-being today and for the stocks of resources shaping future well-being.

A number of these indicators are common to both the measurement of current well-being and its sustainability over time – since in several cases, the same outcomes that are relevant to well-being “here and now” can also serve as a store of value (and/or be a risk factor) for future well-being. Specifically, measures common to both indicator sets are: exposure to PM2.5 air pollution; cognitive skills at age 15; adult skills; life expectancy at birth; long-term unemployment; household net wealth; and voter turnout. In addition, while the headline indicators for current well-being consider the educational attainment of the total working-age population, the upper secondary attainment rates of young adults (aged 25-34) is identified as particularly relevant to the stock of human capital that will be carried forward into the future.

Figure 1.19 summarises countries’ numbers of comparative strengths and weaknesses across each of the four types of capital. Overall, Sweden, Norway, Denmark, Finland and New Zealand have the highest number of strengths across all the indicators of resources for future well-being, with a reasonably balanced spread across the four capitals. By contrast, Greece, Portugal, Hungary, the Slovak Republic and Italy have the lowest number comparative strengths, often with some imbalances between the different types of resources (e.g. Portugal and Hungary perform moderately well on natural capital, but have more weaknesses in relation to social capital). The comparative performance of several OECD partner countries is also reasonably strong, particularly in relation to their natural capital.

Figure 1.19. Countries’ comparative performance on resources for future well-being
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Note : Countries are ranked by their comparative performance on natural capital. To calculate levels of comparative performance, countries’ position on each indicator has been “scored” (1 = bottom third of OECD countries, 2 = middle third of OECD countries, 3 = top third of OECD countries), and the simple average score for each capital has then been calculated (with each indicator weighted equally). The minimum score is therefore 4, while the maximum score is 12. Indicators within each dimension have been weighted equally, with missing data excluded from the analysis.

 StatLink http://dx.doi.org/10.1787/888933595755

Nevertheless, comparative strengths and weaknesses are only one aspect of monitoring resources for future well-being: if all OECD countries are performing poorly, being the “best of a bad bunch” offers little comfort. Similarly, if all OECD countries are doing well on a given indicator, being the worst-performer does not necessarily signal a grave concern. This calls for a more nuanced view of stocks of resources, focusing on target levels and tipping points, rather than placing too much emphasis on a country’s comparative position vis-a-vis the rest of the OECD. It also underscores the importance of a dynamic approach, focusing on how capital stocks, flows and risk factors change over time, rather than just their initial levels.

Change in resources and risks for future well-being over the past 10 years

Data for assessing changes in natural, human, economic and social capital are more limited than is the case for current well-being, but the methods adopted for assessing change remain similar to those used earlier (Box 1.4).

Box 1.4. Assessing changes in resources for future well-being

In the case of resources for future well-being, some data on change are available for all 9 indicators of economic capital, but only for 4 out of 9 indicators of natural capital, 5 out of 8 indicators of human capital, and 2 out of 6 indicators of social capital. However, as is the case with current well-being, limited country coverage or incomplete time series mean that the OECD average often refers to a reduced set of countries – indicated in brackets in the legend of each figure (e.g. OECD 33). The OECD average is typically population-weighted, with exceptions reported in the figure notes, in order to capture the experience of the OECD average person (rather than the OECD average country). Due to large amounts of missing data, changes in OECD partner countries’ resources for future well-being are not considered below. However, the country profiles in Chapter 5 provide detailed change information for all 35 OECD countries and 6 partner countries.

The years covered typically range from 2005 to 2015/16 whenever possible. For measures that are collected on an infrequent basis in most countries (e.g. household net wealth, obesity prevalence, smoking prevalence) or that capture phenomena that occur infrequently (e.g. voter turnout), the OECD average is computed over a multi-year period to increase the number of countries included in the calculation. In the case of data on trust in the national government sourced from the Gallup World Poll, a 3-year average is used to increase the sample size (typically limited to 1 000 people per country, per year) and to reduce short-run volatility in the data.

For the indicators that are common to both the headline indicator set for current well-being and resources for future well-being (i.e. exposure to PM2.5 air pollution; cognitive skills at age 15; adult skills; life expectancy at birth; long-term unemployment; household net wealth; and voter turnout), information on change since 2005 is generally not repeated in the analysis that follows. However, these indicators are included in the summaries at the end of each section.

In the summary figures that describe results across countries (Figures 1.21,  1.23,  1.28,  1.30.  1.A.3 to 1.A.4), changes are calculated as the simple difference between 2005 and 2015 (or the closest years available). The categories “improving”, “little or no change” and “worsening” are defined based on the thresholds detailed in the figure notes, and discussed in Annex 5.A. of Chapter 5.

