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Society at a Glance 2016

OECD Social Indicators

Please cite this publication as:
OECD (2016), Society at a Glance 2016: OECD Social Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264261488-en.

Metadata, Legal and Rights

ISBN: 978-92-64-26153-2 (epub) - 978-92-64-26146-4 (print) - 978-92-64-26148-8 (pdf)

DOI: http://dx.doi.org/10.1787/9789264261488-en

Series: Society at a Glance

ISSN: 1995-3984 (print) - 1999-1290 (online)

This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of OECD member countries.

This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Photo credits: Cover © pio3/Shutterstock.com; Chapter 3: © Stockbyte/Getty Images; Chapter 4: © Maria Taglienti-Molinari/Brand X/Corbis; Chapter 5: © Mauricio Graiki/Shutterstock.com; Chapter 6: © Helen King/Corbis; Chapter 7: © Daniel Boiteau/Fotolia.com

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© OECD 2016

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Foreword

This is the eighth edition of Society at a Glance, the OECD’s biennial overview of social indicators. As with its predecessors, this report addresses the growing demand for quantitative evidence on social well-being and its trends across OECD countries. It updates some indicators included in the previous seven editions and introduces several new ones. Data on Argentina, Brazil, China, Colombia, Costa Rica, India, Indonesia, Lithuania, the Russian Federation, Saudi Arabia and South Africa are included separately where available.

The Great Recession caused sweeping job losses across the OECD, and young people were hit particularly hard. So far, the recovery has been too weak to bring young people – and notably the low-skilled – back into employment. Eight years after the beginning of the crisis, still about 40 million young people are neither employed nor in education or training (NEET). But the roots of the problem go deeper: many lack the qualifications to find a job, while others struggle with personal or social problems. In the long term, joblessness and inactivity can generate isolation and withdrawal from society and endanger social cohesion. The great challenge for governments in the years to come is therefore to devise policies which equip young people with the professional skills they need and help disengaged youth overcome obstacles to education and employment.

This edition of Society at a Glance portrays at-risk youth and surveys policies designed to promote a smooth transition from school to work. Chapter 1 presents and discusses the most recent data on the situation of youth in OECD countries. It also presents evidence on education, training, employment and social policies which can support NEETs. Chapter 2 provides a guide to help readers understand the structure of OECD social indicators. Chapters 3 to 7 then consider these indicators in more detail. Additional information on indicators can be found on the OECD web pages (http://oe.cd/sag).

This report was prepared by Stéphane Carcillo (project leader), Pauline Fron, Raphaela Hyee, Claire Keane, Sebastian Königs and Maxime Ladaique. Nelly Biondi, Chris Clarke, Rodrigo Fernandez, Michael Förster, Gaétan Lafortune, Marlène Mohier all made valuable contributions. Monika Queisser, Head of the OECD Social Policy Division, supervised the report.

Acronyms and conventional signs

OECD country ISO codes

Australia | AUS | Korea | KOR
Austria | AUT | Latvia | LVA
Belgium | BEL | Luxembourg | LUX
Canada | CAN | Mexico | MEX
Chile | CHL | Netherlands | NLD
Czech Republic | CZE | New Zealand | NZL
Denmark | DNK | Norway | NOR
Estonia | EST | Poland | POL
Finland | FIN | Portugal | PRT
France | FRA | Slovak Republic | SVK
Germany | DEU | Slovenia | SVN
Greece | GRC | Spain | ESP
Hungary | HUN | Sweden | SWE
Iceland | ISL | Switzerland | CHE
Ireland | IRL | Turkey | TUR
Israel | ISR | United Kingdom | GBR
Italy | ITA | United States | USA
Japan | JPN |  |

Other major economy and G20 country ISO codes

Argentina | ARG | Indonesia | IDN
China | CHN | Lithuania | LTU
Colombia | COL | Russian Federation | RUS
Costa Rica | CRI | Saudi Arabia | SAU
India | IND | South Africa | ZAF

Other acronyms and abbreviations

NEET | Youth not in employment, education or training
VET | Vocational education and training

Conventional signs

.. Not available

In figures, OECD refers to unweighted averages of OECD countries for which data are available.

(➘) in the legend relates to the variable for which countries are ranked from left to right in decreasing order.

(➚) in the legend relates to the variable for which countries are ranked from left to right in increasing order.

Executive summary

Fifteen percent of the OECD youth population were not in employment, education or training (NEET) in 2015 – about 40 million young people. More than two-thirds of them were not actively looking for work. The total gross income that could have been generated by NEETs in 2014 is estimated to have been between USD 360-605 billion, or 0.9-1.5% of OECD-wide GDP. Job and income uncertainty can keep young people from reaching other traditional markers of adulthood, leaving them disenchanted and discouraged. It can also have serious long-term effects on health, fertility and crime, and eventually endanger social cohesion. Helping young people transition into further education or employment is therefore at the top of the policy agenda in the OECD as evidenced by the G20 target of reducing the number of youth who are low-skilled, NEET or working in the informal sector by 15% until 2025.

One in ten jobs have been destroyed since 2007

Almost one out of every ten jobs held by workers under 30 were destroyed between 2007 and 2014. In Spain, Greece and Ireland, the number of employed youth halved. Young people who had left school at lower-secondary level bore the brunt of these job losses. And while some countries have managed to restore youth employment to pre-crisis levels, the recovery has been too weak to significantly improve young people’s prospects in many countries.

Lower skills make young people particularly vulnerable

Young people with no more than lower-secondary education account for over 30% of NEETs, and they are three times more likely to be NEET than those with a university-level degree. However, poor educational attainment is not only about formal qualifications; young people with weak literacy and numeracy skills are more likely to be NEET, which points to the importance of high-quality alternative education and training paths for early school leavers.

Young women are often NEET because of care-giving responsibilities

Being a woman adds to the risk of becoming NEET particularly on a long-term basis. Many young women care for children and other family members at home. Consequently, women are 1.4 times more likely to be NEET than young men. Single parents find it particularly hard to combine caring for children with employment or further education. The availability of affordable childcare is thus crucial to improving young women’s job prospects.

Some NEETs suffer from additional forms of disadvantage

Young people who are already disadvantaged in other ways often also face a high risk of becoming NEETs. Those born abroad are 1.5 times more likely to be NEET than native-born youth, particularly if they cannot speak the local language and are low-skilled. Young people whose parents have low educational attainment or do not work are also more likely to experience unemployment or inactivity. This indicates intergenerational transmission of disadvantage. Young people who suffer from ill health are also over-represented among NEETs.

Most young people are never NEET, but one-fifth are long-term NEETs

Across a selection of countries analysed, more than half of young people were never NEET over a four-year period. For others, short NEET periods were part of a successful transition from education to work. One-fifth of all young people, however, spent more than one year as NEETs – for them disengagement from work and education is not a transient experience, but a lasting state. Countries hit hard by the crisis have a higher share of long-term NEETs; women, the low-educated and youth with ill health are also at greater risk of becoming long-term NEETs.

NEETs have lower levels of happiness, trust and political interest

Being NEET is likely to influence young people’s happiness, may make them feel disenfranchised, and can affect social cohesion. NEET youth have lower levels of life satisfaction and trust in others compared to non-NEET youth. They also show less interest in politics and are more likely to feel that it is the government’s responsibility to provide for citizens.

Safety nets tend to be weaker for young people

School leavers and young people with patchy employment records often fail to qualify for insurance-based income support. Only around 30% of all unemployed young people receive unemployment benefits, while over 40% of all jobseekers aged 30 and over are covered. Consequently, social safety nets are less effective in fighting poverty among young people: 40% of young people who would have incomes below the poverty line are kept out of poverty by public transfers, compared to 50% of adults aged 30 and over. Roughly every eighth young person lives in poverty, and youth poverty rates are higher than those of the elderly.

Fighting early school leaving is essential

The share of young people who leave school without an upper-secondary qualification has declined in OECD countries over the past decade. Nevertheless, one in six 25-to-34 year-olds still do not have an upper-secondary qualification, particularly young men. To ensure that all young people complete their upper-secondary schooling, comprehensive support is needed: monitoring school attendance to spot warning signs of drop-out; addressing pupils’ social or health problems; and offering after-school programmes to engage pupils and strengthen their motivation.

Quality vocational education and training can help smooth the school-to-work transition

Vocational education and training (VET) is a valuable alternative to academic schooling. It prepares young people for the labour market with a view to responding to employers’ skills needs. The practical training component of VET should be work-based, ideally in the form of apprenticeships matching young people with employers at an early stage. Such programmes may be particularly attractive and beneficial for youth tired of school. Pre-apprenticeship programmes can prepare those who lack the necessary literacy, numeracy or social skills to function in the workplace.

Carefully targeted programmes are needed to re-engage NEETs

Public services need to reach out to NEETs to prevent long-term inactivity. Employment services, social services and non-governmental actors can play a central role in engaging disconnected youth. Once a young person is registered, extensive profiling can help make support adequate to purpose and can save costs by ensuring that interventions target the right youth. Many NEETs may require only a little assistance to find employment, while successful programmes for young people with severe or multiple barriers tend to be intensive and expensive. The most promising programmes combine schooling and practical training with counselling, psychological support and housing. Some have been shown to be cost-effective, by raising earnings potential and reducing criminal behaviour.

Chapter 1.

The NEET challenge: What can be done for jobless and disengaged youth?¹

Introduction

Young people today struggle in the labour market in spite of being the most highly educated generation in history. Unemployment is generally higher among young people than prime age adults, and those who do work tend to have poorer-quality jobs and are much more likely to be on temporary contracts or to earn low wages than older workers.

The Great Recession hit young people particularly hard and, as the recovery has been jobless in many countries, many young people have not seen their situation improve since. This chapter adopts a wide definition of youth, including all 15-29 year-olds, to allow for the fact that young people remain in education for longer, and to include the beginning of family formation. The number of 15-to-29 year-olds not in employment, education or training (NEETs) remains higher than before the onset of the crisis in nearly all OECD countries – 40.0 million in 2015.2 The most vulnerable – those with a poor education, ill health or social problems, and/or a migrant background – are most likely to find themselves without work, quality education or training opportunities. Over two-thirds of all NEETs (28 million young people across the OECD) are inactive, i.e. not even looking for work.

This situation has significant social, political and economic consequences. In the absence of adequate public support, declining household incomes increase the risk of poverty. They may force young people and their families to cut down on essential expenditure on food, housing and health care, so damaging their well-being and health. Periods of inactivity and unemployment in early adulthood have also been shown to have lasting negative effects on future employment prospects and earnings (OECD, 2015a). The growing uncertainty with which young people grapple at the outset of their careers can also keep them from reaching traditional markers of adulthood – securing a steady job is often associated with the decision to leave the parental home and is typically a prerequisite for starting a family. In the long term, inactivity and unemployment can generate isolation and disengagement from society, with adverse consequences for social outcomes such as health, fertility and trust, and can eventually lead to crime (Carcillo et al., 2015).

The social and labour market integration of young people is therefore a policy priority for OECD governments. This chapter takes stock of the current labour market situation of young people, profiles those who are out of employment, education or training, and reviews approaches and policies that OECD countries have adopted to improve youth employment and educational outcomes. It addresses the following sets of questions:

How have young people fared during the Great Recession, and which young people were most affected by the large-scale destruction of jobs? To answer these questions, the first section of this chapter looks at how youth employment rates have evolved across the OECD and at the educational attainment of those who have lost their jobs. Many young people go to school or study and do not participate in the labour market, which makes employment and unemployment rates incomplete measures of the labour market situation of young people. The analysis therefore specifically focuses on NEETs and how their numbers evolved throughout the economic crisis.

Who are the NEETs, and for how long do they typically remain out of employment, education and training? Young people who are unemployed or inactive differ greatly in personal characteristics, family background, and the barriers to their securing a place in education or work. Identifying NEETs has important implications for the type of support they need if they are to (re)gain self-sufficiency. The second section profiles NEETs from country to country in order to identify principal risk factors as well as obstacles to re-integration:

What share of NEETs are early school leavers, and what proportion lack the literacy or numeracy skills required for work or training?

Is there an important gender gap in unemployment or inactivity, and what are its likely drivers?

And to what extent are youth from more disadvantaged families at a greater risk of being NEET?

The negative long-term consequences of joblessness are likely to be greatest for young people who remain NEET for long periods. The third section therefore also studies for how long young people remain out of education, employment and training and seeks to identify factors that put them at risk of being long-term NEETs.

What forms of income support are available to low-income young people, and how successful are they at preventing youth poverty? A consequence of the difficult labour market situation for young people is that a growing share of them struggle to be self-sufficient. Although income support can help absorb severe earnings losses and ensure a decent standard of living, it is often less generous and more difficult to access for young people. The third section looks at income support for young people:

What proportion of young people receive unemployment or disability benefits, social assistance, or other types of cash benefits, and how have their numbers developed over the crisis and its aftermath?

What share of unemployed young people are covered by some form of income support?

What are the implications for the incomes of young people – and the incidence of youth poverty?

What policies and programmes can rise to the NEET challenge? Cross-country analysis of NEETs reveals various barriers to their entering education or employment. Because low educational attainment is such an important risk factor, the final section examines what governments can do to ensure that all young people complete their upper-secondary education:

What measures heighten the chances of spotting students at risk of dropping out of school and giving them support they need?

How can attractive vocational education and training pathways – particularly quality apprenticeships – contribute to providing young people with the skills and work experience required in the labour market?

What interventions can help NEETs back into education or employment? Which ones work best and for whom? What are the challenges of implementation? Under what conditions can youth guarantees make a difference?

1. Youth employment in the aftermath of the Great Recession

Youth employment is an important factor for social cohesion. From a macro-economic perspective, persistently high rates of unemployment or inactivity are a substantial loss of economic opportunity and income. They undermine trust in political institutions and policies while, from an individual viewpoint, under-employment can inflict scars on youth that last for many years. Indeed, there is ample evidence that even short spells of inactivity and unemployment at the outset of a career can have lasting effects. Young people who experience a period of early unemployment are more prone to unemployment later in their careers (Schmillen and Umkehrer, 2013; Möller and Umkehrer, 2014) and have been shown to earn less (Umkehrer, 2015).³

This section examines to what extent the labour market situation of young people differs from one country to another and how it has evolved since the onset of the Great Recession (see also Indicator 4 on “Labour market entry”).

Youth were hit hard by the economic crisis

The sweeping job losses in the wake of the 2007-8 financial crisis hit young people disproportionately hard. They are more likely to work in temporary and atypical contracts that are easier to terminate – in other words, they are what the concept of labour market duality describes as “outsiders”. Moreover, in times of weak labour demand, young people with little or no work experience struggle to find a job. The slow recovery in many countries since the crisis has failed to reverse trends in youth employment and restore it to pre-crisis levels. OECD-wide, the number of employed young people, aged 15 to 29 years, fell by 8% between 2007 and 2015, while overall employment increased slightly (Figure 1.1). The most catastrophic job losses over the period occurred in the countries worst hit by the recession: Spain, Ireland and Greece saw youth employment cut in half. And in Portugal, Slovenia, Italy and Latvia, between one-quarter and one-third of all jobs held by young people were destroyed.

In some countries, such as France and the United States, youth employment fell more moderately, while a handful of countries not only avoided significant job losses, but even saw an increase in employment among young people – particularly Luxembourg (+38%) and Chile (+20%) (Figure 1.1). In both countries’ youth employment rates, especially among young women, were in fact low at the onset of the crisis, and the rise was attributable chiefly to more young women taking up work.

Figure 1.1. Almost one in ten jobs held by young people were destroyed since the beginning of the crisis

1. Data relate to 2011.

Source: OECD Employment Database, www.oecd.org/employment/database.

StatLink  http://dx.doi.org/10.1787/888933404800

The recovery has been too weak to help young people back into employment

While many countries have experienced bouts of recovery, they have generally been too weak to drive up youth employment rates significantly. Indeed, the Southern European countries that have been severely affected by the crisis – such as Spain, Italy and Greece – experienced a second recession in 2011-12, and the share of employed youth is only just levelling out. Other countries – such as Estonia, Hungary and Iceland – have already recovered, or are on their way to recovering, their pre-crisis levels of youth employment. The average share of youth in employment OECD-wide has stagnated since 2010 (Figure 1.2).

Figure 1.2. Youth employment rates have been slow to recover

Changes in youth employment rates in selected OECD countries (in %), 2007 to 2015 where 2007 = 100%

Source: OECD Employment Database, www.oecd.org/employment/database.

StatLink  http://dx.doi.org/10.1787/888933404813

Low-educated youth were particularly vulnerable to job losses

Young people with low levels of educational attainment (below upper-secondary) were most vulnerable during the economic crisis and have continued to be during the slow recovery. They are also at the highest risk of long-term scarring effects. The number of employed youth who had gone no further than lower-secondary education dropped in almost all countries between 2007 and 2014 – including in those where youth employment grew over this period, such as Mexico, Australia and Norway. Indeed, in those seven years, the young people who bore the brunt of job losses across the OECD were those educated to low and medium levels (see explanatory note to Figure 1.3 on levels of education). By contrast, employment among university graduates rose OECD-wide, save in those countries hardest hit by the crisis. In the Czech Republic, for instance, overall youth employment dropped by 17%, while among young people with higher education it increased by 7%.

Figure 1.3. Poorly educated young people were hit hardest by the recession

Percentage change in numbers of employed 15-to-29 year-olds, between 2007 and 2014, by level of education

Note: The figure depicts the absolute change in employed individuals by educational attainment as a percentage of the total change in employed persons.

For Japan, the age bracket is 15 to 24 years old. Data for Chile relate to 2006-13, for Korea to 2008-13, and for Germany, Japan, New Zealand and Turkey to 2007-13.

Education levels are defined as follows: “Low education” denotes a level no higher than lower-secondary education (up to ISCED Level 3C short), “medium education” denotes upper-secondary and post-secondary non-tertiary levels (ISCED Levels 3C long to Level 4), “high education” denotes tertiary level (ISCED Levels 4 and 5).

The education levels medium and high cannot be distinguished for Mexico and New Zealand, and there is no information on the level of education of employed young people in Japan and Korea, so there is no breakdown by level of for the OECD average. Because of a break in the time series in Israel in 2011, 2007-14 comparisons cannot be made for Israel.

Due to missing information on educational attainment for some individuals, there are disparities between the total change in the number of employed youth (diamonds) and the variation aggregated across levels of education for Denmark, Ireland, Korea and Sweden.

Source: OECD calculations based on national labour force surveys and the OECD Education Database (Australia, Germany, Japan and Korea).

StatLink  http://dx.doi.org/10.1787/888933404823

The stark contrast between skilled and unskilled youth certainly reflects rising levels of educational attainment – young people now are generally better educated than they were in 2007 – as well as, in some countries a declining youth population.⁴ It also indicates a growing demand for skills. Many of the jobs destroyed during the crisis are gone for good. It is therefore essential to ensure that all young people who enter the labour market be well qualified. The current climate of relatively weak labour demand is actually a good time to invest in the skills of the most vulnerable young people. (Section 4 discusses policies and programmes that can help upskill poorly qualified NEETs so they are fully prepared when labour demand does pick up.)

Youth employment tends to be higher where many young people combine work and study

As a consequence of the divergent effects of the Great Recession, the OECD-wide youth employment situation has become more unequal. In 2015, over two-thirds of young people were employed in the best-performing countries, such as Iceland, Switzerland and the Netherlands and Australia. In the Southern European countries worst affected by the crisis, by contrast, only one-quarter to one-third of all youth were in work (Figure 1.1).

The disparities in youth employment rates are also linked to a more structural factor – the share of young people who combine studies and work (Figure 1.4). In Iceland, Switzerland and the Netherlands, more than half of all students are also employed, while in Greece, Italy, Hungary, Portugal, Spain or the Slovak Republic, only about one in twenty is. Working a moderate number of hours (below 15 a week) has been shown to lower the risk of early school leaving, possibly because it fosters important life skills such as conscientiousness and motivation, and can steer students towards a career path. There are also benefits for university students, especially if they work moderate hours – i.e. less than half-time – in a job related to their field of study. Accordingly, some countries have encouraged the employment of students by introducing or stepping up the work-based components of school and university courses (Quintini and Martin, 2014).

Figure 1.4. In the best-performing countries, many young people combine work with education

Labour market status of young people, percentages, 2014

Note: Countries are ranked, from top to bottom, in order of youth employment rates. Data for Chile, Korea, New Zealand and Turkey relate to 2013.

Source: OECD calculations based on national Labour Force Surveys and the OECD Education Database (Australia, Germany, Israel and New Zealand).

StatLink  http://dx.doi.org/10.1787/888933404835

Many NEETs are not looking for work

The number of NEETs rose in most OECD countries as a result of sweeping job losses in the wake of the economic crisis. In 2015, the number of NEETs OECD-wide stood at 40.0 million, over two-thirds of whom were not actively looking for work – so called “inactive NEETs”. Averaged across OECD countries, 14.6% of all youth were NEETs in 2015; weighted by the size of countries’ youth populations, this rate rises to 17%.⁵ This share was strikingly high in the countries hit hardest by the recession – between one-quarter and one-fifth of all young people were out of work and not in education in Greece, Italy, and Spain.⁶

A breakdown of NEETs into those actively seeking a job – the unemployed NEETs – and those who are not, the inactive NEETs, shows that in most countries, the majority of NEETs are not even looking for work. The share of inactive NEETs is highest in Turkey and Mexico, but also significant in Italy, Korea and Chile (Figure 1.5, Panel A). Section 2, “Who are the NEETs? A profile of jobless youth”, shows that inactive NEETs are not actively seeking work for a variety of reasons – e.g. care obligations, health problems, substance abuse problems as well as the belief that any job search would be unsuccessful. In some countries – such as Turkey, Mexico, and Chile – low female participation in the labour market leads to high inactive NEET rates among young people. However, since inactive NEETs are not necessarily registered with the public employment or welfare services, they can be particularly hard to reach. Section 4, “Policies to promote self-sufficiency among young people” discusses programmes designed to attract and engage inactive NEETS.

Figure 1.5. Rising NEET rates mostly reflect higher unemployment among young people

Note: Countries are arranged, from top to bottom, in order of overall NEET rates.

Data on Israel are not comparable before and after 2011, so the percentage point change is not presented for Israel. Data for Chile refer to 2006-13, for Korea to 2008-13, and to 2014 for Israel.

Source: OECD calculations based on national labour force surveys and OECD National Educational Attainment Classification (NEAC) Database 2015, https://stats.oecd.org/Index.aspx?datasetcode=EAG_NEAC# (Australia, Israel, Korea and New Zealand).

StatLink  http://dx.doi.org/10.1787/888933404842

The share of young people who are unemployed is significant in some countries, notably those badly hit by the crisis – 18% in Greece and 15% in Spain. By contrast, only 2% of all young people are unemployed and looking for work in Iceland and 3% Norway, the Netherlands, Korea, Germany, Japan and Mexico.

The rise in NEET rates since the beginning of the crisis has been driven wholly by an increase in the share of unemployed NEETs, while the share of inactives has been steady or even declined (Figure 1.5, Panel B). In Greece, for instance, the rise in NEET rates was due solely to the increase in numbers of unemployed NEETs, while the share of inactive NEETs actually fell.

The rise in NEET rates does not exactly match up with the fall in employment rates. This is because the share of young people who do not work but are in education also increased during the crisis period in most countries – on the OECD average, it rose by 3 percentage points between 2007 and 2014 (not shown). This increase was significant in some countries that experienced extreme job losses, such as Spain (+15 percentage points) or Ireland (+12 percentage points), but also Turkey (+9 percentage points) and Denmark (+8 percentage points).

The NEET problem is structural, but has been exacerbated by the crisis

How much of the NEET problem is directly caused by the fall in labour demand during and after the great recession, and how much is structural? That is, are NEET rates expected to fall as labour demand picks up again, or are there obstacles to the employment of NEETs that go beyond business cycles – e.g. a mismatch between the skills of young people and the requirements of employers? Figure 1.6 shows the inactive and unemployed NEET rates on the OECD average since the late 1990s. The rate of inactive youth who are not in employment shows a slight but steady downward trend that continued throughout the crisis years – over the last 17 years, it fell by 2 percentage points. The unemployed NEET rate, however, clearly fluctuates with the business cycle. During the great recession, it increased significantly more than during previous downturns, reaching a maximum of 7.5% in 2013, significantly higher than any value observed during this time period.

Figure 1.6. NEET inactivity has not been influenced by the crisis

Unemployed, inactive and overall NEET rate, OECD average, 1997-2014

Note: The OECD average is based on 25 countries for which data for a sufficient number of years is available.

Source: OECD Education Database.

StatLink  http://dx.doi.org/10.1787/888933404855

This suggests that, while young people suffered severe job losses during the crisis, the average NEET rate across the OECD is not likely to fall more than 2 or 3 percentage points as the economy recovers. The structural component of the NEET problem therefore requires targeted policies that help young people overcome the barriers to employment they face, some of which are discussed in Section 4.

Box 1.1. Measuring youth unemployment

One of the indicators most widely used to assess labour markets is the unemployment rate – the ratio between the unemployed and people who participate in the labour market (be they unemployed or in work). Such a measure can be a misleading when applied to young people, as many are not in the labour market, either because they are students or inactive NEETs.

The proportions of young people participating in the labour market differs widely from one country to another – ranging from a 43% participation rate in Italy to 83% in Iceland (Figure 1.4). Such variations stem not only from different national economic climates, but are influenced by the architecture of a country’s education system: Youth labour market attachment tends to be higher in countries with apprenticeship systems – vocational upper-secondary pathways that combine on-the-job training and classroom instruction. Even if the share of the youth population that is unemployed is the same in two countries, the one with the greater labour force participation will have a lower unemployment rate, as the denominator comprises more young people.

One measure of youth unemployment not influenced by the number of non-working students is the unemployment ratio – the number of the unemployed as a share of the entire youth population. If all young people are either working or seeking work (a labour force participation rate of 100%), the unemployment rate and ratio coincide. At a youth participation rate of around 50%, as in Greece and Spain, the unemployment rate will be about twice as high as the corresponding unemployment ratio (Figure 1.7).

This conceptual issue has a direct impact on youth unemployment rates in the countries worst affected by the crisis, often described as dramatic. Greece’s 45% youth unemployment rate translates into a 22% ratio, and Spain’s 40% rate into a 23% ratio (Figure 1.7).

Figure 1.7. Unemployment rates are always higher than unemployment ratios

Unemployment rates and ratios as percentages, 2015

Source: OECD Employment Database, www.oecd.org/employment/database.

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The high number of NEETs represents a significant cost to OECD economies

The high number of NEETs generates significant opportunity costs for OECD economies, as young people’s time and skills go unused. The fact that the NEET problem is partly structural and therefore unlikely to disappear after OECD economies have fully recovered to the crisis, further adds to the importance of this recurring cost. This section provides a rough estimate of these costs to OECD countries.

NEET costs are defined as the gross labour income NEETs could command if they were employed, measured as the gross labour cost (including social security contributions).⁷ This cost can be considered as a proxy of the forgone productivity of NEETs. This section presents three estimates: upper and lower bound estimates, as well as a point estimate. The point estimate accounts for the fact that jobless young people may have a lower earnings potential than young people in employment – e.g. NEETs tend to have a lower educational background than other youth, and are more likely to have care responsibilities (see Chapter 3).⁸ The upper bound estimate assumes that if employed, NEETs would on average receive the same wages and would choose to work the same hours as employed youth of the same gender and age. The lower point estimate assumes that NEETs could only command a “low-wage”, defined as two-thirds of the median wage among youth of the same gender and age-group.⁹ Hence, both the upper and lower bounds ignore differences in other characteristics, such as education.

This exercise estimates the gross labour cost that could have been generated by NEETs in the OECD in 2014 between USD 360 billion and USD 605 billion, with the point estimate at USD 560 billion. This corresponds to between 0.9 and 1.5% of the OECD GDP. Figure 1.8 depicts the lower bound estimate for each country as a share of GDP.¹⁰

Figure 1.8. NEET costs are significant in many OECD countries

Annual NEET rate and estimation of the cost of NEETs as a percentage of GDP, 2014

Source: OECD calculations based on the EU-SILC, HILDA (Australia), SLID (Canada), CASEN (Chile), SOEP (Germany), ENIGH (Mexico), SILC (Turkey) and the CPS (United States). Data are for 2014 except for Chile and Switzerland (2013), Turkey (2012) and Canada (2011). GDP data from OECD.Stat, http://stats.oecd.org.

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The total costs of NEETs are a function of both NEET rates and wage levels. Countries with the highest NEET rates suffer the highest costs – Turkey at 3.4% of GDP, and Greece at 2%. However, relatively high wage levels can cause significant costs for countries with moderate NEET rates: Belgium, which has a mid-table position with regard to NEET rates (see Figure 1.5), has a similar relative NEET cost as Italy where the NEET rate is nearly twice as high. The countries with the lowest relative NEET costs are Northern European countries with low NEET rates, such as Norway, Sweden or Denmark.

These estimates only provide a proxy for the actual net social cost of the NEET phenomenon, since it disregards that some NEETs might prefer not to work, and their families and wider communities might also benefit from them not working. On the other hand, high NEET rates might affect individuals and the community negatively, e.g. through deteriorated health or more crime. It also ignores the out-of-work benefits paid to some of these NEETs. A further complication is that wages and prices could change if all NEETs were to suddenly take up employment, especially in countries with high shares of NEET youth.

2. Who are the NEETs? A profile of jobless youth

Policies to prevent and curb youth unemployment and inactivity require an understanding of the obstacles that keep NEETs out of employment or education. Only then can they be effectively designed and tailored to needs. NEETs are a diverse group, as are the challenges they face within and between countries. Some young people struggle to secure employment because they left school early, while others – particularly women – have caring obligations. Those affected by illness or disability may face practical difficulties in engaging in education or employment, while migrants may face language barriers.

This section seeks to draw an in-depth profile of NEETs, looks at how long young people stay inactive or unemployed, and identifies the risk factors for being a long-term NEET.

Low levels of education and skills heighten the risk of becoming NEET

As the labour market demands ever higher levels of educational attainment and skills, low education levels are decisive factors in becoming NEET. On average, NEET rates in the OECD are three times higher among young people educated to lower-secondary level and 1.5 times greater at medium level (upper-secondary) than among their highly educated peers with university-level (or tertiary) degrees. Because too many young people fail to complete upper-secondary school – 17% of young adults (25-34) had a maximum of lower-secondary education in 2014 – this means that a large share of NEETs is not fully educated. Actually, just under one-third of all NEETs have only reached lower-secondary school (Figure 1.9, Panel A), while 43% have gone no further than upper-secondary school. Only one quarter of NEETs have higher education qualifications.

The educational gap in NEET rates varies significantly from country to country. As the educational attainment levels of young people rise over time, the absence of qualifications becomes an ever more important impediment to employment. Poorly educated youth in Germany are, for instance, almost 7 times more likely to become NEET than the highly educated (Figure 1.9, Panel B). And in Chile, NEET rates are 2.4 times higher among young people educated to medium level than among the highly educated. In a word, a good education helps protect young people from becoming NEETs, while leaving school early puts them at considerable risk especially when most other young people attain upper secondary or higher education.

Figure 1.9. NEET rates are substantially higher among young people with low education

Note: Data in Panel B refer to 2014, except for Australia, Chile, Germany, Israel, Korea, Mexico, New Zealand and Turkey (2013). No data were available for Japan.

“Low-education” denotes lower-secondary school and lower (Levels 0-2 in the International Standard Classification of Education [ISCED]); “medium education” refers to upper- or post-secondary education (ISCED Levels 3-4); and “high education” means higher, or tertiary, education (ISCED Levels 5-6).

Source: OECD calculations based on the European Labour Force Survey and national labour force surveys; for Australia, Germany, Israel, Korea, Mexico, New Zealand and Turkey, OECD Education Database https://stats.oecd.org/Index.aspx?datasetcode=EAG_TRANS.

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Basic skills are also an important determinant of NEET status. The OECD Programme for International Student Assessment (PISA) also finds a strong link between pre-primary education attendance and better performance in reading, writing and maths later in life. Education begets skills, and skills beget educational attainment. It follows, therefore, that leaving school early may result in low levels of literacy and numeracy, while students who have struggled from an early age with basic literacy and numeracy may also be more likely to drop out as they fall behind their peers. And poor literacy and numeracy also make it more difficult to find a job. Policies should seek to help young people master basic skills to reduce the risk of becoming NEET. Across the OECD, young people with low and medium levels of literacy and numeracy are four times more prone to becoming NEETs than their highly skilled peers (Figure 1.10, Panels A and B).

Figure 1.10. Poor literacy or numeracy skills also greatly increase young people’s risk of being NEET

Note: Literacy and numeracy are rated in accordance with the skills levels in the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC): “Low-skilled” – Level 1 or below, “Medium skilled” – Levels 2 and 3; and “High skilled” – Levels 4 and 5. Low literacy skills, as measured by PIAAC, indicate that and individual can only undertake tasks of limited complexity and is less able to integrate information from multiple sources; low numeracy skills indicate that an individual is less capable of performing complex mathematical tasks and uses fewer problem-solving strategies.

