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Society at a Glance 2014

OECD Social Indicators

Please cite this publication as:
OECD (2014), Society at a Glance 2014: OECD Social Indicators, OECD Publishing, http://dx.doi.org/10.1787/soc_glance-2014-en.

Metadata, Legal and Rights

ISBN: 978-92-64-21997-7 (epub) - 978-92-64-20072-2 (print) - 978-92-64-20665-6 (pdf) - 978-92-64-20669-4 (web)

DOI: http://dx.doi.org/10.1787/soc_glance-2014-en

Serie: Society at a Glance

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

This work is published on the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Organisation or of the governments of its 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 credit: Cover © Inmagine LTD/OJO Images Ltd

Corrigenda to OECD publications may be found on line at: www.oecd.org/about/publishing/corrigenda.htm.

© OECD 2014

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Foreword

This is the seventh 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 six editions and introduces several new ones. Data for the other economies that are members of the G20 are included separately where available.

Before the onset of the financial and economic crisis in 2007-08, social spending across the OECD area accounted for about half of all government outlays. But while there are big demands on social protection systems during all phases of the economic cycle, the need for social support measures is especially acute during deep and extended economic downturns. Against this background, this edition of Society at a Glance takes stock of available information about the social challenges emerging since the beginning of the economic crisis, and countries’ policy responses to meet those challenges.

Chapter 1 presents and discusses the most recent data on the social situation in OECD countries and in selected emerging economies, and it discusses how countries can make social policies more “crisis-proof”. Chapter 2 provides a guide to help readers in understanding the structure of OECD social indicators. Indicators are then considered more in detail in the Chapters 3, 4, 5, 6 and 7. More detailed information on indicators, including some not included in this print edition, can be found on the OECD web page (www.oecd.org/social/societyataglance.htm).

This report was prepared by Pauline Fron, Herwig Immervoll (Chapter 1), Maxime Ladaique and Hilde Olsen. Technical assistance was provided by Laura Quintin. Willem Adema, Nabil Ali, Stéphane Carcillo, Maria Chiara Cavalleri, Eric Charbonnier, Rodrigo Fernandez, Michael Förster, Gaétan Lafortune, Horacio Levy, Kristoffer Lundberg, Thomas Liebig, Pascal Marianna, Marlène Mohier, Andrew Reilly, Dominic Richardson and Linda Richardson 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 | Japan | JPN
Austria | AUT | Korea | KOR
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

Other major economy and G20 country ISO codes

Argentina | ARG | Indonesia | IDN
Brazil | BRA | Russian Federation | RUS
China | CHN | Saudi Arabia | SAU
India | IND | South Africa | ZAF

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.

Editorial

The year 2014 is starting, with the perspective of a more widespread and sustainable recovery from the Great Recession. True, risks remain, and the pace of progress still varies widely: in the Eurozone, for instance, a number of economies remain in a fragile state. Nevertheless, the prospects for both the world economy and the OECD area look brighter than they have for some time.

Encouraging as this may be, it risks seducing us into believing that all is now going well and that, over the next few years, a rising economy will lift all boats. The evidence of the recent past, dating even to before the financial crisis, suggests otherwise. As shown by a series of OECD reports, most notably Divided We Stand (2011) and Growing Unequal? (2008), in recent decades the fruits of economic success have been enjoyed less widely than before.

The crisis of the past years has added to these long-term trends. Many of those who benefited least from growth before the crisis also bore a heavy burden in the recession. And today, while hope for national economies is growing, the economic prospects for far too many of our fellow citizens remain under the cloud of the recent turmoil.

These problems are manifest today in the form of lingering unemployment and flat if not declining incomes for many households. As this edition of Society at a Glance shows, employment rates were falling until recently, with young and low-skilled workers particularly hard hit. Since 2007, the number of unemployed people in OECD countries increased by one-third to reach 48 million and more than one-third of them have been out of work for more than one year.

Growing numbers now say that they have problems making ends meet, a trend visible in 26 OECD countries since 2007. According to the Gallup World Poll, in 2012, one in four people in OECD countries reported income difficulties. In three of the Eurozone members, Greece, Ireland and Spain, the number of people living in households with no income from work has doubled. Across the OECD area, children and young people were hardest hit by income poverty.

Perhaps most worrying, however, is the prospect that these problems may continue to shape people’s lives for many years to come. An obvious and much-discussed impact is “scarring”, or the danger that young people who suffer long periods of unemployment, inactivity or poverty face a lifetime of diminished earnings and weakened job opportunities.

There are others. Take education, one of the most important investments individuals and societies can make in their futures. Public spending in this sector as a share of GDP fell abruptly in more than half of all OECD countries in recent years, and this risks closing off education opportunities for some families.

Health, too, is a concern. In 11 OECD countries, 15% of survey respondents report being unable to meet their health care needs while, across the OECD, only around three out of five low-income individuals report their health status as at least “good” compared with four out of five high earners. Rising numbers of families also say they cannot afford to spend enough on food.

And there are signs that a partial pick-up in fertility rates just before the crisis has now petered out, with falling incomes and joblessness perhaps leading families to delay or put off altogether having children. If this trend is maintained, it risks deepening the ageing challenge facing many OECD societies.

It will take many years before the impact of these trends can be fully understood. Some of the issues that concern us most now may fade away if we enter a period of strong and sustained growth. But, viewed from the present, their potential to produce unwelcome social outcomes looks worryingly high. At the same time, the squeeze on government spending in many OECD economies means our societies’ capacity to cope with these challenges looks increasingly uncertain.

This is not to suggest we should be fatalistic. For our societies to prosper and remain stable, it is essential that social policies support families in troubled times and ensure that short-term problems do not turn into long-term disadvantages. Indeed, survey research in a number of OECD countries suggests strong support for maintaining social spending in key areas.

That will not always be easy, but where cuts are needed they must be done in ways that do not undermine the prospects of the most vulnerable or compromise the long-term well-being of children and young people. That means, in the words of a popular mantra in policy circles, “doing more with less”. But it also means that resources for crucial areas of support, such as social safety nets for the poorest, may need to be increased.

As governments search for new policy approaches that meet our societies’ needs, a swift response to the many social challenges posed by the economic crisis will remain a priority for years to come. But responding to past crises is not enough. One of the most important lessons from the Great Recession is this: we are far less able to predict the gyrations of our economies than we might wish to be. Still, one thing can be said with some certainty: there will be future turbulence and social policy will, once again, be under pressure to deliver.

With this in mind, social policy must be designed to work effectively in both good times and bad. As the crisis revealed, this was regrettably not the case in a number of OECD countries; while social protection programmes helped soften the blow of the crisis for many people, others were left behind with little or no support. In Southern Europe, for example, social support, while expensive, often failed to reach the poor even before the Great Recession.

The priority now must be to ensure that social policies are “crisis-proofed” – ready, in other words, to cope with the worst the global economy can throw at them. Increasingly, social spending and investment will need to be better targeted to ensure they meet our societies’ most pressing needs. Equally, social support systems will need to learn to operate with maximum efficiency, ensuring they are adapted to evolving labour markets and demographics while wasting as little as possible in administration and bureaucracy.

Meeting these challenges will be far from easy and, as the pain of the recession eases, there may be a temptation to put off reforms. That would be a mistake. Evidence of a decline in trust in governments since the crisis should serve as a warning of how quickly the social compact can fray. In response, governments must demonstrate that they can respond effectively to their societies’ needs. They must also ensure that social policy contributes to developing the resilience of their citizens to cope with future crises.

Angel Gurría

OECD Secretary-General

Executive summary

More than five years on from the financial crisis, high rates of joblessness and income losses are worsening social conditions in many OECD countries. The capacity of governments to meet these challenges is constrained by fiscal consolidation. However, cuts in social spending risk adding to the hardship of the most vulnerable groups and could create problems for the future. OECD countries can effectively meet these challenges only with policies that are well designed and backed by adequate resources. Having been spared the worst impacts of the crisis, major emerging economies face different challenges. However, the experience of OECD countries is relevant for emerging economies as they continue to build and “crisis-proof” their social protection systems.

The financial crisis has fuelled a social crisis

The financial upheaval of 2007-08 created not just an economic and fiscal crisis but also a social crisis. Countries that experienced the deepest and longest downturns are seeing profound knock-on effects on people’s job prospects, incomes and living arrangements. Some 48 million people in OECD countries are looking for a job – 15 million more than in September 2007 – and millions more are in financial distress. The numbers living in households without any income from work have doubled in Greece, Ireland and Spain. Low-income groups have been hit hardest as have young people and families with children.

Social consequences could linger for years

With households under pressure and budgets for social support under scrutiny, more and more people report dissatisfaction with their lives, and trust in governments has tumbled. There are also signs that the crisis will cast long shadows on people’s future well-being. Indeed, some of the social consequences of the crisis, in areas like family formation, fertility and health, will be felt only in the long term. Fertility rates have dropped further since the start of the crisis, deepening the demographic and fiscal challenges of ageing. Families have also cut back on essential spending, including on food, compromising their current and future well-being. It is still too early to quantify the longer-term effects on people’s health, but unemployment and economic difficulties are known to contribute to a range of health problems, including mental illness.

Invest today to avoid rising costs tomorrow

Short-term savings may translate into much higher costs in the future, and governments should make funding of investment-type programmes a priority. Today’s cuts in health spending need to avoid triggering rising health care needs tomorrow. Especially hard-hit countries should ensure access to quality services for children and prevent labour market exclusion of school leavers.

Vulnerable groups need support now

To be effective, however, social investments need to be embedded in adequate support for the poorest. Maintaining and strengthening support for the most vulnerable groups must remain a crucial part of any strategy for an economic and social recovery. Governments need to time and design any fiscal consolidation measures accordingly, as the distributional impact of such measures can vary greatly: for example, the poor may suffer more from spending cuts than from tax increases.

Room for cuts in unemployment spending is limited

Weak job markets provide little room for cuts in spending on unemployment benefits, social assistance and active labour market programmes. Where savings can be made, they should be achieved in line with the pace of recovery. Targeted safety-net benefits, in particular, are a priority in countries where such support does not exist, is difficult to access, or where the long-term unemployed are exhausting their unemployment support. Across-the-board cuts in social transfers, such as housing and child/family benefits, should be avoided, as these transfers frequently provide vital support to poor working families and lone parents.

Targeting can deliver savings while protecting the vulnerable

More effective targeting can generate substantial savings while protecting vulnerable groups. Health care reforms, in particular, should prioritise protecting the most vulnerable. However, fine-tuning of targeting is necessary, in order to avoid creating perverse incentives that deter people from finding work. For instance, unemployed people who are about to start a job may suffer losses or may gain very little as they switch from benefits to earning a salary.

Support families’ efforts to cope with adversity

There is a strong case for designing government support in ways that harness and complement – rather than replace – households’ own capacities to cope with adversity. In this light, it is especially important to provide effective employment support, even if this means higher spending on active social policies in the short term. Labour market activation and in-work support should be maintained at reasonable levels. Where there are large numbers of households without work, policy efforts need to focus on ensuring they benefit quickly once labour market conditions improve. For instance, to be as effective as possible, work-related support and incentives should not be restricted to individual job seekers but should be made available to non-working partners as well.

Governments need to plan for the next crisis

To “crisis-proof” social policies and to maintain effective support throughout the economic cycle, governments must look beyond the recent downturn. First, they need to find ways to build up savings during upswings to ensure they can meet rising costs during downturns. On the spending side, they should link support more to labour market conditions – for example, by credibly reducing benefit spending during the recovery, and by shifting resources from benefits to active labour market policies. On the revenue side, they should work to broaden tax bases, reduce their reliance on labour taxes and adjust tax systems to account for rising income inequality. Second, governments need to continue the structural reforms of social protection systems begun before the crisis. Indeed, the crisis has accelerated the need for these. In the area of pensions, for example, some future retirees risk greater income insecurity as a result of long periods of joblessness during working age. In health care, structural measures that strip out unnecessary services and score efficiency gains are preferable to untargeted cuts that limit health care access for the most vulnerable.

Chapter 1.

The crisis and its aftermath: A “stress test” for societies and for social policies

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.

Introduction

Social issues lie at the heart of governments’ policy agendas. Before the onset of the financial and economic crisis in 2007-08, social spending across the OECD area accounted for about half of all government outlay. But while there is great demand for social protection and support in all phases of the economic cycle, the need is especially acute during and after deep and extended economic downturns. The recent global economic crisis is no exception, as it quickly translated into hardships for households, who suffered unprecedented losses of jobs, earnings, and wealth.

A primary purpose of social policies is precisely to help individuals and families cope with the consequences of economic shocks like the Great Recession and to prevent temporary economic problems from turning into long-term disadvantage. They should enable individuals and families to manage risks more effectively and take better advantage of opportunities. Economic shocks have multiple causes which social policies cannot prevent. They can, however, strengthen families’ ability to adapt and respond to economic difficulties when they do occur. Income transfers, health care, and other public services make major shocks both less likely and less damaging. For society as a whole, social policies can prevent cyclical or temporary downturns from turning into protracted social crises.

Against that background, this chapter and the indicators in the rest of the book (see Box 1.1) take stock of what is currently known about the social challenges that have emerged since the onset of the crisis and about countries’ policy responses to those challenges. The book considers and discusses the most recent data on the social situation in OECD countries and in selected emerging economies. The aim of this chapter is to address the following three main sets of questions:

Are the on-going financial, economic, and fiscal crises leading to a social crisis? How have social outcomes evolved in the aftermath of the global economic downturn? To answer those questions, Section 1 of this chapter goes beyond economic “headline” indicators – such as unemployment rates, incomes or GDP – that are commonly used as shorthand for characterising and comparing the impacts of the crisis on individuals and families. As important as these aggregate indicators are, they account only very partially for the realities faced by individuals and families during and after a major downturn. The costs of recessions manifest themselves in a multitude of different ways. Deep economic crises can be expected to have profound knock-on effects on people’s living arrangements, family formation, fertility, health, career choices, or trust in others and in institutions. Understanding these is important not only for monitoring societal well-being, but also because social tensions and a shifting social fabric can trigger and drive fundamental social, cultural and political change (Castells et al., 2012).

How have governments responded? Economic crises are characterised not only by worsening well-being, but also by great uncertainty and a search for solutions to acute policy problems. Have social policy responses been effective so far? To what extent have they cushioned the immediate effects of the crisis on households and have they succeeded in supporting families’ efforts to adapt and respond to the resulting challenges? Economic difficulties put families under significant strain as they seek to contain, offset or adapt to insecure job prospects, the loss of earnings or wealth, precarious housing situations, or to waning public support. Section 2 of this chapter maps the evolution of social policies in OECD countries over the last five years and discusses their likely impact in the context of high and increasingly persistent social risks.

Can governments make social policies more crisis-ready and crisis-proof? Specifically, what are barriers to an effective social policy response and how could they be overcome? The cross-country analysis in Section 2 reveals wide differences in the types and scale of countries’ social policy responses. Such differences are also visible between countries who suffered economic shocks of similar magnitudes. It is not surprising, then, that some have been more successful than others in containing the social and human costs of the downturn. The third and final section seeks to identify factors that could explain why some countries have been able to provide adequate, timely help to families hit hard by the economic crisis. It then calls for a number of concrete measures that governments could take to enable more effective social policy responses to future economic crises.

Box 1.1. About the social and economic indicators in this chapter

OECD social and economic indicators are widely referred to throughout this chapter –particularly in Sections 1 and 2. They consist of an indicator name and appear in brackets after and in support of a statement or assertion. An example might be: “Female employment rates have risen steadily in the last decade across the OECD” (Indicator Employment).

The sentence does two things:

It tells the reader that the statement takes its evidence from the data presented in the data chapters of this edition of Society at a Glance (Chapters 3 to 7).

It refers the reader to the “Employment” indicator. Each indicator includes details on country differences and trends in employment, as well as relevant sources and definitions.

The electronic version of this book features references in hyperlink format that take readers directly to the relevant indicator.

1. Social outcomes in the wake of the economic crisis

Economic losses heighten social risks

The financial crisis in 2007-08 saw a fast, far-reaching deterioration in economic output for the OECD area as a whole and GDP fell steeply from its pre-recession peaks. But while in some countries, the Great Recession was followed by a moderate but continuous recovery, others avoided outright recession. A number of hard-hit countries, notably in Europe, faced a second recession in 2011-12 and output only began to stabilise in late 2013 (Figure 1.1). More than five years after the Great Recession started, economic output in the OECD is still not back to pre-crisis levels.

Figure 1.1. Economic output has begun a recovery everywhere, but employment and wages have not

GDP and total wage bill in real terms, business cycle peak=100

Note: All data are annual and all changes are in real terms. To focus on the effects of the Great Recession, the graph shows OECD countries that saw a drop in annual GDP at least once between 2007 and 2009. Australia, Korea and Poland are therefore excluded. Israel, Mexico, Turkey are also excluded as data on employee compensation are not available. “Peak” refers to the year with the highest GDP prior to the recession (either 2007 or 2008). The shaded area refers to the periods for which data are projected rather than recorded. “Low-growth” (“high-growth”) countries are those where GDP growth between peak and p+4 is below (above) the country average minus (plus) 0.5 standard deviations.

“Low-growth” countries: Estonia, Greece, Hungary, Iceland, Ireland, Italy, Slovenia, Spain.

“High-growth” countries: Austria, Canada, Chile, Germany, New Zealand, Norway, the Slovak Republic, Sweden, Switzerland.

Source: OECD (2013), OECD Economic Outlook 2013, No. 93, www.oecd.org/economy/outlook/economicoutlook.htm and http://dx.doi.org/10.1787/data-00655-en.

StatLink  http://dx.doi.org/10.1787/888932965877

Of all the economic losses, however, the income drops suffered by workers have turned out to be the most difficult to reverse. In most countries, the recovery has not yet translated into significant improvements in labour market conditions. Employment and wages have continued to fall until recently (Figure 1.1).

In the worst-affected countries, labour income – households’ most important income source – keeps on falling, in some instances at a gathering pace, even as GDP stabilises. Most countries have experienced “jobless” recoveries and/or falling wages and it will take several more years for labour incomes to regain their pre-crisis levels. Where the erosion of earnings persists, consumers are unlikely to play much of a role in supporting an economic recovery.

The Great Recession thus continues to cast a particularly long shadow on workers and their families. To policy makers, the negative trends it has generated point to continuing economic hardship, a high risk of growing poverty, and a persistently strong demand for effective support.

The demand for social support has persisted despite a public awareness that something needs to be done about often-unprecedented debt levels and structural fiscal deficits. Figure 1.2 for instance, illustrates the findings from a 2013 survey which shows how, in some countries, attitudes have shifted markedly against government debt and in favour of spending cuts.

Figure 1.2. Most people want to protect social spending, even where support for reducing fiscal gaps is strong

Percentages of respondents saying that spending should be increased, maintained, or reduced, 2013

Note: The data are taken from Transatlantic Trends, an annual survey of public opinion by German Marshall Fund of the United States, Compagnia di San Paolo, Barrow Cadbury Trust, Fundação Luso-Americana, BBVA Foundation, Communitas Foundation, and Swedish Ministry for Foreign Affairs. Polling for the 2013 results took place in June and July by phone interview. In each country, the sample consists of approximately 1 000 randomly chosen men and women of 18 years of age and older. The 95% confidence interval attributable to sampling and other random effects is no more than plus or minus 3 percentage points.

Source: GMF (2013), Transatlantic Trends, German Marshall Fund of the United States.

StatLink  http://dx.doi.org/10.1787/888932965896

Most respondents in France, Italy, Portugal, and the United States supported lowering government expenditure, while in other countries – like the Netherlands, Poland, Sweden, Turkey, and the United Kingdom – people appear much less convinced that spending cuts should be a priority.¹ Strikingly, though, large majorities support protecting or extending social spending, even in those countries where most people consider overall spending too high. That sentiment highlights the essential role of social support measures during and after deep economic downturns. However, concerns about the fiscal situation in some countries also underline the need for cost-efficient social protection and for the difficult task of “doing more with less”.

Social risks are higher when hardship is concentrated in specific groups

Effective, efficient social support measures should be properly targeted and tailored to individual circumstances. To that end, understanding the distributional aspects of recessions is essential. The worsening of aggregate income and employment trends is striking and highlights the scale of the crisis. But aggregate numbers hide wide disparities across population groups and regions within countries. By averaging across diverse populations, they understate the difficulties faced by the worst-off.

Deep recessions do not strike symmetrically. Jobs in sectors that bore the brunt of the initial economic slump in the Great Recession, such as financial services, construction, and manufacturing, were particularly exposed. As reduced incomes and depressed product demand permeated the economy, more and more families were affected, even though the extent and duration of difficulties varied dramatically from one group to another.

Men, youth, and low-skilled workers in labour-market plight

Since 2007, non-employment rates have increased much more markedly among young people, men, and low-skilled workers than among women and older workers (Figure 1.3). The surge in non-employment, especially among youth and men, reflects a combination of increasing numbers of unemployed (those looking for jobs) and so-called labour-market inactive (including discouraged jobseekers who are no longer available for work or not actively looking).