Complete information about the time series for the OECD average and the individual countries is detailed in the Online Data Annex: Current Well-Being and the Online Data Annex: Resources for Future Well-Being that accompany this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

Natural capital

The total stock of forest area in the OECD, when measured per 1 000 people, has fallen by around 5% since 2005 (Figure 1.20). This is driven by falls in around one-fifth of OECD countries, while the majority have seen little or no change (Figure 1.21). OECD average greenhouse gas emissions from domestic production fell by 14% between 2005 and 2015 – but increased in 5 OECD countries. More experimental measures of carbon dioxide emissions from domestic consumption (which take the effects of international trade into account) recorded a lesser fall, of around 8%, between 2001 and 2011 (the latest years available) – and increased in 8 countries overall. Finally, the OECD average exposure to outdoor air pollution by fine particulate matter (PM2.5) – which affects current well-being through the quality of the air breathed today, as well as future well-being through long-term exposure risks – improved in the years to 2011, but has since returned to 2005 levels (Figure 1.14, above). On a country-by-country basis (Figure 1.21 and Figure 1.A.1 in Annex 1.A), air pollution improved for around half of all OECD countries, and remained stable or worsened for the other half.

Figure 1.20. OECD average in selected natural capital indicators, since 2005
Forest area in square kilometres per thousand people (left), greenhouse gas emissions in tonnes per capita CO2 equivalent (middle), and CO2 emissions in tonnes per capita (right)
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Note : For detailed figure notes, see the Online Data Annex: Resources for Future Well-Being (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

Source : For forest area: OECD calculations based on “Land Resources”, OECD Environment Statistics database, http://stats.oecd.org/Index.aspx?DataSetCode=LAND_USE. For greenhouse gas emissions from domestic production: “Greenhouse gas emissions by source”, OECD Environment Statistics database, http://dx.doi.org/10.1787/data-00594-en. For CO2 emissions from domestic consumption: “Carbon Dioxide Emissions embodied in International Trade”, OECD Structural Analysis (STAN) Databases, http://stats.oecd.org/Index.aspx?Data SetCode=IO_GHG_2015.

 StatLink http://dx.doi.org/10.1787/888933595774

Figure 1.21. Change in selected natural capital indicators, relative to around 2005
Share of OECD countries
picture

Note : Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and defined as values greater than or equal to the following thresholds: GHG emissions from domestic production +/- 0.5 tonnes per capita; CO2 emissions from consumption +/- 0.5 tonnes per capita; exposure to PM2.5 air pollution +/- 1.0 micrograms per cubic metre; and forest area +/- 0.5 square kilometres per 1 000 people. For further information, see Annex 5.A in Chapter 5, and for full time series data see the Online Data Annex: Current Well-Being, and Online Data Annex: Resources for Future Well-Being that accompany this report (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595793

Human capital

Several of the indicators used to assess current well-being also form a core part of the human capital indicator set: adult skills, cognitive skills at age 15, life expectancy at birth and long-term unemployment. Of the indicators unique to resources for future well-being, the 10-year change in the educational attainment rate among adults aged 25-34 cannot be assessed due to a significant break in the data for most OECD countries in 2013. However, between 2013 and 2016, the share of young adults with at least an upper secondary education increased from 79.4% to 80.7%. Educational expectancy information is available only for 2015.

Smoking and obesity are human capital risk factors, since they may affect people’s health status in the future. The share of the OECD population who report that they smoke on a daily basis has fallen from 22.2% in 2005 to just under 17.7% in 2016 (Figure 1.22). At the same time, obesity has increased, with the share of the OECD population affected rising, from 21.5% to 23.8%.

Figure 1.22. OECD average smoking and obesity prevalence, since 2005
Share of the population aged 15 and over
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Note : For smoking prevalence, the OECD average is population-weighted and excludes Chile, Finland, Ireland, Mexico and the Netherlands, due to insufficient time series data. For obesity prevalence, the OECD average is population-weighted and excludes Chile, Finland, Germany, Iceland, the Netherlands, New Zealand, the Slovak Republic and Turkey.

Source : “Non-medical determinants of health”, OECD Health Statistics Database, http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_LVNG.

 StatLink http://dx.doi.org/10.1787/888933595812

Looking across the set of human capital indicators, the number of countries experiencing improvements is largest in relation to smoking prevalence and life expectancy at birth, where at least four-fifths of OECD countries have improved since 2005. By contrast, long-term unemployment has worsened in around half of all OECD countries, and obesity has risen in 60% (Figure 1.23, and Figure 1.A.2 in Annex 1.A).