Source: OECD (2015), OECD Skills Outlook 2015: Youth, Skills and Employability, OECD Publishing, Paris (based on the Survey of Adult Skills, PIAAC 2012).

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Non-cognitive skills, too, have been shown to be highly predictive of educational and labour market outcomes (Heckman et al., 2006). Traits such as conscientiousness, emotional stability or openness to new experience are factors that can be as important as IQ in completing school and higher education, finding a job and earning wages (see Box 1.2). They also have a strong impact on health, law-abiding behaviour and other social outcomes. Similarly, it has also been found that non-cognitive skills are more malleable than cognitive abilities in adolescence, and many successful youth programmes emphasise them. In the United States, learning self-discipline, the ability to work in groups and the self-regulation of emotions are, for instance, at the centre of many approaches, including two important national programmes: “Head Start” for children in kindergarten and the “Jobs Corps”, a second-chance programme for teenagers and young adults (see Box 1.6).

Box 1.2. Non-cognitive skills, education and labour market outcomes

While the effect of years of schooling and cognitive abilities (such as attention, memory, and problem-solving as measured by IQ and other ability tests) on income and health has been recognised for many years, the role of personality traits, or non-cognitive skills, is less well known.

A growing body of research finds that non-cognitive skills are associated with educational attainment and outcomes like early school leaving. Of the “big five” personality traits – conscientiousness, openness to experience, extraversion, agreeableness and neuroticism (also referred to as emotional stability) – the first two best predict overall educational achievement (Goldberg et al., 1998 for the United States; Báron and Cobb-Clark, 2010 for Australia; and Van Eijck and De Graaf, 2004; Almlund et al., 2011; and Brunello and Schlotter, 2011 for European countries). Heckman, Stixrud and Urzua (2006) find that personality traits like conscientiousness affect earnings beyond their influence on education, particularly among individuals in lower-skilled jobs. Conscientiousness is also as closely associated with good grades as intelligence is (Poropat, 2009), while a number of studies have found that emotional stability is also often a good predictor of high levels of attainment in school.

Non-cognitive skills can be seen as “internal assets” that will eventually improve academic, family, social and employment outcomes (Almlund et al., 2011; Cunha and Heckman, 2007) Job and academic performance share a number of determinants. For example, both require completing work on time and involve intelligence to varying degrees. It is not surprising, therefore, that non-cognitive skills are also associated with labour market performance. The importance of intelligence increases with the complexity of an occupation, while conscientiousness may be demanded in jobs that range from skilled to semi-skilled and unskilled labour. The principal finding to emerge from the literature is that non-cognitive skills are just as predictive as cognitive ability of education, labour market and other social outcomes, even after controlling for family background and cognition.

Studies show that at least half of non-cognitive abilities stem from children’s home and school environments, with the rest attributable to hereditary factors. Personality traits can, therefore, be changed by experience and specialised interventions, while cognitive abilities form early in life and are more difficult to shape. Many successful interventions for disadvantaged students seek to improve non-cognitive traits, often together with measures to enhance cognitive skills. Such approaches open new directions for social, employment and education policy (Carcillo et al., 2015). Innovative school programmes, after-school support, mentoring, apprenticeship schemes, work experience and second-chance programmes can thus all help to influence non-cognitive skills.

NEET rates are higher among women, mainly due to family responsibilities

While the lack of education is the foremost factor leading to non-employment among young people, being a woman worsens the risks. Women are indeed more likely to become NEET than men – 1.4 times more OECD-wide (Figure 1.11). Behind that average figure, there are variations from country to country. In some, particularly the Nordics, the gap is negligible. By contrast, in Mexico or Turkey, for example, women are at three to four times more risk of becoming NEETs than men. One reason may be the traditional gender-related assignment of roles, with women doing most of the unpaid domestic work and caring for children. However, some higher-income countries like New Zealand and the Czech Republic also have wide gender gaps in their NEET rates, with female rates twice those of men.

Figure 1.11. Young women are more likely to be NEET than young men

NEET rates for women and men as percentages of the 15-to-29 year-old population, 2014

Note: Age group for Japan is 15 to 24 years old, and the United States 16 to 24 years old. For Chile and Turkey, data apply to 2013. In all other countries, the data relate to 2014.

Countries are sorted, from left to right, in ascending order of the overall NEET rate.

Source: OECD calculations based on the European Labour Force Survey (EU LFS), Canada’s Labour Force Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN), Mexico’s National Occupation and Employment Survey (ENOE), the US Current Population Survey (CPS), the Japanese Labour Force Survey and the OECD Education Database (for Australia, Germany, Korea and New Zealand https://stats.oecd.org/Index.aspx?datasetcode=EAG_TRANS).

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Cultural differences aside, the main reason for inactivity among women most often relates to childcare responsibilities, while poor health is the single most widespread cause among males. More than half of women ascribe their inactivity to care-giving and family responsibilities (Figure 1.12, Panel B), which probably means looking after small children, as employment rates are generally low among mothers of very young children – only about half of those with children under 3 years of age are in employment in OECD countries (Figure 1.13). Only small minorities of inactive women attribute not working to poor health or “other” reasons, contrary to men. Some might simply prefer caring for children at home while they are young – more mothers take up employment as children get older (Figure 1.13). Others, however, have no choice because they cannot access or afford childcare for their small children. There is typically a greater, more affordable provision for older children. Indeed, affordable childcare and child-friendly employment arrangements are key requirements for greater labour market participation among young mothers (Box 1.3).

Figure 1.12. NEET rates are particularly high for women in their late 20s, often because of caring responsibilities

Source: OECD calculations based on the European Labour Force Survey (EU LFS), Canada’s Labour Force Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN), Mexico’s National Occupation and Employment Survey (ENOE), the US Current Population Survey (CPS), the Japanese Labour Force Survey and the OECD Education Database (for Australia, Germany, Korea and New Zealand https://stats.oecd.org/Index.aspx?datasetcode=EAG_TRANS). Australia, Germany, Israel, Japan, Korea, Mexico and New Zealand excluded from Panel B due to a lack of information.

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Figure 1.13. Employment rates are low among mothers of young children

Maternal employment rates by age of youngest child, 2013

Note: Data for Australia refer to 2011 and for Denmark and Finland to 2012.

No data are available for Sweden, Iceland, Japan, Korea, Norway, Switzerland or Turkey.

Countries are sorted, from left to right, in descending order of the employment rate of mothers whose youngest child is aged 0 to 14 years old.

Source: OECD Family Database, www.oecd.org/social/family/database.htm.

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Box 1.3. High childcare costs: An obstacle to paid work for mothers

OECD-wide, childcare costs claim an average of 15% of the net family income of an employed single parent or a dual-earner couple (Figure 1.14). Costs vary across the OECD, however, particularly for single parents. In the United States, childcare costs for a lone parent account for over half of net income, while in Ireland the figure is 42%. Couples in New Zealand and the United Kingdom spend around one-third of their income on childcare costs. Such high costs are a strong deterrent to employment. It may not be financially worthwhile for both partners to work, especially in families with several children, and it is usually the mother who stays at home. Resuming employment after some years out of the workforce is difficult, and women often face wage penalties upon their return to work (Budig and England, 2001).

In order to help NEETs with children, particularly females, into employment or facilitate a return to education and training, it is therefore essential that childcare costs are kept to an affordable level, and that childcare, including after-school care, is easy to access. The provision of childcare services may also have an equity role to play – high-quality, formal childcare, particularly at an early age, has been shown to have a positive effect on social development and child cognition and these effects have been found to be stronger for children from lower socio-economic backgrounds (Heckman, 2008). As is shown throughout this chapter, NEETs are not only more likely to have lower educational attainment and skills, but are also more likely themselves to have parents with low educational attainment and parents who are out of work. Ensuring access to high quality childcare can, therefore, help to break the cycle of disadvantage from one generation to the next.

Several OECD countries offer good examples: Denmark operates a system whereby municipalities are obliged to offer all children older than six months a place in publicly-subsidised childcare. In Sweden, municipalities must provide at least 15 hours of childcare per week to children over one. This obligation rises to full-time hours in cases where both parents are employed or in education. Other countries provide additional support for single parents with Iceland (specifically Reykjavik) providing reduced childcare fees and Belgium (Flanders region) providing priority access to childcare services for lone parents.

Figure 1.14. Childcare costs are around 15% of net family income across the OECD

Note: Data relate to i) out-of-pocket childcare costs for full-time care at a typical childcare centre for a single parent with full-time earnings of 67% of average earnings and ii) for a couple with full-time earnings of 100+67% of average earnings. “AW” stands for average wage.

The OECD average is unweighted.

Source: Tax and Benefit System: OECD Indicators, www.oecd.org/social/benefits-and-wages.htm.

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Source: European Commission/EACEA/Eurydice/Eurostat (2014), Key Data on Early Childhood Education and Care in Europe. 2014 Edition. Eurydice and Eurostat Report, Publications Office of the European Union, Luxembourg; Eurydice (2016), Description of National Education Systems, Eurydice, Brussels, https://webgate.ec.europa.eu/fpfis/mwikis/eurydice/index.php/Countries; OECD (2016), Tax and Benefit Systems: OECD Indicators, OECD Publishing, Paris, http://www.oecd.org/els/soc/benefits-and-wages.htm.

Because young people in older age brackets are more likely to have children, the gender gap in NEET rates is more visible here. Male and female NEET rates are the same in the younger 15-to-19 age group, when most young people are still at school (Figure 1.12, Panel A). Later, though, driven by female inactivity rates which are double those of males, a 3 percentage point gap opens up among 20-to-24 year-olds. As for the 25-to-29 age group, female NEET rates stand at 26% – 11 percentage points higher than men’s. And within that percentage, the share of inactive NEETs is more than three times higher than among men.

Young people with poor health are four times more likely to be NEET

A relatively small proportion of NEETs – 7% on average across the OECD – report being in poor health while a larger proportion, just over one fifth, report limitations in their daily activity due to health. But even if a minority of NEETs report poor health, at the individual level illness or disability is a strong predictor of the NEET status, especially among males. Physical or psychological problems often represent serious obstacles to finding employment and accruing work experience. NEET youth are over five times more likely on average to complain of poor health than their non-NEET peers (Figure 1.15, Panel A) and more than twice as likely to report limitations in their daily activity due to their health (Panel B, Figure 1.15). Indeed, a substantial proportion of male and, to a lesser extent, female inactive NEETs state that ill-health or disability is the prime reason for not working (Figure 1.12, Panel B).

Figure 1.15. NEETs are more likely to suffer from poor health

Note: Data are for 2014 except for Chile (2013) and Turkey (2012).

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC), Germanys Socio-economic Panel (SOEP), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN) and the US Current Population Survey (CPS).

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Some NEETs may be permanently unable to work, some able to do only certain types of jobs or a certain number of hours, while others may require special workplace adjustments. They may also have to contend with practical difficulties such as physical restrictions or a lack of flexible working arrangements. Illness and disability may also make going to school or university more difficult if they affect attendance and performance.

As for mental disorders, they are widespread among young people – about one in four 15-to-24 year-olds are affected (OECD, 2012a). Onset often occurs well before any labour market transition – before 14 years old in half of all mental illnesses. Mentally unwell young people are more prone to dropping out of school (OECD, 2015c). A fifth of those with moderate mental health issues – and a quarter of sufferers with severe conditions – do so, compared with only 14% of pupils and students with no such issues. Given the link identified above between early school leaving and drifting into unemployment or inactivity, it is essential that policy measures support mentally unwell young people and keep them fully engaged in education (Section 4).

Migrant youth are more at risk of being NEET…

Similarly, a relatively small proportion of NEETs – 13% on average across the OECD – are foreign-born youth. Nevertheless individually foreign-born young people are significantly more exposed to the risk of being NEETs than the native-born. In most OECD countries, youth born outside their country of residence are 1.5 times more likely to be NEET than native youth. Foreign-born young people are more at risk of becoming NEETs because they might not be sufficiently proficient in the host country’s language, have lower levels of educational attainment, or suffer from discrimination. Indeed, a poor command of language can lead to low educational attainment and thus feed a vicious circle of disadvantage.

The relative risk of becoming NEET is relatively higher in a number of countries with low NEET rates like Germany, Austria, the Netherlands, and Norway, where foreign-born young people are around 2-2.5 times more prone to be NEET than their native-born peers (Figure 1.16, Panel A). In countries such as Poland or the United Kingdom, there is scarcely any gap at all, while in Israel, Ireland and Chile, foreign-born youth are actually less likely to be NEET. This disparity is likely to depend on the country of origin and the reason for migrating. Young people who head abroad to pursue education or employment opportunities are necessarily less likely to be NEET than those who arrive as refugees. And those from countries with high levels of educational attainment are more likely to come equipped with the skills necessary to find a job. Speaking the host country language is also an advantage.

Figure 1.16. NEETs are more likely to have been born abroad and to come from disadvantaged backgrounds

Note: In Panel A, countries are arranged, from left to right, by ascending order of overall NEET rate. The OECD average is unweighted.

Panel B depicts, for each attribute, the ratio of NEETs to all young people who share that attribute. Information on parental education and employment status are available only for young people living with their parents.

No information on country of birth is available from Japan, Korea, Mexico and New Zealand. Family background information is not available from Canada, Israel, Japan, Korea, Mexico, Switzerland and Turkey.

Source: OECD calculations based on national labour force surveys (Panel A); OECD calculations based on national household surveys (Panel B).

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… as are young people with poorly educated or unemployed parents

Many of the disadvantages leading to unemployment or inactivity reviewed so far – e.g. low education, poor skills, health problems such as mental disorders or early pregnancy – are often transferred from parents to children. Parents’ socio-economic status is thus a strong predictor of their children’s (Clark, 2014). NEETs are 80% more likely than other young people to have parents with no upper-secondary schooling and twice as likely to have parents who do not work (Figure 1.16, Panel B).¹¹

For this reason, at the aggregate level, a significant share of unemployed or inactive youth have a disadvantaged background. About a third of NEETs have parents with at most lower secondary education or jobless parents (twice the rates of non-NEET youth).¹²

A number of factors drive the replication of socio-economic disadvantage. Parents’ low educational attainment might directly affect the level of schooling their children attain, for example, if they are less inclined to encourage their pursuit of higher education or less able to help them with schoolwork. Similarly, jobless parents might not have the professional (or other) connections that would help further their children’s career opportunities, which could increase the risk that they become NEET.

Parents may not have passed on to their children desirable social skills, either. Recent research shows that such skills are a key component in matching workers with firms and that the young and poorly educated are at a particular disadvantage (Pellizzari, 2010; Kramarz and Skans, 2013). It often takes intensive, closely targeted programmes with specific methods from a very early age to overcome disadvantage. For instance, some early childhood interventions among poor children in the United States – such as the Perry School Project¹³ and, more recently, “Head Start” – have been found to have significant, durable effects on personality traits and social outcomes (Lee, 2008; Heckman et al., 2009). As Section 4 argues, there is also evidence that specialised interventions for adolescents from underprivileged backgrounds can improve skills and social and economic outcomes (also see Kautz et al., 2015).

NEETs are generally less likely to live with their parents

Beyond the risk factors reviewed so far, living arrangements are important in understanding the situation of young NEETs. Living with their parents may help relieve young people of possible financial strains, but might also relieve them of the need to work. NEET status itself may, of course, influence household formation, as low incomes make it more difficult to move out. On the other hand, in countries where a high proportion of NEETs do not live with their parents, they may be at a higher risk of poverty, particularly if they live alone or in a household where nobody earns an income.

On average, though, young unemployed and inactive NEETs are less likely to live with their parents than non-NEETs (Figure 1.17, Panel A). About half of all NEETs live with their parents, while the figure for non-NEET youth is almost two-thirds. The latter include students, however, who may be more likely to live in the parental home. A substantial proportion of NEETs, 26%, live with a partner and at least one child compared to just 9% of non-NEET youth. This may be an important factor in not being in employment nor education. The link between NEET status and parenthood has been shown above – if one partner is working the other, usually the mother, may be more likely to stay at home with the child, particularly where childcare costs are high.

Figure 1.17. NEETs are less likely to live with their parents, although the practice varies from country to country

Note: In Panel B, countries are sorted, from top to bottom, in descending order of the share of NEETs living with their parents.

In Panel B “alone” denotes a young person living on their own; “with other youth/adults” denotes a young person living with at least one other young person or adult over 30 who is not their partner (and possibly with children); “single parent” means that the young person lives with at least one dependent child and no partner; “partnered, no kids” indicates that the young person lives with a spouse/partner but no children; “partnered, kids” indicates that the young person lives with a spouse/partner and at least one child; “with parents” indicates that the young person lives in the same household as their parent(s).

It was only possible to identify lone parents or couples with children in Canada if they were not living with others, e.g. their own parents or sharing with other adults. The single parent rate and rate of NEETs living with their partners and children may therefore be underestimated.

Data for Chile and Switzerland relate to 2013, data for Turkey relate to 2012 and for Canada to 2011.

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC), Germanys Socio-economic Panel (SOEP), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Canada’s Survey of Labour and Income Dynamics (SLID), Chile’s National Socio-Economic Characterisation Survey (CASEN), Mexico’s Household Income and Expenditure Survey (ENIGH), and the US Current Population Survey (CPS).

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Some single young people living with children may have no choice other than inactivity. Lone parenthood amongst NEETs is five times higher than for non-NEET youth – 5% versus 1%. The lone parent rate amongst NEETs is highest in the United Kingdom where 15% of NEETs are lone parents. They may choose to stay at home to take care of their children rather than seek employment. They might well find it harder to organise childcare than couples who can co-ordinate their work hours and have wider extended families to help them. And, of course, they may well struggle to afford childcare. In addition, many countries run minimum-income support programmes for single parents with young children on very low incomes. Such schemes have little or no activity requirements at all. One example is the Income Support Benefit in the United Kingdom, which is payable to lone parents who care for a child under five years and has no activity requirement.

Most people are not NEET in their youth, although 20% of young people are long-term NEETs

So far, this chapter has sought to profile young NEETs and their attributes without considering how long young people might actually remain NEET. Being NEET for a short time may not in itself be a negative outcome at all – a young person may take time out to care for children or travel, for example. Moreover, many young people go through short bouts of inactivity or unemployment after completing their education, as it takes time to find work and jobs tend to be more unstable at the outset of a career. Longer stretches out of employment or education are, however, more problematic and may even have scarring effects, negatively affecting future employment opportunities and income. This section looks at the duration of NEET periods in 16 countries for which the necessary information is available.¹⁴

On average, nearly half of all young people experience spent time outside of employment, education or training – either long or short – over a four-year period (Figure 1.18, Panel A). The picture ranges, however, widely – from Norway, where nearly three-quarters (73%) of young people were never NEETs between 2009 and 2012, to Greece, where only a minority, 41%, spent no time as NEETs.

Figure 1.18. Half of all young people become NEETs at some point in time, and a substantial minority remains NEET for a year or longer

Note: The necessary data to measure long-term NEET status is not available for all OECD countries. In Panel A, countries are arranged, from top to bottom, in descending order of the percentage young people with over 12 months as NEETs in the four-year period.

Censored NEET periods are included in the calculations with their observed lengths. The OECD average is based on the countries where data are available and is unweighted.

Sample groups were young people aged 15 to 29 years-old in January 2009. They were observed for 48 consecutive months until December 2012. In Estonia, the observation period was January 2008 to December 2011.

Source: OECD calculations based on the longitudinal 2012 European Union Statistics on Income and Living Conditions (EU-SILC) and the Household, Income and Labour Dynamics in Australia (HILDA) Survey, 2009-12.

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Among NEETs, many remain out of employment, education or training for longer. About one-third (31%) of all NEETs across OECD countries are unemployed or inactive for at most six months over the four-year period. Nearly half (47%) remain out of education or work for more than a year. This corresponds to one-fifth of all young people. Youth in the countries affected worst by the crisis are most at risk, with more than 30% of youth in Greece, Italy and Spain spending a year or more as a NEET.

In most cases, times of unemployment or inactivity for young people take the form of one single, uninterrupted NEET “spell”. Only a very small proportion of youth (5%) cycle in and out of being a NEET, i.e. become NEET for a time, resume employment or education, and then become NEET again (not shown).

Young women are especially vulnerable (Figure 1.18, Panel B). The inference may be that, in some OECD countries at least, women may be more prone to long-term NEET status when they have children and little opportunity to resume or take up employment later on. Young people with low levels of education (who did not complete upper-secondary school) are also more likely to be long-term NEETs. The poorly educated account for 17% of the youth population, but 30% of those who spend more than 12 months as a NEET. Young people with poor health are also overrepresented.

NEETs have lower levels of happiness and trust and are less interested in politics

The views on society and the values of NEETs may differ from other youth and this may have long-term consequences on social cohesion. Interestingly, even though they are deprived from employment, NEETs value work as much as other youth. They are just as likely as non-NEETs to think that work would be very important in their life (Figure 1.19). Besides, NEETs are actually less likely than non-NEET youth to think that leisure time is very important, likely due to the fact that those youth in employment or education have less time available for leisure activities due to work or study. Therefore, the lack of a job is likely to have an impact on life satisfaction. NEETs are indeed more likely to report higher levels of dissatisfaction with their lives – 22% of NEETs report low levels of life satisfaction compared to just 14% of non-NEETs. This suggests that for a majority of youth unemployment or inactivity is not a choice and that they would be willing to integrate into the labour market if they could.

Long periods of involuntary inactivity or unemployment do not only have individual consequences but also create a challenge for social cohesion. Over time being a NEET can lead to isolation, a lack of interest in society and a feeling of distrust. Indeed, only 18% of NEET youth report that they feel others can be trusted compared to 29% of non-NEET youth. NEET youth are also less likely to display an interest in politics with just under one-third reporting they are somewhat or very interested in politics compared to 40% of non-NEET youth. Despite this lower interest in politics, NEET youth are more likely to think that it is the government’s responsibility to provide for everyone in the country as opposed to it being the responsibility of individuals themselves – only 26% of non-NEET youth feel the government should take this responsibility compared to 37% of NEETs. This difference in opinion is likely influenced by the higher reliance of NEETs on the benefit system for financial support compared to young people in employment or education.

Figure 1.19. NEETs have less trust in others, lower life satisfaction, less interest in politics and are more likely to feel it is the government’s responsibility to provide for citizens

Average of positive answers for 18 selected countries

The graph shows differences in views of NEET and non-NEET youth in 18 OECD countries. Any country with less than 30 NEETs present in the values surveys used has been excluded from the analysis. The countries excluded are: Australia, Austria, Chile, Denmark, Finland, Iceland, Ireland, Japan, Netherlands, New Zealand, Norway, Poland, the Slovak Republic, Slovenia, South Korea, Sweden and Switzerland.

NEET/non-NEET differences in “importance of leisure time”, “trust”; “interest in politics”, “government responsibility” and “low life satisfaction” are statistically significant at the 95% level.

All measures shown are binary – the importance of work and leisure show the proportion reporting they are “very important”; the trust measure shows the proportion reporting that most people can be trusted; the interest in politics measure shows the proportion reporting they are very or somewhat interested in politics; the government responsibility measure was answered on a scale of 1 (people should take more responsibility) to 10 (the government should take more responsibility) – in the analysis here the measure has been divided into those replying at the most extreme end of the scale i.e. the top 25% which equals those responding 8-10; the low life satisfaction measure shows the proportion reporting their life satisfaction is at the lower end (5 or less) on a scale of 1 (dissatisfied) to 10 (satisfied).

Source: Wave 6 (2010-13) of the World Values Survey for Australia, Chile, Estonia, Germany, Japan, Mexico, the Netherlands, New Zealand, Poland, Slovenia, Korea, Spain, Sweden, Turkey, the United States; Wave 5 (2005-09) for Canada; Wave 4 (1999-04) for Israel. Wave 4 of the European Values Survey (2008-10) for Austria, Belgium, the Czech Republic, Denmark, Finland, France, the United Kingdom, Greece, Iceland, Ireland, Italy, Latvia, Luxembourg, Norway, Portugal, the Slovak Republic and Switzerland.

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3. Safety nets for low-income youth

While the long-term goal of public policies is to help young people on the path to self-sufficiency, those on low incomes, especially the NEETs, may require support to avoid poverty. One way to achieve both objectives is to tie income support payments to young people’s efforts to find a job or upskill. Benefits should allow young people to meet their basic needs so they stay healthy and do not withdraw from society. In that regard, income support programmes have played an important role to protect the most vulnerable groups in the recent crisis and its aftermath.

This section looks at the principal types of benefits available to young people in OECD countries, and how the share of young people in receipt of benefits has evolved since the onset of the crisis. It also discusses the adequacy of income support.

Out-of-work benefits may be less accessible to young people

Only few OECD countries operate income support benefits that exclusively target young people. Instead, young people in most of the OECD have access to the principal income support programmes for working-age individuals:15

Young jobseekers with a previous work and contribution history are typically entitled to unemployment benefits, the primary safety net for unemployed jobseekers.¹⁶ Since benefit eligibility is usually, however, tied to a minimum contribution period – often 12 months, sometimes longer (Figure 1.20) – school leavers and young people with patchy employment records often fail to qualify. Moreover, unemployment benefits for young people tend to be available for shorter times, because the duration of benefit payments generally depends on the length of contribution period.

Unemployed youth who lack a sufficient employment history, those who have exhausted their time-limited unemployment benefits, and those living in low-income households are often entitled to social assistance or housing benefits. Such benefits are usually means-tested at household level, so the incomes of the young person’s parents and spouse or partner are taken into account. Social assistance and housing benefits tend to be less generous than unemployment benefits, but are available for unlimited periods in most countries. In eight countries, young people with no employment record can also receive unemployment benefits (Annex Table 1.A2.1).

Young people who live with their parents often receive family benefits, paid up to the age of 16 in the OECD on average. In most countries, family benefits last longer if the young person is in education (Annex Figure 1.A2.1). Young people with children may also be entitled to child allowances or maternity/paternity benefits.

Youth with permanently reduced work capacity can typically draw disability benefits. In a number of countries, including Austria, Canada and Sweden, invalidity pensions do, however, require a minimum contribution period – youth with limited work capacity who do not fulfil that requirement may receive means-tested disability assistance.

Figure 1.20. Twelve months of work experience nearly always bring entitlements to unemployment insurance benefits, but the duration of benefit payments is often short

Minimum employment/contribution period in months and maximum duration, in months, of unemployment insurance benefits for a 20-year-old with one year of previous employment, 2014

Note: 20-year-olds with a contribution record of one year do not qualify for unemployment insurance benefits in Belgium, Ireland, the Slovak Republic and Turkey. Norway has a minimum earnings requirement instead of a minimum contribution period. In Luxembourg, reduced benefits are paid to school graduates without employment record after a waiting period. No maximum benefit duration applies in Chile.

No unemployment insurance benefit programmes exist in Australia and New Zealand.

For the United States, results are for the State of Michigan.

Source: OECD Tax-Benefit models, www.oecd.org/social/benefits-and-wages.htm.

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Actual income support benefit receipt among youth tends to be low

Unemployment benefits played an essential role in cushioning young people from the blow of the crisis. In response to rising NEET rates, the share of the young in receipt of unemployment benefits rose by 60% between 2008 and 2010 across the OECD (Figure 1.21, Panel A), reflecting increases in receipt rates in essentially all OECD countries. Furthermore, as a result of the weak recovery, especially in many European countries, receipt rates declined only slightly from their crisis-related peak, and appear to have plateaued at a higher post-crisis level since 2012.

Figure 1.21. Proportions of young people (16-29) in receipt of unemployment and disability benefits are generally low

Note: Results are for 16-to-29 year-olds except for Germany (17-29 years) and the United States (16-24 years).

Panel B: Results are for 2014 except for Chile and Switzerland (2013) Denmark and Turkey (2012) and Canada (2011).

Source: OECD calculations based on European Union Statistics on Income and Living Conditions (EU-SILC), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Canada’s Survey of Labour and Income Dynamics (SLID), Chile’s National Socio-Economic Characterisation Survey (CASEN), the German Socio-Economic Panel (SOEP), Mexico’s Household Income and Expenditure Survey (ENIGH), and the US Current Population Survey (CPS).

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The widespread destruction of youth employment has not, by contrast, prompted any particular increase in disability benefit receipt. This is unlike in previous crises, when long-term unemployed youth were moved in large numbers to disability benefits once their unemployment benefit entitlements had expired. Discouraged jobseekers might welcome such a move, as disability benefit programmes usually come with much less rigorous activity requirements and payments are more generous than means-tested social assistance benefits. Public employment services, which may struggle with lack of capacity and few suitable programme options in times of high unemployment, might also wish to see the long-term unemployed youth removed from their records. The approach is dangerous, however, because experience shows that it is extremely difficult to bring young people back into the labour market once they have been on health-related benefits for a while (OECD, 2010, 2012a). Stable receipt rates since the start of the crisis indicate improved gatekeeping mechanisms across OECD countries, which have made progress in restricting access to disability benefits to claimants who are indeed unfit for work.

The share of young people in receipt of unemployment and disability benefits is relatively low in most OECD countries in spite of the recent surge in unemployment benefit receipt rates (Figure 1.21, Panel B):

On average, fewer than one in ten young people received either unemployment or disability benefits in 2013 across the OECD. The proportion was 33% lower than that observed among the general working-age population (aged 15 to 64), and the pattern was consistent across all countries (not shown in Figure 1.21).

Most young recipients receive unemployment benefits. Receipt rates are highest in countries where young people were hit hardest by the crisis – such as Italy, Ireland and Spain. They have also risen, however, in Finland, where youth employment deteriorated more recently, and in Austria and France, where low contribution requirements make benefits more accessible. Receipt is lowest in the Americas – in Chile, Mexico and the United States, though not Canada – and in Turkey.

Few young people receive disability benefits. High receipt rates in some Northern European countries (such as Estonia, Finland and Norway) and in Ireland, however, may be cause for concern.

The rise in receipt rates with the advent of the crisis has been more gradual for means-tested income-support benefits (Figure 1.22):

Figure 1.22. Many young people live in households receiving social assistance or housing benefits

Note: Young people are considered benefit recipients if they received social assistance or housing benefits in the previous year or if they live in a household where any member received such benefits.

Results are for 16-to-29 year-olds except for Germany (17-29 years) and the United States (16-24 years).

Panel B: Results are for 2014 except for Australia, Chile and Switzerland (2013) Denmark and Turkey (2012) and Canada (2011).

Source: OECD calculations based on European Union Statistics on Income and Living Conditions (EU-SILC), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Canada’s Survey of Labour and Income Dynamics (SLID), Chile’s National Socio-Economic Characterisation Survey (CASEN), the German Socio-Economic Panel (SOEP), Mexico’s Household Income and Expenditure Survey (ENIGH), and the US Current Population Survey (CPS).

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Rates of social assistance benefit receipt rose during the crisis but have declined since back to their pre-crisis levels. These benefits play an important role, particularly in Switzerland as well as in the United States, where few young people are in receipt of unemployment benefits and the Supplemental Nutrition Assistance Program (SNAP, formerly known as “Food Stamps”) as well as the Temporary Assistance for Needy Families (TANF) afford support to low-income youth.

The share of young people living in households that receive housing benefits has been trending upwards, but dropped substantially in 2014. This reflects primarily a notable fall in the share of young beneficiaries in Iceland, Ireland and Spain, where many young people live in households receiving housing benefits. Receipt is widespread also in other Northern European countries (Denmark and Finland) and in France, where households with low earnings may receive housing benefits.

More than one-third of all young people are recipients of some type of family allowance (Annex Figure 1.A2.2).

Young people are less well covered by out-of-work benefits

As a result of relatively low receipt of unemployment benefits among young people, benefit coverage – i.e. the share of unemployed youth who receive benefits – is low as well (Figure 1.23). Less than 30% of all young jobseekers receive unemployment benefits, compared to a share of 43% among older jobseekers. Once disability benefits and social assistance are accounted for, the share of unemployed youth in receipt of benefits rises to around 45%, still lower than the 50% rate among jobless adults aged 30 and above.

Figure 1.23. The share of unemployed people covered by benefits is lower among young people than prime-age adults

Percentages of youth and non-youth working-age unemployed in receipt of unemployment benefits or unemployment benefits, disability benefits or social assistance and the unemployed as a percentage of the population in the same age groups, OECD averages, 2007-14

Reading note: In 2014, the unemployed accounted for 10% of all youth (grey bars) on average in the OECD. The share of unemployed youth in receipt of unemployment benefits (dashed line) was 28%. The proportion of unemployed youth in receipt of unemployment benefits, disability benefits or social assistance (solid line) was 46%.

Note: People in formal education are not counted as unemployed.

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC) Survey, Household, Income and Labour Dynamics in Australia (HILDA) Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN), the German Socio-Economic Panel (SOEP) and the US Current Population Survey (CPS).