Figure 1.3. Employment perspectives of youth and low-skilled deteriorated sharply during the crisis

Change in the shares of people without work, by age group, sex and education level

Weighted OECD average, Q4 2007-Q4 2012, in percentage points

Note: “Low”, “medium” and “high” refer to less than upper secondary, upper secondary, and tertiary education. OECD average refers to Austria, Belgium, Canada, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, Mexico, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States.

Source: OECD (2013), OECD Employment Outlook, www.oecd.org/employment/outlook. See also Indicator Employment and Indicator Unemployment.

StatLink  http://dx.doi.org/10.1787/888932965915

Most affected by rising unemployment are low-skilled prime-age workers (Indicator Unemployment), while the doubling of the number of long-term unemployed in the OECD area to 17 million – one in every three jobless people – by the second quarter of 2013 is particularly worrying. Growing numbers of people without recent work experience, depreciating skills, and employers’ reluctance to hire them, swell the ranks of discouraged job seekers, i.e. those who want to work but no longer actively look for a job. Lengthening jobless spells make turning a hesitant recovery into a job-rich economic upswing much more difficult, and can lead to rising structural unemployment.²

Women and older workers have fared somewhat better: their labour market participation had risen prior to the crisis and has mostly continued to do so. They were also less affected by unemployment. Women, for example, are typically overrepresented in the services and public sector that initially suffered less than male-dominated industries like manufacturing and construction. In addition, many inactive women resumed or entered work in an attempt to offset other household members’ loss of earnings. (This so-called “added worker” effect is discussed in detail in Section 3.) Although the crisis had a less adverse effect on the employment situation of women, it spelled the end of the long-term upward trend in employment rates in OECD countries (Indicator Employment).

The collapse in young people’s employment opportunities is of particular concern because it leads to “scarring” – a term commonly used to describe how early working life difficulties can jeopardise long-term career paths and future earnings prospects.³ The share of youth not in employment, education or training (the so-called “NEETs”) has gone up significantly in the OECD area since the onset of the crisis. By late 2012, it stood at 20% or more in Greece, Italy, Mexico, Spain and Turkey [Indicator Youth neither in employment, education nor training (NEETs)]. The sharpest increases were recorded in countries hardest hit by the crisis (Estonia, Greece, Ireland, Portugal, and Spain) and in Italy, Luxembourg, and Slovenia. In the OECD area as a whole, the number of unemployed youth increased by some two million, with young men accounting for the bulk of the rise.

Public sector workers have initially fared better, despite consolidation efforts

Governments plan fiscal savings in a wide range of policy domains (see Figure 1.6). The wage bill for general government employees in the average OECD country accounts for a large share of government expenditures (around 23% on average across the OECD).⁴ As a result, expenditure cuts across all functions of government have often included reductions in staff levels, pay or employee benefits; clearly, public-sector workers are not impervious to the general weakening of the labour market.

At the same time, however, an economic crisis translates into greater demand for social services and other types of labour-intensive public support (e.g. training, education, job-search assistance, and health care). Like other areas of government spending, such services are affected by the conflict that an economic and fiscal crisis generates between a greater need for public support and the reduced fiscal space for financing it. Large drops in staff levels, in particular, may compromise the capacity and quality of social support services (see Section 3).

Figure 1.4 illustrates how general government employment has indeed declined substantially in a number of countries such as Sweden, Italy, and the Slovak Republic. Yet, up to 2011, most countries had safeguarded their public sector jobs more effectively than those in the rest of the economy. Some – like Ireland, Spain, and Slovenia – had actually increased staff levels significantly compared with 2006. However, the latest available international data relate to 2011 and the changes depicted in Figure 1.4 reflect neither governments’ more recent spending cuts nor their future consolidation plans.

Figure 1.4. Public-sector jobs were often more secure despite consolidation efforts

Changes in the shares of working-age individuals in general government jobs and in total employment 2006-11, in percentages

Note: Individuals aged 15 to 64 years old. At the time of writing, 2006 and 2011 are the most recent pre-crisis and post-crisis data on public employment.

Complete or recent data are unavailable for a number of OECD countries (Chile, France, Greece, Iceland, Korea, Portugal, South Africa, Switzerland, Turkey) and are therefore not shown. Data for Australia and Chile refer to the entire public sector (general government and public corporations). Data for Austria, the Czech Republic, Italy, the Netherlands and New Zealand are expressed in full-time equivalents rather than staff headcounts. Data for Germany, Ireland, Norway, Sweden, the United Kingdom are for 2010, not 2011. Data for Hungary, Japan, Mexico, Brazil and the Russian Federation are for 2009, not 2011.

Source: OECD calculations using ILO LABORSTA Database (public employment) and OECD Labour Force Statistics (total employment).

StatLink  http://dx.doi.org/10.1787/888932965934

Individual employment losses leave rising numbers of households with no labour income

The most commonly used statistics of labour-market difficulties refer to individuals rather than households. They therefore do not show how these individual labour-market problems translate into predicaments at the family level. Since 2007 the proportion of people living in households with no income from work has gone up in most countries, approximately doubling in Greece, Ireland and Spain and increasing by 20% or more in Estonia, Italy, Latvia, Portugal, Slovenia, the United States (Figure 1.5). In debates on fiscal consolidation and other policy reforms, such households deserve special attention as they are particularly vulnerable and highly dependent on government support. With more than one in eight working-age individuals in most countries now living in workless households, the success of redistribution measures and active social policies is gauged to a large extent on whether they can improve economic security for families without any income from work.

Figure 1.5. Very large increases in the number of workless households are a major test for social policies

Shares of adults living in workless households, in percentages

Note: Households are defined as “workless” if all household members are either unemployed or labour-market inactive. “Adults” refers to individuals aged 15-64. Data for the United States are for 2013, not 2012.

Source: OECD estimates based on the European Union Labour Force Survey and the United States Current Population Survey.

StatLink  http://dx.doi.org/10.1787/888932965953

Job losses concentrated in economically fragile regions

Geographic concentrations of labour-market disadvantage can threaten social cohesion. They also make it more difficult for governments to respond effectively because they pose greater challenges and because the more economically fragile regions are less able to raise adequate revenue. Regional disparities in unemployment were already high before the crisis (OECD, 2013e). In countries where the unemployment rate has mounted substantially since then, the rise in economically fragile regions has tended to be at least as bad as in the country as a whole. In other words, a large proportion of the increase in unemployment has affected regions where it was above average even before the crisis.

Economic hardship felt most acutely among low income earners and youth

The social impact of the crisis is reflected in the growing numbers of people who struggle to meet their basic needs. According to data from the Gallup World Poll, one in four respondents in the OECD area reported income difficulties in 2012, with the proportion climbing to three out of four in Hungary and Greece and one in two in the United States. The incidence of reported trouble in making ends meet has been on the rise since 2007 in 26 countries, including some where social safety nets have played an important role in cushioning the impact of the crisis (e.g. the Nordic countries, France, and Germany).

Objective measures of household income show both that subjectively reported difficulties are real and that – once again – the burden of income losses has not been evenly shared (Indicator Household income and Indicator Income inequality).

At the onset of the crisis, falling capital incomes lowered top incomes while stimulus packages, along with often powerful automatic stabilisers, helped ease the pain of income losses at the lower end of the income distribution. As adverse economic conditions have persisted, however, lower income households have lost greater proportions of their incomes than the better-off or benefited less from the sluggish recovery – particularly in the hardest hit countries like Estonia, Greece, Ireland, Italy, and Spain, though not in Iceland where well-off households have sustained greater income losses than poor ones.

Across the OECD, the average income of the total population stagnated between 2007 and 2010, while that of the bottom 10% fell at an annual rate of 2%. Clearly, the crisis has worsened longer-term trends of rising income inequality (OECD, 2011), a finding that national studies have confirmed.⁵ More recent aggregate data from OECD national accounts and from national studies using household surveys (such as Cribb et al., 2013 on the United Kingdom) also show that total household incomes often continued to fall after 2010. As social spending comes under pressure from fiscal consolidation, there is a risk that incomes will continue to deteriorate for families with incomes below or close to the poverty line.

Measuring poverty against a relative poverty line (Indicator Poverty) suggests that, between 2007 and 2010, the average share of the poor in OECD countries grew only marginally, by 0.1 percentage points to 12%.⁶ One reason was that social benefits softened the impact of the crisis. But these commonly used relative poverty measures can be difficult to interpret in times of rapid economic change because the poverty line, which is expressed as a percentage of incomes in middle-class households, also moves. Even if those at the bottom of the income ladder suffer significant losses during a downturn, measured poverty might not increase when the average income – and thus the poverty line – falls as well, as often happens during a recession. A more direct way to measure losses at the bottom of the distribution is to take a poverty threshold “anchored” in a given year as the benchmark. This approach reveals a much steeper increase in poverty rates during the first three years of the crisis – as much as two percentage points or more in countries like Greece, Ireland, and Spain.

Thus, even before the bulk of fiscal consolidation programmes kicked in, half of all OECD countries were failing to hold back the rising tide in market income inequality and its impact on those living on incomes at or below the poverty line. However poverty is measured, growing economic hardship at the bottom of the income distribution is unlikely to be a mere “statistical” particularity, where some people shuttle from just above to just below poverty thresholds. Indeed, OECD income distribution data (not reported), together with results from national studies (such as Shaefer and Edin, 2013, for the United States), show that higher poverty rates were frequently accompanied by deepening poverty – a widening gap between families’ incomes and the poverty line.

In a majority of OECD countries, young adults and families with children face considerably higher risks of poverty today than in 2007 (Indicator Poverty). The share of 18-25 year-olds in households where incomes are less than half the national median income has climbed in the vast majority of OECD countries between 2007 and 2010. Rises have been particularly steep in Estonia, Spain, and Turkey (5 percentage points), Ireland and the United Kingdom (4 points), and Greece and Italy (3 points). Lower-income older people did relatively better, as public pension benefits generally changed little and relative income poverty among the elderly fell in most countries. These changes follow a longer-term trend of falling poverty rates among the elderly. Averaged across OECD countries, the proportion of poor people is now, for the first time, lower among the elderly than among young adults and children.

What do these recent trends mean for longer-term inequality trends? Information from earlier downturns provides pointers as to the distributional mechanics which tend to be at work well into the recovery phase. Figure 1.6 offers just such a historical perspective on the income trends among low-, middle- and high-income households across earlier economic cycles. These trends are for market incomes, that is, before adding social transfers or subtracting taxes. By focussing on market income, Figure 1.6 indicates the space that redistribution policies have to bridge if they are to stem widening gaps between household incomes after taxes and government transfers. A number of patterns stand out:7

In spite of long periods of significant aggregate economic growth, low-income households saw market incomes decline over the periods shown in Figure 1.6. Joblessness can take market incomes to very low levels if all family members are without work. (When 10% or more of the population live in such households, the 10th percentile point will be close to zero.) Plummeting incomes during periods of rapidly rising joblessness were, for instance, observed in the early 1990s following the recessions in Australia and the United Kingdom, and during the economic transition in Poland.

Among higher-income groups, any disruptions in longer-term upward trends were short-lived during the downturns of the early 1980s and 1990s.

Market-income inequalities widened in most countries during both downturns and upswings. When incomes at the bottom fell rapidly during and after recessions, incomes in the upper parts of the distribution often continued to rise, albeit at a slower pace. And even where downturns did result in longer-lasting income losses for higher-income groups (as in Australia, Finland and Poland), they nevertheless tended to be smaller than for low earners.

Any episodes of narrowing income differentials did not usually last long enough to offset the gap between high and low incomes that had opened up in preceding years.

These historical trends point in similar directions as data available for the most recent downturn (Cribb et al., 2013; Hoynes et al., 2012). For instance, Hoynes et al. show that, as in earlier recessions, those who are unemployed or in unstable jobs even in good times are, yet again, the main losers in the Great Recession. As they put it with reference to the distribution of jobs and earnings losses, “the Great Recession is different from [earlier] business cycles (…) in size and length, but not in type”.

Figure 1.6. Recessions widen income gaps, and recoveries often fail to close them

Household market incomes for working-age households at different points in the income distribution

In constant prices. Earliest available data point = 100

Note: Initial income gaps between “low” and “high” incomes differ from country to country. Comparisons of the gaps should therefore be made over time, not across countries. Start of series varies due to data availability. Separate series in Canada and the United Kingdom indicate a break due to changing underlying data sources.

Households headed by a working-age individual aged between 15 and 64. “Low” and “high” incomes refer to the 10th (15th in the United Kingdom) and 90th percentiles of the distribution of household market incomes. “Low-growth years” are the bottom third years in terms of real growth between 1979 and 2005 in each country.

Household incomes are market incomes (government transfers are not added and taxes are not subtracted) and account for differences in household size (they are divided by the square root of the household size).

Source: Immervoll, H. and L. Richardson (2011), “Redistribution Policy and Inequality Reduction in OECD Countries: What Has Changed in Two Decades?”, OECD Social, Employment and Migration Working Paper, No. 122, OECD Publishing, Paris, www.oecd.org/els/workingpapers; OECD Economic Outlook: Statistics and Projections for annual growth data, http://dx.doi.org/10.1787/data-00655-en.

StatLink  http://dx.doi.org/10.1787/888932965972

Economic hardship carries serious consequences for families and society as a whole

Economic hardship has a highly tangible impact on well-being and, when they can, households actively adapt to these adverse circumstances. Some types of responses, such as drawing down savings or reducing non-essential consumption, limit negative long-term effects of income losses. But severe, long-lasting economic travails can overwhelm families’ capacity to adapt effectively. Unless there is sufficient public support, they may be forced to cut down on essential consumption, such as food, shelter, and health care. They may also have to curtail investment in their future well-being by, for example, interrupting or cutting short education or training.

Poor households with little savings are more likely to have to resort to coping strategies that are damaging in the long term. Social support measures and policies that ensure adequate access to credit are essential to such households, enabling them to “push through” temporary low-income spells.

Good-quality education may become less affordable as governments spend less

Weak labour markets can make staying on in education a more attractive prospect: opportunity costs – immediate foregone earnings – are lower, which can translate into higher educational attainment (OECD, 2013a; Holzer and Dunlop, 2013).

A good education is expensive, however, and lower wealth, incomes, and profits may affect people’s ability and readiness to invest in education and training (Lovenheim, 2011). To compound matters, fiscal restraint inhibits the provision of the additional resources needed to absorb greater student numbers and maintain quality (Barr and Turner, 2013). Indeed, consolidation efforts halted the long-term trend of rising public spending on education: it declined relative to GDP between 2009 and 2010 in more than half of OECD countries, with cuts especially sharp in Hungary, Iceland, Italy, Sweden, Switzerland, and the United States (Indicator Education spending). Such reductions in public spending are likely to make good-quality education more costly for lower-income households in particular.

The consequences of lower public spending on education will take time to materialise, be it in the form of lower student participation, poorer outcomes, or reduced upwards mobility for children of low-income parents. But, as with cuts in other areas of public investment, it is precisely the longer-term consequences that can be most damaging.

Health outcomes may deteriorate

Difficult economic conditions, people’s behavioural responses to them, and health policy changes may all have impacted on people’s health. There remains, nevertheless, considerable uncertainty as to the net effects of the crisis in the short- and the longer term. At the aggregate country-wide level, studies that consider such broad measures as mortality often find that recessions exert positive short-run effects on health (i.e. mortality is lower). At the same time, there is strong evidence of negative effects on individuals most affected by downturns (unemployed working-age people), especially over the long term (Vangool, 2014).

Indeed, the different ways in which people react to economic downturns have sometimes opposite health effects. For instance, reduced economic activity can curb pollution and lower the risk of road traffic accidents – fatalities on the roads have in fact declined in recent years (OECD, 2013h). Lower incomes may also reduce expenditure on alcohol or tobacco in some groups. At the same time, however, economic troubles can lead to increased substance abuse, anxiety, antisocial behaviour, poor diets, and generally less healthy lifestyles (Catalano, 2009).

Reduced spending on food is one of the main causes of food insecurity, a term that describes a situation where inadequate access to food does not allow all members of a household to sustain a healthy lifestyle.⁸ In the United States, where the incidence of food insecurity is monitored on a regular basis, rates of food insecurity have soared since 2007 (Coleman-Jensen et al., 2013).

While federal food assistance programmes in the United States now support roughly twice as many households as in 2007, the number with inadequate access to food at some time in the year has nonetheless climbed from 13 million (11% of all households) in 2007 to 17.6 million (15%) in 2012. Rates of food insecurity were substantially higher among households with children (20% in 2012) and lone-parent families were particularly affected (35%). Forty-one percent of all food-insecure households received no support through federal food assistance programmes.

While there are no internationally comparable statistics on food insecurity that are as detailed as those of the United States, some unofficial estimates indicate that growing numbers of families and children suffer from hunger or food insecurity in economically distressed countries. Some 10% of students in Greece fall into that category according to Alderman (2013). The Gallup World Poll includes a question on whether respondents feel that they have “enough money to afford food”. Responses confirm that rising numbers of families in OECD countries may have less money to spend on food and a healthy diet. By contrast, while large shares of people in the large emerging economies feel that they cannot afford adequate nutrition, their numbers have mostly declined since 2007 (Figure 1.7).

Figure 1.7. Growing numbers of people feel they cannot afford food

Percentage of survey respondents

Note: Share of “yes” responses to the question “Have there been times in the past 12 months when you did not have enough money to buy food that you or your family needed?”.

Results are averaged over a two-year period to minimise the impact of year-on-year fluctuations.

2008 data for Iceland, Luxembourg and China instead of 2006-07; 2009 data for Switzerland (instead of 2011/12).

For measurement details and limitations of the Gallup World Poll, see Chapter 7.

Source: Gallup World Poll, www.gallup.com/strategicconsulting/en-us/worldpoll.aspx.

StatLink  http://dx.doi.org/10.1787/888932965991

Another critical risk factor for worsening health is constrained access to health care, particularly among the poorest. Economic downturns may result in lower rates of health care use if more people feel they cannot afford it – when private health insurance is tied to employment, for example. Moreover, in response to deteriorating public finances governments may cut health spending and, by the same token, their health care provisions (Vangool, 2014).

With household budgets under pressure, families have indeed reduced their use of routine health care services since the onset of the economic crisis, particularly in countries with high co-pay health insurance plans. For instance, in a survey in the United States, 27% of respondents stated that they had cut back on their use of health care services in 2009 (Lusardi et al., 2010). Similarly, across eleven OECD countries, 15% of respondents said that health costs had stopped them from visiting their doctor, filling prescriptions, and/or having a medical check-up at least once during the previous 12 months (Schoen et al., 2010). For Europe, recent data show that, in all countries, low-income families have above-average “unmet medical needs” (Indicator Coverage for health care). And across OECD countries, the share of low-income individuals reporting a “good” or “very good” perceived health status is significantly lower at 61% than the 80% share among high earners (Indicator Perceived health status). Such patterns highlight the significant risk of income losses translating into lower utilisation of health care services and, subsequently, into poor health.

Lower or delayed utilisation of preventive measures such as breast cancer screening also gives cause for concern as it may lead to additional health risks, greater care needs, and higher spending in the future. Catalano (2003) describes how, during periods of economic stress, the incidence of diagnoses of advanced disease appears to rise. A recent study also finds that a 1% increase in unemployment in the United States is associated with a 1.6% lower use of preventive care facilities (Tefft and Kageleiry, 2013). Poorer individuals, who typically have greater health care needs and are also more likely to cut spending may thus expose themselves to significant risk (Edwards, 2008; Schoen et al., 2011).

Generally speaking, there is overwhelming evidence that long spells of unemployment and joblessness are detrimental to both mental and physical health (OECD, 2008a; Sullivan and von Wachter, 2009). Recent studies of patterns in the prescribing of mental health drugs in the United States suggest that prescriptions rise during recessions (Bradford and Lastrapes, 2013). Even a relatively small rise in unemployment can lead to a substantial increase in the use of drugs. Kozman et al. (2012) report increases of 4% in prescriptions for statins and 3% in PDE inhibitors following a 1% rise in unemployment. In Sweden and Denmark, job loss was found to lead to a higher probability of hospitalisation for alcohol-related conditions, accidents, and mental health problems (Eliason and Storrie, 2009). There also appears to be a close link between the economic crisis and hospital attendance more broadly. For instance, in the United States, Curry and Tekin (2011) and Brooks-Gunn et al. (2013) report an increase in admissions for preventable conditions and the physical abuse of children.

Rise in pre-crisis fertility rates has stalled in several countries

The recovery in fertility rates observed in several OECD countries prior to the crisis now appears to have come to a halt. Up until the early 2000s, fertility in the OECD area dropped dramatically from 3.3 children per woman in the 1960s to 1.63 – significantly below the so-called “replacement level” of 2.1. The subsequent modest rise in total fertility rates (TFRs) to a country average of 1.75 in 2008 was an encouraging development. Since then, however, average TFRs have dropped back – to 1.70 in 2011 – as lower and uncertain incomes may have prompted families to delay parenthood or have fewer children (Indicator Fertility). Even tiny variations in fertility rates affect demographics, patterns of population ageing and, consequently, the sustainability of existing social and health provisions.