Figure 1.23. Change in selected human capital indicators, relative to around 2005
Share of OECD countries
picture

Note : Countries with fewer than 9 years’ time series are excluded from this analysis, with the exception of educational attainment where only the 3 most recent years are considered, due to a break in the time series for the majority of countries. Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and defined as values greater than or equal to the following thresholds: life expectancy at birth +/- 0.5 years; young adult educational attainment +/- 0.5 percentage points; long-term unemployment +/- 0.2 percentage points; obesity +/- 1.0 percentage points; smoking +/- 1.0 percentage points. For further information, see Annex 5.A, in Chapter 5, and for full time series data see the Online Data Annex: Current Well-Being, and Online Data Annex: Resources for Future Well-Being that accompany this report (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595831

Economic capital

Information on changes in economic capital since 2005 is available for the full set of indicators considered in this report in only a small majority of (mostly European) OECD countries. On average, these countries have experienced growth in the volume of their produced fixed assets, the value of intellectual property assets, and the share of GDP invested in R&D (Figures 1.24 and 1.25) between 2005 and 2015. However, produced fixed assets fell in 2008-09, with comparatively weak growth since then – as shown by the sharp drop in gross fixed capital formation (the only indicator in this group for which information is available in all OECD countries), which underwent a dramatic downturn between 2007 and 2009, and in 2015 still remained two percentage points lower than in 2005.

Figure 1.24. OECD average produced fixed assets and gross fixed capital formation, since 2005
USD at 2010 PPPs, per capita (left), and year on year growth rates (right)
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Note : For produced fixed assets, Purchasing Power Parities (PPPs) are those for GDP. The OECD average is population-weighted; it excludes Belgium, Chile, Estonia, Greece, Hungary, Iceland, Ireland, Latvia, Mexico, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Switzerland and Turkey, due to incomplete time series.

Source : For produced fixed assets: OECD calculations based on “9B. Balance sheets for non-financial assets”, OECD National Accounts Statistics Database, http://dotstat.oecd.org/Index.aspx?DataSetCode=SNA_TABLE9B. For gross fixed capital formation: OECD National Accounts Statistics Database, http://dx.doi.org/10.1787/na-data-en.

 StatLink http://dx.doi.org/10.1787/888933595850

Figure 1.25. OECD average intellectual property assets and investment in R&D, since 2005
USD per capita at 2010 PPPs (left), and as a percentage of GDP (right)
picture

Note : For intellectual property assets, Purchasing Power Parities (PPPs) are those for GDP; the OECD average is population-weighted, and excludes Belgium, Chile, Estonia, Greece, Hungary, Iceland, Ireland, Latvia, Mexico, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Switzerland and Turkey, due to incomplete time series. For investment in R&D the OECD average is weighted by the shares of GDP; it excludes Chile, Iceland, Italy, Mexico, Switzerland, Turkey and the United States due to incomplete time series.

Source : For intellectual property assets: OECD calculations based on OECD National Accounts Statistics Database, http://dx.doi.org/10.1787/na-data-en. For investment in R&D: OECD calculations based on “8A. Capital formation by activity ISIC rev4”, OECD National Accounts Statistics Database, http://stats.oecd.org/Index.aspx?DataSetCode=SNA_TABLE8A and the Russian Federal State Statistics Service.

 StatLink http://dx.doi.org/10.1787/888933595869

A country’s net foreign asset position relative to the rest of the world is also relevant to the stability of the economic system. The average financial net worth of the total OECD economy, measured on a per capita basis, switched from negative to positive from 2013 onwards (Figure 1.26), largely due to a recent positive shift in the United States. By contrast, between 2005 and 2015, the financial net worth of the general government sector fell for the OECD on average, from around -42% of GDP in 2005 to -72% in 2015, mainly as a result of the impact of the recession on tax revenues and fiscal deficits and due to public support provided to an ailing banking sector.

Figure 1.26. OECD average financial net worth of the total economy and financial net worth of the general government, since 2005
USD per capita at current PPPs (left); as a percentage of GDP (right)
picture

Note : For the financial net worth of the total economy, Purchasing Power Parities (PPPs) are those for GDP; the OECD average is population-weighted and excludes Japan, Korea, Mexico, New Zealand and Turkey, due to incomplete time series. For the financial net worth of the general government, the OECD average is population-weighted and excludes Korea, Mexico, New Zealand and Turkey, due to incomplete time series.