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Benefit adequacy tends to be lower for youth

The effectiveness of a benefit system cannot be evaluated based on coverage alone – benefit amounts are crucial for household resources. One way to look at a benefit system’s success in fighting poverty is to ask how many households it shields from poverty. How many households are not poor thanks to benefits?

Across the OECD, 22% of working-age adults under 30, and 18% of those over 30, would be poor if they did not receive benefits. Figure 1.24 illustrates how many of these individuals receive benefits that are high enough to lift their income above the poverty line.¹⁷ An average of 49% of working-age adults over 30 who would be poor if they did not receive benefits, receive benefits that are high enough to keep them out of poverty. At 41%, this share is significantly lower for young people. The countries most successful in fighting working-age poverty show the widest differences between the over- and under-30s rescued from poverty. In Norway and Denmark, for example, public transfers keep over three quarters of working-age adults over 30 who are at risk of slipping below the poverty line above it. The proportion is less than one-third for young people. Only in Estonia and Latvia, and to a smaller extent in Slovenia and the United Kingdom, proportionately more under-30s than over-30s are kept out of poverty by public assistance.

Figure 1.24. Income support is less effective in keeping youth out of poverty

Shares, in percentages, of individuals with pre-transfer incomes below the poverty line who are above the poverty line after receiving public transfers, young people (16-29 years old) and non-youth working age (30-64 years old), 2014

Note: United States youth data relate to 16-24 year-olds.

Data for Canada relate to 2011, for Turkey to 2012, and for Chile and Switzerland to 2013.

Individuals are “poor” if they live in a household with an equivalised household income (i.e. adjusted for the number of household members) that is less than 50% of the median income.

The figure shows the percentage of youth and non-youth working-age individuals who were poor before public transfers and who are no longer poor after public transfers.

Public transfers include family allowances, disability benefits, unemployment benefits and social assistance. They exclude public pensions only.

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC) Survey, Household, Income and Labour Dynamics in Australia (HILDA) Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN), and the US Current Population Survey (CPS).

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Young people face a greater risk of poverty than older age groups

Young people are now more likely to be poor than seniors (Figure 1.25). With the exception of Australia, where many retirees draw their pensions as a lump sum instead of receiving monthly payments,¹⁸ the youth poverty rates are higher than seniors’ rates in most OECD countries.

Figure 1.25. Young people are now more likely to be poor than the elderly in most OECD countries

Poverty rates among young people (16-29), non-youth working-age individuals (30-64) and senior citizens (65 and over), in percentages, 2014

Note: Individuals are defined as poor if they live in a household with an equivalised household income (household income adjusted by the number of household members) below 50% of the median income.

United States youth data relate to 16-24 year-olds.

Data for Canada relate to 2011, for Turkey to 2012, and for Chile and Switzerland to 2013.

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC) Survey, Household, Income and Labour Dynamics in Australia (HILDA) Survey, Chile’s National Socio-Economic Characterisation Survey (CASEN), and the US Current Population Survey (CPS).

StatLink  http://dx.doi.org/10.1787/888933405046

Roughly every eighth young person lives in poverty OECD-wide. Youth poverty rates are particularly high in the Nordic countries, where the young tend to move out, so no longer benefit from their parents’ income, earlier than in other countries. They are high in the United States, too, although the population is somewhat younger (see figure note on the age bracket of young people in the United States). The Czech Republic, Iceland and Switzerland enjoy the lowest incidence of youth poverty – at around 5%.

4. Policies to promote self-sufficiency among young people

While income support is essential in the fight against youth poverty by itself, it is seldom enough to put young people back on the path to self-sufficiency. The best way to achieve lasting financial security is to secure stable employment. A lack of affordable childcare is the chief obstacle to labour market participation for many young women (see Box 1.3). Many others, however, lack the skills to find a job, as Section 2 shows. The situation is especially difficult in the lingering aftermath of the crisis, with high numbers of young people requiring new skills to match employers’ needs in sectors where they have never worked before. Public support must therefore seek to ensure that all young people get the qualifications they need to succeed in the labour market. To that end, policy must provide intensive, targeted social support to prevent at-risk young people from slipping through the cracks.

This section discusses policies that look to address the NEET problem and to promote self-sufficiency among young people. It presents strategies to avert early school leaving and facilitate successful school-to-work transitions through high-quality vocational education. It then looks at outreach programmes for disengaged youth and schemes to bring NEETs back into employment or education. The discussion draws on evidence from research into youth programmes and findings from the ongoing series of OECD Investing in Youth country reviews.

Fighting early school leaving is essential for tackling the NEET challenge

To tackle the NEET challenge effectively, governments must ensure that all young people obtain at least an upper-secondary degree that entitles them to pursue their studies or gives them the vocational skills to succeed in the labour market. OECD countries have made considerable progress over the past decade in reducing rates of early school leaving, i.e. the shares of young people who fail to complete upper-secondary schooling (Figure 1.26). Yet, around one in six 25-to-34 year-olds still do not have an upper-secondary qualification, and the rate is substantially higher in Southern European countries such as Italy, Portugal, Spain and Turkey.

Figure 1.26. Early school leaving has declined but remains high, especially in Southern Europe

1. “Below upper-secondary education” denotes a level no higher than Level 3C short of the International Standard Classification of Education (ISCED). The ISCED classification has a structural break in 2014.

2. There are no data for Japan. In Panel B, the OECD average excludes Australia, Chile, Israel, Korea, Mexico and New Zealand for the years 2000-14 and Austria, Iceland and Norway for 2000.

Source: OECD calculations based on the EU-LFS and national labour force surveys, OECD National Educational Attainment Classification (NEAC) Database 2015, https://stats.oecd.org/Index.aspx?datasetcode=EAG_NEAC#.

StatLink  http://dx.doi.org/10.1787/888933405058

To further reduce the number of young people who leave school without an upper-secondary qualification, policies need to ensure that signs of disengagement are detected early, and that young people at risk of dropping out of school receive the support they need to complete their education.

Systematic monitoring of school attendance helps identify at-risk youth

Strategies for keeping at-risk students in education yield the most promising results when they address barriers to educational participation at an early stage. Dropping out is generally not a sudden, unexpected event, but rather the consequence of a longer process of gradual disengagement (Lyche, 2010). It can be driven by a range of different factors – learning difficulties, mental health issues, problems in the family, parents’ attitudes towards education or the school experience – which tend to interact and build up over time (OECD, 2012b). To prevent a young person from dropping out, these challenges need to be addressed as soon as they arise.

Schools should systematically monitor student attendance and keep key stakeholders – notably parents and social services – informed to ensure that troubled pupils are detected and receive the attention they need. In Latvia, for instance, most secondary schools use privately-provided web-based platforms to collect information on students’ timetables, class attendance, performance and any homework due. Teachers enter the information in the system, but it can be accessed by the students and their parents, teachers and the school administration. While the monitoring and reporting of attendance is standard during the years of compulsory school in most countries, such practice is, however, less common in non-compulsory upper-secondary education. A good practice can be found in Sweden, where upper-secondary schools are legally bound to report early school leavers under 20 and students with high absenteeism to the local authorities as part of their so-called “activity responsibility”. The municipalities then try to establish contact with the student, find out about their situation, and offer them activities to help return to upper-secondary education.

Requirements to report attendance to the national education authorities can ensure that teachers, schools and municipalities take non-attendance seriously. At times, schools can, indeed, be slow to react to a student’s poor attendance, either because of a lack of resources or because a “difficult” student’s non-attendance may be perceived as beneficial to the classroom environment. In Sweden, municipalities are required to report to the national education authorities on the situation of the young people identified as being at risk, and on how they have intervened, every six months. In Norway, primary and lower-secondary schools have to transmit attendance figures to the national authorities three times per year, and have to indicate whether they have managed to follow up on students who miss classes. At the national level, such non-attendance data can be a valuable resource to policy makers, for instance for evaluating the quality of educational programmes and the adequacy of student support. One challenge to the reliability of regular attendance information collection can be that schools may not have an incentive to report drop-out promptly, in particular if their funding depends on student enrolment.

At-risk students and their families require comprehensive support

If poor school performance and absenteeism are caused, or aggravated, by non-educational factors – such as problems in the family, health concerns, or substance abuse – they need to be addressed if there is to be a sustainable improvement in educational outcomes.

Specialised support staff in schools is key to quickly identifying and addressing the challenges that a troubled young person may face. Trained psychologists or social workers can be an important first point of contact for students, parents and teachers when problems arise. Where schools lack the resources for such specialised staff, designated teaching staff who have received the appropriate training can provide important support. In Norway, for instance, schools have the freedom to exempt teachers from some of their teaching duties so that they can attend to troubled students and absentees. Such teachers might take students who have concentration or behavioural problems out of the classroom for an hour, or drive out to a student’s home in the morning to pick up a pupil who has failed to show up.

Support networks outside of schools – e.g. social and health services, public employment services and, possibly, NGOs – play an important role in addressing more severe or long-lasting problems that schools are incapable of dealing with on their own. Depending on young persons’ needs, social workers or other support staff might help address family problems, resolve a difficult housing situation, put a young person in touch with health services, or act as a mediator between the young person and the police or courts. Social services sometimes have previous working relationships with a young person’s family, for instance because their parents are benefit recipients. In Portugal, the Educational Territories of Priority Intervention (TEIP) programme, creates partnerships between “priority” schools in certain underprivileged areas and public and private entities like health centres, voluntary associations, and different support agencies. The aim is to provide pupils at risk of dropping out with vocational courses and alternatives to traditional schooling. The Ministry of Education regularly monitors principal outcomes such as improvements in academic achievement, attendance, behaviour and the risk of drop-out. School non-completion rates in priority areas steadily declined after TEIP was introduced, and by 2010, four years after the second version was rolled out, they had converged with national rates (Dias and Tomas, 2012).

External expert support is especially important for helping students with mental health issues. A significant proportion of young people in OECD countries report feeling stressed on a regular basis (OECD, 2013), and the prevalence of conditions like eating disorders, anxieties or depression is high and rising, especially among young women (OECD, 2012a). Identifying mental health problems, however, is not straightforward, as parents and teachers are often not sufficiently familiar with the symptoms. Young people themselves may, moreover, be reluctant to seek help from a person they know out of a sense of embarrassment or shame. Psychological services in schools have a vital role to play in recognising mental health issues when they arise and in providing information and support to teachers, students and parents. External health centres, like those run by the Australian National Youth Mental Health Foundation headspace, are an innovative approach to spotting and treating mental health issues among youth. At headspace centres, young people can confidentially seek help outside their immediate social and educational environment. headspace also provides sex education and contraception to young people (see Box 1.4).

Box 1.4. Headspace: Mental health support for youth in Australia

The National Youth Mental Health Foundation headspace was established by the Australian Government in 2006 to respond to a deficit in access to primary-care mental health services for young people. It provides integrated early-intervention services for 12-to-25 year-olds with, or at risk of, mild to moderate mental illness. Its aim is to promote and facilitate improvements in health, social well-being and economic participation. There are currently 95 headspace centres across the country (as of July 2016), where young people receive help from professionals such as psychologists, social workers, alcohol and other drug workers and GPs, as well as career counsellors, vocational officers and youth workers. Support is provided in four core areas: mental health, physical health, alcohol and other drug use, and work and study support.

The service is designed to be youth-friendly and to provide easy, low-threshold access to health counselling and treatment. Headspace centres tend to be conveniently located, and practice an open-door policy that allows any young people and their families to drop in and receive anonymous help. Services are provided largely free of charge, or at a low cost, and ensure high confidentiality. Online and telephone counselling is provided through eheadspace for young people who live in an area with no local headspace centre or for those who hesitate to go in and seek help.

Headspace has been successful at reaching out to its target population. An independent evaluation considered the frequent referrals to headspace from health, education and community services and concluded that headspace had been effective at creating community awareness (SPRC, 2009). An OECD review team formed the same impression during a fact-finding mission perceiving headspace services to be well-integrated with their local communities. Recent data show that headspace is strongly accessed by youth from marginalised and at-risk groups, including homeless, Indigenous, or lesbian, gay, bi-sexual, transgender or inter-sex youth (SPRC, 2015). Most young people (72.7%) come to headspace with mental health or behavioural issues, primarily anxiety or depressive symptoms, situational problems like bullying, and relationship concerns (13.4%). The vast majority received some form of mental health support, in particular cognitive behaviour therapy and counselling (Rickwood et al., 2015a,b).

There is unfortunately limited evidence at this stage on the impact of headspace services. A recent study into the first 30 centres showed that few measured the effectiveness of their co-ordinated, integrated services or carried out clinical audits (Rickwood et al., 2015c).

The Australian Government has committed a substantial AUD 411.7 million of funding to the programme over the five years from 2013-14. The number of headspace offices is scheduled to increase to 100 in 2016.

Source: OECD (2015), Mental Health and Work: Australia, OECD Publishing, Paris; OECD (2016), Investing in Youth – Australia, OECD Publishing, Paris.

Flexible schooling environments can benefit more disadvantaged youth

Most countries seek to curb the marginalisation of young people with mental and physical disabilities by keeping them in the regular school system and giving specialised support (OECD, 2007). Students with learning difficulties generally benefit from attending mainstream schooling, where they mix with other young people, all the way through to upper-secondary level (OECD, 2012c). Policies should therefore, as far as possible, foster a learning environment that is flexible and supportive enough to cater for at-risk students in standard schools, and keep the share of young people taught in separate special-education programmes to a minimum. But creating such an integrative learning environment is difficult and costly, and mainstream schools may often not have the resources to lend disadvantaged students the support they need.

A number of avenues have been explored in efforts to improve support for disadvantaged students in mainstream schooling environments:

Smaller class sizes can help the most disadvantaged, though younger children tend to benefit more than adolescents. An experiment carried out in the state of Tennessee in the United States showed that putting very young pupils (from kindergarten to third grade) into smaller classes is associated with positive personality changes and higher later-life earnings measured up to the age of 27 years.¹⁹ The observed improvement was, moreover, twice as high among children from minorities than for their peers from majority populations (Dee and West, 2008; Chetty et al., 2011). A key factor seems to have been that the higher teacher-to-student ratios helped improve such non-cognitive skills as concentration, diligence or initiative. Piketty and Valdenaire (2006) found similar results in France, as did Angrist and Lavy (1999) in elementary schools in Israel.

Adapting teaching methods and programme contents to the needs of disadvantaged students can also help improve achievement. In the United States, so-called “charter schools” are public schools that enjoy greater leeway to manage staff, adapt curricula and organise teaching time. They are also set pre-defined outcome targets and are required to report on a range of performance indicators. Charter schools often target students from disadvantaged backgrounds who may not have access to quality public schools in their neighbourhoods or who struggle with traditional curricula. They usually provide better resources (as reflected in smaller class sizes and/or more hours of teaching), complementary services, and better trained teachers for at-risk youth. A substantial body of research finds that charter schools can exert a significant, lasting impact on educational attainment and later employment (Abdulkadiroglu et al., 2009; Angrist et al., 2016; Dobbie et al., 2011).

To what extent the lessons learned from such approaches can be applied on a general scale remains an open question. Every school has its unique features, and charter schools are especially diverse in their methods. Angrist et al. (2012) for instance report a wide range of estimated “charter effects” in a sample of Massachusetts schools, and experience also suggests that certain teaching methods can significantly improve the performance of the most disadvantaged students. Which particular aspects of those successful practices show the greatest promise for helping disadvantaged students to narrow the educational achievement gap remains to be identified.

After-school programmes are particularly valuable for disadvantaged young people

Well-designed after-school programmes can make a considerable contribution to the educational and social development of young people. Attractive opportunities for young people to engage in sports, learn a musical instrument or get involved in handicraft and other practical activities can help build social and professional skills, while countering the risk of isolation. Empirical evidence confirms the positive effects of extracurricular activities on schooling outcomes and career prospects (OECD, 2012b,d; Carcillo et al., 2015) , and these effects tend to be largest for youth from deprived backgrounds (Heckman, 2008). As participation in private after-school schemes is often at the parents’ initiative, however, the young people who take part in such activities tend to come overwhelmingly from well-off backgrounds (OECD, 2011).

Ideally, after-school activities should be offered to all young people, regardless of background, to ensure that the more disadvantaged participate, while averting the possible stigma that attaches to schemes specially for young people from deprived backgrounds. Linking after-school programmes to school establishments can make them easier to access. In Latvia, for instance, municipalities provide an extensive system of extra-curricular “interest education”, in many cases offered on school premises. Activities are voluntary, but since they are attractive and often free of charge, most young people sign up. Where activities are not public like in Latvia, but offered by private organisations such as sports clubs or music schools, schools and private providers can co-ordinate to help channel pupils into the activities that will benefit them most. Municipalities may need to subsidise some activities to enable young people from lower-income groups to pay for fees and any material or equipment needed.

Smaller programmes tailored specifically to the needs of more disadvantaged youth can sometimes be very beneficial, however. Attractive afternoon programmes in problematic neighbourhoods can help get young people “off the streets” and engage them in meaningful activities. Such programmes may also provide assistance with homework and specialised health or psychological support.

A range of successful schemes combine after-school activities for underprivileged youth with a mentoring component. The concept behind them is to provide guidance and propose positive role models to young people who may distrust their teachers and lack authority figures at home. One of the oldest and largest such programmes is “Big Brothers Big Sisters of America” (BBBS), founded in the United States in the early 1900s. The programme operates to a tightly monitored template. Mentors and young participants are selected through an elaborate screening process, then matched by their common interests. Specialised staff keep close track of the mentor-mentee relationships and advise mentors on how to improve their communication, diversify activities, promote child development and address any difficulties that arise. An evaluation demonstrated that adolescents in mentoring relationships that lasted a year or longer reduced their violent behaviour and substance abuse and though their school performance did not improve, their attendance did (Grossman and Rhodes, 2002).²⁰ BBBS has expanded to 14 other countries, including Australia, Austria, Canada, Ireland, Israel, the Netherlands, New Zealand and Poland.²¹

A number of recent initiatives use sports as a vehicle for reaching out to young people, with educators not only teaching sports, but acting as trained mentors. A pilot intervention in the United States, for instance, called “Becoming a Man” (BAM), gives disadvantaged young people with behavioural problems non-academic support during the school year. BAM combines social skills training that draws on cognitive behavioural therapy (CBT) and sports. It has proved cost-effective by reducing crime and improving schooling outcomes (Heller et al., 2015). In Australia, the Sporting Chance Programme, founded in 2006, encourages Indigenous youth to transfer the competitive spirit, discipline and positive mindset of rugby to other areas of life, in particular to schooling. Participants are assigned a personal mentor with an Indigenous background and, together, they develop a plan that commits the young person to a step-by-step improvement in school behaviour and performance using explicitly specified targets and termly evaluations. The impact of the programme is currently being evaluated.

Support for at-risk youth is often difficult to co-ordinate

Comprehensive support for young people with multiple barriers often requires various different actors to work together. For a 360-degree view of a young person’s individual, social and educational attributes and circumstances, all the parties involved should share their knowledge and expertise. To that end, the social services need to co-ordinate with the young person’s parents and school and, if need be, with the police, representatives of the judicial system and even with providers of extracurricular activities.

Managing collaborative work that brings together a large number of actors can be difficult (OECD, 2015e). Responsibilities for youth policies are typically spread across a range of branch ministries, while policy implementation may be located at different tiers of government (local, regional and national). As a result, policies are often poorly co-ordinated and cross-communication is found wanting. Common databases with client information accessible to all government services at all levels can help. They are often unavailable, however, out of privacy concerns or for political reasons. Information therefore has to be shared ad hoc, on a case-by-case basis, and often requires the explicit consent either of the young person concerned or of their parents.

Effective institutional structures can greatly contribute to rapid information exchange and close co-operation between actors. The Australian Commonwealth Government responded to the need to better co-ordinate support policies for young people by placing so-called “partnership brokers” into over a hundred regions.²² The partnership brokers were commissioned to facilitate and strengthen local connections between schools, businesses, community groups and families in order to promote educational attainment, social participation and successful school-to-work transitions of young people. Some of their main tasks were, depending on the local circumstances, to help disadvantaged young people access and navigate local support systems, to improve the collaboration of various actors involved in delivering youth support services, and to identify and help bridge gaps in service delivery. In Norway, a range of different social and employments services were integrated under the umbrella of the Norwegian Labour and Welfare Administration (NAV) established by the so-called “NAV Reform” rolled out from 2006 to 2010. NAV’s state-level arm pays insurance benefits and provides employment services to registered jobseekers, including active labour market policies (ALMPs) for unemployed youth. NAV’s municipal arm pays means-tested social assistance benefits and delivers social services to persons above the age of 18 years.

Quality vocational education and training can help smooth school-to-work transitions

Quality vocational education and training (VET) plays an essential dual role: it prepares young people for the workplace and responds to the skills needs of the labour market. VET enables the young to develop a mix of general and job-specific skills, so helping them to acquire the knowledge and tools that they need to enter employment. Moreover, the combination of classroom learning and practical training is an attractive learning pathway that helps smooth the transition from school to work. To ensure quality and relevance, the practical training component of VET should ideally be in the workplace. On average, slightly less than half of upper-secondary students in the OECD follow a VET course, though proportions vary considerably from country to country (Figure 1.27).

Figure 1.27. There are wide differences between countries in rates of participation in vocational education and training courses

Percentages of upper-secondary students enrolled in general vs. vocational programmes, 2013

Note: There are no results for Canada, Greece and the United States.

Source: OECD (2015), Education at a Glance 2015: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2015-en.

StatLink  http://dx.doi.org/10.1787/888933405064

Apprenticeships are an ideal way of providing relevant practical training

Apprenticeship courses, which match students with private- or public-sector employers early on in the programme, typically for a period of several years, are often regarded as best practice. The combination of on-the-job training and classroom learning, together with the involvement of social partners in drawing up curricula, help ensure that training meets employers’ needs while affording apprentices important initial work experience. Ideally, successful apprentice-employer relationships convert into regular employment. Indeed, empirical research suggests that apprenticeships yield positive returns in the shape of good wages and steady jobs (Carcillo et al., 2015). Apprenticeships may also be effective against early school leaving: they appeal to more practically-minded young people who may lack the motivation for much additional classroom-based learning, and reduce incentives to leave school for paid work.

The positive results produced by apprenticeship programmes – in particular favourable youth labour market outcomes in countries with a tradition of strong apprenticeship systems like Austria, Germany and Switzerland – have revived interest in apprenticeship training. Many governments had long shifted their focus away from VET towards academic education as the preferred path to quality employment. Vocational education programmes in many countries consequently lack appeal (European Commission, 2011) and are viewed as the fall-back choice for young people who fail to succeed in an academic setting. Participation in apprenticeship training tends to be weak in all but a few countries (Figure 1.28). The trend is changing, however, with governments being increasingly concerned with promoting the attractiveness and relevance of VET programmes to boost participation. A number of European countries, such as Italy and Spain, are working closely with Germany to reform their VET systems, and Korea introduced an apprenticeship system inspired by the German, British and Australian systems in 2014. The most disadvantaged young people may, however, struggle to be admitted to apprenticeships because they lack the educational qualifications or basic skills, employers may be reluctant to take them on, or they have to compete with more highly skilled peers.

Figure 1.28. Participation in apprenticeship programmes is low in all but a few countries

Percentage of young people aged 16 to 29 years who were apprentices in 2012 in selected OECD countries

Note: Estimates shown in light blue are based on less than 30 observations for the total and less than 15 observations by gender. These estimates should be interpreted with caution.

The results for Belgium and the United Kingdom refer to Flanders and England plus Northern Ireland, respectively.

Source: OECD calculations based on the OECD Survey of Adult Skills (PIAAC), 2012.

StatLink  http://dx.doi.org/10.1787/888933405075

Strong employer commitment is crucial to allow apprenticeship systems expand into a recognised pathway for the transition from school to work. In many countries, the main challenge does not necessarily lie in the provision of quality training facilities, but in the insufficient number of apprenticeship places offered by firms. The financial burden in terms of wage and non-wage costs deters some companies from taking on apprentices. Accordingly, a number of OECD countries have introduced financial incentives to make it more attractive for employers to create apprenticeship places (Box 1.5).

Box 1.5. Providing employers with incentive to offering apprenticeships

Direct subsidies

Several countries subsidise employers directly to take on apprentices. In the United Kingdom, the National Apprenticeship Service offers apprenticeship grants of GBP 1 500 to employers with up to 1 000 employees who recruit 16-to-24 year-olds. Eligible employers are those who have never before employed an apprentice and those who have not recruited one in the previous 12 months. Up to 10 grants can be made to any 1 employer. In Austria, companies are financially rewarded for every additional apprentice they take above the number hired in the previous year. They also receive a grant if they resume hiring apprentices after a break.

Under the Australian Apprenticeships Incentives Programme, companies are eligible for incentive payments when their apprentices start and complete the programme – up to AUD 4 000 in total. Employers of apprentices and trainees who have faced particular barriers to training and employment can receive additional support. An evaluation found that these subsidies had a significant effect on commencements, although more needed to be done to retain apprentices and prevent them from dropping out (Deloitte Access Economics, 2012).

Tax credits and social security rebates

Another way to subsidise the provision of apprenticeship places is to grant tax credits and/or social security rebates. The French government grants certain firms receive a tax credit of EUR 1 600 per apprentice, which increases to EUR 2 200 if the apprentice has a disability or is considered disadvantaged. Firms may are also exempted from social security contributions for the apprentices they take on. On top of the tax credits, each region offers additional subsidies for hiring apprentices. In Canada, employers can claim up to CAD 2 000 per year for each eligible apprentice under Apprenticeship Job Creation Tax Credit scheme.

Minimum wage

The cost of hiring apprentices can also be lowered by agreeing a special sub-minimum wage. Several countries make use of the practice. In France, the minimum wage for apprentices depends on their age and the year of training they are in, starting at 25% of the national minimum wage for 18-year-olds in their first year and rising to 93% for the over-2s in their fourth year. In Germany, a “training allowance” is agreed upon by the social partners, which also varies according to the apprentice’s age and experience with the firm.

Levy financing

An interesting indirect mechanism for incentivising companies to offer apprenticeships is to require them to contribute to a special training fund, from which only firms who take on apprentices benefit. All companies in Denmark pay a yearly contribution of nearly EUR 400 per employee into the Employers’ Refunds for Apprentices Fund (AER). The AER then compensates companies every 24 months for each apprentice hired. In France, workplace training is funded through an apprentice tax paid by all businesses. It is set at 0.05% of the payroll for firms with fewer than 250 employees and 0.06% for firms with more than 250 employees. Companies may be exempted from the tax if they train a certain number of apprentices.

Source: OECD (2014), Investing in Youth: Brazil, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264208988-en.

Pre-apprenticeships can prepare young people who are not yet ready

Pre-apprenticeship programmes can prepare more disadvantaged young people for VET programmes, by helping them to brush up on patchy literacy or numeracy skills, build motivation, familiarise them with the work routine, and even give them short spells of work experience.

In Germany, young people who cannot find an apprenticeship – because of their poor school results, learning difficulties or other disadvantages – can apply for pre-vocational training. Such programmes last up to one year, introduce trainees to various occupational fields, and place them in companies for subsidised internships. They teach the curriculum of the first year of vocational training. Pre-vocational courses are also open to young people without a lower-secondary qualification, who can attend school part-time during the pre-apprenticeship to obtain their school-leaving certificate. The goal is to help participants to transition into regular apprenticeships after they have completed the programme (OECD, 2012e). Employment outcomes of the German pre-vocational training have been good, albeit not among the most disadvantaged students (Caliendo et al., 2011).

Pre-apprenticeships are also an important feature of Australia’s VET system. They focus on particular occupations or a range of fields, and typically involve classroom-based VET courses and work placements. Students who are still at school can participate part-time. Australia’s pre-apprenticeships seek to introduce young people to a trade, strengthen their motivation before they commit to an apprenticeship, build their basic skills, and increase their technical knowledge and, thereby, their chances of securing an apprenticeship place.²³

Apprenticeship-style programmes can also be built into standard secondary school curricula to give disadvantaged pupils a better chance of being admitted to VET programmes. In the United States, a wide network of Career Academies, located in about 5 000 high schools, seek to keep students engaged in school and prepare them for the transition to post-secondary education and employment. They combine academic and technical training related to a career theme, and form partnerships with local employers to build students’ career awareness and afford them work-based learning opportunities. Career Academies operate as small learning communities of around 150 to 200 students from Grades 9 or 10 through Grade 12 in a larger school. Research suggests that Career Academy graduates benefit from an earnings increment of around USD 2 000 per year over the eight years of follow-up. The programme has also been shown to positively affect a range of social outcomes, such as the likelihood of living independently with children and a partner or spouse. Educational attainment does not benefit, however (Kemple, 2008).

Internships can give students early insights into the labour market

Summer internship programmes can be opportunities for young people to try out trades in which they are interested, to get first work experience, and build links with local employers. They can be of particular benefit to disadvantaged young people, who are more likely than their well-off peers to spend their summer holidays idle. By involving young people in meaningful work experience, internships can help to offset the knowledge lost during school holidays and prevent young people from engaging in illicit activities. Finding a quality internship is not easy, however, especially for young people whose parents lack connections. Available internships may not have a strong enough training component to be valuable, or they are unpaid and hence difficult to afford for low-income youth.

Targeted public programmes are sometimes the solution. New York City has been subsidising summer jobs for disadvantaged students since the 1960s through its Summer Youth Employment Program, the largest of its kind in the United States. It offers a combination of work experience and training geared to equipping youth with the skills necessary for academic success or regular employment. It is open to 14-to-24 year-olds from low-income families or living in poor areas. In 2015, there were 130 000 applicants, of whom 54 000 were placed in a job. The scheme has been shown to be cost-effective – poverty, crime and mortality were lower among participants than among unsuccessful applicants (Gelber et al., 2016).²⁴ Spurred by the positive results of the programme and similar initiatives, the United States Department of Labor is currently developing a Summer Opportunity Project in conjunction with the business community that will provide disadvantaged youth with employment opportunities and class-based training.

Career guidance helps ensure that students make the right choices

Quality career guidance can boost education and training completion rates by improving the match between young people and their chosen path. It can strengthen social mobility by informing young people of career paths that their family and social networks may not suggest, and encouraging them to choose paths more likely to lead to stable employment. Career guidance is of special importance to young people who consider VET programmes – including apprenticeships – as they affect students’ career prospects more directly than general secondary programmes.

Young people’s participation in career guidance is easiest to ensure in the case of school-based career counselling. One downside to it is that counsellors within school tend to show a pro-academic bias emphasising general education programmes at the expense of VET (OECD, 2014a) or to show a preference for programmes offered by the same school over external alternatives (Watts, 2009). The involvement of employers or outside specialists in career guidance helps make information more comprehensive and truer to the realities of the labour market (Sweet, 2009).

In Denmark, the Ministry of Education operates regional guidance centres and services such as the national guidance portal and call centre. Guidance centres work with stakeholders – including the social partners in industry and commerce and local municipalities – to offer a range of activities in various settings in and out of school (workshops, seminars, career fairs, one-to-one counselling, etc.). The centres’ collaboration with educational and labour market institutions makes their guidance relevant to stakeholders in the education system and the labour market (Field et al., 2012), and student participation in career guidance is high (Figure 1.29).

Figure 1.29. Most young people benefit from some form of career guidance

Percentages of 15-year-olds who report having accessed different types of career guidance, selected OECD countries, 2012

Note: Countries are sorted in ascending order of the share of students who accessed all four forms of career guidance.

Source: OECD PISA 2012, www.oecd.org/pisa/pisaproducts/pisa2012database-downloadabledata.htm.

StatLink  http://dx.doi.org/10.1787/888933405084

Comprehensive programmes such as youth guarantees are needed to re-engage NEETs in employment, education or training

Given the negative consequences of early-life unemployment and inactivity on young people’s career prospects, it is important that all NEETs who have trouble finding employment or a suitable educational option participate in active programmes that address their educational and non-educational barriers. Many countries have committed themselves – through so-called “youth guarantees” – to providing all young NEETs with a suitable offer, the most prominent case being the European Union’s Youth Guarantee scheme, introduced in 2013.25 It is meant to ensure that all young people under the age of 25 years – whether registered with employment services or not – receive a good-quality offer of employment, continued education, an apprenticeship or a traineeship within four months of leaving formal education or becoming unemployed. Such initiatives can be a valuable tool to help improve young jobseekers’ employment prospects. Their success relies, however, on effective outreach to inactive and disconnected youth. The quality of options offered, moreover, is important, and solutions must be tailored to young jobseekers’ individual needs.

Reaching out to NEETs not registered as jobseekers is challenging

Reaching out to NEETs as early as possible is critical if they are not to slip into long-term inactivity. For many jobless young people, the availability of benefits – be it cash payments or in-kind support like housing and health care – is an incentive to seek contact with social or employment services and register as unemployed. Some of them may hesitate, however, to claim benefit from government agencies, trying instead to get by on their own for a while or to seek help from family or friends. Opportunities for reaching out to these young people can vanish quickly, as disengagement from school or work may coincide with them moving out of their parents’ home and some even ending up “sleeping rough”. In some cases, disaffected young people may also lose access to a mobile phone or Internet for a while. Re-engaging young people in education or work becomes increasingly difficult after long periods of inactivity, as they slip out of their routine and no longer get up early or take part in structured activities.