Fertility levels and past trends, however, vary hugely across countries, with many emerging economies currently seeing a “youth bulge” resulting in large and growing numbers of young people, while populations are ageing in high-income countries. Where populations decline, migration becomes more significant – both as a factor shaping the demographic composition of a country’s population, and as a possible mechanism for alleviating trends in populating ageing (Indicator Migration). The patterns of crisis exposure and poor economic conditions have altered the dynamics of migration across the OECD area. Australia, Norway, and Switzerland – all countries that were less affected by the crisis – did indeed see an increase in net migration. But migration outflows rose sharply in hard-hit countries such as Estonia, Greece, Spain, Ireland, Italy, Iceland and Portugal. Where young and skilled population groups leave in large numbers, countries face significant additional challenges and the prospect of a worsening demographic outlook and less favourable economic development (OECD, 2013i).

Other social impacts of the crisis are plausible but not always visible in available data

Changes in behaviour or attitudes are a consequence of the strategies that families adopt to cope with economic crises. For instance, although they share resources in all stages of the economic cycle, mutual support becomes vital when economies are weak. Through the support provided by other family members, those affected by job or financial losses thus enjoy greater economic security. However, providing this support places greater demands on family resources, with widespread unemployment or troubled pension investments, for example, prompting a rise in intergenerational support. This pattern is, for instance, documented by studies showing large numbers of unemployed youth returning to the parental home or not moving out in the first place (Morgan et al., 2011 report such a pattern for the United States).

Although the greater need for support may strengthen family ties, economic stress and more acute work-life conflicts can also lead to family breakdown and higher divorce rates. Recent data point to an increase in perceived work-life conflicts (OECD, 2013d) and work pressures resulting from job insecurity and unsocial working hours (McGinnity and Russell, 2013). The net effect of such factors on family bonds and family structure is not clear, however, and may be small (Indicator Family).

Greater economic hardship and dissatisfaction affect not only family ties but also relationships with and attitudes to fellow citizens and social, economic and political institutions. Such changes in outlook may, in turn, drive patterns of civic engagement and collective action for political reform and societal progress. Conversely, indicators of the degree of acceptance of minorities – e.g. immigrants or individuals with a particular sexual orientation – point to significant drops in tolerance in some countries where the crisis has bitten hard. Greece is a notable example. Currently, however, there is little evidence of a systematic link between intolerance and the economic crisis, which suggests that economic factors are neither the sole nor primary drivers of observed change (Indicator Tolerance). Indicators of solidarity, such as charitable donations or voluntary work, also show a significant drop in Greece, while they have risen significantly in other hard-hit countries (Indicator Helping others).

However, the link between economic difficulties and people’s mistrust of national governments appears to be more clear-cut. Such trust declined in most OECD countries from 2007 to 2012, with the largest drops coming in Greece, Ireland, Portugal, and Slovenia. However, young people in Spain and Portugal tended to trust their governments more than their adult counterparts, and their confidence also declined less. There has been a much sharper fall in trust in financial institutions across virtually all OECD countries (Indicator Confidence in institutions).

Where the crisis has bitten, life satisfaction is now lower than in 2007

Societal well-being is a difficult concept to measure and compare on any one-dimensional scale,⁹ be it a traditional metric like GDP or a subjective measure like happiness. As a “satisfactory empirical approximation [of individual utility]” (a phrase used by Frey and Stutzer, 2002), subjective well-being is, however, of considerable interest when assessing the social impact of policy reforms or economic “events” such as the Great Recession.

There have been a number of recent reports of the crisis leading to greater dissatisfaction with life. Some of the most alarming potential symptoms of such a trend relate to rises in suicide rates. A closer look at cross-country data confirms that suicide rates climbed slightly at the onset of crisis in countries such as Ireland, but recent data suggest that the trend has not persisted. Although there was a rise in the number of suicides reported in Greece in 2011 (Liaropoulos, 2012; Karanikolos et al., 2013), the rate stood at one-fourth of the OECD average. Overall suicide rates in the country were stable in 2009 and 2010 despite worsening economic conditions and the changes since then – a rise in 2011 and a drop in 2012 – do not point to any clear trend. Similarly, for the OECD area as a whole, the severe economic crisis does not so far appear to have led to a sharp change in suicide rates (Indicator Suicide).¹⁰

However, as argued above, the major health-related and societal problems that a deep economic crisis may trigger are unlikely to materialise immediately. For instance, research shows that there is a reasonably strong longer-term association between life dissatisfaction and higher risks of suicide (Koivumaa et al., 2001). Waning life satisfaction could thus be seen as a leading indicator that points to serious health or societal problems developing at a later date.

Across the OECD area, average reported life satisfaction in 2012 was only slightly lower than in 2007 (Indicator Life satisfaction). But related data for Europe show that reported well-being declined substantially among groups suffering the biggest deterioration in incomes and labour-market prospects (Eurofound, 2013). There were also sizable fluctuations in the intervening years. In 2008 and 2009, contentedness fell significantly as the scale of the crisis became clear. Then, in 2010, most countries emerged from recession. Life satisfaction climbed before dropping once again in 2011 and 2012 when fiscal problems mounted and recovery turned out to be weaker than hoped. Life satisfaction deteriorated most in Southern Europe (Greece, Italy, Portugal, and Spain), while it improved in countries where the economic impact of the crisis was either less acute or shorter (e.g. Chile, Mexico and, to a lesser extent, Nordic and some Eastern European countries). In the vast majority of OECD countries, however, expectations as to future life satisfaction fell (OECD, 2013d).

Emerging economies were less affected by the crisis, but still face major social challenges

The major emerging economies have made very significant progress towards reducing absolute poverty. Although high inequality and the comparatively low capacity of their social protection systems remain considerable challenges, the economic and social impact of the global downturn was less than in most of the OECD area. The context in which it took place was also significantly different in emerging economies.

Thanks to long periods of strong economic growth, emerging economies have reduced extreme poverty. However, their experience of earlier recessions underscores the need to develop sustainable, “crisis-proof” social protection systems (Box 1.2). Inequality and poverty continue to be daunting policy challenges in emerging economies. Yet their social budgets are smaller than in the OECD area, which leaves many workers and households exposed to economic shocks. The fiscal outlook, while generally much better than in advanced countries, has also become less favourable, due, in part, to higher interest rates and weaker growth prospects (IMF, 2013). In effect, then, OECD and emerging economies must both rise to the challenges of securing adequate resources for their social policies and, where necessary, of “doing more with less”.

Box 1.2. Major emerging economies continue efforts to strengthen redistribution

In contrast to recent and projected austerity measures in much of the OECD area, the large emerging economies have generally sought to bolster redistribution measures as part of their bid to address concerns over high poverty and inequality. Measures to strengthen social protection should also be seen in the context of strategies to support domestic demand and reduce excessive saving in some countries – particularly in China.

The big emerging countries have drawn on their strong economic growth to reduce extreme and absolute poverty – a direction they have pursued since 2007 despite the slow-down in growth. Yet their poverty-reduction achievements cannot be explained by aggregate growth alone. They also reflect effective redistribution policies, as exemplified by well designed, targeted programmes that help to cushion the impact of economic shocks on the most vulnerable.

Income redistribution is a central pillar of Brazil’s growth model. Since the early 1990s, the country has made tremendous progress in lifting millions of people out of poverty and reducing inequality. The Bolsa Familia conditional cash transfer has become a prominent model of successful poverty reduction programmes and a benchmark for anti-poverty measures in other countries.

In India, where fiscal deficits have been much wider than in other emerging economies, spending on social welfare is skewed towards food and other subsidies and employment in public works schemes. Income transfers play a much more limited role, although recent initiatives seek to convert a number of subsidy programmes into direct cash transfers.

High levels of income inequality and poverty in South Africa are, in large part, a reflection of labour-market inactivity and unemployment. Between 2007 and 2011, labour utilisation deteriorated further. However, the introduction of progressive tax measures and the expansion of social transfers since the mid-1990s have strengthened government redistribution.

Although inequality is also high in China, it reached its peak in 2008. In urban areas, it has trended down for some years, reflecting changing wage patterns and more comprehensive health care reimbursements at the lower end of the income spectrum. The gap between rural and urban incomes has also narrowed as rural migrants send earnings back to the countryside. Within rural areas, though, migration has widened income gaps between families with and without migrant workers.

In Indonesia, absolute poverty declined rapidly prior to the Asian financial crisis of the late 1990s, a trend that has continued to this day, albeit at a slower pace. Pro-poor economic growth almost halved the proportion of people living below the national poverty line between 2000 and 2010, reducing it to 13.3%. The policy was, however, accompanied by a significant rise in income inequality, which has also had a powerful effect on the lower part of the income distribution. Overall, Indonesia’s expenditure on poverty reduction schemes remains low: between 2004 and 2010, the country spent approximately 0.5% of GDP on social assistance on average, whereas the average expenditure in other developing countries was some 1.5% (World Bank, 2012).

Selected new policy initiatives

Across emerging economies, there are some bold examples of new policy initiatives and measures to increase the effectiveness of existing social protection measures. Challenges remain, however, as income gaps are often very wide and the effects of structural changes, such as rural-urban migration, can further aggravate them. Reforms have also come up against administrative bottlenecks and challenges which can hamper the effective implementation of social protection measures. Nevertheless there are encouraging examples of well-designed, highly successful programmes.

In Brazil, the poorest 10% of the population enjoyed very fast annual income growth rates in recent decades (comparable with China’s per capita GDP growth), while income gains in the top decile were smaller (closer to per capita GDP growth in Germany). Changes in both labour and non-labour incomes have played equally important roles in sustained inequality reductions. Labour incomes have become more equally distributed as the earnings gaps between high- and low-skilled labour (Brazil’s traditionally very high “education premium”) have narrowed markedly and greater access to education has enabled more households to earn higher wages. At the same time, government transfers have played a crucial role. The Bolsa Familia scheme provides cash transfers to low income households that are conditional on school attendance and health check-ups. Recent refinements have further increased the programme’s generosity, effectively lifting all participants whose income was below the national poverty line above that threshold.

In India, the expansion of the national health insurance system for the poor, known by the acronym RSBY, is important and welcome as large out-of-pocket expenses associated with private hospital stays have long been a barrier to health care access. At the same time, increased investment in public health facilities supports the very poor in areas where no other health services exist. The National Rural Employment Guarantee Scheme (NREGS), a workfare scheme rolled out in 2006 and subsequently expanded nationwide, seeks to guarantee a minimum of 100 days of employment at the minimum wage in rural areas. Its main objectives include boosting rural income, stabilising agricultural production, and curbing rural-urban migration by funding small-scale farm and local infrastructure projects. There is, however, no national equivalent for poor urban dwellers, and the quality of programme implementation has been mixed. The government has renewed efforts to further increase food subsidies, to target them more effectively, and to address some of the very costly inefficiencies in the existing distribution of subsidised food. From July 2013, the National Food Security Act (NFSA) provides some 67% of the population with legal rights to five kilogrammes of food grains at highly subsidised prices, and with an equivalent cash allowance if subsidised food is not available or insufficient. NFSA complements existing food programmes run by states and also provides daily free meals for children, pregnant women and lactating mothers.

South Africa has, over recent years, clawed back some 40% of the increase in market-income inequality through its expansion of social transfer schemes. Social assistance now accounts for two thirds of income in the bottom quintile (the poorest 20% of the population). Nevertheless, the reduction of inequality attributable to taxes and transfers remains well below OECD levels. The South African government’s two main strategic policy documents – the National Development Plan and the New Growth Path – place much emphasis on measures to increase employment and cut unemployment. At the same time, limited administrative capacity, especially at the sub-national government level, is one stumbling block in efforts to build a more inclusive society. In addition, engaging households with poor literacy in administrative procedures is vexed by information barriers and inefficiencies. Partly as a result social programmes do not reach all intended beneficiaries (for instance, the take-up rate of the Child Support Grant, a comprehensive social assistance programme, is only 60%).

The China State Council issued guidelines in February 2013 designed to encourage further measures to reduce inequality and boost consumption, e.g. by strengthening redistribution through better tax collection and pushing ahead with property taxes. The guidelines also called for increased social expenditure (from 36% of government outlays in 2011 to 38% by 2015) with an emphasis on low-income regions through intergovernmental transfers. As in South Africa, effective implementation at the local level is likely to be critical to effectively functioning redistributive policies, especially in big, expanding urban areas.

In Indonesia, a new era of decentralisation in the wake of the Asian financial crisis in the late 1990s brought significant changes to the country’s political structure and social policy strategies. While most centrally managed poverty alleviation policies were universal, decentralisation was accompanied by measures increasingly targeted at helping the poor. In addition to providing resources through social assistance, direct poverty alleviation strategies, such as improved access to health and education, are now much more common. In a culturally, geographically, and economically very diverse country, decentralised intervention strategies have had positive impacts. As in other emerging economies, implementation challenges remain, however, especially in relation to the appropriate targeting of beneficiaries. Dealing with inefficiencies and “leakage” in social assistance is one key policy challenge. Indonesia’s expenditure on major poverty reduction programmes remains small. Low spending levels are in part driven by weak tax collection: although they have increased in recent years, tax revenues still account for only 12% of GDP – significantly lower than in other emerging economies.

Symptoms of a social crisis – and the right policy responses

In summary, the evidence considered in this first section of the chapter suggests that the financial upheaval of 2007-08 led not only to an economic and fiscal crisis in many countries, but to social crises, too. Figure 1.8 presents selected outcome measures for which a “crisis link” is already clearly visible. Life satisfaction has declined much more steeply in countries where household incomes have fallen most (Figure 1.8, Panel A). The same is true for fertility rates (Panel D). Crisis-related effects on other outcomes, including health, take longer to materialise. The indicators presented in Chapters 3 to 7 provide a fuller picture of the social situation across the OECD and how it has changed since the crisis began.

Figure 1.8. Crisis exposure and policy shape key social outcomes

Reading note: The average fall in fertility rates was 0.02 across countries with a “small” drop in household incomes, but 0.10 across countries with a “large” drop in household income.

Note: Country groups were constructed by comparing the change in the relevant indicator to the OECD average, as described in Annex 1.A1, resulting in the following groupings:

Household income. Small decline (or growth): Austria, Canada, Chile, the Czech Republic, Denmark, Finland, Germany, Israel, Poland, the Slovak Republic, Sweden. Large decline: Estonia, Greece, Hungary, Iceland, Ireland, Mexico, New Zealand, Spain.

Public social spending. High growth: Australia, Chile, Estonia, Israel, Korea, New Zealand, Poland, the Slovak Republic, the United States. Low growth: Germany, Greece, Hungary, Iceland, Italy, Portugal.

Recent consolidation effort. Low: Denmark, Estonia, Finland, Germany, Korea, Norway, Sweden, Switzerland. High: Australia, France, Greece, Iceland, Ireland, Italy, Poland, Portugal, Slovenia, Spain, the United States.

Expected future consolidation effort. Low: Australia, Austria, Denmark, Estonia, Germany, Korea, Luxembourg, New Zealand, the Slovak Republic, Slovenia, Switzerland. High: Greece, Japan, Portugal, the United Kingdom, the United States.

Source: See Annex 1.A and Indicator Life satisfaction, Indicator Poverty, Indicator Unemployment and Indicator Fertility.

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The precise patterns differ from one indicator to another and the associations shown in Figure 1.8 are not prove of a causal relationships (for instance a third factor, such as unemployment, is plausibly causing the drops in both household incomes and life satisfaction). But whatever the mechanism behind them, the patterns underline that social outcomes have tended to deteriorate more in countries where households were particularly exposed to economic hardship during the downturn.

In addition to crisis exposure, the policy responses – discussed in Sections 2 and 3 below – matter as well. Fiscal pressures make it more difficult to provide adequate public support in countries where it is most urgently needed. The social and political burden of fiscal pressures is highlighted by the fact that the countries which made the greatest efforts to limit increases in social spending (the “low spending growth” countries in Figure 1.8) or reduce fiscal deficits (the “high recent effort” countries) did so against a background of declining incomes among the poor and increasing unemployment (Figure 1.8, Panels B and C). Importantly, the extent of economic hardship and the deterioration in broad life-satisfaction measures are also more sizable in countries with the greatest future fiscal consolidation needs (“high future effort” countries in Figure 1.8, Panels A, B, and C). Efforts to reduce public debt will therefore continue to come up against the tough task of implementing reform programmes that address immediate social concerns and priorities now, while remaining fiscally, socially and politically sustainable in the future.

2. Social policy responses to date

The nature of problems that households faced in the wake of the Great Recession did not come as a surprise. However, the scale of the resulting social policy challenges and the constraints of the ensuing fiscal crisis were only partially anticipated at the outset. As a result, governments’ responses to the crisis have continued to evolve, as has their general policy stance. Initially, they increased social spending and put in place large fiscal stimulus packages that included greater resources for social measures. But the large fiscal imbalances that governments now face restrict the available policy options (Cournède et al., 2013). Although many European countries and the United States have recently narrowed budget shortfalls significantly, large government debts will see fiscal pressures persisting well into the rest of the decade and often beyond. Social spending, which remains part of most fiscal consolidation plans, looks set to come under further pressure – with potentially serious consequences for the capacity of social policy to provide crucial support.

This section first discusses recent trends in social spending and in the number of people who rely on social support measures. It then assesses countries’ fiscal consolidation efforts, the role social policies play in those efforts, and how the availability and quality of support are affected.

Social spending increased most in countries least affected by the crisis

The global economic crisis has led to a sustained increase in social spending both as a share of GDP and in real terms. On average across the OECD, the ratio of public social spending to GDP rose from around 19% in 2007 to 22% in 2009-10 and has remained at that elevated level (see Figure 1.9 and Indicator Social spending). The sharp decline in GDP in some countries accounts in part for the rising spending/GDP ratios. However, with the exception of Greece and Hungary, social spending has also burgeoned in real terms (Figure 1.10).

Figure 1.9. Social spending keeps rising in real terms, but has stabilised as a share of GDP

Estimated trends in average public social spending in the OECD area

Note: Real-term figures are shown in index form, with a value of 100 in 2007.

Public social spending totals reflect detailed social expenditure programme data for 1980-2009, national aggregates for 2010-12, and estimates for 2013, and are based on national aggregates in national sources, the OECD Economic Outlook (No. 93, May 2013), and the European Commission’s Annual Macroeconomic Database (AMECO, May 2013). Details of estimates for recent years are provided in 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, www.oecd.org/social/expenditure.htm and http://dx.doi.org/10.1787/5kg2d2d4pbf0-en.

Data for Turkey are not available, and information on national spending aggregates is not available for Japan beyond 2010 or for Chile, Korea and Mexico beyond 2012.

Spending totals for 2010 to 2012 (light shade) are subject to revision, but these are likely to be slight. The estimates for 2013 (dark shade) are most likely to be affected by later revisions to expenditure and GDP data.

Source: OECD (2013), OECD Social Expenditure Database (SOCX), preliminary data, www.oecd.org/social/expenditure.htm.

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Strikingly, the biggest increases in expenditure between 2007/08 and 2012-13 came in countries with relatively strong GDP growth and greater spending power, and not in those where deep downturns produced the greatest need for support (Figure 1.10). Some countries with significant GDP drops did, however, respond to deep or long-lasting downturns with substantial hikes in social spending (e.g. Estonia, Finland, Ireland, and Spain). There were others, though, like Italy and Portugal, where increases were only slight over the whole period. Real public social spending was substantially lower than before the crisis in Greece and Hungary, where it was down 17% and 11% respectively. The cuts made by the two countries illustrate the difficulties of maintaining a counter-cyclical policy stance in a severe downturn.

Figure 1.10. Social spending increased least in countries most affected by the crisis

Percentage changes in real public social spending and real GDP, 2007/08 to 2012/13

Note: See notes to Figure 1.9. Estimates for 2007-08 and 2012-13 are averaged over two-year periods to allow for the different years in which the crisis began across countries and to limit the effect of year-on-year fluctuations.

Source: OECD (2013), OECD Social Expenditure Database (SOCX), preliminary data, www.oecd.org/social/expenditure.htm.

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Transfers to working-age individuals driving upward trends in social expenditure

Benefits typically paid to working-age people and their families make up only one-fifth of total public social spending. Yet they account for close to one-third of increases in expenditure since the onset of the crisis. Over the previous two decades, almost all OECD countries reduced transfers to working-age individuals and children – from 27% in 1985 to 21% in 2005 (Immervoll and Richardson, 2011). The Great Recession brought this downward trend to an abrupt end, as unemployment benefits, general social assistance, disability benefits, and cash family benefits increased (see Figure 1.11). On average across the OECD, spending on these “working-age transfers” has risen by some 17% in real terms.