Source : For financial net worth of the total economy: OECD calculations based on OECD National Accounts Statistics Database, http://dx.doi.org/10.1787/na-data-en. For financial net worth of the general government: OECD Financial dashboard Database, http://dotstat.oecd.org/Index.aspx?DataSetCode=FIN_IND_FBS.

 StatLink http://dx.doi.org/10.1787/888933595888

Rising levels of debt over extended periods of time also imply risks for economic sustainability. The OECD average household debt, as a share of household disposable income, rose in 2005-07, before falling until 2012 and stabilising thereafter (Figure 1.27). The leverage of the banking sector has been more volatile over the period, peaking in 2008 and again 2011, and currently weighing in at 17% higher than in 2005.

Figure 1.27. OECD average household debt and banking sector leverage, since 2005
As a percentage of net disposable income (left), ratio of selected assets to own equity (right)
picture

Note : For household debt, the OECD average is weighted by the household net disposable income and excludes Iceland, Israel, Korea, Luxembourg, Mexico, New Zealand and Turkey, due to incomplete time series. For banking sector leverage, the OECD average is population-weighted and excludes the Czech Republic, Iceland, Korea, Mexico, New Zealand, Switzerland and Turkey, due to incomplete time series.

Source : OECD Financial dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=FIN_IND_FBS.

 StatLink http://dx.doi.org/10.1787/888933595907

Figure 1.28 shows the extent to which the experiences of OECD countries differ across the various economic capital indicators. All countries with data available showed an increase in produced fixed assets between 2005 and 2015, although the annual growth rate of gross fixed capital formation fell for 60% of countries. More than half of OECD countries experienced an increase in the stock of intellectual property assets, with a similar number of countries recording an increased share of GDP spent on R&D. The financial net worth of government fell in close to two-thirds of OECD countries, and household debt rose in two‐thirds (despite the improving OECD average picture, which is largely driven by household debt reductions in the United States and Germany). Figure 1.A.3 in Annex 1.A provides a country-by-country analysis.

Figure 1.28. Change in economic capital indicators, relative to around 2005
Share of OECD countries
picture

Note : Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and defined as values greater than or equal to the following thresholds: produced fixed assets +/- 4 500 USD per capita; intellectual property assets +/- 200 USD per capita; investment in R&D +/- 0.2 percent of GDP; financial net worth of the total economy +/- 1 000 USD per capita; household net wealth +/- 9 000 USD; gross fixed capital formation +/- 1.0 percentage point; financial net worth of government +/- 3.0 percent of GDP; banking sector leverage +/- 3.0 change in the ratio of assets to banks’ own equity; household debt +/- 10 percent of household net disposable income. For further information, see Annex 5.A, in Chapter 5, and for full time series data see the Online Data Annex: Current Well-Being, and Online Data Annex: Resources for Future Well-Being that accompany this report (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595926

Social capital

Change over time can be assessed for only two of the six social capital indicators considered in this report: trust in the national government and voter turnout. On both these measures, the OECD average has fallen since 2005 (Figure 1.29). However, not every country follows this trend – with voter turnout rising in around one-third of OECD countries, and trust in the national government rising in one-quarter (Figure 1.30). Changes in trust in others – probably the best indicators of social capital – cannot be assessed based on the indicators used for this report. However, data from the World Values Survey suggest a mixed picture, with a decrease in roughly half of the OECD countries sampled over the period 2005-14, relative to the levels prevailing in 1981-94 (Halpern, 2015).

Figure 1.29. OECD average voter turnout and trust in the national government, since 2005
Percentage of the population responding “yes” to a question about confidence in the national government (left); and voter turnout as a percentage of the population registered to vote (right)
picture

Note : For trust in the national government, the OECD average is population-weighted and excludes Iceland and Luxembourg, due to an incomplete time series. For voter turnout, the OECD average has been calculated across four-year periods. This required excluding Austria, Finland, Italy, Luxembourg and Mexico. Chile is also excluded since compulsory voting was dropped in 2012, introducing a break in the series.

Source : For trust in the national government: OECD calculations based on Gallup World Poll, www.gallup.com/services/170945/world-poll.aspx. For voter turnout: International Institute for Democracy and Electoral Assistance (IDEA) (2017), www.idea.int, the register of the Supreme Electoral Tribunal for Costa Rica, and the Federal Statistical Office (FSO) of Switzerland.