Collaboration between schools and public employment services (PES) is often an important component of successful outreach strategies. Together with school management and class teachers, the PES can give early career advice, raise young people’s awareness of the services available through the PES, and spot at-risk youth early enough to provide timely support and lessen the risk of inactivity after school leaving. While in many OECD countries, the PES and schools are only weakly integrated, Japan and Norway operate models of close collaboration that hold promise:

The Japanese PES “Hello Work” reaches out to students at high schools and universities through specialised youth services (“Hello Work for New Graduates”) to offer counselling, job-search assistance (e.g. interview training and preparation, seminars and student job fairs) and placement. It also informs schools of vacancies, offers regular on-site counselling in schools, and supports school career guidance counsellors. The collaboration between schools and the PES has been extremely successful: virtually all Japanese students who choose not to pursue tertiary education and would like to work have a job offer when graduating from senior high school.

Norway currently runs a pilot project which places youth specialists from the national welfare and employment agency, NAV, into upper-secondary schools for four days a week. The aim of the project is to prevent and reduce school drop-out by providing career guidance, helping students to find work experience opportunities, and supporting the school-to-work transition. A further focus is early detection of and support for young people with multiple barriers.

In many countries, non-governmental actors also play a central role in reaching out to disaffected young people. While their former teachers, municipal youth workers and other public authorities may struggle to track them down and get them to re-engage, disengaged young people may still go to the local youth centre or sports club. A tight network of non-governmental youth activity providers can therefore be helpful in preventing young people who do not regularly engage in education or work from disconnecting entirely. If such actors are aware of the social and educational situations of the young people they deal with, they can work towards putting them in touch with the social services (OECD-LEED, 2014).

A number of countries run active outreach strategies which draw on the services of non-governmental institutions. As part of the former Youth Connections programme, the Commonwealth of Australia paid private providers – many of whom were not-for-profits – to support young people at risk of disengaging from education or training and failing to make the transition into employment.²⁶ Youth Connections used a one-to-one case management approach to provide services such as counselling and mentoring. Providers also performed street outreach, visiting locations frequented by young people to engage with them and bring the disaffected into their programmes. In Japan, outreach to hikikomori youth, i.e. socially withdrawn young people, who sometimes do not leave the family home for months, or even years, is organised through non-governmental Community Hikikomori Support Centres funded by the prefectures.

A very promising, though costly, approach is to explicitly devolve outreach to a single actor who screens all young people to detect those at risk of disengagement. In Norway, county-level Follow-up Services are responsible for contacting all under-21-year-olds who leave school (with or without a qualification) to assess their activity status. Those whom they can track down and who are not in education or employment are either offered counselling or training, or they are put in touch with social support services or the local employment office. As part of their “activity responsibility”, Swedish municipalities are required to establish contact with early school leavers under the age of 20 to determine what they are currently doing and help them back into upper-secondary school. A challenge for follow-up services is that information on the reasons for a student leaving school early is often unavailable. They may therefore spend much of their time chasing students who merely failed to de-register with their municipality when moving to a different city and who do not require any support.

Profiling NEETs is needed for adequate support, and it can save costs

Once young jobseekers register with the public employment service, they should be extensively profiled to ensure that they receive the type and intensity of support they need. Profiling helps caseworkers to determine young jobseekers’ work readiness and assess their skills and training needs. It is also an opportunity for identifying any material barriers such as lack of housing or restricted mobility, social issues and physical or mental health problems that might be obstacles to taking part in a programme or working.

In Australia, all benefit claimants are assessed for their level of disadvantage and anticipated difficulty in finding and keeping employment. This procedure may include screening by a health professional, typically a psychologist or nurse. Jobseekers are then allocated to one of three different-intensity support “streams”, according to the results of the assessment. Similarly, NAV, the Norwegian public employment and welfare service, assigns all clients to one of four jobseeker categories, depending on the expected level of support needed.

Careful profiling should also be considered part of cost control, helping to effectively target expensive interventions at jobseekers who need them and are likely to benefit the most. In Australia, the resources made available to non-governmental employment service providers vary substantially according to stream, with providers receiving more for placing more disadvantaged clients into employment. In Norway, assistance through NAV is primarily directed at jobseekers in the two acutest-needs categories, for whom programme participation can start as soon as they have registered at the public employment office. Jobseekers in the two lower-intensity categories typically simply attend a short job search seminar and are then expected to find work with little or no assistance for the first two or three months. Such strict guidelines also lessen the risk of caseworkers cherry-picking young jobseekers for programme participation, focusing their attention and resources on the least disadvantaged who, although highly motivated, might not necessitate expensive intervention to find employment.

Successful programmes for NEETs have to be targeted tightly

The impact of the many different interventions to improve NEETs’ educational and employment outcomes in OECD countries depends heavily on how well they are designed and targeted. Empirical studies show that sustainable improvements in labour market and social outcomes are difficult to achieve, especially for the most disadvantaged youth, and that effective programmes tend to be very costly. Given the limited financial resources, the capacity constraints that weigh on public employment and welfare services, and the fact that successful programmes are often not easily expanded or replicated, it is vital that existing programmes target those most likely to benefit.

The type of intervention best suited to a young jobseeker depends on the educational and non-educational barriers:

“Education first” is the approach of many countries to early school leavers who have little chance of finding quality employment. The social services or public employment services work with the educational authorities to re-integrate them in mainstream schooling. Some countries even tie eligibility for income support benefits to a return to education. Examples are Australia, through its “learn or earn” requirements, and Denmark.

Comprehensive, full-time, second-chance educational programmes can be a suitable alternative for early school leavers who are unable or unwilling to return to a standard school, possibly because they have been out of school for too long or face additional hurdles, such as family issues or mental health problems. These programmes combine catch-up courses in foundation skills with vocational classes, counselling and career guidance, and often enable participants to obtain their upper-secondary qualification (Box 1.6). Second-chance programmes may be suitable also for young people who have an upper-secondary qualification, but lack the basic skills required to participate in training or find employment.

Work experience programmes or short training courses with a strong practical component may be attractive to NEETs who cannot or will not go back to school because they are frustrated by their previous schooling experience or, possibly, struggling with social and health issues. They can help disadvantaged young people regain self-esteem and build a working routine. And they can prepare them for later participation in education or training programmes.

Work experience measures should, however, always target the most disadvantaged youth. There is now plenty of international evidence that short public-sector employment programmes do not generally improve jobseekers’ prospects of employment in the regular labour market (Card et al., 2010, 2015; Kluve, 2010). Likely reasons are that many schemes have, at best, a weak training component and that private-sector employers think little of the experience gained from these programmes. Some programmes have even been shown to have detrimental effects, as participants eased up on their job seeking during training – the so-called “lock-in” effects. There is also a risk of regular employees being replaced, or “crowded out”, by programme participants who work without pay or for a subsidised wage.

Subsidies for private businesses that hire jobseekers have proven an effective tool for brightening jobseekers’ employment prospects, particularly the programme participants are young. Subsidies should, however, target only low-skilled jobseekers and the long-term unemployed to lessen the risk of employers pocketing the subsidy to recruit jobseekers whom they would have hired anyhow – the “deadweight effect” (Cahuc et al., 2014).

Low-cost, low-intensity interventions like job search assistance, counselling and short training courses (in CV writing and interview techniques) can be sufficient for clients with low barriers to labour market entry. They may also be useful for testing a young person’s readiness for participation in more intensive activity.

Box 1.6. Second-chance learning options for early school leavers: Opportunities and challenges

Early school leavers typically find it very hard to return to school, as the educational, social or personal factors that caused the initial drop-out often persist and remain an obstacle. Depending on their level of schooling and how long it is since they dropped out, young people may also lack the elementary literacy and numeracy skills required to continue schooling or follow a professional training programme.

Second-chance programmes offer a flexible learning environment – often with a residential component – that is well adapted to early school leavers’ needs and designed to help them back into education. They typically combine catch-up classes in literacy and numeracy skills with vocational classes, intensive counselling, health support and career guidance. Simple work experience or community work components – in catering or elderly care, for example – can help them re-gain their work rhythm.

Probably the largest and best-known second-chance programme is the US Job Corps, which has been operating since 1964. It targets disadvantaged 16-to-24 year-olds, giving them academic tuition, vocational training, counselling, and social skills training. It also provides health care and organises job placements. Another important programme – and one which has expanded internationally from the United States – is YouthBuild, which provides skills and work experience in the construction sector. Both schemes rely on strong ties with local employers. And both contain a strong non-cognitive training component aimed at strengthening motivation, building conscientiousness, and coaching young people in interpersonal skills. For some young people, the US Job Corps and YouthBuild function as comprehensive pre-apprenticeships, while for others they are stepping stones to higher education. In France, the École de la Deuxième Chance offers similar curricula. The Swedish Folk High Schools provide young people aged 18 and over with a mixture of intensive counselling, coaching in social and life skills, and formal education. They use their own grading system that measures not only academic performance but also social skills, and public universities set aside quotas for Folk High School graduates. In Australia and the United Kingdom, smaller-scale second-chance programmes are offered in so-called “youth foyers”, which offer training, accommodation and social and psychological support to homeless young people, and which are often located close to the vocational training facilities.

An obstacle to the large-scale roll-out of second-chance learning programmes is that they are very costly. To be successful, they require well-trained and highly motivated staff able to provide intensive support and supervision. Infrastructure requirements are moreover substantial, because facilities for training, housing, leisure activities and even health care need to be provided in the same place. Despite their high immediate costs, second-chance programmes have proven cost-effective for specific groups in the medium and long run, permanently reducing benefit dependency and criminal activity and raising earnings among former participants (Schochet et al., 2008; Cohen and Piquero, 2010, 2015). Given the currently limited capacity of second-chance programmes, they need to be carefully targeted at young people who are motivated and suited to participation in such intensive programmes.

Active programme participation should ideally begin as soon as a young person has registered as jobseeker. One way of securing their continued commitment is by adopting a mutual obligation approach, which links regular income support to a jobseeker’s efforts to find suitable education or work or to their active programme participation.²⁷

References

Abdulkadiroglu, A. et al. (2009), “Accountability and Flexibility in Public Schools: Evidence from Boston’s Charters and Pilots”, NBER Working Paper, No. 15549, www.nber.org/papers/w15549.

Almlund, M., Duckworth A.L., Heckman J. and Kautz T. (2011), “Personality Psychology and Economics”, Handbook of the Economics of Education, Vol. 4.

Angrist, J.D. and V. Lavy (1999), “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement”, Quarterly Journal of Economics, Vol. 114, No. 2, pp. 533-575.

Angrist, J.D. et al. (2016), “Stand and Deliver: Effects of Boston’s Charter High Schools on College Preparation, Entry, and Choice”, Journal of Human Resources, Summer 2014, Vol. 49, No. 3, pp. 572-610.

Angrist, J.D. et al. (2012), “Who Benefits from KIPP?”, Journal of Policy Analysis and Management, Vol. 31, No. 4, pp. 837-860.

Báron, J.D. and D.A. Cobb-Clark (2010), “Are Young People’s Educational Outcomes Linked to Their Sense of Control?, IZA Discussion Paper, No. 4907, Bonn.

Brunello, G. and M. Schlotter (2011), “Non Cognitive Skills and Personality Traits: Labour Market Relevance and their Development in Education & Training Systems”, IZA Discussion Paper, No. 5743, Bonn.

Budig, M. and P. England, (2001), “The Wage Penalty for Motherhood”, American Sociological Review, Vol. 66, No. 2, pp. 204-225, April.

Cahuc, P., S. Carcillo, and K.F. Zimmermann (2013), “The Employment of the Low-Skilled Youth in France”, Les notes du conseil d’analyse économique, No. 4, April.

Cahuc, P., S. Carcillo and A. Zylberberg (2014), Labor Economics, 2nd edition, MIT Press, Boston.

Caliendo, M., S. Künn, and R. Schmidl (2011), “Fighting Youth Unemployment: The Effects of Active Labour Market Policies”, IZA Discussion Paper, Bonn, http://ftp.iza.org/dp6222.pdf.

Carcillo, S. et al. (2015), “Investing in Youth – Policies and Challenges”, OECD Social, Employment and Migration Working Papers, No. 164, OECD Publishing, Paris, http://dx.doi.org/10.1787/5js6363503f6-en.

Card, D., J. Kluve and A. Weber (2015), “What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations”, IZA Discussion Paper, No. 9236, Bonn.

Card, D., J. Kluve and A. Weber (2010), “Active Labour Market Policy Evaluations: A Meta-Analysis”, Economic Journal, Vol. 120, No. 548, pp. F452-F477

Chetty, R. et al. (2011), “How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star”, Quarterly Journal of Economics, Vol. 126, No. 4, pp. 1593-1660.

Clark, G. (2014), The Son Also Rises: Surnames and the History of Social Mobility, Princeton University Press.

Cohen, M.A. and A.R. Piquero (2015), “Benefits and Costs of a Targeted Intervention Program for Youthful Offenders: The YouthBuild USA Offender Project”, Journal of Benefit-Cost Analysis, Vol. 6, No. 3, pp. 603-624.

Cohen, M.A. and A.R. Piquero (2010), “An Outcome Evaluation of the YouthBuild USA Offender Project”, Youth Violence and Juvenile Justice, Vol. 8, No. 4, pp. 373-385.

Cunha, F. and J.J. Heckman (2007), “The Technology of Skill Formation”, American Economic Review, Vol. 97, No. 2, pp. 31-47.

Cunha, F. et al. (2006), “Interpreting the Evidence on Life Cycle Skill Formation”, in E.A. Hanushek and F. Welch (eds.), Handbook of the Economics of Education, Chapter 12, pp. 697-812, Amsterdam.

Dee, T. and M. West (2008), “The Non-Cognitive Returns to Class Size”, NBER Working Paper, No. 13994, Cambridge, United States, www.nber.org/papers/w13994.

Deloitte Access Economics (2012), “Econometric Analysis of the Australian Apprenticeships Incentives Program”, Department of Education, Employment and Workplace Relations, www.australianapprenticeships.gov.au/sites/prod.australianapprenticeships.gov.au/files/publication-documents/EconometricAnalysisAAIP.pdf.

Dias, M. and C. Tomas (2012), “Education and Equity in Semi-peripheral Countries: Current Trends in the Field of Priority Education in Portugal”, Procedia – Social and Behavioral Sciences, Vol. 47, pp.1092-1096.

Diekman, A. and K. Schmidheiny (2008), “The Intergenerational Transmission of Divorce: A Fifteen-Country Study with the Fertility and Family Survey”, Comparative Sociology, Vol. 12, No. 2.

Dobbie, W., Jr. Fryer and G. Roland (2011), “Are High Quality Schools Enough to Close the Achievement Gap? Evidence from a Social Experiment in Harlem”, American Economic Journal: Applied Economics, Vol. 3, No. 3.

DuBois, D.L. et al. (2002), “Effectiveness of mentoring programs for youth: A meta-analytic review”, American Journal of Community Psychology, Vol. 30, No. 2, pp. 157-197.

Ellwood, D. (1982), “Teenage Unemployment: Permanent Scars or Temporary Blemishes?”, in R.B. Freeman and D.A. Wise (eds.), The Youth Labor Market Problem: Its Nature, Causes, and Consequences, University of Chicago Press, pp. 349-390.

Eurofound (2012), NEETs – Young People Not in Employment, Education or Training: Characteristics Costs and Policy Responses in Europe, Publications of the Office of the European Union, Luxembourg.

European Commission (2011), “Attitudes Towards Vocational Education and Training”, Special Eurobarometer, No. 369, http://ec.europa.eu/public_opinion/archives/ebs/ebs_369_en.pdf.

Field, S. et al. (2012), A Skills beyond School Review of Denmark, OECD Reviews of Vocational Education and Training, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264173668-en.

Gelber, A., A. Isen, and J.B. Kessler (2016), “The Effects of Youth Employment: Evidence from New York City Lotteries”, Quarterly Journal of Economics, Vol. 131, No. 1, pp. 423-460.

Goldberg, L.R et al. (1998), “Demographic Variables and Personality: The Effects of Gender, Age, Education, and Ethnic/Racial Status on Self-Descriptions of Personality Attributes”, Personality and Individual Differences, Vol. 24, No. 3, pp. 393-403.

Grossman, J.B. and J.E. Rhodes (2002), “The Test of Time: Predictors and Effects of Duration in Youth Mentoring Programs”, American Journal of Community Psychology, Vol. 30, No. 2, pp. 199-206.

Heckman, J. (2008), “Early Childhood Education and Care: The Case for Investing in Disadvantaged Children”, CESifo DICE, Vol. 6, No. 2.

Heckman, J.J. and G.J. Borjas (1980), “Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence”, Economica, Vol. 42, No. 187, pp. 247-283.

Heckman, J.J., J. Stixrud and S. Urzua (2006), “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior”, Journal of Labor Economics, Vol. 24, No. 3, pp. 411-482.

Heckman, J.J. et al. (2009), “The Rate of Return to the High/Scope Perry Preschool Program”, Journal of Public Economics, Vol. 94, No. 1-2, pp. 114-128.

Heller, S.B. et al. (2015), “Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago”, NBER Working Paper, No. 21178, Cambridge, United States, www.nber.org/papers/w21178.

Jorm, A.F. (2015), “How Effective Are ‘Headspace’ Youth Mental Health Services?”, Australian & New Zealand Journal of Psychiatry, Vol. 49, No. 10, pp. 861-862.

Kautz, T. et al. (2015), “Fostering and Measuring Skills: Improving Cognitive and Non-Cognitive Skills to Promote Lifetime Success”, OECD Education Working Papers, No. 110, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jxsr7vr78f7-en.

Kemple, J.J. (2008), “Career Academies – Long-term Impacts on Labor Market Outcomes, Educational Attainment, and Transitions to Adulthood”, MDRC, www.mdrc.org/sites/default/files/full_50.pdf.

Kluve, J. (2010), “The Effectiveness of European Active Labour Market Programs”, Labour Economics, Vol. 17, pp. 904-918.

Kramarz, F. and O.N. Skans (2013), “When Strong Ties Are Strong: Family Networks and Youth Labour Market Entry”, CEPR Discussion Paper, No. 9620, forthcoming in Review of Economic Studies.

Lee, K. (2008), “The Effects of Children’s Age at Entering Head Start on their Short- and Long-term Developmental Outcomes”, Social Service Review, Vol. 82, No. 4, pp. 663-702.

Lyche, C. (2010), “Taking on the Completion Challenge: A Literature Review on Policies to Prevent Dropout and Early School Leaving”, OECD Education Working Papers, No. 53, OECD Publishing, Paris, http://dx.doi.org/10.1787/5km4m2t59cmr-en.

McIntosh, S. (2004), “The Impact of Vocational Qualifications on the Labour Market Outcomes of Low-Achieving School-Leavers”, CEP Discussion Paper, No. 621, Centre for Economic Performance, London School of Economics and Political Science, London, http://eprints.lse.ac.uk/19982/.

Min, J., M. Silverstein and J. Lendon (2012), “Intergenerational Transmission of Values Over the Family Life Course”, Advances in Life Course Research, Vol. 17, No. 3.

Möller, J. and M. Umkehrer (2014), “Are There Long-Term Earnings Scars from Youth Unemployment in Germany?”, ZEW Discussion Paper, No. 14-089, Centre for European Economic Research, Mannheim.

National Mental Health Commission (2014), Report of the National Review of Mental Health Programmes and Services – Vol. 1, NMHC, Sydney.

OECD (2016), Investing in Youth: Australia, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264257498-en.

OECD (2015a), Employment Outlook 2015, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2015-en.

OECD (2015b), OECD Skills Outlook 2015: Youth, Skills and Employability, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234178-en.

OECD (2015c) Fit Mind, Fit Job, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264228283-en.

OECD (2015d), Mental Health and Work: Australia, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264246591-en.

OECD (2015e), Integrating the Delivery of Social Services for Vulnerable Groups, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264233775-en.

OECD (2015f), Education at a Glance 2015: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2015-en.

OECD (2014), Skills Beyond School: Synthesis Report, OECD Reviews of Vocational Education and Training, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264214682-en.

OECD (2013). PISA 2012 Results: Ready to Learn: Students’ Engagement, Drive and Self-Beliefs (Vol. III), OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264201170-en.

OECD (2012a). Sick on the Job? Myths and Realities about Mental Health and Work, OECD Publishing, Paris, www.oecd.org/els/mental-health-and-work-9789264124523-en.htm.

OECD (2012b), Education at a Glance 2012: Highlights, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag_highlights-2012-en.

OECD (2012c), Equity and Quality in Education: Supporting Disadvantaged Students and Schools, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264130852-en.

OECD (2012d), “PISA in Focus 2012/07 (July)”, OECD Publishing, Paris, https://www.oecd.org/edu/pisa%20in%20focus%20n18%20(eng)–v05.pdf.

OECD (2012e), “OECD Note on ‘Quality Apprenticeships’ for the G20 Task Force on Employment”, 26 September 2016, www.oecd.org/els/emp/OECD%20Apprenticeship%20Note%2026%20Sept.pdf.

OECD (2012f), Activating Jobseekers – How Australia Does It, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264185920-en.

OECD (2011), Quality Time for Students: Learning In and Out of School, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264087057-en.

OECD (2010), Sickness, Disability and Work: Breaking the Barriers – A Synthesis of Findings across OECD Countries, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264088856-en.

OECD (2007), Students with Disabilities, Learning Difficulties and Disadvantages: Policies, Statistics and Indicators, OECD Publishing, Paris, www.oecd.org/edu/school/studentswithdisabilitieslearning difficultiesanddisadvantagespoliciesstatisticsandindicators-2007edition.htm.

OECD-LEED (2014), Local Implementations of Youth Guarantee: Emerging Lessons from European Experiences, OECD Publishing, Paris, www.oecd.org/cfe/leed/THE%20LOCAL%20IMPLEMENTATION%20OF% 20YOUTH%20GUARANTEES_FINAL2015.pdf.

Pellizzari, M. (2010), “Do Friends and Relatives Really Help in Getting a Good Job?”, The Industrial and Labor Relations Review, Vol. 63, pp. 494-510.

Piketty, T. and M. Valdenaire (2006), “L’impact de la taille des classes sur la réussite scolaire dans les écoles, collèges et lycées français”, Les dossiers évaluations et statistiques, No. 173, Ministère de l’Education Nationale, March.

Poropat, A.E. (2009), “A Meta-Analysis of the Five-Factor Model of Personality and Academic Performance”, Psychological Bulletin, Vol. 135, No. 2, pp. 322-338.

Quintini, G. and S. Martin (2014), “Same Same but Different: School-to-work Transitions in Emerging and Advanced Economies”, OECD Social, Employment and Migration Working Papers, No. 154, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jzbb2t1rcwc-en.

Rhodes, J.E. (2008), “Improving Youth Mentoring Interventions Through: Research-based Practice”, American Journal of Community Psychology, Vol. 41, pp. 35-42.

Rickwood, D.J. et al. (2015a), “The Services Provided to Young People by Headspace Centres in Australia”, Medical Journal of Australia, Vol. 202, No. 10, pp. 533-537.

Rickwood, D.J. et al. (2015b), “Changes in Psychological Distress and Psychosocial Functioning in Young People Visiting Headspace Centres for Mental Health Problems”, Medical Journal of Australia, Vol. 202, No. 10, pp. 537-542.

Rickwood, D. et al. (2015c), “Innovation in Youth Mental Health Services in Australia: Common Characteristics Across the First Headspace Centres”, Early Intervention in Psychiatry, Vol. 9, No. 1, pp 29-37.

Schmillen, A. and M. Umkehrer (2013), “The Scars of Youth: Effects of Early-career Unemployment on Future Unemployment Experience”, IAB Discussion Paper, Vol. 6/2013, Institute for Employment Research, Nuremburg.

Schochet, P.Z., J. Burghart and S. McConnell (2008), “Does Job Corps Work? Impact Findings from the National Job Corps Study”, American Economic Review, Vol. 98, No. 5, pp. 1864-1886, December.

SPRC – Social Policy Research Centre (2015), “Is Headspace Making a Difference to Young People’s Lives? Final Report of the Independent Evaluation of the Headspace Program”, SPRC Report, Vol. 08/2015, Social Policy Research Centre, Sydney.

SPRC (2009), “Headspace Evaluation Report – Independent Evaluation of Headspace: The National Youth Mental Health Foundation”, SPRC Report, Vol. 09/2009, Social Policy Research Centre, Sydney.

Sweet, R. (2009), “Apprenticeship, Pathways and Career Guidance: A Cautionary Tale”, in F. Rauner et al. (eds.), Innovative Apprenticeships. Promoting Successful School-to-Work Transitions, Conference Proceedings, InAp Conference, Turin.

Umkehrer, M. (2015), “The Impact of Changing Youth Employment Patterns on Future Wages”, IAB Discussion Paper, Vol. 31/2015, Institute for Employment Research, Nuremburg.

Van Eijck, K. and P.M. de Graaf (2004), “The Big Five at School: The Impact of Personality on Educational Attainment”, Netherlands’ Journal of Social Sciences, Vol. 40, No. 1, pp. 24-40.

Watts, A.G. (2009), “The Relationship of Career Guidance to VET”, OECD Publishing, Paris, www.oecd.org/edu/skills-beyond-school/44246616.pdf.

Annex 1.A1.

Data and methodology of the longitudinal analysis

An analysis of the dynamics of the NEET status comes with substantial data requirements. Identifying young people’s NEET trajectories needs to be based on individual-level longitudinal data determining educational status and labour market participation over a long time horizon and on a monthly basis throughout each year. Since the focus of the analysis is specifically on periods of unemployment or inactivity, the number of individuals in the sample moreover had to be large to identify a sufficient number of NEETs.

The data used in this analysis come from two different sources:

the 2012 European Union Statistics on Income and Living Conditions (EU-SILC) survey of a selection of European countries;

the 2009-12 waves of the Household, Income and Labour Dynamics in Australia (HILDA) survey.

Both surveys supply monthly information on activity statuses – including any periods of unemployment and inactivity – over the 48-month period from January 2009 to December 2012. The sample was restricted to people aged 15 to 29 years at the beginning of the observation period, irrespective of their initial activity status. Persons with missing information on labour market activity for one or several of the 48 months were dropped. A country was included in the analysis as long as no more than 10% of all observed trajectories were incomplete. While HILDA has been observing households since 2001 on an annual basis, the EU-SILC spanned only four years. The analysis was therefore restricted to four years.

The NEET spells studied were defined as consecutive months in which the young respondent reported having been out of employment, education or training. Two periods of NEET status that are interrupted by a single month in education or employment were defined as separate spells. No distinction was made between NEET inactivity and unemployment due to the small sample size.

Annex 1.A2.

Overview of income support benefits

Table 1.A2.1. Means-tested benefits are available almost everywhere to unemployed youth without employment record
Benefits available to a 20-year-old without employment record, 2014
| Unemployment benefits (UB) | Minimum-income benefits | Additional child-contingent benefits
| UI | UA | SA | HB | FB | LP
Australia |  | ● | ● | ● | ● | ●
Austria |  |  | ● | ● | ● | ●
Belgium |  |  | ● |  | ● |
Canada |  |  | ● |  | ● | ●
Chile |  |  |  |  | ● |
Czech Republic |  |  | ● | ● | ● |
Denmark |  |  | ● | ● | ● | ●
Estonia |  |  | ● |  | ● | ●
Finland |  | ● | ● | ● | ● | ●
France |  |  |  | ● | ● | ●
Germany |  |  | ● | ● | ● | ●
Greece |  | ● |  |  | ● | ●
Hungary |  |  | ● | ● | ● | ●
Iceland |  |  | ● | ● | ● | ●
Ireland |  | ● | ● | ● | ● | ●
Israel |  |  | ● | ● | ● | ●
Italy |  |  |  | ● |  | ●
Japan |  |  | ● |  | ● | ●
Korea |  |  | ● | ● |  | ●
Latvia |  |  | ● | ● | ● | ●
Luxembourg | ● |  |  |  | ● | ●
Netherlands |  |  |  | ● | ● | ●
New Zealand |  | ● |  | ● | ● | ●
Norway |  |  | ● | ● | ● | ●
Poland |  |  | ● | ● | ● | ●
Portugal |  |  | ● | ● | ● | ●
Slovak Republic |  |  | ● |  | ● | ●
Slovenia |  |  | ● | ● | ● | ●
Spain |  |  |  |  | ● |
Sweden |  | ● | ● | ● | ● |
Switzerland |  |  | ● | ● | ● |
Turkey |  |  |  |  |  |
United Kingdom |  | ● | ● | ● | ● | ●
United States |  |  | ● |  | ● |
Note: “UI” = Unemployment insurance benefits; “UA” = Unemployment assistance benefits; “SA” = Social assistance benefits; “HB” = Housing benefits; “FB” = Family benefits; “LP” = Lone parent benefits.
Source: OECD Tax-Benefit Models, www.oecd.org/social/benefits-and-wages.htm.

Figure 1.A2.1. Family benefits are typically available to households with children up to the age of 16 or 17 years, except if children are in education

Upper age limits for family cash benefits or non-wastable (i.e. refundable) tax credits for youth and youth in education living with their parents, 2014

Note: For Canada: Province of Ontario; for Switzerland: Zurich.

Source: OECD Tax-Benefit Models, www.oecd.org/social/benefits-and-wages.htm.

StatLink  http://dx.doi.org/10.1787/888933405096

Figure 1.A2.2. More than one-third of all youth receive family benefits

Note: Young people are defined as benefit recipients if they live in a household that received benefits in the previous year.

Results are for 16-to-29 year-olds except for Germany (17-29 years) and the United States (16-24 years).

In Panel B, results relate to 2014, except for Chile and Switzerland (2013) Turkey (2012) and Canada (2011). Countries are sorted, from left to right, in ascending order of the benefit receipt rate.

Source: OECD calculations based on the European Union Statistics on Income and Living Conditions (EU-SILC), Household, Income and Labour Dynamics in Australia (HILDA) Survey, Canada’s Survey of Labour and Income Dynamics (SLID), Chile’s National Socio-Economic Characterisation Survey (CASEN), the German Socio-Economic Panel (SOEP), Mexico’s Household Income and Expenditure Survey (ENIGH), and the US Current Population Survey (CPS).

StatLink  http://dx.doi.org/10.1787/888933405104

Notes

 1. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

 2. Unless stated otherwise, “youth” or “young people” denotes 15- or 16-to-29 year-olds.

 3. Contributory factors in the “scarring effect” are human capital depreciation and the loss of professional networks during out-of-work periods. Employers might also see early periods of unemployment as a sign that a young person is less productive or motivated. Scarring might even negatively impact young people’s preference for work (Heckman and Borjas, 1980; Ellwood, 1982).

 4. The youth population shrunk markedly between 2007 and 2014 in several OECD countries, notably Ireland (-22%), Latvia (-20%), Spain (-19%), and the Czech Republic and Slovenia (both -16%).

 5. This weighted average gives countries with a large youth population, such as Turkey and Mexico, a bigger weight, while the OECD average calculates the NEET rate for each country, and then takes a simple average.

 6. NEETs are defined as youth who are

● “not in employment”: youth who indicate that they are either “unemployed” or “inactive / not in the labour force”;

● “not in education or training”: youth who are not enrolled in a course of formal education or training, such as school, university, or an apprenticeship programme. Youth who are in informal education only are not counted as being in education or training according to this definition.

Eurostat does define youth who are in informal education as non-NEETs, which leads to a significant drop in the NEET rate in some countries, e.g., in 2013, Spain (down by 5 percentage points), Denmark (down 4), and Sweden (down by 3). This report restricts the definition of education to formal education because the labour market connection of informal education is not clear as these could be hobby courses, and information on informal education is not available for non-European countries, which would distort cross-OECD comparisons.

 7. The estimation disregards possible general equilibrium effects of an increase in youth employment rates – especially in countries with high NEET rates, bringing a substantial share of NEETs into employment would significantly increase overall labour supply, which could depress wages. But increasing youth employment would also stimulate demand, and affect aggregate prices and wages.