Figure 1.11. Spending on working-age cash transfers rose steeply

Changes in spending on working-age benefits and their share in changes of total public social spending In percentages, 2007/08-2012/13

Notes: See notes to Figure 1.9.

“Working-age” cash transfers include the following spending categories: incapacity benefits (disability and sickness), family cash benefits, unemployment and so-called “other social policy areas” (which includes minimum-income benefits).

The contribution of changes in “working-age” transfers to changes in total social spending is calculated in relation to spending as a percentage of GDP. Chile, Japan, Mexico and Turkey are not included as breakdowns by spending category are not available.

Estimates for 2007-08 and 20012-13 are averaged over two-year periods to allow for the different years in which the crisis began across countries and to reduce the effect of annual fluctuations.

Source: OECD (2013), OECD Social Expenditure Database (SOCX), preliminary data, www.oecd.org/social/expenditure.htm.

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Much of the increase in social spending early in the downturn was prompted by the rise in out-of-work benefits, especially unemployment insurance, which act as a first line of defence against income drops for job losers. Several countries also boosted spending on “partial” unemployment benefits or “short-term working schemes” (Hijzen and Venn, 2011). Such programmes, which provide income support for those affected by temporary cuts in working hours and earnings, can reduce or slow initial job losses and spread the economic burden of a temporary downturn more evenly across income groups (Bargain et al., 2011; Hijzen and Martin, 2012).

As the crisis progressed, however, expenditure on lower-tier assistance benefits (safety-nets for those who are not, or no longer, entitled to insurance benefits) started rising, too, especially in countries with persistently high unemployment and short-duration unemployment insurance benefits. On average across the OECD, unemployment compensation increased by about 80% in real terms (from an average of 0.7% of GDP in 2007 to 1.1% in 2009). With increases of more than 200%, spending rose most steeply in Estonia, Iceland, and the United States and doubled in Turkey, Ireland, Japan, the United Kingdom, and New Zealand.

Spending increases were driven more by rising numbers of beneficiaries than by higher entitlements per recipient. Although support for the unemployed tended to become less generous in the years prior to the crisis (Immervoll and Richardson, 2013), there was very little change OECD-wide in the overall generosity of jobless benefits between 2007 and 2011. Figure 1.12 shows the net replacement rate (NRR) – the ratio of income received when not in work to that received in work – for a single individual over a long spell of unemployment. NRR changed by less than 5% over a five-year period in around half of all OECD countries and by less than 10% in some others.

Individual countries did, however, introduce sizeable reforms. Among countries showing declining NRR, the drops were largely due to an erosion of benefit levels relative to wage growth, and not to explicit cuts in nominal benefit levels (countries such as Germany, Australia, and New Zealand).¹¹ However, both Norway (prior to the crisis) and Denmark (from 2010) shortened benefit durations, thereby reducing NRRs for people with long unemployment spells.¹² Longer benefit durations increased NRRs for the long-term unemployed in a few countries – the United States, Greece, Canada, and Italy. In the United States, the very large increases were driven by temporary benefit extensions from the standard 26 weeks to 99 weeks. Although the changes in the United States stemmed largely from new legislation, they also reflected automatic extensions that are triggered once state unemployment exceeds or drops below a certain threshold. Canada also operates a system of automatic benefit duration adjustments that depend on provincial unemployment rates.¹³

People not eligible for unemployment benefits may be entitled to receive minimum-income benefits as a follow-up. However, the value of minimum-income benefits generally remained significantly below commonly used relative poverty thresholds across the OECD. Those exhausting unemployment benefits before they find work therefore risk suffering extended periods of income poverty (Indicator Living on benefits).

Figure 1.12. Unemployment benefit amounts changed little, but durations were extended substantially in some countries

Percentage change in long-term net replacement rates, 2007-11

Note: The net replacement rate is calculated for a single individual with a “low-paid” job prior to becoming unemployed (67% of the average wage). It is a synthetic indicator that averages out-of-work incomes over a hypothetical five-year unemployment spell. By showing the replacement rate averaged over a long unemployment spell, the indicator captures changes in both benefit levels and duration. Calculated incomes in work and out of work take into account income taxes, own social contributions, in-work benefits, unemployment insurance and assistance. Means-tested minimum-income and housing benefits are not included. For the generosity of these benefits, see Indicator Living on benefits.

In Ireland, both in-work income and out-of-work benefits fell. The fall in in-work income was stronger, so increasing the NRR.

The only countries which showed relatively large NRR changes since 2010 were Germany (reduced generosity due to the termination of a transition payment for those moving from insurance to assistance benefits) and Greece (higher NRR due to a combination of increased nominal benefit value and wage deflation).

Source: OECD Tax-Benefit Models, www.oecd.org/els/social/workincentives.

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In countries where family support is largely income-tested, public spending on family cash benefits increased as incomes started to fall. In the early years of the crisis (2007-09), average spending on family benefits across OECD countries rose by 0.3 percentage points of GDP – an increase of 10% in real terms. The biggest rises were seen in Korea (50%), Greece (30%), Ireland and Portugal (20%), and in the United Kingdom (10%). Family support is also likely to have gone up in countries where it is delivered as tax credits (although such data are not available for all countries on a comparable basis). In the United Kingdom, for example, Child and Working Tax Credits helped to cushion the effect of the crisis on poor families. Higher numbers of low-income families led not only to more claimants, but also to more receiving the maximum benefit, although policy changes in 2012 reduced the number of recipients (OECD, 2014b; HM Revenue and Customs, 2013).

In sharp contrast with previous recessions, receipt of neither old-age pensions nor disability benefits receipt has increased significantly (Figure 1.13 and Indicator Recipients of out-of-work benefits). In previous downturns, early retirement and disability programmes were frequently used to ease pressures in the labour market. Since those who join such schemes do not typically re-enter the labour market during a recovery, the practice led to large, practically irreversible increases in social expenditures. In the current crisis, there has not been a massive inflow of unemployed people into early retirement or disability benefit programmes. Instead, recent changes in receipt of these transfers have continued to be driven primarily by demographic factors. In the case of disability programmes, structural reforms – designed to strengthen gate-keeping, the assessment of health conditions, and incentives to return to work – appear to have made them more resilient to changes in the economic cycle (some relevant reforms are highlighted below). Pension spending tends to be much less sensitive to the business cycle once countries close access to early retirement, which many have done.

Figure 1.13. More people receive unemployment benefit, but receipt of “inactive” benefits has largely remained stable

OECD total, number of recipients in 2007 shown as 100

Note: Unemployment and benefit recipient ratios relative to the working-age population (total population for old-age benefits).

Source: Calculations based on OECD (2014), Social Benefit Recipients Database.

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But social policies are now at the core of fiscal consolidation

Reduced fiscal space risks compromising continued provision of social support

Fiscal space has been shrinking in most OECD countries, putting more pressure on social spending as governments reduce budget deficits. In 2009 and 2010, the net lending positions of OECD governments slid from their 2007 heights. OECD projections for 2013 and 2014 do not foresee them returning to balance in the near future – with the exception of countries which ran surpluses prior to the crisis, such as the Nordic countries, Australia, and Germany. Structural deficits which existed before 2008 have widened since and will not disappear without consolidation efforts and a return to growth. Planned consolidation is often more far-reaching precisely in countries that where social expenditures have increased as a share of GDP (Figure 1.14, Panel A).

Scrutiny of projected consolidation efforts suggests that pressures to address budget shortfalls are greatest in countries that have experienced the steepest rises in unemployment (Figure 1.14, Panel B). Such is the outlook for a number of Eurozone countries, although a similar picture also emerges for other OECD countries, albeit to a lesser extent. When unemployment rises fast, governments’ fiscal problems are heightened both by increasing expenditures and by contracting revenues. The pattern documented in Panel B of Figure 1.14 is therefore not surprising. But it underlines concerns about the ability of governments to effectively address rising social needs and about the timing and substance of consolidation efforts on the tax and the spending sides. In many countries, consolidation pressures will persist well beyond the next two years, with significant pressures for further consolidation over the next 10 to 15 years (OECD, 2013k; IMF, 2012b).

Figure 1.14. Rising social spending and social needs, but decreasing fiscal space

Note: See notes to Figure 1.9. Averages for 2007/08 and 2011/12 are used as the timing of the downturn and the beginning of any fiscal consolidation efforts varied across countries.

“Consolidation effort”: change in underlying primary balance, percentage points of GDP.

“Increase in social expenditure”: change in social expenditure, percentage points of GDP.

Source: OECD (2013), OECD Economic Outlook: Statistics and Projections, No. 93, May, www.oecd.org/economy/outlook/economicoutlook.htm and http://dx.doi.org/10.1787/data-00655-en; OECD (2013), OECD Social Expenditure Database (SOCX), www.oecd.org/social/expenditure.htm.

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Figure 1.15 shows one possible measure of expected future consolidation pressures. The United States and a number of countries in Europe have already implemented or announced policies that are expected to reduce budget shortfalls very significantly relative to their 2010 levels (light grey bars). Most, however, will need to reduce deficits further and maintain this tighter fiscal stance through to 2030 if they are to put government debt on the downward path to a 60% of GDP target (dark blue bars).

Figure 1.15. Fiscal pressures will persist well into the next decade

Short-term consolidation efforts (2010-14) and medium-term consolidation scenarios (2014-30)

Change in the primary budget balance, in percentage of GDP

Note: Over the 2014-30 projection period countries with gross government debt ratios in excess of 60% of GDP are assumed to gradually reduce debt to this level, whereas other countries stabilise debt ratios at their current levels. Consolidation requirements from 2014 to achieve these objectives are measured as the difference between the underlying primary balance in 2014 and its average over the period to 2030 (or until the debt ratio stabilises). Due to very high initial debt levels, and despite a very large average fiscal consolidation requirement of 11 percentage points relative to the 2014 balance, the scenario for Japan only broadly stabilises gross debt between 2014 and 2030 at a level of over 200% of GDP.

Source: OECD (2013), OECD Economic Outlook, No. 93, http://dx.doi.org/10.1787/data-00655-en.

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Importantly, however, these projections do not account for the expected increases in government spending on health and pensions due to ageing and other factors. If estimates of these additional outlays are factored into projected expenditure, the prospect of achieving the putative 60% target becomes significantly more remote: as the arrows in Figure 1.15 illustrate, significant fiscal pressures will remain in the medium term, even in countries that would otherwise have a more positive fiscal outlook. The inference is that pro-cyclical consolidation efforts during recessions or low-growth periods are no substitute for longer-term, structural measures that put government finances on a sustainable footing.

Social transfers have been the main target of consolidation measures

Of all areas of public spending areas, social transfers have been the focus of by far the greatest number of consolidation measures since 2011. Country responses to OECD policy questionnaires reveal that the category most frequently selected for savings was “working-age transfers” (unemployment, social assistance, disability and family benefits), followed by health care and old-age pensions (Figure 1.16). In addition, many consolidation plans include unspecified savings – in other words, no details are given on savings that take the form of general spending cuts across departments. Although such unspecified measures may involve sizeable cutbacks (e.g. EUR 3 billion between 2011 and 2014 in Ireland) and affect social policy areas, they are not included in the breakdown in Figure 1.16.

Figure 1.16. Social transfers are more often part of consolidation plans than other areas of public spending

Major programme measures in fiscal consolidation plans, by area of public spending

Reading note: 70% of countries have planned to cut welfare spending in 2012.

Note: “Working-age transfers” include unemployment benefits, social assistance, housing benefits, disability benefits and family benefits. “Pensions” denotes old-age pensions only.

Source: OECD (2012), Restoring Public Finances, 2012 Update, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264179455-en.

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More than two-thirds of OECD countries reported plans to reduce spending on “working-age transfers” in 2012. Greece planned to reduce them by 1.9% of GDP (through cuts in social security funds and social spending). This is the largest reduction in the OECD area. Under the same heading, Ireland, Hungary, Poland, Germany and the United Kingdom planned spending cuts totalling more than 1% of GDP. The United Kingdom revised and increased its planned expenditure reductions from 0.4% of GDP in 2011 to 1.1% in 2012 through cuts in child and disability benefits. France, Iceland, and the Netherlands planned to make savings on working-age transfers that accounted for more than 0.6% of GDP.

Health care was the second most frequently targeted area for fiscal savings, with some 50% of countries reporting planned reductions. Health was a major focus of consolidation efforts in the countries with IMF/EU Economic Adjustment Programmes: Greece, Ireland and Portugal. Ireland and Portugal expect to reduce health expenditure by as much as 1% of GDP. Belgium, too, raised its savings target in health care to 1% of GDP and Spain to 0.7%.

Countries use different approaches to achieve savings

Working-age benefits so far the main focus of expenditure reductions

Recent savings measures to reduce expenditures on income support for working-age people and their families have focused mainly on unemployment insurance programmes and on family and child benefits. Until now, there have been no major changes to lower-tier assistance programmes that secure minimum living standards. Some countries have however introduced several smaller changes that, in combination, made safety-net benefits considerably less accessible or generous.

Some temporary measures to extend the duration or coverage of unemployment insurance programmes are being phased out (Table 1.1). Some countries, e.g. Greece, have not renewed temporary unemployment benefit measures taken in 2009-10, while others are now reversing planned extensions of benefit durations (e.g. Spain). In the United States, several states have begun cutting benefit durations, sometimes significantly, even as federal extensions have remained in place until the end of 2013. However, because federal extensions are conditional on state benefit rules, they were also affected by the cuts. Some other countries have reduced the maximum duration of insurance programmes (Denmark, Hungary, Portugal) or tightened eligibility conditions (Czech Republic, Spain) in order to strengthen job-seeking incentives or contribute to fiscal consolidation. However, Portugal has recently eased eligibility requirements, making benefits available to those with shorter employment histories, and has introduced a bonus payment for families where both parents receive benefits.

In parallel, unemployment assistance programmes for those not, or no longer, entitled to insurance benefits have been bolstered in some countries. Portugal increased benefit durations and payments, before reversing the measures in 2010. Greece more than doubled the income limits that determine eligibility to unemployment assistance. But eligibility remained restricted to those aged 45 or older, coverage among the long-term unemployed remains very low as a result, and the real value of benefits has declined as nominal amounts have remained unchanged for the past ten years (Matsaganis, 2013). From 2014, the government plans to extend eligibility to all low-income long-term unemployed, irrespective of age. Finland raised its basic allowance, while Austria has improved benefits for the unemployed who attend training programmes. In the Netherlands, a temporary assistance benefit for older unemployed people was introduced in 2010 (and is to expire in 2016). France extended a similar type of programme earlier on during the crisis. There are only a few instances of assistance benefits being cut: Hungary abolished unemployment assistance, tightened access to social assistance, and reduced the duration of unemployment insurance; Portugal introduced stricter means testing; and Germany abolished a transitional payment for those moving from insurance to assistance benefits (though the measure was not crisis-related).

Some countries have pursued structural reforms of disability benefits by introducing stronger gate-keeping mechanisms, time-limiting benefits, or reassessing the eligibility of existing recipients. Reforms aim to avert the risk of the long-term unemployed drifting into disability benefit schemes and contribute to curbing long-term expenditure. Such policies have been introduced in Sweden, the Netherlands, Switzerland, and the United Kingdom – all countries that have generally been able to put disability benefit claims on a declining trend. By contrast, other countries that have experienced steep rises in unemployment but failed to reform disability benefit now face mounting beneficiary rates – in Estonia and the United States they have risen by over 10% (OECD, 2014b). However, without appropriate employment support, comprehensive reassessments of health entitlements and tighter eligibility criteria can also increase poverty as vulnerable people are excluded from income transfers altogether.

Some countries bolstered lower-tier social safety-net programmes, such as minimum income schemes, prior to the crisis. In comparison with unemployment benefits, minimum-income benefit reforms were fewer and less far-reaching. Measures to strengthen benefit provisions included reforms in the Czech Republic, Estonia, Finland, France, Poland and the United States [although increased allotments under the Supplemental Nutrition Assistance Program (SNAP) are to be widely reversed in late 2013]. Korea is to provide a wider range of separate social assistance transfers from 2014, which is expected to increase the number of people receiving support while reducing some benefits. Italy has announced plans for a new minimum-income programme, while Greece is to introduce a minimum-income benefit on a pilot basis and intends to introduce means-tested housing assistance. However, some countries have reduced the generosity of benefits or made them subject to more stringent job-search requirements with the stated objective of raising the incentive to work. Two examples are New Zealand and the United Kingdom. In other countries, the main motivation was, arguably, to reduce spending. In Hungary and Portugal, measures to reduce benefits and make them less easily accessible were followed by substantial drops in recipient numbers despite high rates of long-term unemployment. In Portugal, for instance, the number of families receiving the Social Integration Income fell by some 30% between early 2010 and July 2013 (SPC,  2013; Farinha Rodrigues, 2013).

Savings measures have included child or family-related benefits since 2010. Before 2010, several countries increased such benefits (which included tax allowances) on a temporary basis (Table 1.2) after having extended them in pre-crisis years. In Germany, Italy and Hungary, one-off benefits were paid to families in need, while France has reduced income taxes for low-income families (France also recently passed a law that will raise them for better-off families with children from 2014). Since 2010, consolidation measures have frequently included lower benefits for children or for childcare. However, such moves constitute a mixed bag and include both cuts and new entitlements, as in the United Kingdom, for example. A number of countries have simply frozen benefits and/or tightened eligibility conditions (e.g. Australia, Greece, Hungary, the Netherlands and the United Kingdom), while others, like the Czech Republic and Estonia, have capped or cut birth-related benefits or reduced the generosity of their parental leave policies. While less visible than explicit benefit reductions, “freezing” benefit payments by delaying, suspending, or reducing regular adjustments in line with consumer prices or earnings can yield significant savings over time. However, such moves typically erode the incomes of families, particularly of those with children (Whiteford, 2013; Joyce and Levell, 2011; OECD, 2007; Immervoll and Richardson, 2011).