 StatLink http://dx.doi.org/10.1787/888933595945

Figure 1.30. Change in selected social capital indicators, relative to around 2005
Share of OECD countries
picture

Note : Changes are calculated as the simple difference between 2015 and 2005 (or closest years available) and defined as values greater than or equal to the following thresholds: voter turnout +/- 1.0 percentage point; trust in the national government +/- 3.0 percentage points. For further information, see Annex 5.A, in Chapter 5, and for full time series data see the Online Data Annex: Current Well-Being, and Online Data Annex: Resources for Future Well-Being that accompany this report (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595964

Statistical agenda ahead

This chapter demonstrates the richness of well-being statistics now available in OECD countries, including new data on household wealth, job strain, feelings of having a say in what the government does, exposure to air pollution, and subjective well-being. OECD work has continued to support the statistical agenda on measuring well-being (Box 1.5), and many National Statistical Offices are taking serious steps to improve data availability (see OECD, 2015a, for examples). Nevertheless, in terms of comprehensively monitoring well-being and its sustainability for OECD and partner countries, a number of significant data gaps remain:

  • First, OECD country coverage is incomplete for several of the headline indicators. Key gaps for current well-being include time devoted to leisure and personal care (missing for 14 OECD countries in the latest available year), household net wealth (missing for 8), adult skills, and having a say in government (both missing for 7), and life satisfaction (missing for 5). On resources for future well-being, there are sizeable gaps in relation to produced fixed assets and intellectual property assets (both missing for 9 OECD countries), trust in others and trust in the police (missing for 8), volunteering (missing for 7), household debt (missing for 5) and threatened species (missing for 3-5 countries).

  • Second, several indicators are not collected on a routine basis (e.g. trust in others; trust in the police; threatened species), or are collected infrequently (e.g. time devoted to leisure and personal care; adult skills; having a say in government; volunteering). In other cases, methodological breaks interrupt the time series for a sizeable number of countries (e.g. educational attainment; educational expectancy; long-term unemployment; life expectancy). This makes it difficult to provide a comprehensive account of whether life is getting better for people. While change since 2005 can be assessed for 21 out of the 25 headline indicators for current well-being shown in this edition, and for 20 of the 32 resources for future well-being indicators, country coverage for these analyses is often limited. This in turn limits the conclusions that can be drawn about OECD-wide trends, and about comparative performance.

  • Third, since the first edition of How’s Life? (OECD, 2011) several indicators drawn from non-official sources have been used as “placeholders” until other internationally harmonised data become available. This includes social support, satisfaction with water quality, feelings of safety, and life satisfaction. Thanks to new data from national statistical offices, it has been possible to replace the original life satisfaction placeholder (see Exton, Siegerink and Smith, forthcoming, for an overview). Yet non-official sources remain an important source of information for several dimensions of well-being.

  • Fourth, a number of dimensions remain poorly covered in terms of the available internationally-comparable evidence. Natural, human, economic and particularly social capital have important gaps in terms of the concepts covered – and the issues of global public goods and transboundary impacts (see Box 1.2) require further conceptual and statistical work. Some of the progress needed in the measurement of social capital (including on trust and governance) are discussed in more detail in Chapter 4 of this edition, and are also addressed in recent OECD work (Box 1.5). On current well-being, social connections continue to be poorly captured: there is just one headline indicator, based on a simple “yes/no” question about having someone to count on in times of trouble, which suffers from ceiling effects some OECD countries. On personal safety, a placeholder measure on reported assault has been removed from the headline indicator set since data are no longer collected routinely. Internationally comparable data on the incidence of crimes, other than homicide, should be a priority for the future. For environmental quality, there are important data gaps to fill regarding access to green space and objective measures of water quality. As yet, it has also not been possible to identify a suitable measure of mental health for the health status dimension, a major omission.

  • Fifth, inequalities in well-being are often difficult to measure. Capturing the distribution of well-being outcomes is central to the How’s Life? measurement approach (see Box 1.1) and also of importance to the UN Agenda 2030’s aspiration to “leave no-one behind”. Chapter 2 of this report provides a comprehensive account of the inequalities that can be measured across the headline indicators of current well-being. This includes the size of the gap between the top and bottom of the distribution, and differences in outcomes between groups (by gender, age and education). Chapter 3 (on migrants’ well-being) focuses on the experiences of a sizeable minority group in many OECD countries. Both of these Chapters discuss in detail the statistical agenda ahead, to enable a more complete story to be told.