 8. This estimate imputes wages for NEETs given their observable characteristics, such as education, age, work experience, gender and household characteristics, taking into account that youth who are offered higher wages are more likely to work. Wages are imputed using the Heckman correction which corrects for selection effects into employment. Wages are only observed for youth who are employed, and employed youth are likely to have a higher earnings potential and / or lower costs of working than non-working youth. Therefore, predictions of wages for non-working youth based on data on working youth can be biased. The Heckman correction remedies this by directly estimating the probability of working for each youth, and using this probability to adjust the estimates of the wage equation. The model estimates the hourly wages of NEETs using micro-data from the EU-SILC, HILDA (Australia), SLID (Canada), CASEN (Chile), SOEP (Germany), ENIGH (Mexico), SILC (Turkey) and the CPS (United States). For some countries, data from several years are pooled to increase the sample size. Explanatory variables are education (below upper secondary, upper secondary and post-upper secondary non-tertiary, and tertiary education) potential work experience (and a quadratic term), gender, region, whether the young person has a spouse or live-in partner, and a year control were applicable. For countries were information on potential work experience is not available or of poor quality, it uses age and its quadratic term. The selection equation controls for a variety of household characteristics: having a child under the age of five, living with parents, and income received by other persons in the household. The effect of having a child and having a spouse or live-in partner is also allowed to vary between men and women. Suffering from poor health is also allowed to influence the selection into employment. The model is estimated separately for each country. The imputed wages and costs only pertain to employment income, except for Turkey, where information on months worked during the year is only available for employment and self-employment. Data are for 2014 except for Chile and Switzerland (2013), Turkey (2012) and Canada (2011), however, these values are uprated to 2014 USD. The necessary data for this estimation was not available for Israel, Japan, Korea and New Zealand.

 9. Average annual wage costs (i.e. mean gross annual employment income plus employer’s social security contributions) are computed for each country for the age groups 15-19, 20-24, and 25-29, separately by gender. Multiplication with the number of NEETs of each gender and age-group yields the upper bound estimate. For the lower bound estimate, the number of NEETs in each age-group-gender cell is instead assigned a wage of 67% of the median wage in his or her group.

 10. These estimates are in line with a recent estimate for EU countries in 2011 (Eurofound, 2012).

 11. An important limitation of these statistical calculations is that parental education is observed only for young people who are living in their parents’ home. Results may therefore be unreliable if the decision to leave the parents’ home is related to the parents’ level of education (e.g. because parental education influences income) and if it differs between NEETs and non-NEETs.

 12. Intergenerational transmission has been examined extensively in educational attainment and poverty – and a wide range of other beliefs and behaviour, e.g. Diekman and Schmidheiny (2008) on the intergenerational transmission of divorce and Min et al. (2012) on the intergenerational transmission of values.

 13. The Perry Preschool Program, which ran between 1962 and 67, was targeted at low-income black children with initial IQs below 85 at the age of 3. Preschool was provided each weekday morning in 2.5-hour sessions. The average child-teacher ratio was 6:1. The curriculum emphasised social skills and active learning, in which the children engaged in activities that i) involved decision making and problem solving, ii) were planned, carried out, and reviewed by the children themselves, with support from adults and iii) involved working with others when problems arose. In addition, there were home visits to promote parent-child interaction. The programme ended after two years of enrolment. Participants in the program were followed for over 40 years.

 14. The analysis follows 15-to-29 year-olds over a four-year period, 2009 to 2012. Young people are tracked and their activity status examined each month over the four years (see the annex to this chapter for more information on the data and analysis). The data necessary for the analysis were available only for 16 OECD countries.

 15. One exception is Australia’s Youth Allowance, the main income support benefit for young jobseekers aged 16 to 21.

 16. Unemployment benefits include unemployment insurance benefits as well as the less generous unemployment assistance available in some countries.

 17. Public pensions (and, therefore, seniors) are excluded from this analysis, because pensions would just tautologically lift a large share of seniors out of poverty and would make a comparison between countries with public and capital-based pension systems difficult.

 18. As poverty rates are based on monthly income, retirees drawing on their retirement funds “appear poor” in these statistics, because they do not receive regular income.

 19. As part of the so-called project STAR (Student/Teacher Achievement Ratio), pupils were randomly assigned to classes with sizes ranging from 15 to 22 students on average. The experiment was implemented across 79 schools in Tennessee between 1985 and 1989, and the outcomes of young participants could be tracked to the age of 27 years.

 20. Older adolescents and those who had suffered from emotional, sexual or physical abuse were most likely to be in relationships that terminated early.

 21. The success of mentoring programmes depends crucially on the participation of charismatic and credible mentors. Mentors should have a background that the young person can relate to, and they need to be given the time to invest in their relationship with the mentee to build up trust for the programme to have an impact (DuBois et al., 2002; Rhodes, 2008). Accordingly, the main bottleneck to the expansion of mentoring programmes is the recruitment of suitable volunteer mentors. One promising approach has been to find mentors through partnerships with private companies. Meetings with the mentees can take place directly on-site, so saving the mentors commuting time. Partnerships with schools are an alternative approach: BIG Futures, a new initiative in Australia, will try to bring BBBS mentoring directly into Australian schools. The Australian iTrack programme provides high school students with mentors for an 18-week period to motivate them to complete school and provide them with career guidance.

 22. The former School Business Community Partnership Broker programme was one component of the National Partnership for Youth Attainment and Transitions, a set of initiatives agreed upon in 2009 by the Commonwealth and State/Territory governments with the aim of improving educational outcomes and school to work transitions. The National Partnership expired in 2013, the Partnership Brokers programme was extended by another year before ending in 2014.

 23. In 2010, an estimated 28% of all apprentices had completed a pre-apprenticeship.

 24. This is true even though not all employment possibilities were in the private sector, which probably reduced the programme’s impact.

 25. Youth guarantees were first developed in the Nordic countries in the late 1980s.

 26. Youth Connections, like the School Business Community Partnership Broker programme, was part of the National Partnership for Youth Attainment and Transitions, which expired in 2014. In principle, support for school-age youth is the responsibility of Australian States and Territories.

 27. In fact, the concept of “mutual obligations” was initially introduced in Australia for employable young jobseekers only, requiring them to undertake an activity like part-time work, voluntary work or training in exchange for income support (OECD, 2012f).

Chapter 2.

Interpreting OECD social indicators

The purpose of Society at a Glance

Society at a Glance 2016 aims to address the growing demand for quantitative evidence on the social situation, its trends, and its possible drivers across OECD countries. One objective is to assess and compare social outcomes that are currently the focus of policy debates. Another is to provide an overview of societal responses, and how effective policy actions have been in furthering social development. This edition of Society at a Glance discusses policy actions in response to the situation of youth Neither in Employment, Education, nor Training (NEET). Indicators on youth are therefore a particular focus.

The indicators are based on a variant of the “Pressure-State-Response” framework that has also been used in other policy areas [United Nations (1997), Glossary of Environment Statistics, Studies in Methods, Series F, No. 67, New York]. This groups indicators into three areas:

“Social context”: refers to general indicators that, while not usually direct policy targets are relevant information for understanding the social landscape. An example is the proportion of elderly people to working-age people.

“Social status”: describes the social outcomes that policies try to influence. Ideally, the selected indicators can be easily and unambiguously interpreted. As an example all countries would rather have low poverty rates than high ones.

“Societal response”: provides information about measures and activities to affect social status indicators. Examples are governmental policies, but also activities of NGOs, families and broader civil society.

In addition, the framework used in Society at a Glance groups social status and societal response indicators according to the broad policy fields they cover:

“self-sufficiency”

“equity”

“health status” and

“social cohesion”.

A related OECD publication, How’s Life – Measuring Well-being, presents a large set of well-being indicators, with an aim to give an accurate picture of societal well-being and progress. Compared with Society at a Glance, How’s Life uses a broader set of outcome measures but excludes indicators of policy responses. In addition, the special chapter in Society at a Glance provides policy analysis and recommendations.

OECD countries differ substantially in their collection and publication of social indicators. In selection of indicators for this report, the following questions were considered.

What is the degree of indicator comparability across countries? This report strives to present the best comparative information for each of the areas covered. However the indicators presented are not confined to those for which there is “absolute” comparability. Readers are, however, alerted as to the nature of the data used and the limits to comparability.

What is the minimum number of countries for which the data must be available? This report includes only primary indicators that are available for two thirds of OECD countries.

What breakdowns should be used at a country level? Social indicators can often be decomposed at a national level into outcomes by social sub-categories, such as people’s age, gender and family type. Pragmatism governs here: the breakdowns presented vary according to the indicator considered, and are determined by what is readily available.

Chapters 3 to 7 describe the key evidence. Some of these indicators are published by the OECD on a regular basis (e.g. Social Expenditure Database and OECD Health Statistics). Others have been collected on an ad hoc basis. Yet others involve some transformation of existing indicators.

The selection and description of indicators

General social context indicators

When comparing social status and societal response indicators, it is easy to suggest that one country is doing badly relative to others, or that another is spending a lot of money in a particular area compared with others. It is important to put such statements into a broader context. General context indicators including household income, fertility, migration, family and the demographic trends, provide the general background for other indicators in this report (see Chapter 3).

Table 2.1. List of general context indicators

Household income

Fertility

Migration

Family

Demographic trends

Self-sufficiency indicators

Self-sufficiency is an underlying social policy objective. Self-sufficiency is promoted by ensuring active social and economic participation by people, and their autonomy in activities of daily life. A selection of indicators is shown in Chapter 4.

For many people, paid employment provides income, identity and social interaction. Social security systems are also funded by taxes levied on those in paid employment. Thus promoting higher paid employment is a priority for all OECD countries. To be unemployed means that supporting oneself and one’s family is not always possible. Skills also play a central role in ensuring people find and keep employment, particularly important for young people. Finding a job is especially difficult for recent labour market entrants. Experience shows, moreover, that the bad luck of entering the labour market in recession years has long-lasting consequences for young people’s employment and earnings prospects. Hence a major societal response to enable people to become self-sufficient is public and private spending in education.

The table below lists the chosen indicators for assessing whether OECD countries have been successful in meeting goals for assuring the self-sufficiency of people and their families.

Table 2.2. List of self-sufficiency indicators
Social status | Societal responses
Employment |
Unemployment | Education spending
Skills |
Labour market entry |

Equity indicators

Equity is another common social policy objective. Equitable outcomes are measured mainly in terms of access by people to resources.

Equity has many dimensions (Chapter 5). It includes the ability to access social services and economic opportunities, as well as equity in outcomes. Opinions vary as to what exactly entails a fair distribution of opportunities or outcomes. Additionally, as it is hard to obtain information on all equity dimensions, the social status equity indicators presented here are limited to inequality in financial resources.

Income inequality is a natural starting point for considering equity across the whole of society. Often however, policy concerns are more strongly focussed on those at the bottom end of the income distribution. Hence the use of poverty measures, in addition to overall inequality. Consideration of guaranteed minimum income benefits shows financial support and obtainable living standard for low-income families. This indicator of living on benefits complements the more general measures of income inequality and poverty. All OECD countries have social protection systems that redistribute resources and insure people against various contingencies. These interventions are summarised by public social spending. Equity indicators are clearly related to self-sufficiency indicators. Taken together, they reveal how national social protection systems address the challenge of balancing adequate provision with system sustainability and promotion of citizens’ self-sufficiency. In periods with high unemployment, cash transfers for working-age people are a major income safety net (recipients of out-of-work benefits).

Table 2.3. List of equity indicators
Social status | Societal responses
Income inequality | Social spending
Poverty |
Living on benefits |
Recipients of out-of-work benefits |

Health indicators

Health status is a fundamental objective of health care systems, but improving health status also requires a wider focus on its social determinants, making health a central objective of social policy (Chapter 6).

The links between social and health conditions are well-established. Indeed, educational gains, public health measures, better access to health care and continuing progress in medical technology, have contributed to significant improvements in health status, as measured by life expectancy. Often the health focus is on objective health indicators. More subjective population-based indicators of health, such as perceived health status can be important to assess overall well-being. Suicide rates give additional information about health and societal challenges, since there are a complex set of reasons why some people commit suicide. Health spending is a more general and key part of the policy response of health care systems to concerns about health conditions. Another health indicator for total population and youth is Tobacco and alcohol consumption, both associated with numerous harmful health and social consequences.

Nevertheless, health problems can sometimes have origins in interrelated social conditions – such as unemployment, poverty, and inadequate housing – beyond the reach of health policies. Moreover, more than spending levels per se, the effectiveness of health interventions often depends on other characteristics of the health care system, such as low coverage of medical insurance or co-payments, which may act as barriers to seeking medical help. A much broader range of indicators on health conditions and interventions is provided in OECD Heath Statistics and in Health at a Glance.

Table 2.4. List of health indicators
Social status | Societal responses
Life expectancy | Health spending
Perceived health status |
Suicide |
Tobacco and alcohol consumption |

Social cohesion indicators

Social cohesion is often identified as an over-arching objective of countries’ social policies. While little agreement exists on what it means, a range of symptoms are informative about lack of social cohesion. Social cohesion is positively evident in the extent to which people participate in their communities or trust each other (Chapter 7).

Life satisfaction is determined not only by economic development, but also by the diverse experiences and living conditions. The extent of trust that citizens have in the political institutions of their community and participation in voting are two important dimensions of the extent to which individuals are well integrated and taking part in social life. A general measure on crime and prisoners may indicate the degree to which economic and social exchange is facilitated, enhancing well-being and facilitating socially beneficial collective action. Social networks is another important element of social cohesion indicator, through relatives and friends, online connectedness or voluntary work.

It is difficult to identify directly relevant and comparable response indicators at a country level on social cohesion issues. Policies that are relevant to other dimensions of social policy (self-sufficiency, equity and health) may also influence social cohesion.

Table 2.5. List of social cohesion indicators
Social status | Societal responses
Life satisfaction |
Trust |
Voting |
Crime and prisoners |
Social networks |

What can be found in this publication

In each of the five domains covered in Chapters 3 to 7 of this report, each of the five indicators chosen provides a page of text and a page of charts. Both charts and text generally follow a standardised pattern. Most indicators integrate a youth focus. The choice of the time period over which change is considered is partly determined by data constraints. However, ideally changes are examined 1) over the last generation, to compare how society is evolving in the longer term, or 2) over the period since the last economic crisis (typically between 2007-08), so the extent to which recent economic fluctuations are influencing social indicators can be studied.

Finally, a box on “Definition and measurement” provides the definitions of data used and a discussion of potential measurement issues.

The data underlying each indicator are available on the OECD website (http://oe.cd/sag), or by typing or clicking for “electronic books” on the “StatLink” at bottom right of each indicator (where data for more countries are also available).

Further reading

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

United Nations (1997), Glossary of Environment Statistics, Studies in Methods, Series F, No. 67, New York.

Chapter 3.

General context indicators

Household income

Fertility

Migration

Family

Demographic trends

Household income

Disposable household income provides an indication of the goods and services families can purchase on the market. It is thus an objective indication of material quality of life, and it is used to measure poverty and inequality.

In 2013 half of the population in Mexico had incomes of less than USD PPP 4 800, whereas half of the people in Luxembourg had incomes eight times higher (Figure 3.1). Countries with low median household income included countries in Southern Europe, Turkey and much of Eastern Europe, as well as two Latin American countries – Chile and Mexico. Those with higher household incomes included Luxembourg, Norway and Switzerland.

On average, between 2007 and 2013, OECD real median household disposable income remained stable, thanks to the effect of public cash transfers and personal income taxes (Figure 3.2). Real median disposable income fell most in Greece by 8% per year, but also in Ireland and Spain by more than 3%. By contrast, disposable income increased significantly in Chile as well as to a lower extent in Israel and Poland.

Figure 3.2 focuses on the top and bottom 10% of the population. While on average across OECD countries real median household disposable income and the average income of the top 10% remained almost stable, the income of the bottom 10% fell by almost 1% per year over the period 2007 to 2013. Out of the 34 countries where data are available, the top 10% has done better than the poorest 10% in 21 countries (see also the “Income inequality” indicator in Chapter 5). This pattern was particularly strong in some of the countries where household income decreased the most. Incomes of poorer households fell by more than 5% annually in Italy and Spain, and by more than 10% in Greece.

Household income data are also available by age group. Between 2007 and 2013, young people (18 to 25) suffered the most severe income losses, while elderly people (over 65) were largely shielded from the worse income effects of the crisis (Figure 3.3). Across OECD countries, average household disposable income fell in real terms by around 0.6% per year among youth. Meanwhile, income was stable among the elderly (over 65). Large income losses among the youth took place particularly in Greece, but also in Ireland and Spain. Some gains were experienced in Chile and Sweden. Overall, young people were better off than elderly in only seven countries (Canada, Germany, Iceland, Mexico, Norway and Sweden) by less than 1%.

Definition and measurement

Data on annual median equivalised household disposable income come from the OECD Income Distribution Database. Disposable income is market income (income from work and capital) after taking into account public cash transfers received and direct taxes and social security contributions paid. It excludes in-kind services provided to households by governments and private entities, consumption taxes, and imputed income flows due to home ownership. People were attributed the income of their household. After subtracting taxes and adding cash transfers, household income provides an indication of the goods and services families can purchase on the market. Household income is adjusted for differences in the needs of households of different sizes with an equivalence scale that divides household income by the square root of household size. The adjusted income is then attributed to every person in the household.

For cross-country comparison, national currency measures of income were converted into US dollars (USD) using purchasing power parity (PPP) for private consumption exchange rates. These PPPs reflect the amount of a national currency required in each country to buy the same basket of goods and services as a dollar does in the United States. Both income and PPP estimates are affected by statistical errors, so differences between countries of 5% or less are not considered significant.

Further reading

OECD Income Distribution Database, http://oe.cd/idd.

OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, http://oe.cd/init2015.

Figure notes

Figures 3.2 and 3.3: Data were adjusted for a break in series due to a change in the standard methodology of household income as from 2012; no change available for Switzerland.

3.1. Median income varies by a factor of 8 from USD 4 800 and USD 38 500

Annual median equivalised disposable incomes, in 2013, USD at PPP rates

StatLink  http://dx.doi.org/10.1787/888933405112

3.2. Poorer households tended to lose more or gain less between 2007 and 2013

Annual percentage changes in real household disposable income between 2007 and 2013, by income group

StatLink  http://dx.doi.org/10.1787/888933405123

3.3. Disposable income has fallen most among youth between 2007 and 2013

Annual percentage changes in real disposable income between 2007 and 2013, by age group

Source: OECD Income Distribution Database (http://oe.cd/idd).

StatLink  http://dx.doi.org/10.1787/888933405130

Fertility

The total fertility rate indicates the number of children an average woman would have if she were to experience the exact age-specific fertility throughout her life. Allowing for some mortality during infancy and childhood, the population is replaced at a total fertility rate of a little over two.

Over the last decades, fertility declined dramatically across OECD countries, falling on average from 2.7 children per woman of childbearing age in 1970 to 1.7 in 2014 (Figure 3.4). Falls were especially pronounced – by at least four children per woman on average – in Korea, Mexico and Turkey. Before the recent crisis, there was a moderate recovery in average fertility rates between 2000 and 2008. This rebound stalled in many OECD countries in 2009, probably as a consequence of the crisis.

In 2014, fertility was well below the replacement level in most countries, averaging 1.7 across the OECD (Figure 3.4, bars). The highest rate was recorded in Israel at 3.1, where women had almost one child more than women in Mexico and Turkey, the countries with the second and third highest rates, respectively. These three countries were the only OECD countries with a level above the replacement fertility rate (2.1 children per woman). Anglophone and Nordic countries were typically at the higher end. France has the highest fertility rate in Europe (and the 4th highest rate in the OECD) while the lowest fertility rates are found in Japan and South Europe. Fertility rates were notably low in Korea and Portugal, with two parents replacing themselves in the next generation by little more than one child, on average.

Fertility rates are generally higher in key partner economies than in OECD countries; rates are above replacement levels in Argentina, India, Indonesia, Saudi Arabia and South Africa. While fertility increased in the Russian Federation by four decimals between 1995 and 2014, fertility decreased in other key partner economies.

Rising female education and employment, a need to secure a job and income, growing housing problems and in some cases insufficient support for families juggling work and children, have all played a role in declining fertility. Family formation postponement is reflected in recent trends in OECD age-specific fertility rates (births per 1000 women) in Figure 3.5. Since 2000 fertility rates have been declining for under 30s whereas at the same time they been on the rise for over 30s. In the last few years, the average OECD fertility rate of 30-34s exceeded the fertility rate of 25-29s, and so did the rate of 35-39s compared to 20-24s. Also the 40-44 fertility rate is about to surpass the adolescent fertility rate. The adolescent fertility rate has fallen to low levels at under five births per 1 000 adolescents in Denmark, Japan, Korea, Slovenia and Switzerland, but it still remains high at above 50 in Chile and Mexico.

Family formation postponement is also reflected in the increase in the mean age of women at first child birth, in all 26 OECD countries for which data are available (Figure 3.6). Between 1995 and 2014, the average mean age at first birth has risen by almost three years, from 26.1 to 28.7 years old. In 2014 mean ages at first birth were lowest at 27 or under in the United States and in some Eastern European countries (Estonia, Latvia, Poland, Slovak Republic), whereas they were highest at 30 or above in some Asian (Japan, Korea), continental European (Luxembourg, Switzerland) and Mediterranean countries (Italy, Spain).

Definition and measurement

The total fertility rate is the expected number of children born to each woman at the end of the childbearing years (i.e. if the likelihood of her giving birth to children at each age was the current prevailing age specific fertility rates). It is computed by summing up the age-specific fertility rates defined over five year intervals. Assuming there is no net migration and mortality remains unchanged, the total fertility rate of 2.1 children per woman (“replacement”) ensures broad population stability.

The age-specific fertility rates are the number of births per 1000 women of a given age in a given year. They are presented here per five year age group.

Fertility data typically come from civil population registers or other administrative records. These are harmonised according to United Nations and Eurostat recommendations.

Mean ages of women at first birth are from OECD Family Database, based from Eurostat demographic statistics and national statistical institutes.

Further reading

OECD (2016), “SF2.1 Fertility rates”, OECD Family Database, www.oecd.org/social/family/database.htm.

OECD (2016), “SF2.3 Age of mothers and age-specific fertility”, OECD Family Database, www.oecd.org/social/family/database.htm.

Figure notes

Figure 3.4: 2012 instead of 2013 and 2014 for Canada, 2013 instead of 2014 for Chile and non-EU non OECD countries.

Figure 3.6: Data for the United Kingdom refer to England & Wales only; 2011 for Canada instead of 2014; 1998 for France and Sweden, 1999 for the Slovak Republic instead of 1995; no data available around 1995 for Germany; no data available for both years for Australia, Chile, Mexico, New Zealand and Turkey.

3.4. Fertility rates across the OECD are typically below replacement level

Number of children per woman aged 15 to 49, in 1970, 1995 and 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405149

3.5. Decline in fertility rates for under 30s and increase for over 30s

Births per 1 000 women by five-year age group, 2000 to 2014, OECD average

StatLink  http://dx.doi.org/10.1787/888933405155

3.6. The average mean age of women at first birth has risen by almost three years in the last two decades

Mean age of women at first birth, 1995 and 2014 (or nearest year)

Source: OECD Family Database (Indicator SF2.3), based on Eurostat demographic statistics (http://ec.europa.eu/eurostat/web/population-demography-migration-projections/population-data/database) and national statistical institutes.

StatLink  http://dx.doi.org/10.1787/888933405161

Migration

Europe recorded in 2015 an unprecedented number of asylum seekers and refugees with up to 1.2 million asylum applications (Figure 3.7) ; an estimated 250 000 to 350 000 people could be granted refugee or similar status, more than in any previous European refugee crisis since World War II. As during previous refugee crises in the 1990s the impact is concentrated in a few countries. In the OECD, Turkey is the most affected, currently hosting as many as 1.9 million Syrians as well as a large number of people from Iraq. Within the European Union, Italy, Greece and Hungary are on the front line but the main destination countries are Germany, in absolute terms, and Sweden and Austria, relative to their population.

More than in previous crises, asylum seekers are very diverse in terms of country of origin, profile and motivation. Recent refugees from the Syrian Arab Republic (Syria) are more skilled than other groups and those who came, for example, during the Yugoslav wars in the 1990s. There are more unaccompanied minors (children without a responsible adult to care for them) arriving now than previously. Refugee flows tend to concentrate in countries with the most favourable economic conditions. A strong jobs market seems to be the most important determinant of flows for main refugee groups.

On average in the OECD, 13% of the population was foreign-born in 2014. The share of foreign-born within the population was highest in Australia, Canada, Israel, Luxembourg, New Zealand and Switzerland, where at least one in five people were foreign-born (Figure 3.8). Nearly two-thirds of the OECD countries had an immigrant population exceeding one in ten of the population, whereas the share of immigrants is under 5% in seven countries. This ratio increased in most OECD countries over the last decade where on average the immigrant population constituted around 9.5% of the population around 2000s.

Immigrant offspring account for more than 1 in 4 in the population aged 15-34, a sizeable share of the young in OECD countries. In the 22 OECD countries for which recent data are available, in 2013, nearly one in five 15-34 year-olds was the child of an immigrant or had immigrated as a child. A further 9% arrived in the host country as adults (Figure 3.9). The United States, Germany and France host the largest numbers of native-born offspring with two foreign-born parents. However, in relative terms, the highest shares of immigrant offspring are to be found in European countries whose total populations have substantial proportions of immigrants (Luxembourg, Israel and Switzerland) and in countries with a significant population growth through migration like in Australia and Canada. In the more recent migration destinations of southern Europe as well as in Finland, by contrast, less than 1% of young people were born in the host country to foreign-born parents.

Definition and measurement

The terms asylum-seeker and refugee are often confused: an asylum-seeker is someone who applies for international protection, but whose claim has not yet been definitively evaluated. National asylum systems are there to decide which asylum seekers actually qualify for international protection. Those judged through proper procedures not to be refugees, nor to be in need of any other form of international protection, can be sent back to their home countries. Data on asylum seekers are from the United Nations High Commissioner for Refugees (UNHCR).

Immigrants are, in the first instance, defined as those who are foreign-born, whatever their citizenship at birth. In general, the foreign-born population is substantially larger than the share of foreign nationals. Immigrants offspring include different categories of people: ie. they can either be born in their parents’ host country to two foreign-born parents, or to mixed parentage (one foreign-born parent), be foreign-born and arrived as children or be foreign-born and arrived as adults.

Further reading

OECD (2016), OECD International Migration Outlook, OECD Publishing, Paris.

OECD (2015), OECD Indicators of Immigrant Integration 2015, Settling In, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234024-en.

OECD (2015), “Is this Humanitarian Migration Crisis Different?”, Migration Policy Debates, No. 7, OECD, Paris, September, https://www.oecd.org/migration/Is-this-refugee-crisis-different.pdf.

Figure notes

Figure 3.8: Data are not available for Greece. Data refer to 2000 or to the closest year with available data and to 2014 or most recent available year. OECD average refers to the average of countries presented. Data refers to foreign instead of foreign-born population for Japan and Korea.

Figure 3.9: The full split according to the categories of immigrants and immigrant offspring among 15-34 year-olds is not available for New Zealand. Data on foreign-born as a percentage of the total population are not available for Greece. OECD and EU averages refer to population weighted averages.

3.7. Unprecedented number of asylum seekers recorded in Europe

Number of new asylum seekers since 1980 in the OECD, the EU and Germany

Source: United Nations High Commissioner for Refugees (UNHCR).

StatLink  http://dx.doi.org/10.1787/888933405170

3.8. On average in the OECD, 13% of the population is foreign-born

Foreign-born as a percentage of the total population, 2000 and 2014, percentages

Source: OECD (2016), International Migration Outlook 2016 and OECD International Migration Database.

StatLink  http://dx.doi.org/10.1787/888933405181

3.9. One in four young people has a migrant background

Categories of immigrants and immigrant offspring among 15-34 year-olds, 2013, percentage of the population aged 15 to 34

Source: OECD (2015), OECD Indicators of Immigrant Integration 2015, Settling In, OECD Publishing, Paris and the OECD International Migration Database.

StatLink  http://dx.doi.org/10.1787/888933405197

Family

The living arrangements of youth are important for a variety of reasons. Moving out of the parental home is an important step on the way to adulthood as is cohabitation, marriage or having children. The living arrangements of youth can also influence their welfare and poverty rates – those living with their parents may have a lower risk of poverty as they can depend more on their parents financially and may face little or no costs for housing, food or other daily expenses.

Large variation in the living arrangements of youth exists across the OECD. In countries such as Italy, Slovenia and Greece over three quarters of 15-29 year-olds live with their parents (Figure 3.10, Panel A). In others, notably Canada and the Nordic countries, a small proportion live with their parents and youth are much more likely to live independently, particularly on their own. On average, around a quarter of young people live with a partner ranging from 11% in Italy to 41% in Finland.

The recession appears to have had a small overall impact on the living arrangements of youth across the OECD but the average change masks larger changes in some countries (Figure 3.10, Panel B). For example, in France there was a 12.5 percentage point rise in the share of youth living with their parents. Countries hit strongly by the crisis, such as Greece and Italy saw smaller increases but these countries already had some of the highest shares of youth living in their family home before the crisis hit. Overall across the OECD there was an increase of 0.7 percentage point in the proportion of youth living with their parents and a 1 percentage point decline in the proportion of youth living with partners or spouses. This seems to suggest that the recession may have lengthened the time young people live at home and delayed their transition to forming their own families.

Across the OECD the average age of those getting married has significantly increased (Figure 3.11). At the start of the 1990s the average age at first marriage across OECD countries was 24.9 for women and 27.4 for men. By 2014, this average age had increased from 28 years old to 31 for women and from 31 years old to 34 for men. Despite common declining trends in increasing ages at first marriage, there remain notable differences between countries. The average age is very high in the Nordic countries. In Israel and in Turkey, by contrast, the average age of women at first marriage is below 25 and of men is less than 28. The difference between countries points to a variety of transition paths towards the formation of long-term partnerships: cohabitation has become an important form of long-term partnership in, for example, the Nordic countries, postponing and frequently replacing marriage as the partnership standard.

Higher ages of marriage were accompanied by declining marriage rates and stabilising divorce rates. In 2014, crude marriage rates were between 4 and 5.5 marriages per 1 000, with the OECD average standing at 4.6 (Figure 3.12). But rates are very low in Italy, Luxembourg, Portugal, Spain and Slovenia at 3.5 or fewer marriages per 1 000 people, while rates are almost twice that at 6 per 1 000 or above in Israel, Korea, Turkey and the United States. In contrast, in 1990 most OECD countries had a marriage rate of around five to seven marriages per 1 000 people. Only Sweden and Turkey experienced an increase in marriage rates between 1990 and 2014. Crude divorce rates also vary across countries, from as low as 0.1 divorces per 1 000 people in Chile to above 3 per 1 000 in Denmark and the United States in 2014. Between 1990 and 2014, the picture was mixed: they increased in 21 OECD countries but decreased in 14 others. Decline was most pronounced in the United States, from 4.8 divorces per 1 000 in 1992 to 3.2 in 2014.

Definition and measurement

Household types are defined as follows: “alone” describes a young person living on their own; “single parent” means that the young person lives with at least one dependent child and no partner; "partnered’ describes a young person living with a spouse or cohabiting partner (and potentially with children in the same household); “with other youth/adults” describes a young person living with at least one other young person or adult (aged 30 years or older) and potentially with children in the same household; “with parents” means that the young person lives in a household with at least one of their parents.

The mean age at first marriage is defined as the mean average age in years of marrying persons at the time of first marriage. This measure is disaggregated by sex with separate averages for men and women.

The crude marriage rate is defined as the number of legal civil unions or marriages each year per 1 000 people. The crude divorce rate (CDR), defined as the number of legal civil unions or marriages that are dissolved each year per 1 000 people.

Figure notes

Figure 3.10: Data for 2014 refer to 2013 for Switzerland, 2012 for Turkey and 2011 for Canada; data for 2007 refer to 2006 for Chile and 2008 for Switzerland and Mexico. No data for Israel, Japan, Korea and New Zealand.

Figure 3.11: Median age at first marriage for Australia, Israel, New Zealand and United States; civil unions are included in New Zealand; data refer to all marriages for Mexico.

3.10. Most youth live with their parents and patterns have changed since the recession

Source: OECD calculations based on EU-SILC, HILDA (Australia), SLID (Canada), CASEN (Chile), HLFS (New Zealand) and CPS (United States).

StatLink  http://dx.doi.org/10.1787/888933405203

3.11. Increase in mean age at first marriage for both women and men of 4.6 years on average since 1990

Mean age at first marriage, by gender, 1990 and 2014 (or nearest years)

Source: OECD Family Database (Indicator SF3.1) based on national statistical offices and Eurostat.

StatLink  http://dx.doi.org/10.1787/888933405210

3.12. Decline in marriage rates and stabilisation of divorce rates on average since 1990

Crude marriage and divorce rates, per 1 000 people, 1990 and 2014 (or nearest years)

Source: OECD Family Database (Indicator SF3.1) based on national statistical offices and Eurostat.

StatLink  http://dx.doi.org/10.1787/888933405228

Demographic trends

Age-dependency ratios are a measure of the age structure of the population. They provide information about the demographic shifts that have characterised OECD countries in the past and that are expected in the future.