Table 1.1. Significant changes to unemployment, minimum-income, and incapacity benefits
|  | Eligibility | Benefit level or duration | Programme starts (+) or ends (-) | Details
Guaranteed minimum income
Austria | 2011 |  | + |  | Benefit rules unified across states, resulting in higher benefits in some.
Czech Republic | 2012 |  | + |  | Minimum living and subsistence benefit harmonised.
Estonia | 2011 |  | + |  | Nominal base for calculating guaranteed minimum income adjusted.
Finland | 2012 |  | + | + | Higher benefit value and new supplement for lone parents.
Greece | 2009 |  |  | +/- | Lump-sum benefits for civil servants and support towards heating costs: introduction and subsequent termination.
Hungary | 2010-12 | - | - |  | Eligibility tightened and benefit levels lowered.
New Zealand | 2013 | - |  |  | Lone parents expected to look for work once child aged 14.
Poland | 2012 | + | + |  | Adjusted benefit level, also to offset erosion since last adjustment in 2006.
Portugal | 2010-13 | - | - |  | Lower supplements for children and spouse, lower income and asset ceilings, means testing now includes resources of those outside nuclear family.
United Kingdom | 2012 | - |  |  | Lone parents expected to look for work once child aged 5.
| 2013 |  | - |  | Cap on total amount of state benefits that can be received by working-age claimants. Lower housing benefits for larger housing units.
United States | 2009-13 |  | +/- |  | Increased real value of maximum SNAP allotments, mostly reversed in 2013.
Unemployment benefits
Austria | 2013 | + | - |  | Supplement for recipients who attend public employment services training schemes.
Australia | 2012 |  |  |  | Wage subsidy to encourage employers to take on eligible people who have been unemployed for at least two years.
Canada | 2009 |  | + | + | Duration of unemployment insurance extended, more generous earnings exemption, new programme for self-employed and for parents of ill children.
Czech Republic | 2011-12 | - | - |  | Contribution requirements tightened and benefit levels reduced.
Denmark | 2010 |  | - |  | Duration of unemployment insurance reduced.
Finland | 2009-12 | + |  | - | Basic allowance increased in 2009 and again in 2012.
| 2013 |  | + |  | Spouse’s income no longer included in means test for assistance benefits.
France | 2009-10 | + | + | -/+ | Contribution requirements for unemployment benefit reduced . Duration of unemployment benefit slightly lengthened. One-off payment for jobseekers not entitled to unemployment benefit (introduced 2009) phased out.
Germany | 2009-12 |  | - |  | In 2011 transitional UBII discontinued. (UBII was a payment that offset loss of benefit loss for people transitioning from unemployment benefit to unemployment assistance). The real value of UBII levels themselves had changed little since 2009
Greece | 2010 |  |  | -/+ | Lump-sum benefit introduced then phased out.
| 2012 | + | - |  | UI benefit cut by 22%. UA income limits made less stringent.
| 2013 |  | - |  | Maximum total benefit duration during any four-year period reduced.
Hungary | 2011 | - | - |  | Eligibility tightened, duration of unemployment benefits shortened.
| 2012 |  |  | - | Unemployment assistance abolished.
Japan | 2011-12 | + | + |  | Duration of unemployment insurance extended.
Netherlands | 2010 |  |  | + | Temporary unemployment assistance for older workers introduced.
Poland | 2010 |  | + |  | Increased benefit over first 3 months.
Portugal | 2009 |  | + |  | Unemployment assistance: temporary increase in amount and length of benefit.
| 2010 |  | - |  | Unemployment assistance: temporary increases discontinued and means testing tightened.
| 2012 | + | - | + | Unemployment insurance: eligibility eased; duration shortened, ceiling lowered; reduced 10% after 6 months; 10% supplement when both parents receive unemployment benefit; new unemployment benefit for self-employed in quasi-employment.
Spain | 2009 |  |  | + | Temporary lump-sum payment introduced for participants in labour-market preparation schemes.
| 2011/12 |  | - |  | Lower unemployment insurance benefit after 6 months/lower lump-sum payment for participants in labour-market preparation schemes.
| 2013 | - |  |  | Unemployment insurance: job-search requirements tightened; reduced scope for keeping benefits after moving abroad. Unemployment subsidy for older people: age threshold raised from 52 to 55; more restrictive income test.
Slovenia | 2011 |  | + |  | Higher benefit during first 3 months of unemployment.
United States | 2008-11 |  | + |  | Discretionary and automatic extensions of UI benefit duration (state and federal).
| 2012 |  |  | - | Beginning reversal of state-level extensions (also reduces federal extensions).
Disability and sickness benefits
Austria | 2013 | - |  |  | Access to pensions tightened for white-collar employees and skilled workers.
Australia | 2012 | - |  |  | Disability pensions eligibility tightened.
| 2011/12 | + |  |  | List of recognised disabilities amended/carer benefits eligibility extended.
Czech Republic | 2012 |  |  |  | Ten benefits for the disabled merged into 2 new benefits.
Finland | 2012 |  | - |  | Changes in reimbursements for pharmaceutical expenses.
Greece | 2012 | - |  |  | Eligibility rules improved and simplified to enhance fraud control
Netherlands | 2013 | + |  |  | Incentives for temporary workers and employers in the event of long-term illness.
New Zealand | 2013 | - |  |  | Sickness benefit combined with unemployment benefit as incentive to seek work.
Poland | 2013 | - | + |  | Stricter eligibility rules, higher benefits for parents of disabled children.
Spain | 2013 |  | - |  | Benefit calculation index revised for less generous amounts (law 27/2011).
| 2013 |  |  | + | Convenio especial (special scheme) ensures continued contributions to old-age pensions for certain groups of incapacitated unemployed.
Japan | 2015 | + |  |  | Increased benefits for low-income pensioners.
Note: Reforms were selected if their design or timing was plausibly linked to the economic downturn or fiscal consolidation.
A “-” means less generous: cancellation of a programme, stricter eligibility conditions, or lower benefit levels or indexation rules. A “+” means the reform heads in the opposite direction.
For Canada, information does not include Quebec.
SNAP: Supplemental Nutrition Assistance Program (formerly “Food Stamps”).
Years refer to the time when measures first took effect but do not indicate the planned duration or any phase-in provisions. The table does not show measures that are planned but not yet implemented.
Source: OECD Tax-benefit Policy Database and OECD 2013 questionnaire on social policies in the crisis.

Table 1.2. Significant changes to family-related benefits (family/child/child-birth/childcare benefit)
| Type of benefit |  | Eligibility | Benefit level or duration | Programme starts (+) or ends (-) | Details
Australia | Various | 2009-13 | - | - | + | Less generous or no indexation of benefit amounts and/or income limits, new paid parental leave, new benefit for school-age children.
Austria | Family benefit | 2009 |  | + |  | One-off family allowance.
| Tax credit | 2009 |  | + |  | Higher tax credit for childcare.
Canada | Tax Credits | 2011 |  | + |  | Higher non-refundable credits for children.
| Maternity Leave | 2012 | - |  |  | Maternity and parental benefits subject to authorisation to remain in Canada.
Czech Republic | Income tax | 2009 | + |  |  | Temporary reduction for low-income families.
| Family benefit | 2011/12 |  | - | - | Social allowance abolished/parental allownc. reduced.
| Maternity leave | 2009 |  | - |  | Lower replacement rate.
| Birth grant | 2011 | - | - |  | More restrictive and less generous.
Estonia | Tax break | 2009 |  | + |  | Increase for families with 2 or more children.
| Tax credit | 2009 |  | - |  | Additional tax-relief removed.
| Family benefit | 2011 | - |  |  | Can no longer be combined with paid parental leave.
| Study loans | 2009 |  | - |  | For parents with children in school.
Finland | Child benefit | 2013 |  | - |  | Suppression of inflation adjustments (2013-15).
France | Family benefit | 2009 |  | + |  | One-off family allowance top-up.
| Income tax | 2009 | + |  |  | Reduced bottom-tier tax.
| Childcare | 2009/12 | + | + |  | One-off increase in childcare vouchers/easier access to childcare benefit for lone parents.
Germany | Homecare allowance | 2013 |  |  | + | For children aged 15-36 months who are not in subsidised childcare.
Greece | Maternity leave | 2009 | + |  |  | Mothers working in the private sector included.
| Child benefit | 2012 |  | + | + | New means-tested benefit.
| Family benefit | 2012 | - | - | - | Benefits for large families (3 or more children) abolished.
Hungary | Family benefit | 2009 |  | + |  | One-off payment for low-income families.
| Childcare provision | 2009 |  | + |  | Extension for low-income families.
| Family benefit | 2011 |  | - |  | Temporary freeze on universal allowance.
Ireland | Maternity leave | 2009 |  | + |  | Higher replacement rate.
| Childcare | 2009 |  | + |  | Free pre-school year.
| Child benefit | 2009 | - | - |  | Restricted age range and lower benefit.
Israel | Family benefit | 2013 |  | - |  | Lower benefit and new income ceiling.
Italy | Family benefit | 2009 |  | + | +/- | Lump-sum to low-income families; temporary increase in family allowance.
| Birth grant | 2009 |  | + |  | Temporary lump sum payment.
| Childcare | 2013 |  |  | + | Childcare voucher for mothers not using parental leave.
Japan | Child benefit and birth grant | 2010/11/12 |  | + |  | Amount increased.
Korea | Childcare | 2013 | + |  |  | Childcare subsidy no longer income-tested.
Luxembourg | Childcare provision | 2009 |  | + |  | New voucher for children under 12.
Netherlands | Childcare | 2013 | - | - |  | Support and income ceiling lowered. New asset test.
Poland | Family benefit | 2012 | + | + |  | Higher benefit level and income limit, also to offset erosion since last adjustment in 2004.
Portugal | Child benefit | 2009 |  | + |  | Low-income education allowance extended to all income groups.
2010 |  |  |  | Reversals of education allowance extension and of 2008 25% benefit bonus.
|  | - |
2011 | - |  |  | Income ceiling lowered; More frequent assessments to reduce overpayments.
Spain | Birth grant | 2008-10 |  |  | +/- | Birth grant introduced in 2008, abolished in 2010.
Sweden | Family benefit | 2010 |  | + |  | Amount increased.
United Kingdom | Child benefit | 2009 |  | + |  | Amount increased.
| 2013 |  | - |  | Income ceiling for benefit receipt introduced.
| Tax credits | 2009 | - |  |  | Income ceiling lowered.
|  | 2011 |  | +/- |  | Higher child element, baby element abolished, steeper benefit withdrawal.
|  | 2012 | - |  |  | Work requirement for couples with children increased.
|  | 2011-13 |  | - |  | Disregards for income changes made stricter.
| Birth grant | 2011 |  |  | - | “Health during pregnancy” grant abolished.
| Childcare | 2011 |  | - |  | Childcare elements of tax credits cut to 70% of cost.
| 2013 | + |  |  | 15 hour-per-week free childcare extended to 2-year old children from disadvantaged families or in care.
United States | Tax credit | 2009-11 |  | - |  |
Note: See notes to Table 1.1.
Source: OECD Family Database and OECD 2013 questionnaire on social policies in the crisis.

Resources for active labour market policies and services have not kept up with rising demand

With an OECD average of around 1% of general government spending, active labour-market polices account for a much smaller share of public expenditures than cash benefits. But while spending on income support for the unemployed is strongly counter-cyclical, expenditure on active labour market policies (ALMP) tends to expand only modestly during downturns, with the notable exception of the Nordic countries. During the recent economic downturn, total spending did increase more than in previous ones. Nevertheless, averaged across OECD countries, ALMP spending per unemployed person declined by some 20% (OECD, 2012). When dwindling resources have to contend with greater demands on employment services and other ALMPs it becomes more difficult to serve job seekers effectively. Lower resources per unemployed person are a concern during high-unemployment periods when jobseekers struggle to find work on their own and the demand for job-seeking assistance and labour market programmes increases.

Pre-crisis reform plans for old-age pensions brought forward

While pension payments were sometimes included in stimulus packages in the early phase of the crisis, they are now targets of fiscal consolidation (Table 1.3 and OECD, 2013i). A number of countries – e.g. Austria, Greece, the United Kingdom, and the United States – initially introduced one-off payments for retirees and these sometimes came on top of more targeted safety-net measures. New means-tested safety-net benefits for the elderly were introduced in Chile, Finland, Greece and Mexico. Australia and Spain have enhanced existing safety-net provisions for some or all low-income elderly. Iceland allowed early access to pension savings in order to support domestic demand.

Table 1.3. Significant changes to the generosity or accessibility of old-age pensions
Selected countries, 2009-13
| Pension age (a minus sign denotes less generous eligibility rules) | Contribution period (a minus sign denotes less generous eligibility rules) | Benefit level or indexation | Safety nets for elderly
Australia, 2009-10 |  |  | + | +
Austria, 2010-11 | - |  | - |
Belgium, 2012 | - |  |  |
Chile, 2011 |  |  |  | +*
Czech Republic, 2011-13 | - | - | - |
Estonia, 2009-10 | - |  | - |
Finland, 2010-13 | - |  | + | +
France, 2010 | - | - |  |
Greece, 2010-13 | - | - | - | +*
Hungary, 2009-11 | - |  | - |
Ireland, 2010-11 | - |  | - |
Italy, 2011 | - | - | - |
Netherlands, 2012-13 | - |  |  |
Norway, 2011 |  |  | - |
Mexico, 2013 |  |  |  | +*
Poland, 2011 | - | - | - |
Portugal, 2011-13 | - |  | - |
Spain, 2011 | - | - | - | +
Slovak Republic, 2011 | - |  | - |
Slovenia, 2011-12 | - | - | - |
Sweden, 2009 |  |  | + |
United Kingdom, 2012 | - | + | +/- |
United States, 2011 |  |  | + |
Note: See notes to Table 1.1.
A “-” means less generous: stricter eligibility conditions, or lower benefit levels or indexation rules that became less generous. A “+” means the reform heads in the opposite direction and “+*” indicates a newly introduced programme.
Source: OECD Pension Database and OECD 2013 questionnaire on social policies in the crisis.

In parallel, however, reforms also continued to address the structural weaknesses of pension provisions that became increasingly evident as GDP declined. More recently, pension reforms have focused either on immediately lowering public expenditure on retirement benefits or on restoring the long-term financial sustainability of pension systems by lengthening contribution periods. Measures that bring savings quickly include across-the-board benefit cuts, such as the abolition of the 13th and 14th monthly instalments in Greece, pension freezes, as in Austria, Greece, Italy, Portugal and Slovenia, or less generous indexation, as in the Czech Republic, Hungary and Norway. However, some countries, such as Australia, Finland and the United States, have altered the standard indexation mechanism to prevent benefit levels from dropping (indicated as a “+” in Table 1.3). Large benefit reductions were sometimes designed to protect smaller pension payments. For instance, successive reduction in Greece in 2010, 2011 and 2012 exempted pensions below EUR 1 200.

Many countries have sought to reduce costs and improve economic efficiency by raising retirement ages (most countries in Table 1.3) and by tightening early retirement conditions (e.g. Italy). Others, however, partially reversed earlier reforms. By early 2014, discussions were underway in Germany to lower retirement ages for specific groups, such as those with long employment histories. Several countries have partially or entirely diverted mandatory contributions to second-pillar private pension plans into public schemes (Estonia, Hungary, Poland and the Slovak Republic). Some of these reversals were introduced on a temporary basis (e.g. Estonia) while others are permanent (Hungary, Poland). Some involve a complete retreat from compulsory private pensions (Hungary) and others a partial change of the system (the Slovak Republic, Poland).

After long, rapid growth, health care spending at standstill since 2008

Unlike spending on social transfers, the rise in health expenditure had already come to a halt in 2008 across the OECD after long periods of rapid growth. In the fifteen years prior to the crisis, public and private health spending grew three times as fast as GDP. Between 2009 and 2011, it remained unchanged in real terms and it fell as a share of GDP on average (Indicator Health expenditure, see also OECD, 2013h). In a number of European countries, health care expenditure fell drastically, with Greece at 11% and Ireland at 7% making the greatest reductions. Other hard-hit countries – such as Iceland, Portugal, and Spain – also made cuts. Only Israel and Japan have accelerated their health care spending.

Some three-quarters of health care spending in the OECD is publicly funded, and much of the overall drop can be attributed to falling government expenditure, or to substantially slower expenditure growth. In the immediate aftermath of the economic slowdown, public spending on health was largely stationary – even in some of the worst-hit countries. From 2010, however, cuts became significantly more widespread. Countries that cut expenditures (like Ireland, Iceland, Estonia, and Greece), or where the growth in spending slowed significantly, reversed pre-crisis trends across all the main health care spending categories – in-patient, out-patient and pharmaceuticals.

Cost saving in health care is a daunting challenge because, if doing so compromises health outcomes, it will trigger even higher health care costs in the future (OECD, 2010b). Nevertheless, a few countries reformed their health care systems precisely to make short-term savings.

In the aftermath of the crisis years, countries made substantial changes to their health policies – even if it is not always easy to distinguish between measures taken in response to the crisis and previously planned structural reforms to contain health care costs. Policy responses varied across countries, but some general patterns can be identified (Vangool, 2014). Denmark, Germany, Poland, the Slovak Republic, and Switzerland had already planned to curb their public health care provision before the crisis. When it bit, however, they took swift, intensive action to implement their reforms. Countries like Australia, which have avoided deep recession, also introduced measures to make health care-related cost savings.

To achieve savings, countries have sought either to reduce the cost of health care services and products or to limit coverage. Many have restricted coverage by requiring income tests so that lower socio-economic groups retain their entitlements and the wealthy face higher costs. The Czech Republic and Spain, however, have curbed public health entitlements for undocumented foreign nationals. Many more countries (Australia, Austria, Belgium, France, Denmark, Estonia, Iceland, Ireland, Portugal) moved to require larger out-of-pocket payments. This affects low-income households although exemptions and caps can ease the impact on vulnerable groups; Portugal and Spain are among the countries that have taken action to that end.

Reducing the cost of public health care provision often involves cutting the wages of health care professionals or renegotiating pharmaceutical prices. A short-term focus on bringing down the prices of health care provision can help to maintain levels of service provision in the short run. But it can be contentious, nonetheless. For instance, in the longer run, wage-cutting policies may drive people out of the health care profession so creating staff shortages and compromising quality of service in the future.

Many OECD countries have undertaken more structural reforms to improve the efficiency of the health care sector, changing payment mechanisms, merging key institutions, and rethinking purchasing arrangements. Major structural reforms – such as the provisions of the US Affordable Care Act which first came into force in 2013 – are however often not directly related to the crisis or to short-term fiscal objectives. And even when they are, they are likely to require lead-in times before they have the desired effect and pay dividends of improved long-term efficiency, productivity, and coverage. Countries such as Greece and the Czech Republic are implementing output-based hospital funding mechanisms that have been shown to increase productivity in other countries. Stronger competition in areas such as community pharmacies may also provide greater responsiveness to consumers’ needs and reduce prices.

Current fiscal measures have implications for wellbeing and social cohesion now and in years to come

Fiscal consolidation hampers progress in reducing inequality and poverty

Before the recession, fiscal policies, through taxes and social benefits, have played a significant role in reducing poverty and inequality in OECD countries. Previous work has shown that the redistributive effect of government expenditures and taxes acted as a significant “break” to the trend increases in inequality and poverty among the working-age population over recent decades (OECD, 2008b; Immervoll and Richardson, 2011). In the mid-2000s, taxes and transfers together reduced poverty by about 60% on average in the OECD (about 80% in Sweden and France, and 40% in the United States and Japan).

In most countries, social transfers contribute twice as much to inequality reduction as taxes do. However, since the mid-1990s, transfers in half of the OECD countries have in fact become less redistributive, largely as a result of falling benefit coverage among the working-age population. This has added to the long-term trend of rising inequality that was already apparent before the crisis (Immervoll and Richardson, 2011).

The patterns and mechanisms of redistribution discussed above prompt two important observations in a context of constrained social budgets:

It is very difficult to cut social spending – particularly transfers – without increasing inequality. A simple simulation, for example, reveals that cutting benefits in the same proportions across all income groups would widen income inequality significantly, while tax-based consolidation (a proportionate tax increase across all income groups) had the opposite effect (Rawdanowicz et al., 2013).

There is scope for strengthening existing targeting mechanisms – e.g. by ensuring that low-income jobseekers do not go without any support. Improving coverage of the neediest families should be a priority at a time when market incomes remain depressed and government support measures are being reviewed and often rolled back.

Countries with strongly redistributive taxes and transfers contained income losses in the early phases of the crisis as they were better equipped to provide automatic income stabilisation. As shown in Figure 1.17, the poorest 10% of households lost considerably more income in countries where automatic income stabilisers were weak. In these countries, tax reductions and higher benefits provide less income cushioning for those becoming unemployed or losing earnings. In some hard-hit countries with particularly large drops in disposable incomes of the poorest it is likely that automatic stabilisers were not operating at their full capacity (e.g. in Greece or Spain). Fiscal pressures may have led to cuts in income support through discretionary measures. Likewise, some of the groups with particularly high unemployment risks in these countries (e.g. young people or those losing their jobs after working on a non-standard employment contract) were not entitled to full income support and therefore did not benefit from any automatic stabilisers that provided support for other, less affected groups.

Figure 1.17. Stronger automatic stabilisers were crucial in limiting income losses among the poorest

Note: The “Strength of automatic income stabilisers” is a coefficient that shows how changes in market income translate into changes in disposable income. The higher the coefficient, the stronger the stabilisation effect – e.g. a coefficient of 0.4 denotes that 40% of the earnings shock due to higher unemployment is absorbed by the tax benefit system. The income changes are simulated based on EUROMOD (EU countries) and TAXSIM (United States) for an increase in unemployment of 5 percentage points.

Source: Indicator Household income for income changes in the bottom 10% of the income distribution; Dolls, M., C. Fuest and A. Peichl (2012), “Automatic Stabilizers and Economic Crisis: US vs. Europe”, Journal of Public Economics, Vol. 96, No. 3-4, pp. 279-294 ,for automatic stabilisers (using simulations based on tax and transfer systems that were in place before the crisis).

StatLink  http://dx.doi.org/10.1787/888932966181

Pre-crisis trends in redistribution policies and income disparities can either moderate or reinforce the effects of fiscal consolidation (Immervoll et al., 2011; Jenkins et al., 2012). Where the redistributive capacity of tax and benefit policies had already weakened before the crisis (OECD, 2011), further consolidation measures may put income adequacy at risk.

Similarly, in countries where most transfers are already mainly received by low-income groups, cuts in transfer spending are much more likely to widen income inequalities. Figure 1.18 shows that transfers received by lower-income groups (the “poorest 30%”) were close to double the average benefit payment in Australia, New Zealand and Denmark, and about 1.5 times the average in the United Kingdom, Switzerland, Sweden and the Netherlands. In these countries, reducing benefit spending without hurting low-income groups is more difficult than in countries providing significant income support across the income spectrum.

Figure 1.18. When social transfers are highly targeted, spending cuts are more likely to hurt the poor

Average total cash transfers received by low- and high-income groups, percentage of average transfers in 2010

Reading note: In Portugal, the average total transfer payment received by low-income families (in the bottom 30% of the income distribution) is 71% of the average payment across all families, and less than half of the average benefit payment received by high-income families, who receive 52% more than the average family.

Note: Transfers include all public social benefits. The reference year is 2009 for Hungary, Japan, New Zealand, Switzerland and Turkey. “Bottom 30%” and “top 30%” refer to average public transfers received by decile groups 1 to 3 and 8 to 10, respectively. Decile groups are determined in relation to household disposable income after accounting for taxes and transfers. All incomes and transfer amounts are adjusted for household size (see www.oecd.org/social/inequality.htm).

Source: OECD Income Distribution Database, www.oecd.org/social/inequality.htm.