Box 1.5. OECD contributions to the statistical and policy agenda on well-being

It is now 6 years since the OECD the launched its Better Life Initiative. Well-being statistics and analysis are now published regularly, both as part of the How’s Life? series, and in several different web-formats, such as the interactive Better Life Index (www.oecdbetterlifeindex.org); a regional well-being data explorer (www.oecdregionalwellbeing.org); the Child Well-Being Portal (www.oecd.org/social/child-well-being); and the Gender Data Portal (www.oecd.org/statistics/datalab/gender-data-portal.htm). Several studies have provided a more in-depth analysis of well-being in specific countries, including Israel (OECD, 2016a), Mexico (OECD, 2015b), Denmark (OECD, 2016b) and Slovenia (through work supporting the National Development Strategy). The OECD’s Economic Surveys (www.oecd.org/economy/surveys), Better Policies Series (www.oecd.org/about/publishing/betterpoliciesseries.htm) and Multi-Dimensional Country Reviews (www.oecd.org/development/mdcr/) now also routinely make use of well-being data in the analyses presented. Two of theOECD’s leading data collections, the Programme for International Student Assessment (PISA, www.oecd.org/pisa), and the Survey of Adult Skills (or Programme for the International Assessment of Adult Competencies, PIAAC, www.oecd.org/skills/piaac) include a range of indicators that are valuable for assessing various dimensions of well-being, beyond education and skills. The 2015 PISA exercise also included a special focus on students’ well-being, considered from a psychological, physical, cognitive and social perspective (OECD, 2017c).

A range of OECD methodological projects have contributed to the statistical agenda on measuring well-being. This includes international guidelines, such as the: Guidelines on Measuring Subjective Well-Being (OECD, 2013b); Guidelines for Micro Statistics on Household Wealth (OECD, 2013c); OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth (OECD, 2013d), Guidelines on Measuring Trust (OECD, 2017d); and Guidelines on Measuring the Quality of the Working Environment (OECD, 2017e). Several databases have also been developed or enhanced in recent years, including the Income Distribution database; the Wealth Distribution database; and the Job Quality database (see database references, below). Forthcoming projects include conceptual work on measuring the impact of business on well-being; digitalization and well-being; capturing the well-being experiences of different ethnic groups; and unlocking the potential of time-use data for well-being measurement.

The 17 UN Sustainable Development Goals, underpinned by 169 targets, and a (still evolving) list of 232 indicators, pose formidable measurement challenges for the statistical systems of all countries. Since the overlap between the SDGs and the OECD well-being framework is large (Figure 1.2), there are many commonalities in terms of the statistical agenda. The OECD is participating in the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators (UN Statistics Division, 2017), and supporting the UN Global monitoring framework in a number of respects (OECD, 2016c). These include providing indicators directly (such as data on Official Development Assistance), collaborating with other agencies (such as with UNESCO on education-related indicators), and helping to fill data gaps in key areas, such as governance statistics, through the work of the UN Praia Group, (Instituto Nacional de Estatística, Cape Verde, 2017). Finally, statistical capacity-building assistance is being provided through joint work with PARIS21, a body that promotes the better use and production of statistics throughout the developing world.

References

Alkire, S. and M.B. Sarwar (2009), Multidimensional Measures of Poverty and Well-being, Oxford Poverty and Human Development Initiative, Oxford Department of International Development, University of Oxford.

Anand, P., M. Durand and J. Heckman (2011), “Editorial: The Measurement of Progress – some achievements and challenges”, Journal of the Royal Statistical Society, Vol. 174, pp. 851-855.

Exton, C., Siegerink, V. and Smith, C. (forthcoming), Measuring subjective well-being in national statistics: taking stock of recent OECD activities, OECD Publishing, Paris.

FSO (2015), Sustainable Development – A Brief Guide 2015: 17 key indicators to measure progress. Swiss Federal Statistical Office, Neuchâtel www.bfs.admin.ch/bfsstatic/dam/assets/349919/master (accessed 28 September 2017).

Halpern, D. (12 November 2015) “Social trust is one of the most important measures that most people have never heard of – and it’s moving”, Behavioural Insights Team blog, www.behaviouralinsights.co.uk/uncategorized/social-trust-is-one-of-the-most-important-measures-that-most-people-have-never-heard-of-and-its-moving/.

Instituto Nacional de Estatística, Cape Verde (2017), Praia Group on Governance Statistics (website), www.ine.cv/praiagroup/ (accessed 29 September, 2017).

OECD (2017a), Measuring Distance to the SDG Targets: An Assessment of Where OECD Countries Stand, OECD Publishing, Paris, www.oecd.org/std/measuring-distance-to-the-sdgs-targets.htm.

OECD (2017b), Green Growth Indicators 2017, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264268586-en.