OECD populations became older and will continue to become older. In 2015, there were on average across OECD countries 28 persons aged 65 and over for every 100 persons aged 20 to 64, an average level up from 18 in 1970 (Figure 3.13). Cross country differences are large, varying in 2015 from less than 15% in Mexico and Turkey, to over 35% in Finland, Italy and Greece and to over 45% in Japan. By 2060, this average ratio is projected to almost double in the OECD area (to 57%) and to quadruple in Korea. By 2060, the old-age dependency ratio will almost reach 80% in Korea and Japan while remaining below 45% in Israel, Mexico and Turkey. This increase will contribute to higher public spending in health, long-term care and pensions.

Conversely, the youth-dependency ratio declined between 1970 and 2015. In 2015, there were on average across OECD countries 38 persons aged below 20 for every 100 persons aged 20 to 64, an average level down from 69% in 1970 (Figure 3.14). In 2015, the youth-dependency ratio ranged between 29% in Germany and 65% or more in Israel and Mexico. In most OECD countries, this ratio will stop declining, reaching an average level of 40% in 2060, except in Israel, Mexico and Turkey. Lower youth dependency means lower public spending in education and towards families. But overall, the declines are not large enough to offset higher spending towards the elderly.

In emerging economies, old-age dependency ratios are in general lower than in OECD countries, particularly in India, Indonesia and South Africa. By contrast youth dependency ratios are higher.

Figure 3.15 also presents the past, current and future shares of youths aged 15 to 29 – those in age to enter the labour market – in percentage of the total population. On average it declined from 23% in 1970 to 19% in 2015, with strongest declines in those “ageing” countries Japan, Finland, Italy and Spain. The average ratio is forecast to decline even further to 16% of the total population by 2060, with highest declines in countries which will particularly become older in the next decades like Chile, Mexico, Korea and Turkey.

Definition and measurement

Age-dependency ratios relate the number of individuals that are likely to be “dependent” on the support of others for their daily living – elderly or youths – to the number of those individuals who are capable of providing such support.

The old-age dependency ratio relates the number of individuals aged 65 and over in percentage of the population aged 20 to 64. The youth dependency relates the number of individuals aged less than 20 in percentage of the population aged 20 to 64. An additional ratio is also shown here: the share of youth aged 15-29 in percentage of the total population.

Estimates prior to 2015 and projections from 2015 are drawn from the United Nations, World Population Prospects – 2015 Revision. Projections used here are based on the most recent “medium fertility variant” population projections, which for each country corresponds to the median of several thousand projected trajectories of each demographic component.

Note that these forecasts do not take into account the 2015 refugee crisis in Europe from Syria. It may have an impact in the population structures of countries which integrated many migrants, like Germany, Turkey and some Eastern and Nordic European countries.

Further reading

OECD (2015), Pensions at a Glance 2015 – OECD and G20 Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/pension_glance-2015-en.

United Nations (2015), World Population Prospects – 2015 Revision, Washington DC, http://esa.un.org/unpd/wpp.

3.13. The old-age dependency ratio will double in the next 45 years on average in OECD countries

Number of people of retirement age (65+) per 100 people of working age (20-64), in 1970, 2015 and 2060

StatLink  http://dx.doi.org/10.1787/888933405231

3.14. The youth dependency ratio will not decline in the next 45 years in most OECD countries

Number of young people (under 20) per 100 people of working age (20-64), in 1970, 2015 and 2060

StatLink  http://dx.doi.org/10.1787/888933405240

3.15. Decline of the share of youth in total population in most countries

Number of young people (15-29) in total population, percentages, in 1970, 2015 and 2060

Source: Calculations from United Nations, World Populations Prospects – 2015 Revisions.

StatLink  http://dx.doi.org/10.1787/888933405255

Chapter 4.

Self-sufficiency indicators

Employment

Unemployment

Skills

Education spending

Labour market entry

Employment

Employment is a key factor in self-sufficiency. On average, two out of three working-age adults in the OECD area are employed (Figure 4.1.A). In Iceland and Switzerland, more than eight out of ten are employed, compared to about one out of two in Greece and Turkey.

Eight years after the start of the financial crisis, employment rates across the OECD are slowly rebounding. On average, the employment rate reached its pre-crisis level in 2015, and employment has been rising above its pre-crisis level in half of all countries (Figure 4.1). Much like the crisis itself, however, the recovery has been highly unequal across countries. Of the countries hit hardest by the recession, some have already reached, or even surpassed, their pre-crisis level of employment. Estonia and Latvia have seen impressive and steady employment growth since 2010, and also Iceland has returned to its 2007 level of employment. Spain, Ireland and Portugal are roughly at the half-way point to full recovery, while employment growth in Greece only started to pick up in the last two years.

A number of countries with comparatively low employment rates at the outset of the crisis experienced substantial increases over recent years: employment in Hungary grew by 10 percentage points, in Turkey by 7 and in Chile and the Czech Republic by 6. These increases were to a large extent driven by rising female employment rates.

Youth employment rates followed these broad trends, although employment losses for youth were generally more pronounced during the crisis than for the entire working-age population. As a result, OECD-wide the number of employed young people, aged 15 to 29 years old, fell by 8% between 2007 and 2015 (see Chapter 1, and Figure 1.1).

While employment has been increasing overall, many do not find the full-time job they are seeking. In Italy, Spain and Ireland, around every tenth employee is an involuntary part-time worker (Figure 4.2). For youth, these rates are twice as high. Especially young women are likely to work shorter hours than they want to: around a quarter of young women aged 15-29 in Spain and Italy, but also in Australia. At around 15%, these rates are lower for young men, yet still considerable.

On the OECD average, immigrants are almost as likely to be employed as their native peers: in 2014, the employment rate of the foreign-born population of working age was 66%, compared to 67% for their native born peers. This average, however, masks pronounced differences across countries: in traditional Northern and Western European destination countries such as the Netherlands, Sweden and Belgium, but also France and Germany, natives are 6-15 percentage points more likely to be employed than those born abroad (Figure 4.3). This is mainly driven by differences in female employment rates (OECD, 2015). In contrast, in Chile, Israel, Luxembourg and Hungary, female employment rates are actually higher among immigrants. Because seeking employment tends to be an important reason to migrate to these countries, as well as to the United States, also immigrant men tend to have higher employment rates than native men.

Looking at young migrants, aged 15-29, the gap in employment rates reverses – they are 1 percentage point more likely to work than their native peers. Young migrants are less likely to be in education than their native peers, and therefore enter the labour market at younger ages, and while they are more likely to be working than their native peers, they are also more likely to be neither in work nor in education (see Figure 1.16 in Chapter 1).

Definitions and measurement

A person is employed if working for pay, profit or family gain for at least one hour per week, even if temporarily absent from work because of illness, holidays or industrial disputes. The data from labour force surveys of OECD countries rely on this work definition during a survey reference week. The basic indicator for employment is the proportion of the population aged 15-64 who are employed. These employment rates are presented by gender and migrant status. Migrants are defined as those who were born outside their country of residence, regardless of their citizenship status, or how long they have lived in the country.

Involuntary part-time workers are part-timers (working less than 30 usual hours per week) because they could not find a full-time job.

National definitions broadly conform to this generic definition, but may vary depending on national circumstances. For more information www.oecd.org/employment/database.

Further reading

OECD (2016), OECD Employment Outlook 2016, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2016-en.

OECD and EU (2015), Indicators of Immigrant Integration 2015: Settling In, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234024-en.

Figure notes

Figure 4.1: Q1-2016 for Australia, Canada, Chile, Colombia, Israel, Japan, Korea, Mexico, the Netherlands, New Zealand and the United States; Q4-2015 otherwise. Q2-2007 for Switzerland and Q1-2008 for South Africa.

Figure 4.2: 2015, 25-64 and 16-24 for Australia, Germany, Israel, Japan, New Zealand, Slovenia; 2013 for Chile. No data for Korea, Mexico and Turkey.

Figure 4.3: No data on Japan and Korea. No data for youth for Australia, Germany, Mexico and New Zealand. OECD average excludes those six countries. Data for Chile is for 2013, for Israel for 2015. For Israel, youth data refers to 15-24 years old.

4.1. The labour market impact of the crisis and recovery has been highly uneven across countries

Employment rate, percentage of the working-age population (aged 15-64), Q4 2007-Q1 2016

Source: OECD calculations based on the OECD Short-term Labour Market Statistics (database), http://dx.doi.org/10.1787/data-00046-en.

StatLink  http://dx.doi.org/10.1787/888933405261

4.2. Involuntary part-time work is increasingly prevalent in some countries

Incidence of involuntary part-time employment, percentage of total employment, people of working age and youth, by gender, 2015 (or nearest year)

Source: OECD calculations based on national labour force surveys and OECD Employment Database.

StatLink  http://dx.doi.org/10.1787/888933405277

4.3. Migrants are still less likely to be employed in some countries

Difference in percentage points in employment rates between native and foreign-born, people of working age and youth, 2014 (or nearest year)

Source: OECD calculations from national labour force surveys.

StatLink  http://dx.doi.org/10.1787/888933405284

Unemployment

In addition to putting a strain on household and public finances, unemployment can have a demoralising effect on individuals and diminish their career prospects. This is especially true for young people at the beginning of their working lives.

The great recession led to record unemployment rates across the OECD. Even though the situation has improved since the peak of the crisis, still 8% of the active working-age population on average was unemployed in early 2016 across the OECD, and the average unemployment rate is projected to still be above pre-crisis levels by the end of 2017 (OECD, 2016) (Figure 4.4). Countries present a diverse picture. Iceland and Japan exhibit rates as low as 3%, while many countries including the United States, the United Kingdom and Germany, cluster around 5%. On the other hand, unemployment is still strikingly high in the southern European countries hit hardest by the crisis, such as Greece (24%), Spain (20%) and Portugal and Italy (12%).

Some countries have seen impressive falls in unemployment since the crisis peak, particularly Estonia and Latvia, where the unemployment rate halved. The fall has also been also substantial in Spain, Portugal and Hungary with decreases of around 5 percentage points, as in the United States.

The unemployment rate of youth (aged 15-24) is twice as high as adult unemployment (25 years+) in most countries (Figure 4.5). It is nearly 4 times as high in Italy, New Zealand, Sweden and the United Kingdom. Young people were more severely affected by the sweeping job losses during the great recession (Chapter 1), as they were more likely to hold temporary jobs that were easy to terminate, or entered the labour market when firms were not hiring. A lack of work experience also makes young people vulnerable during times of high unemployment. However, the fact that many 15-24 year-olds are still in full-time education and therefore not active on the labour market, also tends to inflate the rate of youth unemployment (Box 1.1 in Chapter 1). Greece’s youth unemployment rate of 50% does not mean that 50% of all young people aged 15-24 are out of work. Instead, half of all young people who are active on the labour market – 28% of all young Greeks of this age group – are unemployed. This corresponds to nearly 15% of all 15-24 year-olds, still a high number.

Unemployment is a transient phenomenon for most unemployed, but in some countries, it is persistent, especially for prime age and older workers. Across the OECD, on average around 40% of all unemployed aged 55 and over had been unemployed for at least a year or longer in 2014, whereas only 20% of all young people aged 15-24 had been unemployed for this long (Figure 4.6). Long-term unemployment was especially high in countries with high overall unemployment, such as Greece (56% of youth aged 15-24, and 85% of unemployed aged 55+) and Italy (56% of youth and 65% of those aged 55 and over).

Definitions and measurement

The unemployment rate is the ratio of people out of work and actively seeking it to the population of working age either in work or actively seeking it (aged 15 or over). The data are gathered through labour force surveys of member countries. According to the standardised ILO definition used in these surveys, the unemployed are those who did not work for at least one hour in the reference week of the survey, but who are currently available for work and who have taken specific steps to seek employment in the four weeks preceding the survey. Thus, for example, people who cannot work because of physical impairment, or who are not actively seeking a job because they have little hope of finding work are not considered as unemployed. These unemployment rates are presented by broad age group.

Long-term unemployment is defined here as lasting for one year or more.

For more information, see www.oecd.org/employment/database.

Further reading

OECD (2016), OECD Employment Outlook 2016, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2016-en.

Figure note

Figure 4.4: Q1-2016 for Australia, Canada, Chile, Colombia, Israel, Japan, Korea, Mexico, the Netherlands, New Zealand, the Russian Federation and the United States; Q4-2015 otherwise. Q2-2007 for Switzerland and Q1-2008 for South Africa.

Figure 4.5: Q4-2015 for Estonia, Greece, Hungary, Norway, Switzerland, Turkey and United Kingdom. Data for Iceland, Luxembourg and Norway are based on small sample sizes. No data for Chile, Latvia and Switzerland.

4.4. Unemployment is on the decline, albeit slowly in some countries

Unemployment rate, percentage of the labour force (aged 15+), in Q4 2997, peak since Q4 2007 and Q1 2016 (or Q4 2015)

Source: OECD calculations based on the OECD Short-term Labour Market Statistics (database), http://dx.doi.org/10.1787/data-00046-en.

StatLink  http://dx.doi.org/10.1787/888933405293

4.5. Youth unemployment is twice as high as adult unemployment in most OECD countries

Youth (15-24) and adult (25+) unemployment rates, Q1 2016

Source: OECD calculations based on the OECD Short-term Labour Market Statistics (database), http://dx.doi.org/10.1787/data-00046-en.

StatLink  http://dx.doi.org/10.1787/888933405302

4.6. Incidence of long-term unemployment increases with age

Incidence of long-term unemployment (12 months and over) as a percentage of total unemployment by age group, 2015

Source: OECD (2016, OECD Employment Outlook 2016, http://dx.doi.org/10.1787/empl_outlook-2016-en.

StatLink  http://dx.doi.org/10.1787/888933405311

Skills

Skills play a central role in ensuring people find and keep employment. They are particularly important for young people as general education levels have increased in most OECD countries over the last few decades. Young people who lack basic literacy and numeracy skills will find it particularly difficult to make the transition from school to the workplace and may be left behind as countries skills demands continue to increase. Chapter 1 displayed the large gaps in NEET rates between those with low and high literacy and numeracy skills. In today’s digital economy technological skills have also become much more important for a range of employment opportunities than was the case in the past. Skill levels are related more broadly to educational attainment. Those who leave school before completing upper secondary are twice as likely to have a low level of numeracy skills (OECD, 2015). Skill levels are not fully determined by educational attainment however, the quality of education systems is of importance in ensuring that students reach a minimum proficiency level. Skill levels can vary considerably among individuals with similar educational qualifications (OECD, 2013).

On average across the OECD about one person aged between 30 and 54 years in six has either low literacy, low numeracy or low problem solving skills. Skill levels differ substantially across OECD countries (Figures 4.7 and 4.8). In countries such as Japan and Finland only a small proportion of individuals have low literacy, numeracy and technological skills. Spain, Italy and France have the highest proportions of 30-to-54 year-olds with low literacy skills, and Spain, Italy and the United States have the highest proportion of this group with low numeracy skills. These countries, as well as the United Kingdom, also tend to show relatively high shares of low numeracy and literacy skills among youth, while Japan, Finland, as well as Korea have the lowest shares.

In recent decades, skill levels have improved in most OECD countries for younger generations in line with more general increases in educational attainment by young people compared to their older counterparts. As a result, there is a higher share of 30-54 year-olds with lows skills than among 16-29 year-olds. The generational divide is particularly striking for problem solving in technology-righ environments (Figure 4.9). Japan stands out across the OECD in having little change in literacy and numeracy levels between younger and older persons but this is due to the fact that a large proportion of those aged 30-54 already had high levels of performance, while Korea has made substantial improvements between generations. On the other hand, a few countries have experienced a deterioration in literacy and numeracy performance between the older and younger population. The United Kingdom and Norway have higher rates of youth with low literacy and numeracy levels compared to those aged 30-54 while the proportion with low numeracy skills in the United States is high and performance levels have stagnated between the older and younger groups.

Definition and measurement

The OECD Programme for the International Assessment of Adult Competencies (PIAAC) assesses skill levels of adults aged 16-65 in literacy, numeracy and problem solving in technology-rich environments. The latter, also known as “information-processing skills”, is defined as the ability to use digital technology, communication tools and networks to acquire and evaluate information, communicate with others and perform practical tasks. PIAAC is carried out in more than 40 countries and is designed to be comparable cross-culturally and cross-nationally.

Literacy and numeracy proficiency are split into five levels while problem-solving in technology rich environments is split into three. The graphs presented here show the proportion of youth with “low” skills. For literacy this is defined as those who can, at best, read relatively short texts to locate a single piece of information that is identical to the information given in the question or directive or to understand basic vocabulary. Those with low numeracy skills can, at best, perform one-step or simple mathematical processes involving counting, sorting, basic arithmetic operations, understanding simple percentages, and locating and identifying elements of simple or common graphical or spatial representations. Low achievers at problem-solving in technology rich environments can, at best, use widely available and familiar applications such as email or a web browser where little or no navigation is required to access information or solve a problem.

Further reading

OECD (2015), OECD Skills Outlook 2015: Youth, Skills and Employability, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234178-en.

OECD (2013) OECD Skills Outlook 2013: First Results from the Survey of Adult Skill, OECD Publishing, Paris, www.oecd-ilibrary.org/education/oecd-skills-outlook-2013_ 9789264204256-en.

Figure notes

Figure 4.3: Results present the share of 16-29 year-olds and of 30-54 year-olds failing to reach Proficiency Level 2 in literacy and numeracy and Proficiency Level 1 in problem solving in technology-rich environment.

4.7. A substantial proportion of youth in the OECD have poor literacy skills

Percentage of individuals with low literacy skills, by age group, in 2012

StatLink  http://dx.doi.org/10.1787/888933405321

4.8. An even higher proportion of youth have poor numeracy skills

Percentage of individuals with low numeracy skills, by age group, 2012

StatLink  http://dx.doi.org/10.1787/888933405330

4.9. When it comes to technology youth tend to perform better than their older counterparts

Percentage of individuals with low problem-solving skills in technology-rich environments, by age group, 2012

Source: OECD (2015), OECD Skills Outlook 2015: Youth, Skills and Employability, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234178-en.

StatLink  http://dx.doi.org/10.1787/888933405341

Education spending

On average, OECD countries spent USD 10 000 per child per year from primary through tertiary education in 2012 (Figure 4.10). Spending was highest in Luxembourg with just over USD 22 000 per child, followed by Switzerland, Norway and the United States. On the opposite end, spending was at around USD 3 500 in Mexico and Turkey. Spending was also relatively low (between USD 4 000 and 8 000) in several Eastern European countries.

All emerging economies for which data are available had education spending comparable to the low-spending OECD countries, except Indonesia at the lowest level of USD 1 400.

The crisis has halted the long-term trend of increasing spending in education. While public spending as a percentage of GDP for all levels of education increased by 6% between 2008 and 2010 on average across OECD countries, it fell by 3% between 2010 and 2012 (Figure 4.11). Public expenditures on educational institutions as a percentage of GDP decreased in three-quarters of those OECD countries for which data are available, most likely as a consequence of fiscal consolidation policies. Drops of more than 8% were seen in Australia, Estonia, Hungary, Norway, Portugal and Spain. In Australia, the drop related to a stimulus spending programme on infrastructure that stopped in 2012.

On average across the OECD countries, less investment is put into early education as compared to later years, with spending per child ranging from USD 7 900 at the early childhood level to USD 15 100 at the “bachelor, master or doctoral” tertiary level (Figure 4.12). These averages mask a broad range of expenditure per student by educational level across the OECD countries. The ratio of the top spending to the bottom spending country varies from 3 at the tertiary level to 9 at secondary level.

Investing in vocational education can be key to smooth school-to-work transitions, notably for vulnerable youth. Also on average, only USD 330 more per student is spent in vocational than in general programmes at upper secondary and post-secondary non-tertiary education. Exceptions to this pattern are Australia, Belgium, Chile, Hungary, Slovenia, Switzerland and the United Kingdom, where expenditure per student enrolled in a general programme is higher than expenditure per student in a vocational programme. On the other hand, some countries with large enrolments in dual-system apprenticeship programmes at the upper secondary level (e.g. Finland, Germany and the Netherlands) tend to have higher expenditure per student in vocational programmes than in general programmes by 20% to 30% more. The underestimation of the expenditure made by private enterprises on dual vocational programmes can partly explain some of the differences across countries.

Definition and measurement

Data on education spending is calculated using total annual spending from primary to tertiary education (including research and development activities). Figures are for public and private spending combined, and are reported in US dollars based on purchasing power parities for the respective years.

The trends in expenditure on educational institutions in percentage of GDP refer to the spending originating in, or generated by, the public sector.

Levels of education are based on the International Standard Classification of Education (ISCED 2011), which distinguishes six levels of education, classified here into four groups: early childhood (ISCED-0), primary (ISCED-1), lower secondary (ISCED-2), upper secondary (ISCED-3),post-secondary non-tertiary (ISCED-4), short cycle tertiary (ISCED-5), and Bachelor, Master or Doctoral or equivalent (ISCED 6-7-8).

Further reading

OECD (2015), Education at a Glance 2015: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2015-en.

Figure notes

Figure 4.10: Data refer to 2013 for Chile, Colombia and Indonesia. Data are not available for Canada, Denmark and Greece.

Figure 4.11: Data are not available for Austria, Denmark, Greece, Latvia, Luxembourg, New Zealand and United Kingdom.

4.10. Variation in per student education spending across the OECD

Annual expenditure per student from primary through tertiary education, in USD at current prices and current PPPs in 2012 (rounded at nearest 100)

StatLink  http://dx.doi.org/10.1787/888933405357

4.11. Decline in public education spending in percentage of GDP between 2010 and 2012

Index of change in public expenditure on educational institutions in percentage of GDP, for all levels of education

StatLink  http://dx.doi.org/10.1787/888933405368

4.12. Spending per child tends to increase with the level of education

Annual expenditure per student by level of education, in USD at current prices and current PPPs in 2012

Source: OECD (2015), Education at a Glance 2015: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2015-en.

StatLink  http://dx.doi.org/10.1787/888933405374

Labour market entry

Finding a stable job after leaving school or university can be a lengthy process involving bumps and false starts. Labour market entrants face a much greater risk of unemployment than young people who have been out of education for a while (Figure 4.13). Their unemployment rate is more than twice as high as that of all youth out of education (33 vs. 16% across OECD countries). Young people who recently left education have particular difficulties finding work in countries where youth unemployment remains high in the aftermath of the crisis – in Italy and Greece, over 60% of labour market entrants are looking for work.

Starting a career is especially hard during recessions, when fewer jobs are available and competition is intense. Young people who transitioned from education to the labour market in 2009 had substantially lower employment rates over the next four years than a comparable cohort who entered the labour market before the crisis in 2004 (left-hand panel of Figure 4.14). Even four years into their careers – in 2008 and 2013, respectively – the crisis cohort had substantially lower employment rates than their pre-crisis peers – the difference was 5 percentage points for young men and 10 percentage points for young women.

Experience from previous recessions shows, moreover, that even those who manage to secure employment have fewer opportunities for promotion and training and therefore tend to suffer from persistently depressed earnings (e.g. Oreopoulous et al., 2012). Also the 2009 cohort of labour market entrants suffered from a much more meagre earnings growth than their pre-crisis peers (right-hand panel of Figure 4.14): for women, earnings rose by a modest 3% per year over the first four years. For men, earnings plummeted by 11% in the year after labour market entry, and only barely recovered over the following three years. The pre-crisis cohort, by contrast, enjoyed solid earnings growth of around 6-7% per year for both women and men during the initial years after labour market entry.

Earnings have grown more slowly for the 2009 cohort of labour market entrants in nearly all countries studied (Figure 4.15). In the Netherlands, for example, a country with one of the highest youth employment rates (see Chapter 1), youth who left education in 2008 saw their real earnings shrink by 2% over five years, compared to a 29% earnings increase for those who graduated in 2003. The drop in earnings growth is particularly large again, however, in the countries hit hardest by the crisis: Spain (-56 percentage points), Estonia (-47 percentage points) and Ireland (-42 percentage points).

Definition and measurement

In Figures 4.13 to 4.15, labour market entrants are defined as 15-to-29-year-olds who are not enrolled in formal education in the reference year but who were in education the year before. Figure 4.13 gives the share of unemployed out of all active young people for labour market entrants and for all youth not in education.

Figures 4.14 and 4.15 show the shares of young people in (part-time or full-time) employment and the development of real monthly earnings for the 2004 (pre-crisis) cohort and the 2009 (crisis) cohort of labour market entrants for the first four years after labour market entry. Real earnings are expressed in relation to the earnings in the initial year after labour market entry (2004 or 2009, respectively). No earnings data are available for Estonia in 2008, Hungary in 2005 and 2006, Iceland in 2012, Mexico in 2008 and Spain in 2013. Therefore, the earnings development was interpolated for Hungary between 2004 and 2007 and for Iceland between 2011 and 2013, and extrapolated for Estonia and Mexico in 2008 and Spain in 2013 (end of the cohort tracking periods).

Further reading

Carcillo, S. et al. (2015), “NEET Youth in the Aftermath of the Crisis – Challenges and Policies”, OECD Social, Employment and Migration Working Papers, No. 164, OECD Publishing, Paris, http://dx.doi.org/10.1787/5js6363503f6-en.

Oreopoulos, P., T. von Wachter and A. Heisz (2012), “The Short- and Long-Term Career Effects of Graduating in a Recession”, American Economic Journal: Applied Economics, Vol. 4, No. 1, pp. 1-29, http://dx.doi.org/10.1257/app.4.1.1.

Quintini, G. and S. Martin (2014), “Same Same but Different: School-to-work Transitions in Emerging and Advanced Economies”, OECD Social, Employment and Migration Working Papers, No. 154, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jzbb2t1rcwc-en.

Figure notes

Figure 4.13: 2013 for Germany and Turkey. Data are missing for Australia, Canada, Chile, Israel, Japan, Korea and New Zealand.

Figures 4.14 and 4.15: OECD averages shown are for all countries with available data: Austria, Belgium, the Czech Republic, Estonia, France, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, Mexico, the Netherlands, Poland, Portugal, the Slovak Republic, Slovenia Spain and the United Kingdom.

4.13. Labour market entrants have much greater difficulties to find work than other young people

Unemployment rate in percent among labour market entrants and all youth not in education, 2014

Source: Calculations from EU-LFS and national labour force surveys for the Mexico, the United States and Turkey.

StatLink  http://dx.doi.org/10.1787/888933405381

4.14. Young people who entered the labour market during the crisis had lower employment and earnings growth

Employment rates (in percent) and relative earnings (initial earnings=100) over a four-year period for the 2004 and 2009 cohorts of labour market entrants, OECD average, by gender

Source: Calculations from EU-SILC and the Mexican National Occupation and Employment Survey (ENOE).

StatLink  http://dx.doi.org/10.1787/888933405393

4.15. The drop in earnings growth for labour market entrants was largest in economies hit hard by the crisis

Four year earnings increase for the 2004 and 2009 cohorts of labour market entrants, in percent of the starting wage, by country

Source: Calculations from EU-SILC and the Mexican National Occupation and Employment Survey (ENOE).

StatLink  http://dx.doi.org/10.1787/888933405407

Chapter 5.

Equity indicators

Income inequality

Poverty

Living on benefits

Social spending

Recipients of out-of-work benefits

Income inequality

Income inequality is an indicator of how material resources are distributed across society. Some people consider that high levels of income inequality are morally undesirable; others regard income inequality as harmful for social cohesion. Beyond its impact on social cohesion, growing inequality can also be harmful for long-term growth and harms opportunities. Often the policy concern is focussed more on the direction of change of inequality, rather than its level.

Income inequality varied considerably across the OECD countries in 2014 (or nearest year available) (Figure 5.1). The Gini coefficient ranges from around 0.25 in Denmark, Iceland, Norway and Slovenia to almost twice that value in Chile and Mexico. The Nordic and central European countries have the lowest inequality levels of disposable income while inequality is high in Chile, Israel, Mexico, Turkey and the United States. Alternative indicators of income inequality suggest similar rankings. The gap between the average income of the richest and the poorest 10% of the population was 9.4 to 1 on average across OECD countries in 2014, ranging from 5 to 1 in Denmark to more than four times larger (21 to 1) in Chile and Mexico. Keeping measurement-related differences in mind, emerging countries have higher levels of income inequality than most OECD countries, particularly in Brazil, Colombia and South Africa.

The distribution of income from work and capital (market income, pre-taxes and transfers) widened during the crisis. Between 2007 and 2014, market income inequality rose by 1.6% on average (bars in Figure 5.2). The increase was particularly large in Estonia, Greece, Ireland and Spain. On the other hand, market income inequality fell in Israel and Turkey, and to a smaller extent in Chile and Poland. The distribution of income that households “take home” (disposable income, post-taxes and transfers) remained unchanged on average, due to the effect of cash public transfers and direct taxes. Between 2007 and 2014, the Gini coefficient for disposable income remained broadly stable in most OECD countries (markers in Figure 5.2). It fell the most in Iceland, Latvia and Portugal, and increased the most in Estonia, Slovak Republic and Sweden.

Household wealth is much more unequally distributed than income. On average, households in the top 10% of the wealth distribution own half of all total household wealth, and as much as 76% in the United States. In comparison, the richest 10% of income earners get on average around a quarter of all cash income, ranging from 28% in the United States to 20% in Norway (Figure 5.3). But countries with lower income inequality levels are not necessarily those with low wealth concentration, as witnessed by the examples of Austria, Germany and the Netherlands.

Definition and measurement

The main indicator of income distribution used is the Gini coefficient. Values of the Gini coefficient range from 0 in the case of “perfect equality” (each person receives the same income) and 1 in the case of “perfect inequality” (all income goes to the person with the highest income). Measures of income inequality can be based on people’s household disposable income – post-taxes and social transfers – or on people’s household market income – pre-taxes and transfers (for more details, see “Definition and measurement” in the indicator on “Household income” in Chapter 3). Gini coefficients are based on equivalised incomes for OECD and EU countries, Colombia, Lithuania and the Russian Federation – i.e. adjusted for differences in the needs of households of different sizes with an equivalence scale that divides household income by the square root of household size, on per capita incomes for all key partners (dark bar) except India and Indonesia for which per capita consumption was used (light bar) typically showing lower inequality measures.

An alternative indicator is the S90/S10 income decile share, corresponding to the gap between the average incomes of the richest and the poorest 10% of the population, also based on equivalised disposable income. Income data are from the OECD Income Distribution Database available at www.oecd.org/social/income-distribution-database.htm.

Wealth data refers to net private household wealth, that is the value of all assets owned by a household less the value of all its liabilities at a particular point in time, around 2012 here. Data are from the OECD Wealth Distribution database at http://stats.oecd.org/Index.aspx?DataSetCode=WEALTH.

Further reading

OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, http://oe.cd/init2015.

Figure notes

Figures 5.1 and 5.2: Data refer to 2014 for Australia, Finland, Hungary, Israel, Korea, Mexico, the Netherlands, the United States and China; to 2012 for Japan and New Zealand, Argentina, Colombia and South Africa; 2011 for Brazil; 2010 for Indonesia and the Russian Federation; and to 2013 for all other countries. Data shown for 2007 refer to 2008 for Australia, France, Germany, Israel, Mexico, New Zealand, Norway, Sweden and the United States; to 2006 for Japan; and to 2009 for Chile. No change available for Switzerland.

5.1. Large differences in levels of income inequality

Gini coefficient of household disposable income and gap between richest and poorest 10%, in 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405418

5.2. Market income inequality rose between 2007 and 2014

Percentage point changes in the Gini coefficient of household market and disposable incomes between 2007 and 2014

StatLink  http://dx.doi.org/10.1787/888933405420

5.3. Wealth is more concentrated at the top than income

Share of top 10% of household disposable income and top 10% of household net wealth, 2012 (or nearest year)

Source: OECD Income Distribution Database (http://oe.cd/odd) and OECD Wealth Distribution Database.

StatLink  http://dx.doi.org/10.1787/888933405431

Poverty

Income Poverty rates measure the share of people at the bottom end of the income distribution. Often a society’s equity concerns are greater for the relatively disadvantaged. Thus poverty measures often receive more attention than income inequality measures, with greater concerns for certain groups like older people and children, who have no or limited options for working their way out of poverty.

The average OECD relative poverty rate in 2014 was 11% for the OECD (Figure 5.4). Poverty rates were highest at around 18% in Israel and the United States, while poverty in Iceland and Denmark affected only about one in 20 people. Mediterranean countries and Chile, Japan, Korea and the United States have relatively high poverty rates.

The crisis had a limited impact on relative income poverty (i.e. the share of people living with less than half the median disposable income in their country annually). Between 2007 and 2014, poverty increased by more than 2 percentage point only in Estonia and Hungary (Bars in Figure 5.5). Over the same period, it fell in Australia, Iceland, Latvia and the United Kingdom, while changes were below 2 percentage point in the other OECD countries.

By using an indicator which measures poverty against a benchmark “anchored” to half the median real incomes observed in 2005 (i.e. keeping constant the value of the 2005 poverty line), recent increases in income poverty are much higher than suggested by “relative” income poverty. This is particularly the case in Greece, Ireland, Italy and Spain (“symbols” in Figure 5.5). While relative poverty did not increase much or even fell in these countries, “anchored” poverty increased by 4 percentage points or more between 2007 and 2014, reflecting disposable income losses of poorer households in those countries. Only in Chile and Korea “anchored” poverty fell.