StatLink  http://dx.doi.org/10.1787/888932966200

However, several countries that face particularly strong fiscal pressures in fact appear to spend more on transfers to well-off families (the “top 30%”) than to low-income ones. This pattern – which is one factor behind structural fiscal deficits – is particularly strikin in Italy, Greece, Portugal, Spain, and, to a lesser extent, in France. In these countries, there is scope for lowering transfer spending without weakening redistribution and for shifting additional resources towards support for the poorest families.

An additional factor should be considered when weighing the benefits and costs of reduced social spending. Structural reforms in recent years have made social protection programmes significantly more employment-friendly. Examples are the introduction of measures to “make work pay” or to help reconcile work and family life. Insofar as countries have successfully reformed social protection in ways that encourage rather than hinder employment, cuts in social spending can now be expected to have a more adverse effect on poverty, inequality, and growth than in the past.

Who loses most from fiscal austerity measures?

Across all countries, rolling back inequality-reducing policy measures is bound to magnify income disparities in the short term. (By the same token, fiscal consolidation measures on the expenditure side also restrict room for manoeuvre in tackling the well documented medium-term trends towards rising inequality across OECD countries.) The opposite holds true of increasing progressive taxes. This is simply a “mechanical” consequence of the distributional profiles of taxes and transfers and establishing it does not require sophisticated analyses of historical data.

The precise economic consequences of fiscal consolidation measures are however the subject of an on-going, and still evolving, debate. In part, the controversy comes from the use of different outcome measures. A primary concern is the severe and immediate income difficulties that the crisis has brought onto families and most studies have therefore focussed the attention on the short-run effects of fiscal consolidation. But the full consequences of consolidation measures typically show up only after a number of years – the cumulative impact of consolidation on income inequality, for example, has been found to peak only after five to six years and fades by the tenth year (IMF, 2012a).

A second reason for the on-going debate is that some studies are interested in the impact on inequality, while others are mainly concerned with growth. From a social policy point of view, both dimensions are important, as tackling poverty and inequality is fraught with difficulty when the economy contracts or growth is weak. Indeed, stronger economic growth is a requirement for financing redistribution measures, reducing unemployment, and strengthening incomes at the bottom of the distribution.

On balance, the main lessons from recent authoritative studies based on data from earlier economic cycles point to four main conclusions (see Box 1.3):

In the medium term, fiscal consolidation appears to damage growth – a finding that applies particularly firmly to consolidation programmes enacted during downturns or fragile recoveries, and when consolidation efforts get underway simultaneously across several countries.

Spending cuts appear less damaging (or more beneficial) to medium-term GDP growth than tax-based consolidation. However, there is lingering uncertainty over such findings, as the measured effect may actually be due to other policies that are undertaken at the same time (such as monetary easing).

Any GDP losses resulting from fiscal consolidation are not shared equally. Labour incomes appear to fall substantially more strongly than profits or rents, and losses suffered by workers also persist for longer.

In line with the “mechanical” effect of fiscal savings measures, analyses of past consolidation programmes tend to find that spending cuts increase inequality more than tax increases (Woo et al., 2013). Tax increases’ effects on inequality in particular depend on the type of tax increased – whether it is direct or indirect, for example.

Box 1.3. Fiscal consolidation, inequality and growth: An on-going and evolving debate

Although fiscal adjustments have an impact on economic outcomes, economic outcomes also affect the size of fiscal adjustments. The correlation between the two cannot therefore be viewed as a cause-effect relationship that operates in any one direction. The standard approach to solving this problem is to statistically separate changes in fiscal balances from (other) economic changes (Alesina and Adragna, 2012).

Along these lines, Agnello and Souza (2012a) find that successful fiscal consolidation episodes – defined as those that bring public debt down to a lower level within three years – may actually reduce income inequality. Their study draws on a long data series for 18 OECD countries covering consolidation periods between 1970 and 2010. However, in a more detailed analysis the same authors show that the impact on income inequality depends on the size and make-up of fiscal consolidation policy, with adjustments based primarily on large spending cuts leading to rising inequality (Agnello and Souza, 2012b). Inequality rises when consolidation is modest (below 1% of GDP) and spending cuts exceed 0.8% of GDP. By contrast, inequality falls if taxes rise by more than 0.6% of GDP.

Alternatively, fiscal consolidation episodes can be identified directly from policy documents to ensure they are not simply responses to the economic cycle – the so-called “historical” approach. This line of research tends to confirm that adjustment costs are not shared equally and fall mostly on lower-income groups. Drawing on 173 episodes of consolidation in 17 OECD economies over the past 30 years, Ball et al. (2011) find that wage earners lose out more: for every 1% of GDP of fiscal consolidation, wage income typically shrinks by 0.9%, while profit and rents fall by only 0.3%. The decline in wage income also tends to be more persistent, particularly due to increased long-term unemployment, and labour-market inactivity.

Short-term effects, which include any adverse impact on inequality, are a primary policy concern in countries where lower-income groups suffered significantly during the downturn. But there is a parallel debate on the relative merits of tax- and spending-based consolidation strategies with regard to subsequent economic growth.

Alesina and Ardagna (2010, 2012) find that fiscal adjustments based on spending cuts are less likely to be reversed than measures that rely primarily on tax rises, that they have a less detrimental effect on economic activity, and that in combination with other policies they may be associated with stronger economic growth in subsequent years. The main mechanism underlying the findings is that a decline in public spending without significant increases in taxes stimulates private domestic demand in the short term. This is the “expansionary austerity hypothesis”.

However, the “historical” approach described above tends not to support the expansionary austerity hypothesis. Actually, spending-based adjustments do appear less contractionary than those that are tax-based, particularly after the first year. This, however, stems primarily from the difference in monetary policy responses, with central banks easing their policies more often in support of spending-based adjustments. In addition, some of the spending cuts in earlier downturn episodes took place in a more favourable economic context, which probably helped to reduce any damaging effects on longer-term growth (Guajardo et al., 2011).

In new work that adopts a more refined statistical approach, Jordà and Taylor (2013) confirm that fiscal consolidation damages medium-term growth. They go further, however, and look at the crucial question of timing. Results indicate that damage to growth is more likely if consolidation takes place pro-cyclically at a time when growth is already weak, and that particular care needs to be exercised when implementing savings measures during or shortly after a deep downturn. This type of result is also consistent with studies indicating that government spending has larger expansionary effects in recessions than in periods of expansions (e.g. Auerbach and Gorodnichenko, 2012). In addition, growth tends to fall more markedly if consolidation efforts get underway simultaneously across several countries as they create negative “spillover” effects via international trade channels (Goujard, 2013).

In addition to the question of timing, important further distinctions need to be made according to the specific policy measure being taken. For instance, OECD (2013k) draws on existing empirical work to argue that such distinctions need to go beyond spending-based versus revenue-based consolidation. For instance, they illustrate that increasing (progressive) personal income taxes damages long-term growth, but reduces inequality, whereas the opposite pattern holds true for (largely regressive) indirect tax raises.

Generally speaking, carefully balanced offsetting measures – such as higher tax progressivity and targeted cash benefits – can limit any negative effects of consolidation on inequality. Beyond direct redistribution through taxes and transfers, there is evidence that longer-term trends in both equality and growth can be promoted by ensuring adequate resources for ALMPs and education for low- and middle-income workers (Woo et al., 2013).

The consequences of fiscal adjustment for household income therefore depend not only on the extent of fiscal measures but, crucially, on their design and timing. Simulations based on household data can provide deeper insights into the distribution of consolidation burdens across different income groups. While the backward-looking studies mentioned above paint a useful “big picture”, micro-simulation studies are valuable for the way they identify the effects of very specific policy measures – they can, for example, go beyond the very crude distinction between spending-related and tax-based consolidation measures.

Avram et al. (2013) use the simulation approach to estimate the impact of actual fiscal packages in Estonia, Greece, Italy, Portugal, Spain, and the United Kingdom. Although it is difficult to account in a realistic way for possible consolidation-induced changes in labour market behaviour, the study gives a sense of the most relevant distributional mechanics of recently enacted reforms and of their immediate impact on household incomes. Results confirm that the distribution of adjustment costs between income groups depends heavily on the details of fiscal packages and on population characteristics. As might be expected, spending cuts made between 2010 and 2012 typically weigh more heavily on the bottom income groups, while tax increases have mostly affected higher-earning families.

Overall, the early consolidation measures analysed by that study seem to have been borne mainly by upper-income groups – largely because most means-tested benefits were protected from early cuts, while progressive taxes were increased. There are, however, wide differences between countries, and accounting for significant increases in typically regressive indirect taxes could change the overall conclusion (European Commission, 2013a). Also, consolidation efforts that came into effect after the study’s 2012 cut-off would change the combined effect of consolidation measures. For instance, more recent tax and benefit reforms implemented in the United Kingdom in 2012-13 were found to produce disproportionate income losses among families in the bottom half of the income distribution (Joyce, 2012).

3. Can social policies be made more crisis-proof?

Crisis “readiness” is not just about spending levels

Ensure essential support for the least well-off: benefits and costs of targeting

Reforms to cash-transfer policies and social and health care services should make protection of the neediest their priority. Across-the board cuts are not compatible with the important global agenda of ensuring effective social protection floors (ILO and OECD, 2011). Fiscal consolidation measures should steer clear of indiscriminately cutting supplementary benefits such as housing and child/family support which may be vital to poor working families and lone parents. Reducing benefit levels directly, as in Ireland, or progressively through de-indexing, as Finland, the Netherlands and the United Kingdom have done, does create savings. Such an approach, however, needs to treat the most vulnerable families differently in order to avoid poverty and long-term ill-effects on children’s well-being.

As long-term unemployment spreads, accessible, adequate assistance benefits have become crucial for averting steep rises in poverty and inequality. The central role of assistance benefits as fall-back options for those who are not or are no longer entitled to unemployment support should be reflected in the design, timing, and implementation of necessary fiscal consolidation strategies. Indeed, well-targeted safety-net benefits are more cost effective than other measures – such as expensive and difficult-to-target price subsidies for food or energy – that also aim to help households to make ends meet. Cash benefits should continue to adequately support families in hardship, while minimum income benefits should be made more accessible where unemployment and poverty remains high and those affected have little access to other forms of support.

Greater means testing could help target and protect the most vulnerable while reducing benefit expenditures. However, work disincentives associated with tight targeting of low-income families income are likely to become a more significant concern once labour demand starts to pick up during a recovery and people’s labour supply decisions become a more powerful determinant of employment levels. Means-tested programmes can also be difficult to roll out quickly and often suffer from low benefit take-up. As a result, it can be difficulties to reach the most vulnerable groups, and coverage of targeted populations can be low.

Targeting behaviour or non-income characteristics is an alternative that can save costs while leaving incentives intact. In the context of fiscal consolidation, adequate administrative and operational resources are, however, required to effectively implement targeting measures.

Broad indicators of deprivation, such as those that many countries use for determining eligibility for social housing, could be a good basis for effectively targeted services or in-kind transfers. These deprivation indicators can be a more reliable metric of living conditions than income. They are also less volatile and do not compromise short-term work incentives.

Some forms of conditional cash transfers, such as those pioneered in Mexico and Brazil, can in fact create positive externalities by promoting beneficial health and educational outcomes (Fiszbein and Schady, 2009).

When support is directed at children, it can help to ensure more equal opportunities and reduce the likelihood that poverty is transmitted from one generation to the next. For instance, subsidised or free school meals exist in a number of OECD countries, including France, the United Kingdom, and the United States (Richardson and Bradshaw, 2012). In hard-hit countries, such as Greece, they should be considered as one element in strategies to reduce the negative long-term consequences of increasing economic hardship.

The concept of “mutual obligations” makes benefits conditional on claimant behaviour and aims to restore self-sufficiency and prevent long-term benefit dependency. A stricter enforcement of job-search and other work-related conditions is controversial and difficult to implement when labour markets are very weak and greater job search may not produce the desired effect. As more job vacancies are posted during a recovery, there is however a strong case for linking benefit receipt more tightly to job-search or availability-for-work requirements.

Efficient public or private services are essential to delivering good social policy

Services are an integral part of support for vulnerable groups, such as children in disadvantaged families, jobseekers, people with health problems, or groups facing extreme economic hardship. The public provision of services, or the public funding of private provision, is also an effective way of making important aspects of life less dependent on income.

Governments should consider whether structural reforms in public service delivery can save costs and increase efficiency. However, because service provision needs to be efficient in its utilisation of inputs and delivery of outputs, it is equally important that they also look at whether essential services meet demand. More broadly, debates of public expenditure cuts should critically examine the impact that such cuts have on service users.

Service cuts are problematic when large numbers of people can no longer afford market-based services or when trying economic conditions increase the demand for public services. Reducing staff levels in labour-intensive services impairs their effectiveness: at public employment offices jobseekers may not get the person-to-person support they need, for example, and understaffed childcare centres will lack capacity, making it harder for parents to resume work. Similarly, cuts to education budgets affect skills development and school environments and may swell future youth unemployment. Where possible, governments should seek to reduce costs while protecting the delivery of essential services, for instance by redeploying staff from lower-priority activities to areas of greater need.

Lower spending on service provision may not translate into overall savings if reduced capacity and quality increase the demand for cash support or for services in other areas. For instance, lower funding for homeless shelters may redirect support seekers to much more costly hospital services. There is also evidence that a good public service provision helps to keep prices low, while cutbacks may trigger price hikes and rising demand for cash support (Cunha et al., 2013). Similarly, scaling back service infrastructure does not produce longer-term efficiency gains if significant human or institutional capital is lost in the process. There may be trade-offs between quick cost-cutting fixes (such as budget ceilings or envelopes) and measures to improve long-term efficiency – especially in services for which demand will rise in the future, like long-term care, or which support an economy’s productive capacity, such as childcare.

Service cuts are typically not easily reversed. Temporary reductions in service capacity may eventually lead to higher costs than temporary changes to cash transfers or taxes, as staff need to be rehired or retrained or infrastructure rebuilt. Finally, if service delivery is highly decentralised, savings measures instituted at different levels of government may give rise to considerable co-ordination challenges – especially in federal countries, even though all countries devolve service delivery to some extent.

Prioritise funding in investment-type programmes, especially for children and youth

In some areas of social spending, there is strong evidence of distinct long-term benefits which should inform decisions on how to share savings efforts across the health and social-protection budgets. Good quality health care and effective income safety nets are not only crucial for safeguarding individual well-being, but also to maintain the capacity and productivity of the current and future workforce.

Any savings measures should take special care to factor in the increased health care needs arising from the crisis. It is well-documented, for instance, that unemployment is detrimental to mental health (see the discussion in Section 1). Although mental health problems often become chronic, most of them can be treated, symptoms reduced and conditions stabilised (OECD, 2012c). Yet, even when the economy is robust, one of the biggest challenges for the health system is the high rate of under-treatment of mental illness. A lack of effective prevention, diagnosis, and treatment for groups at risk of poor mental health translates into significant social and economic costs later on.

Similarly, governments should prioritise social support for children and youth – particularly during the formative years of early childhood and the transition from school to work. While poverty is a concern in itself, it also has damaging long-term consequences, particularly its “scarring” effects on children. These “scarring” effects of low-income spells mean that when the recession ends, its impact on children do not. Ensuring that the basic needs of children and youth are met can therefore be one of the most important social investments and should be a central pillar of social protection.

Governments need to take swift action to address the widely observed increase in youth poverty and joblessness. A number of countries, like Portugal, have introduced support measures for unemployed youth, while others – such as the United Kingdom, Denmark, and New Zealand – have implemented comprehensive strategies to offer a way forward to all young people who are neither in employment, education, or training. The principles underlying the European Union’s Youth Guarantee scheme and the OECD Action Plan for Youth go in the same direction.

Under the European Youth Guarantee, EU member states make all under-25s a tailored offer – for a job, apprenticeship, traineeship, or continued education – within four months of their quitting formal education or becoming unemployed. Ideally, cash transfers for young people should be conditional on young people taking up the offers made to them, and should include access to affordable health care (see recommendations in OECD, 2013c). Implementing these strategies will require planning – and financing – additional infrastructure and training capacities in the short term. But if carefully designed and implemented, it should boost employment rates and lower dependence on social transfers throughout adult life.

Provide accessible employment support adapted to the labour market situation

Government support should harness and supplement – rather than substitute – the ability of households to adjust to troubled circumstances. Finding alternative earnings opportunities is no easy matter in the depths of a recession. But the evidence shows that even in such trying times there is considerable hiring – in the order of 15% of total annual employment (OECD, 2009) – and that firms in some sectors grow while others reduce staff levels or close.

The high fiscal cost of joblessness reinforces the case for well-funded active labour-market policies (ALMPs), even if they are costly in the short term. While ALMPs account for a small share of public expenditures, spending in this area nevertheless has a crucial bearing on fiscal consolidation as successful employment support policies boost growth and reduce other social expenditures. Weak labour markets, coupled with the need to tackle large fiscal imbalances, have renewed interest in the role of activation policies that promote the (re-)integration of jobseekers into employment.¹⁴ When fewer vacancies complicate the task of effectively matching jobs and jobseekers, there are, more than ever, sound arguments for making adequately resourced, suitably designed active labour market policy a priority (Immervoll and Scarpetta, 2012).

Governments should maintain labour market activation strategies and suitably designed in-work support at a reasonable level – including for part-time workers. When the number of jobseekers grows during a downturn, a prime focus for governments should be to ensure adequate resources for public employment services and benefit and programme administration. These services act as “gateways” to programmes such as training and job-search assistance. Maintaining effective service capacity is crucial for avoiding inappropriate and inefficient assignments of unemployed persons to costly labour-market programmes. To address these challenges, Australia, Denmark and Switzerland automatically adjust budgets for active labour market policies in line with labour-market conditions (OECD, 2009). Similar provisions should also be considered in other countries in order to protect this crucial area of social spending during times of fiscal restraint.

However, how ALMP resources are allocated and used is as important as how much is spent on them overall. The best combinations of policies are those that meet labour market conditions and jobseeker needs, both of which generally change significantly over the course of a downturn and into recovery. As a recovery gains momentum, more vacancies are posted, and active job-search becomes a more decisive factor for employment outcomes, policies should shift from labour demand to activation and in-work support for low-income working families. The type, sequence and intensity of activation measures should be continually reviewed and adapted to evolving labour-market challenges, while fiscal constraints may require a rapid transition from wide-ranging stimulus packages to selective, customised employment support.

Policy changes in other areas may also require reviews of activation strategies. Generally, when benefit provisions are altered, this typically also shifts the balance of “mutual obligations” which underlies the relationship between claimant, benefit administrations and employment services. Unemployment benefit extensions, for instance, should go hand in hand with adequate resources for effective job-search services and employment support. To ensure that the focus stays on re-employment, governments should consider “soft sanctions” such as requiring claimants to re-apply for benefit extensions, introducing waiting periods between consecutive claims, or reducing benefit amounts over time.¹⁵

Moreover, as the number and profiles of jobseekers changes, governments should monitor whether back-to-work policies continue to target and prioritise the intended groups. Activation measures and support for recipients of lower-tier assistance benefits become, for instance, more important as people exhaust their unemployment insurance or where many unemployed do not receive insurance benefits in the first place.

If support services do not have the capacity to serve everybody, then authorities need to make difficult choices. The best track may be to prioritise those who are, in some sense, closest to the labour market as they hold the best prospects for returning to employment. However, people who are essentially job-ready may in fact not need intensive public assistance to find work. Instead, a more urgent priority may be to focus on those most in need of support services and intensive case management. The best targeting strategy depends on available resources, on the types of activation and employment support measures that are available, and on the specific employment barriers faced by the different groups of jobseekers.

Reinforce household resilience and encourage support between family members

Successful active social and labour-market policies should, as much as possible, factor in the family situation of jobless individuals. To date, policy responses to the crisis have concentrated on individual job losses and circumstances while frequently ignoring household and family context. However, when there are large numbers of workless households (see Figure 1.15 above), back-to-work and in-work support should not be restricted to individual job losers, but include partners and all working-age family members (even if they are not registered as unemployed). Policies that strengthen work incentives and support for the partners of primary earners and jobseekers are cost-effective as second earners’ employment decisions are known to respond strongly to such measures.

Households where both partners work, have work experience, or are actively looking for a job are in a better position to minimise income losses in the event of unemployment. They are also likely to benefit more quickly from improving labour-market conditions. However, it is in fact not clear whether a recession strengthens or weakens the so-called “added-worker” effect – where spouses compensate for some of their partners’ loss of earnings by starting employment or working longer hours. On the one hand, accelerating job losses, less stable employment patterns, and reduced working hours clearly increase families’ need to make up for falls in income. On the other hand, the weak labour market makes it harder to do so.

The objective of strengthening families’ ability to absorb and offset temporary earnings losses has brought gender into play, as more women now have labour-market experience than in previous recessions. This, and the fact that men have suffered significantly greater job losses in the OECD area, has increased the chance that women will be able to compensate for some of their partners’ earnings losses through the added-worker effect.