OECD (2017c), PISA 2015 Results (Volume III): Students’ Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264273856-en.

OECD (2017d), OECD Guidelines on Measuring Trust, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264278219-en.

OECD (2017e), OECD Guidelines on Measuring the Quality of the Working Environment, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264278240-en.

OECD (2016a), Measuring and Assessing Well-being in Israel, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264246034-en.

OECD (2016b), Well-being in Danish Cities, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264265240-en.

OECD (2016c), “Better policies for 2030: An OECD Action Plan on the Sustainable Development Goals”, OECD Week 2016, www.oecd.org/dac/Better%20Policies%20for%202030.pdf

OECD (2015a), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en.

OECD (2015b), Measuring Well-being in Mexican States, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264246072-en.

OECD (2013a), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264201392-en.

OECD (2013b), OECD Guidelines on Measuring Subjective Well-being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264191655-en.

OECD (2013c), OECD Guidelines for Micro Statistics on Household Wealth, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264194878-en.

OECD (2013d), OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264194830-en.

OECD (2011), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264121164-en.

Sen, A. (1985), Commodities and Capabilities, North-Holland Publishing, Amsterdam.

Statistics New Zealand (2011). Key findings on New Zealand’s progress using a sustainable development approach: 2010. Statistics New Zealand, Wellington, www.stats.govt.nz/browse_for_stats/snapshots-of-nz/Measuring-NZ-progress-sustainable-dev-%20approach/key-findings-2010.aspx (accessed 28 September 2017).

Stiglitz, J.E., A. Sen and J.-P. Fitoussi (2009), Report by the Commission on the Measurement of Economic Performance and Social Progress, www.insee.fr/en/information/2662494.

UNECE (2014), Conference of European Statisticians Recommendations on Measuring Sustainable Development, United Nations, New York and Geneva, www.unece.org/fileadmin/DAM/stats/publications/2013/CES_SD_web.pdf.

United Nations (2009), Measuring Sustainable Development, United Nations, prepared in cooperation with the OECD and the Statistical Office for European Communities (Eurostat), New York and Geneva.

UNU-IHDP and UNEP (2012) Inclusive Wealth Report 2012. Measuring progress towards sustainability. Cambridge: Cambridge University Press.

UN Statistics Division (2017), Inter-Agency Expert Group on SDG Indicators (website), https://unstats.un. org/sdgs/iaeg-sdgs/ (accessed 10 September 2017).

UN Department of Economic and Social Affairs (2012), Future We Want – Outcome Document (webpage) https://sustainabledevelopment.un.org/index.php?menu=1298 (accessed 24 September 2017).

Database references

Gallup World Poll, www.gallup.com/services/170945/world-poll.aspx.

International Institute for Democracy and Electoral Assistance (IDEA) (2017), www.idea.int.

OECD Average annual wages database, http://stats.oecd.org/Index.aspx?DataSetCode=AV_AN_WAGE.

OECD Employment and Labour Market Statistics database, http://dx.doi.org/10.1787/lfs-lfs-data-en.

OECD Environment Statistics (database), http://stats.oecd.org/Index.aspx?DataSetCode=LAND_USE.

OECD Exposure to air pollution database, http://dotstat.oecd.org/Index.aspx?DataSetCode=EXP_PM2_5.

OECD Financial Dashboard database, http://dotstat.oecd.org/Index.aspx?DataSetCode=FIN_IND_FBS.

OECD Health Statistics database, http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_STAT.

OECD Income Distribution database, http://stats.oecd.org/Index.aspx?DataSetCode=IDD

OECD Job Quality database, http://dotstat.oecd.org/Index.aspx?DataSetCode=JOBQ.

OECD National Accounts Statistics database, http://dx.doi.org/10.1787/na-data-en.

OECD Structural Analysis (STAN) databases, http://stats.oecd.org/Index.aspx?DataSetCode=IO_GHG_2015.