There has been a gradual shift in the age profile of poverty over the last decades, with young people replacing the elderly as the group most at risk of poverty (OECD, 2015). With an average poverty rate of almost 14% in 2014, youth have a higher poverty risk than any other group. (Figure 5.6). Poverty rates amongst youth were particularly high in Denmark, Netherlands and Norway, countries where youth leave parent’s home early and become economically independent (Figure 1.25). But rates were also high in Greece and to a lesser extent in Spain where youth unemployment rates ballooned during the crisis.

Rates for the elderly were below those observed for youth in two-thirds of countries and even below the rates for the 26-65 in half of the countries. Very high poverty rates among elderly are observed in a few countries, which is often related to their pension systems. For instance, poverty rates were highest in Korea, where the recent pension system has not fully matured, as well as in Australia and Switzerland partly due to the fact that many pensioners have taken their accumulated pensions as lump sums (which are not counted as current income) rather than annuitising them to provide income streams.

Definition and measurement

As with income inequality, the starting point for poverty measurement is the concept of equivalised household disposable income (see “Definition and measurement” of the “Household income” or “Income inequality” indicators).

The poverty rate is a headcount of how many people fall below the poverty line. People are classified as poor when their equivalised disposable household income is less than 50% of the median prevailing in each country. The use of a relative income threshold means that richer countries have the higher poverty thresholds. Higher poverty thresholds in richer countries capture the notion that avoiding poverty means an ability to access to the goods and services that are regarded as customary or the norm in any given county. Poverty rates by age group are computed based on the median income for the entire population.

Changes in relative poverty referring to the current median income can be difficult to interpret during recessions. In a situation where the incomes of all households fall, but they fall by less at the bottom than at the middle, relative poverty will decline. Therefore, more “absolute” poverty indices, linked to past living standards, are needed to complement the picture provided by relative income poverty. Therefore changes in poverty are also presented in Figure 5.5 using an indicator which measures poverty against a benchmark “anchored” to half the median real incomes observed in 2005.

Data are from the OECD Income Distribution Database available at www.oecd.org/social/income-distribution database.htm.

Further reading

OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, http://oe.cd/init2015.

Figure notes

Data refer to 2014 for Australia, Finland, Hungary, Israel, Korea, Mexico, the Netherlands and the United States; to 2012 for Japan and New Zealand; and to 2013 for all other countries.

Figure 5.5: 2007 refer to 2008 for Australia, France, Germany, Israel, Mexico, New Zealand, Norway, Sweden and the United States; to 2006 for Japan; and to 2009 for Chile. No change available for Switzerland. OECD average is based on 25 OECD countries for which data are available for both poverty definitions. Poverty rates are “anchored” in 2006 for Chile, Japan, Korea and Turkey; and 2007 for Austria and Spain.

5.4. Large differences in levels of relative poverty

Percentage of persons living with less than 50% of median equivalised disposable income, in 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405446

5.5. The evolution of poverty differs if the threshold is “anchored” at the time of the crisis

Percentage point changes in relative and “anchored” poverty rates between 2007 and 2014

StatLink  http://dx.doi.org/10.1787/888933405454

5.6. In 2014, poverty was highest among youth and children and lowest among adults and elderly

Percentage of persons living with less than 50% of median equivalised income, by age group, in 2014 (or nearest year)

Source: Provisional data from OECD Income Distribution Database (http://oe.cd/idd).

StatLink  http://dx.doi.org/10.1787/888933405464

Living on benefits

Most OECD countries operate transfer programmes that aim at preventing extreme hardship and use a low-income criterion as the central entitlement condition. These guaranteed minimum-income benefits (GMI) provide financial support for low-income families to ensure an acceptable standard of living. As such, they play a crucial role as last-resort safety nets, especially during prolonged economic downturns when long-term unemployment rises and increasing numbers of people exhaust their entitlements for unemployment benefits.

GMI benefits are particularly important for youth who live on their own. When out of work, many youth are not eligible for unemployment insurance schemes because they do not have enough contribution records. In most countries young individuals and couples are entitled to the same benefits than adults, but some countries restrict access to GMI programmes to youth (France, Luxembourg) while others provide lower amounts for single youth (Netherlands, Denmark, the United Kingdom, Switzerland, Australia, New Zealand and Israel).

In a large majority of OECD countries, incomes for the long-term unemployed are much lower than for the recently unemployed (Figure 5.7). Making GMI benefits more accessible is key to maintaining a degree of income security for the long-term unemployed. In addition, high numbers of people who have neither a job nor an unemployment benefit means that the generosity of GMI benefits is likely to receive more public attention.

Benefits of last resort are sometimes significantly lower than commonly used poverty thresholds (Figure 5.8). Poverty avoidance or alleviation are primary objectives of GMI programmes. When comparing benefit generosity across countries, a useful starting point is to look at benefit levels relative to commonly used poverty thresholds.

The gap between benefit levels and poverty thresholds is very large in some countries (Figure 5.8). In a few countries there is no generally applicable GMI benefit (Greece, Italy and Turkey). For GMI couples living in rented accommodation, housing-related benefits like rent allowances can provide significant further income assistance, bringing overall incomes close to or somewhat above the poverty line (Iceland, the Netherlands, Denmark and Ireland). Family incomes in these cases depend strongly on the type of housing and on family arrangements. In all countries, income from sources other than public transfers is needed to avoid substantial poverty risks.

Definition and measurement

One way of looking at how countries’ social protection systems perform is to show how the level of net minimum cash income benefits (including housing assistance) compares to relative poverty thresholds of 50% or 60% of median household incomes. These income levels account for all cash benefit entitlements of a family with a working-age head, with no other income sources and no entitlements to primary benefits such as unemployment insurance. They are net of any income taxes and social contributions. Median disposable incomes (before housing costs) come from the OECD Income Distribution Database (www.oecd.org/social/income-distribution-database.htm). They are for a year around 2014 expressed in 2014 prices and are adjusted for family size using the “square root of household size” equivalence scale. Similarly, net minimum cash benefits are converted to 2014 prices.

The net replacement rate (NRR) measures the fraction of net income in work that is maintained when unemployed. It is defined as the ratio of net income while out of work divided by net income while in work. The NRR presented here corresponds to a 40 years-old single person without children who earns 100% of the average wage. Initial phase of unemployment refers to the first month of benefit following any waiting period, and long-term unemployment refers to the 60th month of benefit receipt.

Family incomes are simulated using the OECD Tax-Benefit Model (methodology available in Benefits and Wages 2007 and on-line: www.oecd.org/els/social/workincentives). The amounts calculated for means-tested benefits should be considered upper-bound estimates. While housing benefits frequently provide the largest part of benefit income, they are computed for rental expenses equal to 20% of average worker earnings or the applicable ceiling of “allowable” rental expenses, whichever is lower. This may well exceed actual housing costs, particularly for low-income households. No data are available for Mexico.

Further reading

Immervoll, H., S.P. Jenkins and S. Königs (2015), “Are Recipients of Social Assistance ‘Benefit Dependent’? Concepts, Measurement and Results for Selected Countries”, OECD Social, Employment and Migration Working Papers, No. 162, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jxrcmgpc6mn-en.

OECD (2015), “Minimum Wages After the Crisis: Making Them Pay”, Directorate for Employment, Labour and Social Affairs, OECD, Paris, May 2015, www.oecd.org/social/Focus-on-Minimum-Wages-after-the-crisis-2015.pdf

Figure notes

Figure 5.7: No data for Mexico.

Figure 5.8: No data for Mexico; the standard indicator corresponds to a 40 years-old individual, youth is a 20 years-old individual; countries are ranked from left to right by increasing level of cash minimum-income benefit including housing benefit.

5.7. In most countries, benefit incomes decline significantly for people with long unemployment spells

Net income while out of work in percentage of net income in work (NRR), 40 years-old single, 2014

StatLink  http://dx.doi.org/10.1787/888933405479

5.8. Minimum-income benefits alone cannot typically prevent income poverty, notably for youth

Net income level provided by cash minimum-income benefit (MIB), with and without housing benefit (HB), in percentage of median household income, 2014

Source: OECD Tax-Benefit Models, www.oecd.org/els/soc/benefits-and-wages.htm.

StatLink  http://dx.doi.org/10.1787/888933405488

Social spending

In 2016, public social spending average an estimated 21% of GDP across the 35 OECD countries (Figure 5.9). Public social spending-to-GDP ratios are highest in France, at 32% of GDP, followed by Finland, at over 30% of GDP. Belgium, Italy, Denmark, Austria, Sweden, and Greece devote more than a quarter of their GDP to public social spending. At the other end of the spectrum are non-European countries such as Latvia, Turkey, Korea, Chile and Mexico which spend less than 15% of GDP on social support. Social spending in the emerging economies in the early 2010s was lower than the OECD average, ranging from around 2% of GDP in India to about 17% in Brazil.

While in most OECD countries social spending has not fallen much in recent years, in some countries there has been a significant decline since spending peaked in 2009-10. Spending-to-GDP ratios declined by 3 percentage points in Hungary and Luxembourg and by 4 and 6 percentage points of GDP in Latvia and Ireland respectively.

When comparing current social spending levels with pre-crisis levels in 2007, public social-spending-to-GDP ratios are about more than 5 percentage points higher in 2016 in Finland, Greece, Norway, Estonia, Japan (2013).

On average in the OECD, pensions and health services account for two-third of the total expenditures. In a majority of OECD countries, pensions are the largest expenditure area (Figure 5.10). In Anglophone countries and most other countries outside Europe, health makes the bulk of public social expenditure. In a few countries, such as Denmark and Ireland, the largest share is devoted to income support of the working-age population.

Accounting for the impact of taxation and private social benefits (Figure 5.10) leads to some convergence of spending-to-GDP ratios across countries. Net total social spending is 21-28% of GDP in about half of countries. It is even higher for the United States at 29% of GDP, where the amount of private social spending and tax incentives is much larger than in other countries. It remains highest in France at 31% of GDP.

Investing on early ages is important for future development and capacities of young people. Public spending on family benefits and education varies according to the age of the child (Figure 5.11). Social expenditure during early childhood is focused mainly on cash benefits/tax breaks and childcare, while spending on older children is dominated by public investment in education. On average public spending on education is dominant when children are aged 12-17. Most children are in secondary education at this stage of childhood, and consequently education spending forms a large part of total spending.

Definition and measurement

Social expenditure is classified as public when general government controls the financial flows. Sickness benefits financed by compulsory contributions to social insurance funds are considered “public”, whereas sickness benefits paid directly by employers to their employees are classified as “private”. The spending shown in Figure 5.9 is recorded before deduction of direct and indirect tax payments levied on these benefits and before addition of tax expenditures provided for social purposes. Data after considering the impact of private social spending as well as the tax system (Total Net social) are presented in Figure 5.10. Spending by lower tiers of government may be underestimated in some federal countries.

Public social spending totals reflect detailed social expenditure data for 1980-2013/14. Consistent with these historical series, public social expenditure totals were calculated for 2014, 2015 and estimated for 2016.

The OECD country’s age-spending profile maps public social and education expenditure by year of age for the first 28 years of life and the pre-natal period of nine months. Expenditure is allocated by benefit rules, population data by age, and reported receipt by age, where this data is available. Detailed childhood age-spending profiles for each individual age are available online via the OECD Family Database, www.oecd.org/social/family/database.htm.

Further reading

Adema, W., P. Fron and M. Ladaique (2011), “Is the European Welfare State Really More Expensive? Indicators on Social Spending, 1980-2012 and a Manual to the OECD Social Expenditure Database (SOCX)”, OECD Social, Employment and Migration Working Papers, No. 124, OECD Publishing, Paris, http://dx.doi.org/10.1787/5kg2d2d4pbf0-en.

OECD (2015), Integrating Social Services for Vulnerable Groups, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264233775-en.

OECD (2011), Doing Better for Families, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264098732-en.

OECD Family Database, www.oecd.org/social/family/database.htm.

Figure notes

Figure 5.9: Data for Mexico refer to 2012, 2013 for Japan, 2014 for Turkey and 2015 for Canada, Chile and New Zealand.

Figure 5.10: Countries are ranked by decreasing order of public social expenditure as a percentage of GDP. Spending on Active Labour Market Programs (ALMPs) cannot be split into cash and service spending. Income support to the working-age population refers to spending on the following SOCX categories: Incapacity benefits, Family cash benefits, Unemployment and Other social policy areas. Total net social expenditure data are not available for Latvia.

5.9. Public social spending is worth 22% of GDP on average across the OECD

Public social expenditure as a percent of GDP, 2007, peak level after 2007, and 2016

StatLink  http://dx.doi.org/10.1787/888933405490

5.10. Most spending goes to pensions and health

Public social spending by broad policy area and total net social spending, 2013/14, in percentage of GDP

StatLink  http://dx.doi.org/10.1787/888933405505

5.11. Public spending on family benefits and education varies according to the age of the child

Average social expenditure (including education) by intervention in OECD countries, in PPP per child and age in early 2010s (USD), OECD average

Source: OECD Social Expenditure Database, www.oecd.org/social/expenditure.htm.

StatLink  http://dx.doi.org/10.1787/888933405515

Recipients of out-of-work benefits

Cash transfers for working-age people provide a major income safety net in periods of high unemployment. In most countries two different layers of support can be distinguished: a primary out-of-work benefit (generally unemployment insurance benefits); and a secondary benefit (unemployment assistance or minimum-income benefits such as social assistance) for those who are not or no longer entitled to insurance benefits.

In 2012, the shares of working-age individuals receiving primary out-of-work benefits were highest in Belgium, France, Finland, Spain, Portugal and Australia, with rates above 4% (Figure 5.12). At the other end of the spectrum, in Turkey, the United Kingdom, Japan, Hungary and Korea, less than 1% received unemployment insurance payments. On average, 2.3% of the working-age population received primary out-of-work benefits in the OECD. There is no nation-wide unemployment insurance programme in Mexico.

The large variation in the numbers in part reflects labour market conditions and partly the design of social benefit systems. Low participation in unemployment insurance programmes reduces coverage among the unemployed. An example is Chile, where unemployment insurance is organised as an individual saving scheme and take-up is voluntary. In Sweden, where unemployment insurance membership is voluntary, recipient numbers dropped despite rising unemployment. Between 2007 and 2012, benefit receipt increased most in Spain, Portugal, Finland, the United States and Slovenia (Figure 5.12), all countries where unemployment soared during the economic crisis.

Receipt of secondary out-of-work benefits also increased between 2007 and 2012 (Figure 5.13, Panel B). Rising long-term unemployment and increasing joblessness among people without access to insurance benefits led to a substantial rise in Lithuania, Ireland, Latvia, the United States (Supplemental Nutrition Assistance Program, SNAP) and Spain. Receipt rates dropped somewhat in the Czech Republic (due to stricter conditions in access to minimum income benefit) and in Germany (due to more favourable labour-market developments).

By 2012, on average about 4% of the working-age population received secondary out-of-work benefits. Receipt rates were highest in Ireland, the United States, Lithuania and Finland (Figure 5.13, Panel A) and lowest in Chile, Belgium, Korea and Japan. The composition of these safety nets differs across countries. Social assistance dominates in Mexico (Prospera) and the United States (SNAP and Temporary Assistance for Needy Families, TANF). Unemployment assistance is important in Ireland, Germany, Spain and the United Kingdom. Australia, Iceland and New Zealand also provide targeted income support to a large number of lone parents. In Chile, Turkey and Italy, there is no nation-wide minimum income benefit.

Out of work benefit are often less accessible to young people. Only around 30% of all unemployed young people receive unemployment benefits, while over 40% of all jobseekers aged 30 and over are covered (see Chapter 1 and Figure 1.23). Consequently, social safety nets are less effective in fighting poverty among young people than among other adults.

Definition and measurement

Primary out-of-work benefits are those that are typically received during an initial phase of unemployment (unemployment insurance in most countries). Some countries that have no unemployment insurance instead operate means-tested unemployment assistance as the primary benefit. Eligibility for primary benefits typically requires previous employment or insurance contributions. Exceptions are assistance benefits in Australia and New Zealand, which are not conditional on earlier employment. All primary out-of-work benefits are subject to active job search and related requirements, although implementation and enforcement differs across countries and programmes.

Where unemployment insurance is the primary benefit, unemployment assistance or social assistance provide secondary income support. In addition, many countries operate targeted benefits for specific groups, such as lone parents.

In-work benefits (like partial unemployment insurance or social assistance for individuals who are working) are not included.

Statistics are based on the OECD Social Benefit Recipients Database (SOCR), which covers all main income replacement benefits in 40 EU and OECD countries. Depending on the data made available by countries, SOCR includes caseloads, flows and average amounts of benefits, and currently covers four years (2007-12).

The charts show number of recipients as shares of working-age individuals. Benefits that are awarded at family level (e.g. social assistance) are only counted once per family.

Further reading

Immervoll, H., S.P. Jenkins and S. Königs (2015), “Are Recipients of Social Assistance ‘Benefit Dependent’?: Concepts, Measurement and Results for Selected Countries”, OECD Social, Employment and Migration Working Papers, No. 162, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jxrcmgpc6mn-en.

Figure notes

Figures 5.12 and 5.13: Data are missing or incomplete for Greece, Iceland and Poland. For comparability reasons, Canada and Switzerland were also excluded. Countries are ranked from left to right in increasing order of all benefits covered.

5.12. Increase in recipients of primary out-of-work benefits in most OECD countries since 2007

Number of recipients of primary cash transfers as a percentage of the working-age population, 2012 and percentage point change between 2007 and 2012

StatLink  http://dx.doi.org/10.1787/888933405527

5.13. Increase in recipients of secondary out-of-work benefits (safety net) in most OECD countries since 2007

Source: OECD Benefit Recipients Database (SOCR), www.oecd.org/social/recipients.htm.

StatLink  http://dx.doi.org/10.1787/888933405535

Chapter 6.

Health indicators

Life expectancy

Perceived health status

Suicide

Health spending

Tobacco and alcohol consumption

Life expectancy

Life expectancy at birth continues to increase steadily in OECD countries, going up on average by three to four months every year. These gains in longevity can be attributed to a number of factors, including improved lifestyle, better working conditions and education, as well as progress in health care.

In 2014, life expectancy at birth on average across OECD countries reached 80.6 years, an increase of more than ten years since 1970 (Figure 6.1). Japan, Spain and Switzerland lead a large group of almost three-quarters of OECD countries in which life expectancy at birth now exceeds 80 years. A second group, including Chile, the United States, and a number of Central and Eastern European countries, have a life expectancy between 75 and 80 years.

Among OECD countries, life expectancy was lowest in Latvia and Mexico in 2014, still slightly below 75 years. Since 2000, life expectancy in Mexico has increased more slowly than in other OECD countries, with a gain of just over a year compared with an average gain of more than three years across OECD countries. In the United States, the gains in life expectancy since 1970 have also been much more modest than in most other OECD countries. Possible explanations include: 1) the highly fragmented nature of the US health system, with relatively few resources devoted to public health and primary care, and a large share of the population uninsured; 2) health-related behaviours, including higher calorie consumption per capita and obesity rates, higher consumption of prescription and illegal drugs, higher deaths from road traffic accidents and higher homicide rates; and 3) adverse socioeconomic conditions affecting a large segment of the US population, with higher rates of poverty and income inequality than in most other OECD countries, which can have adverse effects on health-related behaviours and access to treatment.

Emerging countries such as Brazil, China, Indonesia and India have also achieved large gains in longevity over the past decades, with life expectancy in these countries converging rapidly towards the OECD average. There has been much less progress in South Africa (due mainly to the epidemic of HIV/AIDS) and the Russian Federation (due mainly to the impact of the economic transition in the 1990s and the rise in risky behaviours among men).

Life expectancy varies by gender, and also by socio-economic status as measured for instance by education level (Figure 6.2). Higher education levels not only provide the means to improve the socioeconomic conditions in which people live and work, but may also promote the adoption of more healthy lifestyles and facilitate access to appropriate health care. On average among 15 OECD countries for which data are available, people with the highest level of education can expect to live six years more than people with the lowest level of education at age 30 (53 years versus 47 years). These differences in life expectancy by education level are particularly pronounced for men, with a gap of almost eight years on average. They are particularly large in Central and Eastern European countries (Czech Republic, Estonia, Hungary and Poland), where the life expectancy gap between higher and lower educated men reaches more than ten years. Differences are less pronounced in Italy, Norway, Sweden, the Netherlands and Portugal.

Higher health spending per capita is generally associated with higher life expectancy at birth, although this relationship tends to be less pronounced in countries with the highest spending per capita (Figure 6.3). Japan, Italy and Spain stand out as having relatively high life expectancies while the Russian Federation and the United States have relatively low life expectancies, given their levels of health spending.

Definition and measurement

Life expectancy at birth measures how long, on average, people would live based on a given set of age-specific death rates. However, the actual age-specific death rates of any particular birth cohort cannot be known in advance. If age-specific death rates are falling over time (as has been the case over the past decades), actual life spans will be higher than life expectancy calculated with current death rates. The methodology used to calculate life expectancy can vary slightly between countries. This can change a country’s estimates by a fraction of a year.

Life expectancy at birth for the total population is calculated for all OECD countries using the unweighted average of life expectancy of men and women. To calculate life expectancies by education level, detailed data on deaths by sex, age and education level are needed. However, not all countries have information on education as part of their deaths data.

For Health spending per capita, see indicator “Health spending”.

Further reading

OECD (2015), Health at a Glance 2015 – OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2015-en.

Figure notes

Figures 6.1 and 6.3: 2014 data refers to 2011 for Canada and 2013 for Brazil, China, Costa Rica, India, Indonesia, Russian Federation and South Africa; 1970 refers to 1971 for Canada, Israel, Italy, Luxembourg; no data for 1970 for Colombia.

Figure 6.2: 2013 data refers to 2012 for Israel, Mexico and the Netherlands.

6.1. Life expectancy has increased remarkably in OECD countries

Life expectancy at birth, in years, 1970 and 2014 (or nearest years)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en.

StatLink  http://dx.doi.org/10.1787/888933405548

6.2. People with highest level of education can expect to live six years more than people with lowest level of education

Gap in life expectancy at age 30 between the tertiary and below upper secondary level of education, by sex, 2013 (or nearest year)

Source: Eurostat database complemented with national data for Israel, Mexico and the Netherlands.

StatLink  http://dx.doi.org/10.1787/888933405552

6.3. Higher health spending per capita is generally associated with higher life expectancy at birth, 2014 (or nearest year)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en.

StatLink  http://dx.doi.org/10.1787/888933405561

Perceived health status

In almost all OECD countries, a majority of the adult population reports their health as good or better than good (Figure 6.4). Australia, Canada, New Zealand and the United States are the four leading countries, with almost nine out of ten people reporting to be in good health. However, the response categories offered to survey respondents in these three countries are different from those used in European countries and Asian OECD countries: they offer one more option on the positive side of the scale (“excellent”) and one less option on the negative side (very poor). This introduces an upward bias in the results. On the other hand, less than half of the adults in Japan, Korea and Portugal rate their health as good or very good. The proportion is also relatively low in Chile, Estonia, Hungary, Latvia and Poland, where less than 60% of adults consider themselves to be in good health. Such differences in self-assessed health status could in part stem from cultural biases.

In all OECD countries except in Australia and New Zealand, men are more likely than women to report being in good health. The gender gap is especially large in Chile, Portugal, the Slovak Republic and Turkey.

There are also large disparities in self-reported health across different socio-economic groups, as measured for instance by income or education level. Figure 6.5 (Panel A) shows that, in all countries, people with a lower level of income tend to report poorer health than people with a higher income, although the gap varies. On average across OECD countries, nearly 80% of people in the highest income quintile reports being in good health, compared with just over 60% for people in the lowest income group. These disparities may be explained by differences in living and working conditions, as well as differences in health-related lifestyles (e.g. smoking, harmful alcohol drinking, physical inactivity and obesity problems). In addition, people in low-income households may have more limited access to certain health services, for financial or non-financial reasons. It is also possible that the causal link goes the other way around, with poor health status in the first place leading to lower employment and lower income.

Self-reported health across different socio-economic groups is also available for the youth population aged 16-29 for European countries (Figure 6.5, Panel B). Unsurprisingly youth report higher levels of health status. Again youth with a lower level of income tend to report poorer health than youth with a higher income but the gap tend to be smaller than the one measure than for the population as a whole. However, the socio-economic gap is particularly important with 12 to 14 percentage points in Estonia, Germany and Portugal.

Definition and measurement

Perceived health status reflects people’s overall perception of their health, including both physical and psychological dimensions. Typically ascertained through health interview surveys, respondents are asked a question such as: “How is your health in general? Is it very good, good, fair, poor, very poor?”. OECD Health Statistics provides figures related to the proportion of people rating their health to be “good/very good” combined.

Caution is required in making cross-country comparisons of perceived health status, for at least two reasons. First, people’s assessment of their health is subjective and can be affected by factors such as cultural background and national traits. Second, there are variations in the question and answer categories used to measure perceived health across surveys and countries. In particular, the response scale used in Australia, Canada, New Zealand and the United States is asymmetric (skewed on the positive side), including the following response categories: “excellent, very good, good, fair, poor”. The data in OECD Health Statistics refer to respondents answering one of the three positive responses (“excellent, very good or good”). By contrast, in most other OECD countries, the response scale is symmetric, with response categories being: “very good, good, fair, poor, very poor”. The data reported from these countries refer only to the first two categories (“very good, good”). Such a difference in response categories biases upward the results from those countries that are using an asymmetric scale.

Self-reported health by income level is reported for the first quintile (lowest 20% of income group) and the fifth quintile (highest 20%). Depending on the surveys, the income may relate either to the individual or the household (in which case the income is equivalised to take into account the number of people in the household).

Further reading

OECD (2015), Health at a Glance 2015 – OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2015-en.

Figure notes

Figures 6.4 and 6.5: Results for Australia, Canada, Chile, Israel, New Zealand and the United States are not directly comparable with those for other countries, due to methodological differences in the survey questionnaire resulting in an upward bias. Data refer to 2009 for Chile and 2013 for Japan instead of 2014. No data for Mexico nor for youth aged 16-29 for non-European countries. Countries are ranked from left to right in decreasing order as in Figure 6.4.

6.4. A majority of the adult population reports their health as good

Percentage of adults aged 15 and over reporting to be in good or better than good health, by gender, 2014 (or nearest year)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en.

StatLink  http://dx.doi.org/10.1787/888933405577

6.5. People in the highest income quintile report being in better health than people in the lowest income group

Percentage of people reporting to be in good or better than good health, by income level, in percentage, 2014 (or nearest year)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en and EU-SILC for European countries.

StatLink  http://dx.doi.org/10.1787/888933405588

Suicide

Suicide is a significant cause of death in many OECD countries, and accounted for over 150 000 deaths in 2014, or 12 suicides per 100 000 people. There are a complex set of reasons why some people choose to attempt or commit suicide, with multiple risk factors that can predispose a person to attempt to take their own life.

In 2014 suicide rates were lowest in Turkey, Greece, Mexico, and but also in South Africa and Colombia, at five or fewer deaths per 100 000 population (Figure 6.6). In Hungary, Slovenia, Japan, Korea, Latvia, Lithuania and the Russian Federation, on the other hand, more than 18 deaths per 100 000 population were caused by suicide. There is a eleven-fold difference between Turkey and Korea, the two countries with respectively the lowest and highest suicide rates. However, the number of suicides in certain countries may be under-reported because of the stigma that is associated with the act, or because of data issues associated with reporting criteria.

Suicide rates increased in the 1970s and peaked in the early 1980s (Figure 6.7). Since the mid-1980s, suicide rates have decreased by around 30% across OECD countries, with pronounced declines of over 50% in Hungary for example. On the other hand, death rates from suicides have increased in countries such as Japan and Korea. In Japan, there was a sharp rise in the mid- to late 1990s, coinciding with the Asian financial crisis, but rates have started to decline in recent years. Suicide rates also rose sharply at the same time in Korea until 2011.

Previous studies have shown a strong link between adverse economic conditions and higher levels of suicide (Van Gool and Pearson, 2014). Suicide rates rose slightly at the start of the economic crisis in a number of countries, but more recent data suggests that this trend did not persist. In Greece and Spain, overall suicide rates were stable in 2009 and 2010, but have increased since 2011 from very low levels. This underlines that countries need to closely monitor high-risk populations such as the unemployed and those with psychiatric disorders.

Death rates from suicide are three-to-four times greater for men than for women across OECD countries (Figure 6.6). In Poland and Slovak Republic, men are at least six times more likely to commit suicide than women. While the gender gap is smaller in Netherlands and Sweden, male suicide rates are still at least twice as high as those of females.

On average, older people are more likely to take their own lives, with 20 people aged 70 years or more per 100 000 (Figure 6.8), but this pattern is not general across the OECD. Austria, France, Germany, Hungary and Korea are examples where older people take their own lives more often than young people. The largest increasing age gradient is found in Korea, where rates amongst the eldest group are almost 15 times higher than those of teenagers. Differences in suicide rates between men and women become particularly important from 75 years old, where suicide rates are six times greater for men than for women. This pattern may reflect higher social isolation, possibly following ending of a long term partnership by dissolution or death, of older men compared to older women. It could also come from higher incidence of diseases among men leading to suicides.

Except in a few countries, youth are much less likely to commit suicide with only 9young people aged between 15 and 29 years out of 100 000. However, in a minority of OECD countries like Ireland, New Zealand and Norway, young people are more likely to take their own lives than older people. Suicide rates among under 30s are highest in Finland, Japan, Korea and New Zealand, with 15 or more suicides per 100 000 youth. They are lowest in Mediterranean European countries and Luxembourg.

Definition and measurement

The World Health Organization defines suicide as an act deliberately initiated and performed by a person in the full knowledge or expectation of its fatal outcome. Comparability of data between countries is affected by a number of reporting criteria, including how a person’s intention of killing themselves is ascertained, who is responsible for completing the death certificate, whether a forensic investigation is carried out, and the provisions for confidentiality of the cause of death. Caution is required therefore in interpreting variations across countries.

Mortality rates are based on numbers of deaths registered in a country in a year divided by the size of the corresponding population. The rates have been directly age-standardised to the 2010 OECD population to remove variations arising from differences in age structures across countries and over time. The source is the WHO Mortality Database. Deaths from suicide are classified to ICD-10 codes X60-X84.

Further reading

OECD (2015), Health at a Glance 2015 – OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2015-en.

Van Gool, K. and M. Pearson (2014), “Health, Austerity and Economic Crisis: Assessing the Short-term Impact in OECD Countries”, OECD Health Working Papers, No. 76, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jxx71lt1zg6-en.

Figure notes

Figures 6.7 and 6.8: See Statlink for precise latest years ranging from 2009 and 2014.

6.6. Eleven-fold difference between countries with lowest and highest suicide rates

Age-standardised suicide rate per 100 000 persons, by gender, 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405598

6.7. Falling suicide rates in most countries since the mid-1980s, and slight increase for countries hit by the crisis

Trends in age-standardised suicide rate per 100 000 persons, selected OECD countries, 1970-2014

StatLink  http://dx.doi.org/10.1787/888933405605

6.8. Suicide rates increase with age, except in the first years of retirement

Suicide rate per 100 000 persons by age group and gender, OECD average, 2013 (or nearest year)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en and OECD Secretariat calculations form WHO Mortality Database.

StatLink  http://dx.doi.org/10.1787/888933405611

Health spending

How much countries spend on health and the rate at which such expenditure grows from one year to the next reflects a wide array of market and social factors, as well as countries’ diverse financing and organisational structures of their health systems.

In 2015, the United States continued to outspend all other OECD countries by a wide margin, with the equivalent of USD 9 450 per person (Figure 6.9). This level of health spending is two-and-a-half times the average of all OECD countries (USD 4 010) and nearly 40% higher than Switzerland the next biggest spending country. Around half of countries fall within a per capita spending of between USD 3 000 and USD 6 000. Countries spending below USD 3 000 include most of the southern and central European members of the OECD, together with Chile, Israel and Korea. The lowest per capita spenders on health were Mexico and Turkey with levels of just above 1 000 USD per person.

Among the key emerging economies, China, Indonesia and India spent 18%, 8% and 7% respectively of the OECD average on health in per capita terms in 2015.

Figure 6.9 also shows the breakdown of per capita spending on health into public and private sources. The ranking according to per capita public expenditure remains broadly comparable to that of total spending. On average, private spending represents 27% of total spending. However, private source represents around half of total outlays in Mexico and the United States. Even if the private sector in the United States continues to play the dominant role in financing, public spending on health per capita is still greater than that in all other OECD countries, with the exception of the Norway and Switzerland.