New labour-market data show that female employment has in fact been an important factor in limiting economic hardship in families (Figure 1.19). Between 2007 and 2011 job losses and reduced working time among partnered men lowered total working hours of couples (i.e. the number of hours worked by both partners in all couple families in the country) – by some 3% in Canada, Portugal, Slovenia, and the United States, and by between 6% and 9% in hard-hit Estonia, Greece, Ireland, Latvia and Spain (Figure 1.19, Panel A). Although women’s unemployment rates also rose, their total working hours fell less than men’s – and often went up – in all the countries shown. For women who already worked full-time, working significantly more was not an option. Many women work part-time, however, which yields considerable scope for increasing total working hours even in countries where their employment rates were comparatively high, such as in France and the Netherlands.¹⁶ Partnered women were more likely to work more (or less likely to see their hours reduced) than single women (Figure 1.19, Panel B). Although this pattern is not conclusive evidence of an added-worker effect, it is plausible that their partner’s earnings loss was one of the factors driving women’s additional hours of work.

Policy factors explain in part why women in some countries increase their working hours more than in others. The need to do so may be perceived as less pressing if men’s earnings losses are temporary (due to short-time working schemes, for example) or largely offset by government transfers. In addition, disincentives created by tax breaks and out-of-work benefits can affect the job hunting and work commitment not just of a household’s principal earner but of its second earner, too. Even though people entitled to means-tested benefits generally have very low incomes – and therefore stand to gain substantially from the added-worker effect – benefit reductions that kick in as soon as a family member works or earns more are a barrier to a household enjoying a stable income.

Figure 1.19. Women’s employment greatly improves families’ resilience to economic shocks

Change in total hours worked by men and women, 2007-11

Note: Changes are relative to family pre-crisis hours (i.e. the sum of men’s and women’s hours) in Panel A, and relative to individual pre-crisis hours in the different groups in Panel B. Changes in total hours capture differences in both employment levels and hours worked.

Source: OECD calculations based on national labour force data and European Labour Force Surveys.

StatLink  http://dx.doi.org/10.1787/888932966219

In most OECD countries, families with one long-term unemployed member are much better off when his or her partner finds employment, even if it is relatively low paid (Figure 1.20). However, Figure 1.20 also shows that some tax-benefit systems do little to accommodate added workers. In Luxembourg, the Netherlands, Switzerland, Iceland, Japan, Norway, and Sweden, for example, a relatively high tax burden of the spouse taking up employment, and/or reduced benefits as a result of family means testing limit the income gains from an added-worker effect. Countries should consider giving added support to the partner making the transition into employment in the form of childcare support, for example, or carefully designed back-to-work allowances that benefit not only registered jobseekers, but their partners too. Finland has recently changed the means test for unemployment assistance benefits along these lines, by ensuring that employment of one partner does not reduce benefits of the other (see Table 1.1). However, some reforms that are aimed at helping workless households – such as “bonus” payment for families where both parents are unemployed (see Table 1.1 for examples) – could discourage active job search if benefits are withdrawn too quickly once a family member starts to work.

In general, policies that address gender-specific employment barriers strengthen families’ resilience to economic shocks and improve their prospects of benefiting from a recovery (OECD, 2012b). At the same time, however, households are shrinking, with growing numbers of single-person and lone-parent families and fewer multigenerational ones. Single-person households obviously face a complete loss of earnings in the event of unemployment, while lone parents may find it particularly difficult to adjust to income shocks because of their childcare obligations and restricted mobility. Such constraints point to the crucial need for governments to continue providing lone parents with child benefit and employment-friendly tax-breaks across the economic cycle.

Figure 1.20. A working partner makes family incomes more resilient to income losses

Net incomes at different sages of unemployment, with and without a working partner, percentage of in-work income, 2011

Note: Incomes are shown for a married couple with one unemployed spouse (previously earning 100% of the country’s average wage) and the other spouse either labour-market inactive or working and earning 67% of the average wage. Percentages relate to the family’s net income before the primary earner became unemployed. Net incomes include unemployment benefits, as well as any minimum-income or family-related benefits that are available. Results are averages over two family situations: a married couple with and without children.

Source: OECD Tax-Benefit Models, www.oecd.org/els/social/workincentives.

StatLink  http://dx.doi.org/10.1787/888932966238

Enable labour-market adjustments through employment-friendly regulations

Labour market regulations should protect workers but not hinder the creation of new jobs. Deep recessions typically produce sizable sectoral shifts in the economy. In the countries most affected by the Great Recession, hard-hit sectors like construction and manufacturing will often not regain their pre-crisis employment levels. Recessions and subsequent recoveries also lead to substantial numbers of job transitions within sectors – e.g. when firms that had shed personnel in response to faltering demand start to rehire. Regulations that make it costly to hire new workers slow down or inhibit the dynamic job creation that is needed for a swift labour market recovery. When vacancies cannot be filled, this leads to longer periods of unemployment, and a poor match between job requirements and a worker’s skills and aspirations.

With disadvantaged workers bearing the brunt of job and earnings losses during the on-going crisis, concerns over labour-market inequality have become more pressing. Governments in several countries have taken positive steps towards fostering under-represented groups’ access to employment and address labour market segmentation and discrimination. Recent reforms in this area need to be seen as a response to policy trends initiated in the 1990s, such as the deregulation of temporary contracts. This unbalanced deregulation heightened labour market duality between growing numbers of temporary workers, or “outsiders”, who cycle between temporary contracts, and “insiders” on open-ended contracts who enjoy a high degree of employment protection and greater job stability.¹⁷ Partly as a result of dual or highly segmented labour markets, disadvantaged workers in Southern Europe experienced particularly steep job losses during the recession (Carneiro et al., 2013). Facilitating their reemployment in better-quality jobs is a priority and labour-market reforms have been high on the policy agenda, particularly in a number of Southern European countries.

Since the onset of the financial crisis, more than one-third of OECD countries have relaxed regulations governing individual or collective dismissals. The most far-reaching changes have generally come in countries which had the most stringent regulations before the crisis, such as Greece, Italy, Portugal and Spain (OECD, 2013b). Greece and Portugal have made severance pay less generous and shortened notice periods. In Portugal, an important plank in the country’s reform to support young workers is the abolition of the need for redundancies to proceed by age, with the most senior workers laid off last. Italy has reduced legal uncertainty on the employer side by restricting the grounds on which courts can order reinstatements to severe cases of wrongful dismissal, such as discrimination. Italy and Spain have also streamlined dispute resolution procedures and Italy has abolished provisions that allowed employers to terminate certain atypical contracts at will.

In early 2012, Spain enacted a labour market reform to address some of the main causes of dual labour markets (OECD, 2014a). The reform provides firms with alternatives to layoffs when product demand is weak (e.g. giving them greater scope for renegotiating wages and working time), halved notice periods, reduced monetary compensation for unfair dismissal, simplified administrative procedures for mass (or “collective”) redundancies, and introduced a new, less regulated employment contract for small firms with fewer than 50 staff. In France, a 2013 reform of the labour code relaxed regulations on regular (open-ended) contracts, introduced an additional payroll tax applicable if fixed-term contracts are not converted to open-ended ones at the end of the fixed term, and allowed social partners – in times of serious company difficulties – to negotiate temporary firm-level agreements on wages and shorter working times in exchange for job guarantees.

Adequate resources for counter-cyclical support measures

Ensure fiscal measures are carefully timed and balance measures on spending and revenue sides

The fiscal crisis is not just a spending crisis. Recessions cause slumps in a range of revenue sources and a possibility of extended periods of sluggish revenue growth. During some phases of the Great Recession, reduced government revenues in many countries have consequently had greater impacts on budget balances than inflated benefit expenditures. For instance, if 2010 revenues in Spain had been the same as in 2007 in real terms, this would have reduced the budget deficit by more than 6 percentage points (Figure 1.21). Returning to 2007 benefit expenditure levels would have narrowed the deficit as well, but by much less (3 percentage points).

Figure 1.21. Budget deficits after the initial downturn: role played by changes in transfers and revenues

Changes in benefit expenditure and revenues as percentages of 2010 GDP, 2007-10

Reading note: If 2010 revenues in Spain had been the same in real terms as in 2007, the country’s budget deficit would have been more than 6 percentage points smaller. Returning to 2007 benefit expenditure levels would have reduced the deficit by under 3 percentage points.

Note: Changes in both transfers and revenues are measured in real terms (in 2010 currency). The vertical y axis is inverted (a positive number indicates an increase in social benefit expenditure and a worsening budget balance).

Government transfers: all cash social benefits paid by government. Government revenues: total tax and non-tax receipts of the general government sector (central and sub-central) plus social security contributions.

Source: OECD (2011), “Economic Crisis and Beyond: Social Policies for a Recovery”, Background document for OECD Ministerial Meeting on Social Policy, 2-3 May, OECD, Paris.

StatLink  http://dx.doi.org/10.1787/888932966257

Revenue-side measures have an important role to play. Both historical income trends and recent data signal sizable shifts in relative “tax capacity” from lower- to higher-earning groups in the aftermath of steep downturns. Governments should factor those shifts into tax measures that seek to balance revenue needs with distributional concerns such as the very unevenly shared benefits of economic growth, both before and since the crisis, and the very large income gains of top earners in some countries (Förster et al., 2014). Like expenditure cuts, tax measures should be designed, timed, and targeted carefully so as to avoid choking off the fragile economic recovery. Moreover, revenue requirements are such that tax increases in any one area are unlikely to be sufficient to close the revenue gap. The consolidation efforts of recent years have focused mostly on income and consumption taxes. Governments should now consider action such as tackling evasion and avoidance, shifting tax burdens away from labour (particularly low earners) to broad-based consumption and also residential property (European Commission, 2013b; IMF, 2013; LeBlanc et al., 2013). Addressing tax policy challenges, broadening the tax base, tackling tax avoidance and reducing labour tax burdens for low-income groups in particular could also help the resumption of growth and make revenues less volatile during the economic cycle.

A need for counter-cyclical policies

Governments find it hard to build up savings. This difficulty can be explained by political considerations (Alesina and Tabellini, 1990; Amador, 2003), and is strikingly illustrated by the fact that many OECD countries ran budget deficits in most or all years in the past three to four decades. One risk of a long-term rise in government debt is that a combination of increasing debt-servicing costs and spending increases for old-age support reduce the room for redistribution and investment-related social policy measures targeted at children and working-age individuals (Streeck and Mertens, 2013; Immervoll and Richardson, 2011). The failure to address fiscal misalignments during economic upswings creates strong pressures to consolidate in a pro-cyclical manner (i.e. during a downturn or periods of low growth), which risks delaying and slowing the recovery (see Box 1.2). Indeed, a recent IMF study of 17 OECD countries confirms the pattern of pro-cyclical consolidation and points out that large fiscal adjustment programmes have almost always taken place in the context of “initially weak [macro-financial] fundamentals” (Dell’Erba et al., 2013).

Counter-cyclical support is needed for two reasons. First, because the objective need for support is greater during and after a downturn (equity argument). And second, because economic upswings alone are unlikely to undo the damage inflicted by recessions, e.g. because income losses suffered during downturns become entrenched. Counter-cyclical social policy is then an efficient use of public funds and can increase total welfare by reducing future social and economic costs (efficiency argument). Spells of poverty and unemployment give rise to longer-term scars and there is in fact overwhelming evidence that scarring does lead to lower future employment and earnings, and also negatively impacts a range of other important outcomes, including health.¹⁸ When scarring is substantial, rising poverty and unemployment during and after a downturn strengthens the case for redoubling social policy efforts.

OECD countries have used counter-cyclical social policies of different types and to different extents, and these differences offer pointers as to how policies could be made more responsive to changing economic conditions and to household needs. For instance, countries such as France, Portugal and the United States, have actively extended out-of-work benefits at the onset of the crisis, and most countries with strong out-of-work benefits in place have allowed them to operate to the full extent by keeping them accessible to a rapidly growing number of jobseekers and so helping to stem income losses (see Figure 1.17 above).

Some of the worst-affected countries in Southern Europe, however, were ill prepared for the social consequences of the crisis. Their social protection arrangements were weak and discretionary policy measures did not significantly strengthen support for such hard-hit groups as the long-term unemployed or people with little or piecemeal work experience. Their poorly targeted and expensive benefit systems actually contributed to the deep fiscal crisis, which in turn severely constrained the scope for discretionary support when most needed. A significant reconfiguration of welfare systems to improve targeting would arguably protect disadvantaged groups more effectively and affordably (Matsaganis, 2011; OECD, 2013f).

The United States, where out-of-work transfers were relatively modest before the crisis, has done much more to strengthen income support in a counter-cyclical manner. The country’s transfer system directs a large share of working-age cash payments towards low-earning working families. To address the social risks of such a policy configuration when more and more people were finding themselves jobless for long periods, the United States combined a number of swift discretionary policy measures with “automatic” policy changes that extended unemployment insurance and safety-net benefits during the downturn, albeit from a comparatively low level (Immervoll and Richardson, 2013). Canada also combined discretionary and automatic policy adjustments. Although discretionary measures accounted for the majority of additional spending in both countries, the automatic benefit extensions described in Section 2 made support significantly more counter-cyclical, and it directed extra support to economically more fragile regions.¹⁹ Importantly, such automatic provisions also strengthen the credibility of expenditure reductions in line with the recovery. These experiences are relevant to countries considering how to adapt social support systems more readily to variations in economic circumstances and household needs.

Striking the right balance between benefit recipients’ rights and responsibilities is one way to make transfers more responsive to labour-market conditions. Job-search requirements and activation measures help ensure that benefit expenditures decline when labour demand picks up. They also allow benefit administrations some room for manoeuvre to make benefits more accessible (e.g. by tailoring eligibility criteria to labour-market conditions) when job prospects are poor or when increasing numbers of jobseekers have no recent work experience. Moreover, activation policies contribute to better targeting by making support conditional on job-search efforts (Immervoll, 2012; OECD, 2013g). If well designed, such targeting can, in turn, create the fiscal space, and possibly the political support, that is needed to ensure support for individuals and families who require it.

Yet, a credible commitment to counter-cyclical redistribution rests on consistency between social spending and the revenues that finance them. In the United States, the pre-crisis boom years saw a budgetary and arguably a political marginalisation of first-tier transfers (unemployment insurance) and second-tier benefits [e.g. Temporary Assistance for Needy Families (TANF)] for workless individuals and households.²⁰ At a time when the recovery is still weak and poverty high, the pre-crisis erosion of revenue sources produced by pro-cyclical tax reductions (notably in the case of unemployment insurance funds) has now created strong pressures for across-the-board budget cuts and specific benefit cuts at state and federal levels.

Unemployment benefit, general social assistance, and active labour market programmes together account for an average of less than 10% of public social spending in the OECD. However, the downturn placed heavy additional demands on them. Even in the current economic context, margins for savings are still narrow and, ideally, reductions in benefit duration and recipient numbers should be paced to match recovery. Governments can improve both fiscal and social sustainability by committing to funding with a long view in order to balance finances across the economic cycle and maintain effective income support during extended downturns.

Structural policy reforms need to continue and be fiscally and socially sustainable

Key structural reforms of pensions and health care systems begun before the crisis should continue. Pensions and health care each account for 30% of total public spending in the OECD on average, and successful reforms in these areas create the fiscal space that enables governments to provide disadvantaged groups with adequate support, notably in the context of often rapid population ageing. Structural health care reforms should focus on identifying and reducing unnecessary supply of services and on savings through efficiency gains. Untargeted cuts, for example in the form of higher co-payments, should be avoided as they restrict access to health services for the most vulnerable.

As for pensions, short-term and temporary reforms – like freezing benefit levels – have an immediate impact on public finances. But they may also heighten the risk of poverty among the low-income elderly unless supplementary measures are taken in parallel. Structural reforms that seek to restore the long-term sustainability of pension systems – e.g. raising retirement ages and lengthening contribution periods – can achieve greater savings, albeit with a longer time lag. While short-term fiscal pressures may cast the spotlight on certain elements of public pension provision, it is important to consider retirement income more broadly. The economic crisis has already had a serious effect on households. And it will not end there. It will also affect the retirement situation of the current working-age population. Across all spending areas, an overarching challenge is to identify reforms that are effective in alleviating the impact of economic crises on both households and government budgets, not only now, but for later years as well.

Notes

 1. While such recent data are not available for the entire OECD area, they cover countries with very different degrees of exposure to the crisis.

 2. There is new evidence that the duration of unemployment determines chances of obtaining a job interview. This effect can be expected to be stronger when labour markets are relatively tight and is therefore of growing concern once labour markets recover (Kroft et al., 2013).

 3. The negative effects of unemployment during someone’s initial years on the job market tend to be both strong and long-lasting, especially for disadvantaged youth (OECD, 2010a; Scarpetta and Sonnet, 2012).

 4. General government includes central, state and local government as well as social security funds. The latest data are for 2012 and are available for 26 countries. Source: OECD National Accounts.

 5. Immervoll and Richardson (2013) summarise a number of recent studies for Europe and the Unite States.

 6. Relative income poverty is the share of people with income below half of the national median.

 7. The figures show relative income changes for different income groups. Starting points are very different across countries, with the United States, the United Kingdom or Poland recording much higher levels of market-income inequality than Denmark or Finland.

 8. Food insecurity is therefore a different concept from hunger or undernourishment, (FAO, 1996 and 2012; Radimer, 2002).

 9. The OECD’s How’s Life? framework monitors a comprehensive set of well-being outcomes to assess trends in well-being at the individual level and differences in those trends across countries (OECD, 2013d).

 10. Reporting of suicides differs across countries. In particular, trends in recorded suicides need not parallel numbers of attempted suicides, which could be of similar or greater interest from a “life dissatisfaction” perspective.

 11. In Germany, the end of the transitional benefit, which had eased income losses for job seekers moving from insurance to assistance benefits, further reduced the NRR.

 12. In Norway the termination of the “Waiting Benefit” in 2008 reduced the maximum duration from five years to two. In 2010, Denmark reduced the maximum duration of unemployment insurance benefits from four years to two.

 13. In Canada, the number of insured hours of employment that jobseekers need in order to qualify for unemployment benefits also varies with provincial unemployment rates.

 14. Activation policies are a combination of measures that provide support and incentives for: i) job search and job finding; ii) productive and rewarding participation in society; and iii) self-sufficiency and independence from public income support. See OECD (2013g).

 15. Immervoll (2013) discusses options and priorities for reforming out-of-work benefits in the context of a weak labour market.

 16. Since women are, on average, paid significantly less than their partners, households often suffer earnings losses overall even if women attempt to compensate their partner’s earnings loss by working significantly more. Persistent gender wage gaps reduce women’s ability to help stabilise family incomes. In addition, adverse labour market conditions and the fear of losing their job lead some to work longer hours without being paid accordingly.

 17. In 2011, 12% of employees in the OECD area were on fixed-term contracts. The proportion was much higher among youth. One-quarter of employees aged between 15 and 24 years is on a fixed-term contract in the OECD area, but more than one-half of young people in France, Germany, Italy, the Netherlands, Poland, Portugal and Switzerland have temporary jobs and over 70% in Slovenia (OECD, 2013b).

 18. Key results, especially among youth, include the findings of Bell and Blanchflower (2009), Mroz and Savage (2006), Oreopoulos et al. (2012), Gregg and Tominey (2005), Arulampalam (2001), Kletzer and Fairlie (1999), Ellwood (1982). Findings of substantial scarring of low-income and out-of-work spells are consistent with the historical income data shown in Figure 1.6 in Section 1 of this chapter, and the role that cyclical income changes play in shaping long-term trends of rising inequality: low-income groups fall significantly further behind the rest of the population during recessions, opening up gaps that subsequent upswings often fail to close.

 19. The most important automatic provision, extended unemployment benefit durations, were first put in place in 1970. Since job losses during the recent “Great Recession” have exceeded numbers seen in earlier recessions, the relevant provisions were triggered in most states (Vroman et al., 2003). In addition to unemployment insurance, a number of safety-net benefits also include provisions that make them more generous, or more easily accessible, once state-level unemployment rates exceed a pre-defined threshold (USDA, 2012). Both discretionary and automatic adjustments need to be designed carefully, in order to avoid such unintended consequences as hindering mobility between regions with high and low unemployment.

 20. On TANF policies and the decline in beneficiary numbers, see Anderson et al. (2011); Trisi and Pavetti (2012). For discussions and assessments policy challenges related to Unemployment Insurance, see Vroman (2011, 2012); McKenna and Wentworth (2011); Evangelist (2013).

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Annex 1.A1.

Approach used to construct country groupings in Figure 1.8

The indicators used for the groupings are as follows:

Household income (change 2007 to 2010): Real average household disposable income. Source: OECD Income Distribution Database. See also Indicator Household income.

Public social spending (change 2007/08 to 2012/13): Real public social spending. Data are missing for Japan and Turkey. Source: OECD Social Expenditure database. See also Indicator Social spending.

Recent consolidation effort (2011/12 to 2014): Change in general government underlying balances as a percentage of GDP. Data are missing for Chile, Mexico, the Slovak Republic and Turkey. Source: OECD Economic Outlook, No. 93 (May 2013).