OECD Wealth Distribution database, http://stats.oecd.org/Index.aspx?DataSetCode=WEALTH

Figure 1.A.1. Countries’ changes in selected natural capital indicators, relative to 2005
Share of indicators (out of 4 indicators in total)
picture

Note : Further information on which indicators have improved, seen little or no change, or worsened can be found in the country profiles of Chapter 5, and full time series information is available in the Online Data Annex: Current Well-Being and the Online Data Annex: Resources for Future Well-Being that accompany this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933595983

Figure 1.A.2. Countries’ changes in selected human capital indicators, relative to 2005
Share of indicators (out of 5 indicators in total)
picture

Note : Countries with fewer than 9 years’ time series are excluded from this analysis, with the exception of educational attainment where only the 3 most recent years are considered for all countries, due to a major break in the time series. Further information on which indicators have improved, seen little or no change, or worsened can be found in the country profiles of Chapter 5, and full time series information is available in the Online Data Annex: Current Well-Being and the Online Data Annex: Resources for Future Well-Being that accompany this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933596002

Figure 1.A.3. Countries’ changes in selected economic capital indicators, relative to 2005
Share of indicators (out of 9 indicators in total)
picture

Note : Countries with fewer than 9 years’ time series are excluded from this analysis, with the exception of household net wealth, where only two observations are available for all countries. Further information on which indicators have improved, seen little or no change, or worsened can be found in the country profiles of Chapter 5, and full time series information is available in the Online Data Annex: Current Well-Being and the Online Data Annex: Resources for Future Well-Being that accompany this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933596021

Figure 1.A.4. Countries’ changes in selected social capital indicators, relative to 2005
Share of indicators (out of 2 indicators in total)
picture

Note : Further information on which indicators have improved, seen little or no change, or worsened can be found in the country profiles of Chapter 5, and full time series information is available in the Online Data Annex: Current Well-Being and the Online Data Annex: Resources for Future Well-Being that accompany this volume (www.oecd-ilibrary.org/economics/how-s-life-2017_how_life-2017-en).

 StatLink http://dx.doi.org/10.1787/888933596040

Notes

← 1. The OECD’s Better Life Index (www.oecdbetterlifeindex.org) is a website where people can explore OECD well-being statistics through a set of interactive data visualisations. A key feature of the site is that users are able to build their own customised index of overall well-being, by rating the importance of the 11 different dimensions of life covered by the OECD framework. Users can then see how countries rank in terms of overall performance, based on their own customised index.

← 2. In the online annex that accompanies this publication, the number of countries covered by the OECD average is indicated in brackets in the legend (e.g. OECD 33). The results reported in this section typically refer to population-weighted averages in order to capture the experience of the OECD average resident (rather than the OECD average country). This procedure gives proportionately more weight to countries with a larger population, and proportionately less weight to countries with a smaller population, in the calculation of the average.

← 3. Comparative strengths are defined as those falling in the top third of OECD countries, while comparative weaknesses are defined as those falling in the bottom third. Tiers have been determined by ranking countries from worst outcome (1) to best outcome (35), and then dividing that rank by the total number of OECD countries in the sample. The resulting values (ranging from 0 to 1) are then categorised as follows: countries with values ranging from zero to 1/3 are assigned to the bottom tier; values greater than 1/3 but less than or equal to 2/3 are categorised as middle tier; and valuesgreater than 2/3 but less than or equal to 1.0 are assigned to the top tier. For OECD partner countries, the “OECD equivalent” rank is shown – i.e. the rank that the country would attain when compared to OECD countries only.

← 4. The methodology for assigning countries to the top, middle or bottom third of the OECD is the same as that applied in the country profiles shown in Chapter 5. Namely, countries have been ranked from worst outcome (1) to best outcome (35), and this rank has then been divided by the total number of OECD countries in the sample. The resulting values (ranging from 0 to 1) are then categorised as follows: countries with values ranging from zero to 1/3 are assigned to the bottom tier; values greater than 1/3 but less than or equal to 2/3 are categorised as middle tier; and values greater than 2/3 but less than or equal to 1.0 are assigned to the top tier. For OECD partner countries, the “OECD equivalent” rank is shown – i.e. the rank that the country would attain when compared to OECD countries only.

← 5. The cumulative growth rate for household net adjusted disposable income between 1995 and 2005 was 18.7%, but due to data availability this considers a slightly smaller number of OECD countries than the 2005-15 analysis shown (25 countries, rather than 28). The corresponding cumulative growth rate for those 25 countries between 2005-15 is 8.5%. The cumulative growth rate for earnings between 1995 and 2005 was 14%. This is based on the same sample of 34 OECD countries as the 2005-16 growth rate.

← 6. This calculation is based on the 26 OECD countrieswith at least two relevant data points, and represents a fall from 2.1% in 2005-10 to 1.3% in 2011-15. The OECD average is population-weighted and excludes Australia, Canada, Chile, Israel, Japan, Korea, Mexico, New Zealand and the United Kingdom, due to incomplete time series for these countries.

← 7. This was largely due to a substantial increase in the homicide rate in Mexico, which has driven up the OECD average as a whole, despite decreases in almost all other countries.