Since 2009, health spending has slowed markedly in several countries after years of continuous growth. However, health spending patterns across the 35 OECD countries have been affected to varying degrees. On average, per capita health spending over the period 2005-09 is estimated to have grown, in real terms, by 3.4% annually (Figure 6.10). In contrast, over the subsequent six years (2009-15), average health spending across the OECD grew at only 1.1% per year as the effects of the economic crisis took hold.

The extent of the slowdown has varied considerably across the OECD. While a number of European countries have experienced drastic cuts in spending, in the context of fiscal consolidation, other countries outside Europe have continued to see health spending grow, albeit in many cases at a reduced pace. Since 2009, the pre-crisis increases in spending have been reversed in Greece (4.5% annual growth rate over the 2005-09 period vs. -6.6% after 2009) and in Ireland (6.9% vs. -0.3%). The pace of spending has slowed down in the vast majority of OECD countries. Outside Europe, health spending growth also slowed down significantly between 2009 and 2015, notably in Canada (0.5%) and New Zealand (0.8%). Only five countries – Chile, Hungary, Israel, Luxembourg and Switzerland – recorded higher growth rates after the crisis than before.

Definition and measurement

Heath expenditure measures the final consumption of health goods and services. This includes spending by both public and private sources on medical services and goods, public health and prevention programmes and administration, but excludes spending on capital formation (investments).

To compare spending levels across countries, per capita health expenditures are converted to a common currency (US dollar) and adjusted to take account of the different purchasing power of the national currencies. Economy-wide (GDP) Purchasing Power Parities (PPPs) are used to that effect.

For the calculation of growth rates in real terms, economy-wide GDP deflators are used. In some countries (e.g. France and Norway) health-specific deflators exist, based on national methodologies, but these are not used due to limited comparability.

Further reading

OECD (2015), Health at a Glance 2015 – OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2015-en.

Figure notes

Figure 6.9: Data for Brazil, Colombia, China, Costa Rica, India, Indonesia, Latvia, Russian Federation and South Africa refers to 2013 and includes investments.

6.9. Large differences in health spending across the OECD

Health expenditure per capita, in USD PPPs, 2015 or latest year available

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en; WHO Global Health Expenditure Database.

StatLink  http://dx.doi.org/10.1787/888933405623

6.10. Since 2009, health spending has slowed markedly in several countries after years of continuous growth

Annual average growth rate in per capita health expenditure, real terms, 2005-09 and 2009-15 (or nearest years)

Source: OECD Health Statistics 2016, http://dx.doi.org/10.1787/health-data-en.

StatLink  http://dx.doi.org/10.1787/888933405637

Tobacco and alcohol consumption

Tobacco and alcohol are major risk factors for at least two of the leading causes of premature mortality – cardiovascular diseases and cancer.

On average, in 2014 about 19% of the adult population smoked daily, but this proportion varies greatly across the OECD (Figure 6.11). Rates were lowest in Mexico and Sweden (less than 12%). On the other hand, smoking rates remain high in Latvia at 36%. Smoking prevalence is also generally higher among men than among women in all OECD countries except in Sweden and Iceland. Smoking rates across most OECD countries have shown a marked decline. On average, smoking rates have decreased by about one fourth since 2000, from 26% in 2000 to 19% in 2014. Large reductions occurred in Denmark, Luxembourg and Norway. Smoking rates only increased in Latvia and the Slovak Republic. Smoking rates were also low at 12% or less in Brazil and Colombia, and they were high at above one third in Indonesia.

Alcohol consumption, as measured by recorded data on annual sales, stands at 8.9 litres per adult, on average, across OECD countries, based on the most recent data available (Figure 6.12). Austria, Estonia, France and the Czech Republic reported the highest consumption of alcohol with 11.5 litres or more per adult per year. Low alcohol consumption was recorded in Turkey and Israel, as well as in emerging economies Indonesia and India, where religious and cultural traditions restrict the use of alcohol in some population groups. Although average alcohol consumption has gradually fallen in many OECD countries since 2000 – by about two-thirds of liters per adult –, it has risen by one liter or more in Chile, Latvia, Poland and Sweden, as well as in key partner countries China, Lithuania and the Russian Federation. OECD analysis based on individual- level data show that men of low socioeconomic status are more likely to drink heavily than those of a higher socioeconomic status, while the opposite is observed in women (OECD, 2015).

Adolescent smoking and drinking often have adverse consequences for physical and mental health. Adolescents establish addictions more quickly than adults and regular drinking is associated with poorer psychological, social and physical health outcomes, as well as poorer educational outcomes, violence, injuries, smoking, drug use and risky sexual behaviour (OECD, 2015a). On average, one in eight 15-years-old reported smoking at least once a week. Adolescent smoking rates ranged from less than 5% in Canada, Iceland and Norway to around 20% in France, Hungary and Italy (Figure 6.13). Boys reported significant higher prevalence in Finland, Israel, Lithuania and Russian Federation, while the opposite pattern prevailed in the Czech Republic and Luxembourg.

As for drunkenness at 15-years-old, on average one in five adolescent old reported have been drunk at least twice in their life. Rates ranged from 10% in Israel to above 35% in Denmark, Hungary and Lithuania (Figure 6.14). Boys are more likely to report higher prevalence than girls particularly in Austria, Hungary, Israel, Italy, Latvia, Lithuania, Russian Federation and Switzerland. The United Kingdom is the only country where girls (from England and Wales, not Scotland) report significant higher prevalence than boys.

Definition and measurement

The proportion of daily smokers is defined as the percentage of the population aged 15 years and over who report smoking every day. International comparability is limited due to the lack of standardisation in the measurement of smoking habits in health interview surveys across OECD countries. Variations remain in the age groups surveyed, the wording of questions, response categories and survey methodologies (e.g. in a number of countries, respondents are asked if they smoke regularly, rather than daily). Self-reports of behaviours may also suffer from social desirability bias that may potentially limit cross-country comparisons.

Alcohol consumption is defined as annual sales of pure alcohol in liters per person aged 15 years and over. The methodology to convert alcoholic drinks to pure alcohol may differ across countries. Official statistics do not include unrecorded alcohol consumption, such as home production.

Tobacco and alcohol consumption rates for 15 years old by gender are from the 2013/14 Health Behaviour in School-aged Children (HBSC) study, which collects information on many socio-economic factors that affect health behaviour among children for 26 OECD countries. Indicators shown here by gender are the percentage of 15-year-olds who smoke at least once a week and those who have been drunk on two or more occasions.

Further reading

HBSC (2016), Health Behaviour in School-aged Children Study: International Report from the 2013/2014 Survey, www.hbsc.org/publications/international.

OECD (2015a), Tackling Harmful Alcohol Use – Economics and Public Health Policy, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264181069-en.

OECD (2015b), Health at a Glance 2015 – OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2015-en.

Figure notes

Figures 6.11 and 6.12: See Statlink for precise years.

Figures 6.13 and 6.14: Data for Belgium were computed using population shares for Flemish (60%) and French (40%); data for the United Kingdom were computed using population shares for England (85%), Scotland (9%) and Wales (5%).

6.11. Marked decline in smoking rates among adults in most OECD countries

Percentage of population 15 years and over smoking daily, in 2000 and 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405648

6.12. Gradual decline in alcohol consumption among adults in most OECD countries

Liters of pure alcohol per person aged 15 years and over, 2000 and 2014 (or nearest year)

StatLink  http://dx.doi.org/10.1787/888933405654

6.13. On average one in eight 15 years old reported smoking at least once a week

Percentage of 15-year-olds who smoke at least once a week, by gender; in 2013/14

StatLink  http://dx.doi.org/10.1787/888933405662

6.14. On average one in five 15 years-old reported have been drunk at least twice in their life

Percentage of 15-year-olds who have been drunk on two or more occasions, by gender, 2013/14

StatLink  http://dx.doi.org/10.1787/888933405675

Source: HBSC (2016), Health Behaviour in School-aged Children (HBSC) Study: International Report from the 2013/2014 Survey, www.hbsc.org/publications/international.

Chapter 7.

Social cohesion indicators

Life satisfaction

Trust

Voting

Crime and prisoners

Social networks

Life satisfaction

Life satisfaction is determined not only by economic development, but also by people’s diverse experiences and living conditions. People in Switzerland and Denmark are most satisfied with their lives (Figure 7.1). The measured level in these countries was 2.5 steps higher than in Greece or Portugal, the countries at the bottom of the 11-step ladder in 2014/15. Indeed life satisfaction deteriorated during the crisis, particularly in European Mediterranean countries. Countries which experienced the greatest deterioration in incomes and labour-market prospects are more likely to have low levels of subjective well-being.

There are broad regional or cultural country groupings of life satisfaction. Four of the top five countries are Nordic. Continental Western, Eastern European and Asian OECD members are less satisfied with their lives, with the notable exceptions of Switzerland and, to a lesser extent, Austria and the Netherlands. Predominantly Anglophone OECD countries are all in the top half of the list when measuring life satisfaction, and follow in a tight group after the predominately Nordic top cluster. As for emerging economies, life satisfaction also varies between them, from above 6 in Argentina, Brazil, Costa Rica, Colombia and Saudi Arabia, to below 5 in India and South Africa.

Young people are happier that older groups, as the level of life satisfaction tends to decrease with age. Youth from Switzerland, Israel and Norway are the most satisfied with their lives in OECD, whereas in Hungary, Turkey and Estonia, they report the lowest levels. However, life satisfaction is “u-shaped” in some countries, increasing from about the age of 55.

Teenage boys usually report higher life satisfaction than girls. Figure 7.2 presents the data on the proportion of teenagers aged 15 years old that reported levels of life satisfaction in the top half of the scale (6 or above). On average the proportion of teenagers reporting high levels of life satisfaction is almost 10 percentage points higher among boys compared to girls. The gap is even larger in Poland and France (i.e. 15 percentage points). Overall highest levels of life satisfaction were reported in the Netherlands and Denmark where about 90% of teenagers were satisfied with their life.

Life satisfaction is higher among people who are satisfied with their work-life balance. The difference is largest in Austria where people who are satisfied with their work-life balance report an average life satisfaction score more than 1.7 point greater than people who are dissatisfied with their work-life balance (Figure 7.3). Policies aiming at enhancing the combination of work and private life are important factors of quality of life.

Definition and measurement

The Gallup World Poll asked respondents to: “Imagine an eleven-rung ladder where the bottom (0) represents the worst possible life for you and the top (10) represents the best possible life for you. On which step of the ladder do you feel you personally stand at the present time?”. The main indicator used in this section is the average country score. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire. With few exceptions, all samples are probability based and nationally representative of the resident population aged 15 years and over in the entire country. While this ensures a high degree of comparability across countries, results may be affected by sampling and non-sampling errors, and variation in response rates; for example, data, especially for youth, should be interpreted carefully.

Data on life satisfaction among children are taken from the Health Behaviour in School-aged Children survey. Children aged 11 to 15 years are asked to report on the quality of their current life based on the Cantril life satisfaction scale of 0 to 10 (Cantril, 1965), with 0 representing the worst possible life and 10 representing the best possible life. A child is said to be satisfied with his or her own life if they report a score in the top half of the scale – 6 or above (Currie et al., 2012). Thus, life satisfaction is presented as the proportion of children reporting a score of 6 or above.

Data on the relationship between life satisfaction and work-life balance are based on the European Quality of Life (EQLS) survey. To measure life satisfaction, the survey asks persons aged 15 and over to rate the level of satisfaction with their life overall based on the questions “All things considered, how satisfied would you say you are with your life these days?” on a scale of 1 to 10, where 1 means very dissatisfied and 10 means very satisfied. Life satisfaction is presented as the average score among adults. Dissatisfaction with work-family life balance is based on a person reporting home and work conflict.

Further reading

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

Figure notes

Figure 7.1: Data for Iceland refer to 2013. Data for age 15-29 are not available for Iceland and Japan.

Figure 7.2: Data for Belgium were computed using population shares for Flemish (60%) and French (40%); data for the United Kingdom were computed using population shares for England (85%), Scotland (9%) and Wales (5%).

7.1. Young people generally report higher levels of life satisfaction

Average points of life satisfaction on an 11-step ladder from 0-10 by age group, 2014/15

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888933405682

7.2. Teenage boys usually report higher life satisfaction than girls

Proportion of teenagers aged 15 years old reporting a life satisfaction score of 6 or above, on a scale of 0 to 10, 2014

Source: Health Behaviour in School-aged Children survey (HBSC 2013/14) www.hbsc.org/.

StatLink  http://dx.doi.org/10.1787/888933405690

7.3. People satisfied with work-life balance are more happy

Life satisfaction by satisfaction in work-life balance, 2012

Source: European Quality of Life Survey (EQLS), www.eurofound.europa.eu/surveys/european-quality-of-life-surveys.

StatLink  http://dx.doi.org/10.1787/888933405707

Trust

A cohesive society is one where citizens have confidence in others and public institutions. Trust may affect economic performance and policies can affect trust and well-being (Algan and Cahuc, 2013)

The share of people expressing trust in others varies greatly across countries (Figure 7.4). In OECD countries, about 36% of interviewees expressed interpersonal trust. In Nordic countries over 60% of interviewees trust each other compared to less than 13% in Chile, Mexico and Turkey. Among the key partner economies, levels of trust are usually lower than in OECD countries ranging from 4% of the population in Columbia expressing trust in others to 33% in India.

Confidence in the national government is comparable, with about 42% of people. People in Switzerland, Luxembourg, Norway and New Zealand express higher confidence in their governments, while rates are lower in Slovenia, Portugal, Poland and Spain with large differences across countries (Figure 7.5). Among the key partner economies, confidence in national governments is highest in India and Indonesia and lowest in Colombia.

On average youth levels of confidence in others and in national government are similar to those of the total population. However in Belgium, Estonia and the United States, young people tend to have more confidence in government than the overall population whereas in Chile, Korea and Greece, this is the opposite. Among young people, NEETs are half as likely to report that they feel others can be trusted compared to other youth (see Chapter 1 and Figure 1.19). Over time being a NEET can lead to isolation, a lack of interest in society and a feeling of distrust.

Worries about finding or keeping a job are usually higher for youth than for the whole population. This gap is highest in Sweden, the United States, New Zealand and Spain. In Mexico, Korea, Japan, Estonia and Spain, over 65 % of people reported worrying very much about losing, or not finding, a job , whereas in Sweden and the Netherlands less than one in four do (Figure 7.6).

Definition and measurement

Data on trust in others is based on the question: “Generally speaking would you say that most people can be trusted or that you need to be very careful in dealing with people?”. The response is 1 “most people can be trusted” or 2 “need to be very careful”. Data come from two surveys: the World Values Survey (wave 6) for all countries present in wave 6, wave 5 for Canada, wave 4 for Israel and the European Values Survey (wave 4) for European countries not in the WVS, wave 6. Data comparability across countries may be affected by sample sizes and response rates. Rates refer to people reporting that “most people can be trusted”. Note that the overall figures for trust amongst youth does not match Figure 1.19 as it only includes OECD countries with a large enough sample size of NEETs.

Data on confidence in national government comes from the Gallup World Poll where the question asked is “In this country, do you have confidence in each of the following, or not? In the national government?“. With few exceptions, samples are probability-based and nationally representative of the resident population aged 15 years and. While this ensures a high degree of comparability across countries, results may be affected by sampling and non-sampling error, and variation in response rates; for example, data, especially for youth, should be interpreted carefully.

Data on worries about losing or not finding a job comes from the World Values Survey Wave 6: 2010-14. The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life. The WVS consists of nationally representative and comparable surveys conducted in almost 100 countries which contain almost 90% of the world’s population. It is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including almost 400 000 respondents. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people worldwide.

Further reading

Algan, Y. and P. Cahuc (2013), “Trust, Well-Being and Growth: New Evidence and Policy Implications”, IZA Discussion Paper, No. 7464, Bonn.

OECD (2015), Government at a Glance, OECD Publishing, Paris, http://dx.doi.org/10.1787/gov_glance-2015-en.

Figure notes

Figure 7.4: Data refer to wave 6 (2010-13) of the World Values Survey for Australia, Chile, Estonia, Germany, Japan, Mexico, Netherlands, New Zealand, Poland, Slovenia, South Korea, Spain, Sweden, Turkey, United States; Wave 5 (2005-09) for Canada; Wave 4 (1999-04) for Israel. Wave 4 of the European Values Survey (2008-10) for other countries.

Figure 7.5: Average 2014/2015 except for Switzerland, New Zealand, Canada, Australia, Israel, Chile, Japan, United States, Korea and Brazil where the data refer to 2014. Due to small sample size, data for Iceland are not available.

Figure 7.6: 2014 for Brazil, India; 2013 for Argentina, Germany; 2012 for Australia, Colombia, China, Mexico, Netherlands, Poland; 2011 for Chile, Estonia, New Zealand, Russian Federation, Slovenia, Spain, Turkey, United States; 2010 for Japan, Korea.

7.4. Nordic countries tend to report higher levels of trust

Percentage of people reporting trust in others, 2014

Source: World Value Survey (wave 6: 2010-14) for countries present in wave 6, WVS wave 5 (2005-09) for Canada and WVS Wave 4 (1999-2004) for Israel. European Values Survey (EVS) (2008-10 wave 4) for all other countries.

StatLink  http://dx.doi.org/10.1787/888933405711

7.5. Confidence in national government varies across countries

Percentage of people reporting confidence in national government by age, average 2014/2015

Source: Gallup World Poll (www.gallup.com) extracted at January 2016.

StatLink  http://dx.doi.org/10.1787/888933405726

7.6. Worries in the labour market is higher for young people

Percentage of people reporting to worry very much or to a great deal about losing their job or not finding one, by age, around 2010-14

Source: World Values Survey (wave 6: 2010-14).

StatLink  http://dx.doi.org/10.1787/888933405731

Voting

Voter turnout rates vary substantially across the OECD. A high voter turnout is a sign that a country’s political system enjoys a strong degree of participation or that voting is mandatory. Voting-age turnout rates in parliamentary elections are above 80% in Australia, Belgium, Denmark and Turkey where voting is mandatory, as well as in Iceland, Korea and Sweden. They are below 50% in Switzerland (Figure 7.7). Low turnout not only reflects limited participation by registered voters, but possibly also that many potential voters do not register. Among non-OECD countries, voter turnout is highest in Indonesia (83%) and lowest in Colombia (52%).

In general, younger voters are less likely to cast their vote than the electorate in general: voter turnout among 18 to 24 year-olds is, on average, 16 percentage points lower than for adults aged 25 to 50 inclusive (Figure 7.8). In the United Kingdom, Slovak Republic and Israel, young people are much less likely to vote than prime-age individuals. Only in Korea are younger voters more likely to cast their vote than prime-age individuals. On average there is no significant difference in voter turnout between men and women.

Overall interest in politics is an important factor for social cohesion. This constitutes a key challenge for politicians to ensure that most citizens feel concerned by politics and participate as actors into the political life of the society. On average one in four young people reports to be not at all interested in politics compared to one in five for the total population in OECD (Figure 7.9). Chile and Portugal report the highest level of disinterest in politics among the total population whereas Denmark, Germany, Japan and Norway report the lowest levels. Among young people aged 15 to 29, disinterest in politics is high in Chile, the Czech Republic and Hungary. In Brazil and Colombia, more than 40% reported to be not at all interested in politics.

Definition and measurement

Voting in national parliamentary elections is one indicator of people’s participation in their community’s national life. The indicator used here to measure the participation of individuals in the electoral process is the “Voting age population turnout”, i.e. the percentage of the voting age population (VAP) that actually voted – as available from administrative records of member countries. The VAP is an estimate as it is difficult to accurately account for people who are of voting age but who are not registered voters, whatever the reason. In countries where registration is compulsory and/or automatic (e.g. based on the civil register), as, for example in Scandinavian countries, the number of registered voters will be close to the VAP.

Cross-national comparisons for voter turnout data can be affected by a variety of factors including, the legal voting age, the voting registration system (automatic or requiring action by the potential voter) and whether voting is compulsory or not. In most OECD and European countries, the legal voting age in the national elections is 18 years old, but young people can vote from age 16 in Austria and from age 17 in Korea.

Different types of elections occur in different countries according to their institutional structure and different geographical jurisdictions. For some countries, it should be noted, turnout for presidential elections and regional elections may be higher than for national parliamentary elections, perhaps because those elected through these ballots are constitutionally more important for how those countries are run. Data about voter turnout are extracted from the international database managed by the Institute for Democratic and Electoral Assistance (IDEA).

However, IDEA does not involve a disaggregation of voters by age and gender. For information on younger voters and by gender, surveys had to be relied upon. Data have been taken from both the Comparative Study of Electoral Systems (CSES module 4: 2011-16), and the European Social Surveys (ESS). The ESS has data on participation in the last national election (parliamentary or presidential). However, sample sizes are small, and for that reason CSES data has been used where available.

Data on interest in politics, are from the European Social Surveys (ESS) and the Word Value Survey Wave 6: 2010-14 (WVS). The questions in both surveys ask about How interested in politics and the respondent to choose between four categories: very interested, quite interested, hardly interested and not at all interested. Data refer to the rate of people answering to be not at all interested in politics.

Figure notes

Figure 7.7: Data refer to parliamentary elections, with the exceptions of Colombia, France, Korea, Mexico, Poland, the Russian Federation and the United States, where presidential elections are considered due to higher participation rates. Instead of 2015: 2014: Belgium, Hungary, Japan, Latvia, New Zealand, Slovenia, Sweden; 2013: Australia, Austria, Chile, Czech Republic, Germany, Iceland, Italy, Luxembourg; 2012: France, Korea, Lithuania, Mexico, Netherlands, Russian Federation, Slovak Republic, United States; 2011: Canada, Ireland, Spain, and Switzerland. Data for Norway are not available. Data for Norway are not available.

Figure 7.8: Data for Chile, Latvia, Luxembourg and Turkey are not available.

Figure 7.9: Data for Canada, Greece, Latvia and Luxembourg are not available.

7.7. Large variation in electoral participation in OECD countries

Voter turnout in latest parliamentary election, percentage of the voting age population, 2015 (or nearest year)

Source: International Institute for Democracy and Electoral Assistance (IDEA) voter turnout database.

StatLink  http://dx.doi.org/10.1787/888933405740

7.8. Young people tend to vote less

Voter turnout ratios for different population groups, around 2012/13

Source: Module 4 of the Comparative Study of Electoral Systems (CSES 2011-16) and European Social Survey (ESS) for other countries.

StatLink  http://dx.doi.org/10.1787/888933405752

7.9. One in four young people in OECD not at all interested in politics

Share of people reporting to be not at all interested in politics, by age group, around 2012-14

Source: European Social Survey ESS6-2012, ESS7-2014 and World Values Survey Wave 6: 2010-14.

StatLink  http://dx.doi.org/10.1787/888933405767

Crime and prisoners

On average in 2013, 2 100 persons per 100 000 were brought into formal contact with the police and/or criminal justice system. Crime rates among total population declined slightly on average (by almost 3%) from 2008 to 2013 (Figure 7.10). The declines were highest in Japan, Netherlands and the United States by more than 20%, while the increase was strongest in Luxembourg and Turkey. During the same period, crimes among juveniles declined even further, by almost 20% on average. The declines were highest in the Czech Republic and the Netherlands by more than 50%, while the increase was strongest in Turkey by almost 50%, and in Australia as well among youth.

Prison population stood at 147 per 100 000 people in the mid-2010s on average in the OECD. There are large variations across countries (Figure 7.11). For instance, the United States reports the highest rate at almost 700 per 100 000 population in 2013 down from a peak at 755 in 2008. This is three times higher than second highest country (Israel). The prison population rate is also high in key partner countries, except in China, India and Indonesia where it is below the OECD average.

Youths (aged under 18) represent only 1.3% of the prison population (Figure 7.12). They account for less than 1% in 22 OECD countries, but almost 3% in Germany, 4% in Canada and up to 14% in Mexico. This may reflect the specific forms of correction applied to minors in different OECD countries – i.e forms which may fall outside the scope of the statistics used here. These youth are typically not included in statistics on NEETs since they are not covered by surveys.

Most OECD countries have seen their prison population rates rise in the 1990s and the 2000s followed by a slight decrease in the early 2010s. On average across the 35 OECD countries, this rate increased from 117 persons per 100 000 in the early 1990s to 156 persons in 2010; it then declined slightly to 147 in the mid-2010s (Figure 7.11). Since 1992, the prison population rate has more than doubled in Mexico and Turkey, while it has declined only in Canada, Estonia, Korea, Latvia and three Nordic countries Denmark, Finland and Sweden. Since 2010, the prison population rate has decreased in two-thirds of the OECD countries, including in the United States.

In several countries, the rise in the prison population has stretched beyond the receptive capacity of existing institutions (Figure 7.12). Occupancy levels are above 100% in almost half of OECD countries, and above 120% in Belgium, Hungary and Mexico. Occupancy rates are also generally high in non-European key partner countries. Such overcrowding feeds violence and rebellion against institutions.

Definition and measurement

Data on crime rates are based on the United Nations Office on Drugs and Crimes (UNODC) Database, based on administrative data. UNODC collects data on crime and the operation of criminal justice systems in order to make policy-relevant information and analysis available in a timely manner to the international community. The index is based on the total number of persons – and juveniles aged under 18 – brought into formal contact with the police and/or criminal justice system, all crimes taken together, per 100 000 population. Data may include persons suspected, or arrested or cautioned. Any cross-national comparison should be conducted with caution because of the differences that exist between the legal definitions of offences in countries, or the different methods of offence counting and recording.

Crime causes great suffering to victims and their families, but the costs associated with imprisonment can also be considerable. These costs are normally justified by the need to inflict retribution to offenders, to deter others from behaving in a similar way, and to prevent re-offending. The size of the prison population depends on the level of crime, the legislative measures and the efficiency of the enforcement measures. The basic indicator of the size of the prison population is each country is the number of persons in prison (including pre-trial detainees and remand prisoners) per 100 000 of national population. Data on the prison population can also be broken down according to their demographic characteristics and legal status, including the share of juveniles under age 18. It should be noted that not everyone in prison has been found guilty of a crime, due to the inclusion of those awaiting trial or adjudication. The occupancy level refers to the prison population as a percentage of the official capacity. The indicators shown here are gathered in the World Prison Brief by the Institute for Criminal Policy Research (www.prisonstudies.org).

Figure notes

Figure 7.10: 2012 in Chile, New Zealand and Turkey; 2011 in Estonia, Greece and Luxembourg instead of 2013.

Figures 7.11 and 7.12: Instead of 2016: 2015 for Australia, Austria, Chile, Japan, Denmark, Finland, Germany, Greece, Hungary, Israel, Korea, Luxembourg, Mexico, Norway, Slovenia, Sweden, China, Indonesia, Lithuania, and South Africa; 2014 for Belgium, Canada, Iceland, Netherlands, Switzerland, Brazil, Costa Rica, India and Latvia; 2013 for United States. 2000 instead of 1992 for China, Colombia, Costa Rica, India, Latvia and Lithuania.

Figure 7.12: No youth data for Brazil and Colombia; no occupancy level for China.

7.10. Downward trends in crimes in most OECD countries between 2008 and 2013, particularly among youths

Rate of persons brought into formal contact with the police and/or criminal justice system per 100 000, in 2013 (or nearest year), all crimes taken together, index 100 in 2008

Source: United Nations Office on Drugs and Crimes (UNODC) Database (www.unodc.org/unodc/en/data-and-analysis/statistics/crime.html) accessed on 15 March 2016.

StatLink  http://dx.doi.org/10.1787/888933405777

7.11. Prison population rates increased till 2010 then slightly decreased

Prison population per 100 000 population, in 1992, 2010 and 2016 (or nearest year)

Source: World Prison Brief, Institute for Criminal Policy Research, World Prison Brief (www.prisonstudies.org) accessed on 15 March 2016.

StatLink  http://dx.doi.org/10.1787/888933405783

7.12. Variation in share of youth in prisons and prison occupancy rates

Percentage of youth (under 18) in the prison population and share of prison population in percentage of official capacity, in 2016 (or nearest year)

Source: World Prison Brief, Institute for Criminal Policy Research (www.prisonstudies.org) accessed on 15 March 2016.

StatLink  http://dx.doi.org/10.1787/888933405797

Social networks

Social networks consist of a group of individuals interacting with each other, be it in person or virtually. Social networks and connectedness have been shown to be important for a variety of outcomes. Loneliness has been shown to be more prevalent amongst those on the edge of social networks and has been shown to be detrimental to health and increase mortality rates, particularly for older people (Cacioppo et al., 2011).

Young people tend to have a higher level of social support than older age groups. On average across the OECD 93% of 15-29 year-olds report having a relative or friend they can count on to help them if they were in trouble (Figure 7.13). This compares to 89% of those aged 30-49 and 87% of those over 50. In most OECD countries at least 90% of youth report having family or friends to turn to, just Turkey and Mexico fall below this level (83 and 84% respectively). Turkey and Korea both experience relatively low levels of social support for older age groups with only 61% of Korean and 68% of Turkish persons over 50 reporting having someone to turn to in times of need. Iceland, Ireland and Denmark have some of the highest rates of social supports across the different age groups.

This higher level of social support amongst young people may, in part, be due to their higher level of online connectedness. Online social networking by young people has been show to allow them to explore interests outside of their physical social network and teach them about social norms (Mizuko et al., 2009). 16-24 year-olds are 1.4 times more likely to engage in online social networking than the overall (16-74) population (Figure 7.14). On average across the OECD 89% of youth use online social networks, ranging from 53% in Mexico to 97% in Iceland. Online social connectedness is, of course, linked to internet access and the owning of a smartphone. 35% of individuals in Mexico report owning a smartphone compared to over 90% in countries like the United States the United Kingdom and Canada (PEW, 2016). Only 26% of households in Mexico report having access to the internet compared to 95% of households in Iceland (OECD, 2016).

Volunteering, as well as providing a contribution to society, has been shown to increase the wellbeing of volunteers themselves (Meier and Stutzer, 2007). Just over one-fifth of young people across the OECD report volunteering in the past month (Figure 7.15). This is slightly lower than for those aged 30 and over. Participation in volunteer activities varies greatly across the OECD. Countries like Greece, Turkey and Hungary tend to have low volunteering rates with 10% or less of adults volunteering their time. At the other end of the spectrum a large proportion (in excess of one-third) of adults in New Zealand, the United States and Ireland volunteered in the last month.

Definition and measurement

Data on social support and volunteering in an organisation comes from the Gallup World Poll where the questions asked to respondents are respectively: “If you were in trouble, do you have relatives or friends you can count on to help you?” and “Have you done any of the following in the past month? How about volunteered your time to an organisation?”. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire. With few exceptions, all samples are probability-based and nationally representative of the resident population aged 15 years and over in the entire country, including rural areas. While this ensures a high degree of comparability across countries, results may be affected by sampling and non-sampling error, and variation in response rates; for example, data, especially for youth, should be interpreted carefully. Rates refer to people who answered “yes” and include Don’t know” and “Refused” in the denominator.

Data on online activities among Internet users are from the OECD Information and communication technology (ICT) database. The part for ICT Access and Usage by Households and Individuals is based on Eurostat Statistics on Households and Individuals for the OECD countries that are part of the European Statistical system. Otherwise for non EU countries the information is based on an OECD data collection where the data originate from National Statistical Offices official surveys.

Further reading

Cacioppo, J. et al. (2011), “Social isolation”, Annals of the New York Academy of Sciences, Vol. 1231, pp. 17-22.

Mizuko, I. et al. (2009), “Living and Learning with New Media: Summary of Findings from the Digital Youth Project”, MacArthur Foundation Reports.

OECD (2016), “Internet access (indicator)”, http://dx.doi.org/10.1787/69c2b997-en (accessed 24 May 2016).

OECD (2015), OECD Science, Technology and Industry Scoreboard 2015: Innovation for Growth and Society, OECD Publishing, Paris, http://dx.doi.org/10.1787/sti_scoreboard-2015-en.

Pew Research Center (2016) “Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies”, www.pewglobal.org/files/2016/02/pew_research_center_global_technology_report_final_february_22__2016.pdf.

Figure notes

Figure 7.14: No data available for the United States and for 16-24 year-olds in Canada and Japan. Data refer to 2014 except for Australia and Israel: 2013 and Canada and New Zealand: 2012.

7.13. Social support is highest amongst youth

Percentage of people who report having relatives or friends they can count on, by age, pooled results 2006-14

Source: OECD (2015), How’s Life 2015 – Measuring Well-being, Gallup World Poll, www.gallup.com/services/170945/world-poll.aspx.

StatLink  http://dx.doi.org/10.1787/888933405801

7.14. Young people are more likely to engage in online social networks

Proportion of each age category engaging in social networking online, 2014

Source: OECD ICT Database; Eurostat, Information Society Statistics Database, March 2016.

StatLink  http://dx.doi.org/10.1787/888933405818

7.15. Young people are less likely to volunteer

Proportion of each age category who volunteered time to an organisation in the past month, pooled results 2006-15

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

StatLink  http://dx.doi.org/10.1787/888933405829

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