Expected future consolidation effort (2014 to 2030): Average annual consolidation from 2014 onwards to achieve a notional target of gross government debt (60% of GDP) in 2030. Data are missing for Chile, Mexico, Norway and Turkey. Source: OECD Economic Outlook, No. 93 (May 2013).

Countries with a change above the country average plus 0.5 standard deviations were classified as “high” or “large”, those with a change below the country average minus 0.5 standard deviations were classified as “low” or “small”.

Chapter 2.

Interpreting OECD social indicators

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.

The purpose of Society at a Glance

Society at a Glance 2014 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 recent and on-going financial, economic and fiscal crisis. Indicators of policy responses 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 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 old age support rate, 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

Old age support rate

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. The rate of youth neither in employment, education nor training, NEETs, signals an important dimension of hampered human capital accumulation, measured towards the end of compulsory education in most countries. High NEET rates not only reduce self-sufficiency among young people, but can also more permanently reduce their possibility to establish in paid employment. The number of expected years in retirement is a societal response, determined by employment among older people and age of pension eligibility, to issues of self-sufficiency in old age. A major societal response to enable people to become self-sufficient is public and private expenditure 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 | Expected years in retirement
Unemployment | Education spending
NEETs |

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 give additional information about health and societal challenges, since there are a complex set of reasons why some people commit suicide. Health expenditure is a more general and key part of the policy response of health care systems to concerns about health conditions. Coverage for health care gives additional information about the access to health care.

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 expenditure
Perceived health status |
Suicide |
Coverage for health care |

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 feel safe (Chapter 7).

Life satisfaction is determined not only by economic development, but also by the diverse experiences and living conditions. One of these experiences can be the degree of tolerance and social cohesion between traditional majorities and those often historically considered to be outsiders. A cohesive society is one where citizens have confidence in institutions and believe that social and economic institutions are not prey to corruption. A general measure of safety and crime may indicate the degree to which economic and social exchange is facilitated, enhancing well-being and facilitating socially beneficial collective action. One way of helping others can be donations to charities, voluntary work or help to a stranger.

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 |
Tolerance |
Confidence in institutions |
Safety and crime |
Helping others |

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. Both text and charts address the most recent headline indicator data, with countries ranked from highest to lowest performer. 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 of the current economic crisis (typically between 2007-08), so the extent to which recent adverse economic events 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 (www.oecd.org/social/societyataglance.htm), 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 (2013), How’s Life? 2013: Measuring Well-being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264201392-en.

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

3.

General context indicators

Household income

Fertility

Migration

Family

Old age support rate

Household income

In 2010 half of the people in Mexico had incomes of less than USD 4 500. Half of the people in Luxembourg had incomes about eight times higher (Figure 3.1, Panel A). Countries with low 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 Norway and Switzerland.

In most OECD countries incomes from work and capital (i.e. market income) fell considerably between 2007 and 2010 (Figure 3.1, Panel B). Higher unemployment and lower real wages brought down household market income, particularly in Estonia, Greece, Iceland, Ireland, Mexico, New Zealand and Spain (5% or more per year). By contrast, market income increased significantly in Chile and Poland as well as to a lower extent in Austria, Germany and the Slovak Republic. On average, between 2007 and 2010, real household disposable income declined by much less than the market income (-0.5%), thanks to the effect of public cash transfers and personal income taxes. At the same time, incomes from work and capital fell by 2% per year.

Figure 3.2 focuses on the top and bottom 10% of the population. While on average across OECD countries real average household disposable income and the average income of the top 10% remained almost stable, the income of the bottom 10% fell by 2% per year over the period 2007 to 2010.

Out of the 33 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. In Italy and Spain, while the income of the top 10% remained broadly stable, the average income of the poorest 10% in 2010 was much lower than in 2007. Incomes of poorer households also fell by more than 5% annually in Estonia, Greece, Iceland, Ireland and Mexico. Among these countries, Iceland was the only one where the decrease in average annual income at the top (-13%) exceeded that of the bottom (-8%).

Definition and measurement

Household income provides an indication of the goods and services families can purchase on the market. It is thus an absolute objective indication of material quality of life.

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 deduction of direct taxes and payment of social security contributions. 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 welfare benefits, household income provides an indication of the goods and services families can purchase on the market.

Household income is adjusted for family size and the adjusted measure is attributed to every person in that household. Half of all people have higher income than the median and the other half lower.

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, www.oecd.org/social/income-distribution-database.htm.

OECD (2011), Divided We Stand: Why Inequality Keeps Rising, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264119536-en.

Figure notes

Figures 3.1, Panel B and 3.2: 2007 refers to 2006 for Chile and Japan. 2008 for Australia, Finland, France, Germany, Israel, Italy, Mexico, New Zealand, Norway, Sweden and the United States. 2010 refers to 2009 for Hungary, Japan, New Zealand, Switzerland and Turkey. 2011 for Chile.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

3.1. Household income of OECD countries varies between USD 4 500 and USD 36 400

Source: OECD Income Distribution Database (www.oecd.org/social/income-distribution-database.htm), accessed on 10 September 2013.

StatLink  http://dx.doi.org/10.1787/888932966276

3.2. Poorer households tended to lose more or gain less between 2007 and 2010

Annual percentage changes in disposable income between 2007 and 2010, by income group

Source: OECD Income Distribution Database (www.oecd.org/social/income-distribution-database.htm), accessed on 10 September 2013.

StatLink  http://dx.doi.org/10.1787/888932966276

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.

In 2011, fertility was well below the replacement level in most countries, averaging 1.70 across the OECD (Figure 3.3, Panel A). The highest rate was recorded in Israel, where women had almost one child more than in the second country, New Zealand. Israel was in fact the only OECD country with a level above the replacement fertility rate (2.1 children per woman). Anglophone and Nordic countries were typically at the higher end, while continental Europe (France being the one major exception) reported low fertility, along with even lower fertility rates in Japan and South Europe. Fertility rates were notably low in Hungary and Korea, with two parents replacing themselves in the next generation by little more than one child, on average.

Persistent economic uncertainties can reduce the number of children women may have over their reproductive life. During the crisis years (i.e. between 2008 and 2011), ferti-lity rates fell in more than two-thirds of the OECD countries (Figure 3.3, Panel B): by almost two decimal points in the United States (a relatively high fertility country) and by one decimal point in five European OECD countries (Denmark, Estonia, Hungary, Iceland and Spain) and New Zealand and Turkey. The US rate fell to an all-time low in 2011 at 1.89, down from 2.12 in 2008.

Over the last 50 years, fertility declined dramatically across OECD countries, falling on average from 3.3 children per woman of childbearing age in 1960 to 1.7 in 2011 (Figure 3.4, Panel A). The reasons were postponement of family formation and a decrease in desired family size. Rising female education and employment, insufficient support for families juggling work and children, a need to generate a secure job and income, or growing housing problems may have all also played a role. Falls were especially pronounced – by at least four children per woman on average – in Korea, Mexico and Turkey.

Before the crisis, there was a moderate recovery in average fertility rates between 2000 and 2008. However, trends have been quite heterogeneous (Figure 3.4, Panel B). Ferti-lity rates remained stable in Austria, Japan and Switzerland – all low fertility countries. Fertility was more likely to rebound in countries with higher initial fertility rates, and even exceeded the replacement level in New Zealand and Iceland. This fertility rebound stalled in many OECD countries in 2009, possibly as a consequence of the economic crisis.

Fertility rates are generally higher in emerging economies; rates are above replacement levels in Argentina, India, Saudi Arabia and South Africa. While fertility increased in Russian Federation by one decimal between 2008 and 2011, fertility decreased in other emerging economies (except Brazil).

Definition and measurement

The total fertility rate is the expected number of children born to each woman at the end of her 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. Data typically come from civil population registers or other administrative records. These are harmonised according to United Nations and Eurostat recommendations. The exception is Turkey, where fertility data are survey-based.

Further reading

OECD (2013), “SF2.1 Fertility rates”, OECD Family Database, www.oecd.org/social/family/database.

Figure note

Figure 3.3: 2010 instead of 2011 for Chile.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

3.3. Fertility rates across the OECD are typically below replacement level with a moderate decline since the crisis

Source: National statistical offices and World Development Indicators (http://data.worldbank.org) for non-OECD G20 countries.

StatLink  http://dx.doi.org/10.1787/888932966295

3.4. Decline in fertility over the last 50 years, and moderate recovery between 2000 and 2008

Source: National statistical offices and World Development Indicators (http://data.worldbank.org) for non-OECD G20 countries.

StatLink  http://dx.doi.org/10.1787/888932966295

Migration

The migrant population represents a growing share of the total population. The share of foreign-born within the population increased in all OECD countries between 2001-11, with the exception of Estonia, Israel and Poland (Figure 3.5, Panel A).

On average in the OECD, 12.6% of the population was foreign-born in 2011. 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 3.5, Panel A). Nearly two-thirds of the OECD countries had an immigrant population exceeding one in ten of the population. There is, however, a large variation across the OECD countries in the share of immigrants. In Japan, Korea, Mexico and Poland less than 2% of the population was foreign-born.

Increased unemployment after the crisis in 2008 has had an impact on trends in net migration in the last part of the decade (Figure 3.5, panel B). Notably, high positive net migrant rates in Iceland and Ireland in the period 2005-07 were turned into substantial negative rates in the period 2008-10. Also Spain experienced a sharp decline in net migration over the same period. The rates were highest in Australia, Luxembourg, Norway and Switzerland. For these countries, an increase in the net migration rates was observed between the periods 2005-07 and 2008-10. Overall within the OECD, net migration declined slightly during the same period.

The OECD countries most affected by the economic crisis (notably Greece and Spain) experienced the largest relative increase in outflow of nationals to other OECD countries (Figure 3.6).

The economic crisis has also affected the composition of the inflows of foreigners, although family migration and free mobility (i.e. migration within a free-movement zone) still represent the bulk of permanent-based migration (Figure 3.7). Over the period 2007-11, the free movement category has shown the sharpest decline. A substantial share of free movements can be assumed to be work-related. Although work-related migration had increased from 2010 to 2011, it is still at much lower levels than prior to the crisis.

Definition and measurement

Net migration rate refers to the difference of inflows and outflows of an area in a period of time per thousand people in the population. A positive value represents more people registered as entering the country than leaving it, while a negative value means more people leaving than entering. Both nationals and foreigners are included.

Immigrants are, in the first instance, defined as those who are foreign-born. In general, the foreign-born population is substantially larger than the share of foreign nationals.

Five categories of permanent-type international migration can be identified:

Work-related: Persons admitted for employment on a permanent-type basis, including with a temporary permit that can in principle be renewed indefinitely.

Free movement: Nationals moving within a free-mobility zone (e.g. EU/EFTA; Trans-tasman agreement), except students and temporary mobility (e.g. seasonal labour mobility); in general it refers to nationals from other countries in the free-mobility zone (other than students) who stayed for more than a year.

Accompanying family of workers: Persons accompanying a principle migrant admitted for work-related reasons (see above).

Family migration: Persons admitted for family reunification and family formation purposes to both foreigners and nationals.

Humanitarian: Persons admitted for international protection and other humanitarian means, including their accompanying family.

Other: Includes ancestry-based migration, retirees, persons of independent means, etc.

All of these categories can include status changes of people already residing in the country under a different, but temporary category (e.g. international students changing status after their studies to take on employment in the host country or because they married a national).

Further reading

OECD (2013), International Migration Outlook 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/migr_outlook-2013-en.

OECD (2007), “OECD Standardised Statistics on Immigrant Inflows: Results, Sources and Methods”, by Georges Lemaitre, Thomas Liebig, Cécile Thoreau and Pauline Fron, available at www.oecd.org/els/mig/38832099.pdf.

Figure notes

Figure 3.5, Panel A: Data for France exclude persons born abroad who were French at birth. Data on the foreign-born are not available for the Slovak Republic. Data are from the OECD International Migration Database except Japan and Korea in 2011 (UN Population Division) and Greece in 2011 (Eurostat).

Figure 3.7: Excludes the Czech Republic and the countries for which standardised data by category of entry are not available.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

3.5. Net migration rates declined slightly after the crisis

Source: International Migration Outlook 2013 (www.oecd.org/migration).

StatLink  http://dx.doi.org/10.1787/888932966314

3.6. The OECD countries most affected by the economic crisis experienced the largest relative increase in outflow of nationals

Outflows of nationals from selected OECD countries to other OECD destination countries, 2011 Index 100 in 2007

Source: International Migration Outlook 2013 (www.oecd.org/migration).

StatLink  http://dx.doi.org/10.1787/888932966314

3.7. The economic crisis has also affected the composition of the inflows of foreigners

Permanent immigration in OECD countries by category of entry or change of status, standardised statistics, 2007-11

Source: International Migration Outlook 2013 (www.oecd.org/migration).

StatLink  http://dx.doi.org/10.1787/888932966314

Family

The number of adults in a household illustrates additional information about household composition and how people live together, while indicators on marriage and divorce reflect on “adult partnership” status.

Marriage is the most common form of adult partnership in the OECD as a whole (Figure 3.8). There are, however, large differences across countries. The share of married adults is highest in Japan and Turkey where more than 65% were married in 2012. The share is lowest in Chile and Estonia where only about 40% were married. The share of cohabitation is generally high in countries with low marriage rates. In Estonia, Iceland and Sweden, about one in five adults cohabited with a domestic partner.

The share of single/never been married also varies greatly across countries. In Chile and Korea, close to four out of ten adults were single/had never been married. In Estonia, Hungary and the United Kingdom this share was close to one out of five.

Measurable changes in family patterns and the breakdown of families may, however, not be immediately observable. In a long run perspective, marriage rates have dropped significantly (Figure 3.9). Both the level of and change in divorce rates differ across countries (Figure 3.10), but in a long run perspective, back to 1970, divorce rates have increased significantly. Overall, it is difficult to gauge the effect of the crisis on family breakdown. Economic stress may lead to family breakdown and more divorces, but due to the increased economic costs of divorce and the greater income potential for couples, these factors may also account for a decrease in the number of divorces.

As a result of changing partnership patterns and lower fertility rates, the share of households without, or with only one or two children has increased. Children today are also more likely to live with just one parent. This can change the role of family as a safety network. The share of multigenerational households varies across the OECD area, but on average, there is no observed correlation between change in GDP and change in the number of people above the age of 15 in the household over the period 2007 to 2012.

The family structure varies across the emerging economies. While more than 70% of the adult population are married in China, India and Indonesia, the share is close to 25% in South Africa (Figure 3.8). Only Argentina and Brazil have shares of cohabitation above the OECD average. The Russian Federation has a divorce rate of 9% which is higher than all of the OECD countries except for the Czech Republic and Finland.

Definition and measurement

Data on household structure are drawn from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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, variation in response rates. Sample sizes vary between around 1 000 and 4 000, depending on the country and data should be interpreted carefully. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error for at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

Respondents aged 15 years and over are asked to reply to the following question ie. What is your current marital status? The categories are self-assessed by the respondent.

Marriages and divorces rates are taken from OECD Family Database. The crude marriage rate is the number of marriages formed each year as a ratio to 1 000 people. This measure disregards other formal cohabitation contracts and informal partnerships. The crude divorce rate expresses the ratio of the number of marriages which are dissolved in a given year to the average population in that year. The value is given per 1 000 inhabitants. For more information, www.oecd.org/social/family/database.

Further reading

OECD (2013a), OECD Family Database, www.oecd.org/social/family/database.

OECD (2013b), “Changes in Family Policies and Outcomes in EU and OECD Countries: Is There Convergence”, OECD Publishing, Paris, forthcoming.

United Nations (2011), UN Demographic Yearbook 2011, Washington, DC.

Figure note

Figure 3.8: Data for Chile refer to 2011.

Figure 3.9: No data for Turkey in 1970.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

3.8. Large differences in households composition

Proportion of respondents aged 15 and over by relationship status, 2012, percentages sorted by married

Source: Gallup World Poll (www.gallup.com), OECD Family Database as in September 2013 (www.oecd.org/social/family/database), UN Demographic Yearbook 2011.

StatLink  http://dx.doi.org/10.1787/888932966333

3.9. Marriage rates have decreased in the last four decades

Number of marriages formed each year as a ratio to 1 000 people (crude marriage rate) in 1970 and 2010

Source: Gallup World Poll (www.gallup.com), OECD Family Database as in September 2013 (www.oecd.org/social/family/database), UN Demographic Yearbook 2011.

StatLink  http://dx.doi.org/10.1787/888932966333

3.10. Divorces slightly picked up during the crisis in some countries

Number of divorces per 1 000 population (crude divorce rate), selected countries, 1970 and 2000-10

Source: Gallup World Poll (www.gallup.com), OECD Family Database as in September 2013 (www.oecd.org/social/family/database), UN Demographic Yearbook 2011.

StatLink  http://dx.doi.org/10.1787/888932966333

Old age support rate

The old age support rate is the ratio of the population who are economically active to older people who are more likely to be economically inactive. It thus provides an indicator of the number of active people who, potentially, are economically supporting inactive people. It also gives a broad indication of the age structure of the population. Changes in the old age support rate depend on past and present mortality, fertility rates and, to a much lesser degree, on net migration.

On average there were about four people of working age for every older person across the OECD in 2012 (Figure 3.11, Panel A). Rates in Mexico (nine people) and Turkey (eight people) exceeded this rate by a big margin. At the other end of the spectrum, in Germany, Italy and Japan, there were three or fewer working-age people for every older person.

Support rates are projected to decline in all OECD countries over the next 40 years (Figure 3.11, Panel B). Mexico and Turkey are expected to lose five to six working-age people per older person. In many of the other OECD countries, numbers will decline to fewer than two working-age people per older person. Countries which have the highest old age support rates are currently experiencing the biggest falls, indicating that support rates are gradually converging between countries and will continue to do so over the next 40 years.

The historical and projected pattern of evolution of support rates differs greatly according to country (Figure 3.12). The chart illustrates future convergence of support rates between countries. The main reason for convergence in support rates is the lagged effect of convergence in fertility rates across the OECD. The projections of support rates are highly conditional on projections of likely fertility rates over the next forty years. The extent of the policy challenges caused by these expected support rates will depend in part on the health and labour market attachments of those over age 65, which will influence their abi-lity to support themselves.

The old age support rates in emerging countries are in general higher than in OECD countries (Figure 3.11, Panel A). The variation is, however, huge; from about five people of working age for every older person in Argentina and the Russian Federation to about twenty in Saudi Arabia. The support rates are, however, projected to drop sharply over the next 40 years. Saudi Arabia is expected to lose as much as about 17 working-age people per older person. Also Brazil, China, India and Indonesia will lose five to seven working-age people per older person.

Definition and measurement

The old age support rates relate to the number of those who are capable of providing economic support to the number of older people that may be materially dependent on the support of others. The support rate indicator used here is the population aged 20 to 64 as a ratio to those aged 65 and over. It is the inverse of the old age dependency ratio. The projections for old age support rates used here are based on the most recent “medium-variant” population projections. They are drawn from the United Nations, World Population Prospects – 2012 Revision.

Further reading

OECD (2013), Pensions at a Glance 2013: OECD and G20 Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/pension_glance-2013-en.

United Nations (2012), World Population Prospects – 2012 Revision, Washington, DC.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

3.11. Population are ageing and the old age support ratio will halve in the OECD

Source: OECD (2013) and United Nations (2012).

StatLink  http://dx.doi.org/10.1787/888932966352

3.12. Convergence in the old age support ratio across the OECD

Number of people of working age (20-64) per person of pension age (65+) in selected countries, 1950-2050

Source: OECD (2013) and United Nations (2012).

StatLink  http://dx.doi.org/10.1787/888932966352

4.

Self-sufficiency indicators

Employment

Unemployment

Youth neither in employment, education nor training (NEETs)

Expected years in retirement

Education spending

Employment

Access to paid work is crucial for people’s ability to support themselves. On average, two out of three working age adults in the OECD area are employed (Figure 4.1, Panel A). In Iceland and Switzerland about eight out of ten are employed, compared to about one out of two in Greece and Turkey. Gender differences in employment rates are small in the Nordic countries, but such differences tend to be largest in Chile, Korea, Mexico and Turkey.

The economic crisis has had a large impact on the employment rates in many countries (Figure 4.1, Panel B). On average, the employment rate declined by 1 percentage point in the OECD area from mid-2007 to mid-2013, but the variation across countries is large. While the rates dropped by 10 or more percentage points in Greece and Spain; Chile, Israel and Turkey experienced an increase of 5 or more percentage points over the same period.

Women have improved their relative position in the labour market compared to men (Figure 4.1, Panel B). Only in Estonia, Korea and Poland, was the change in the employment rate the same for both sexes. In spite of this relatively more favourable development for women, the long-term increasing trend in female employment rates came to a halt in OECD countries after the onset of the crisis.

While employment has dropped, part-time work has increased in many countries. Even if these people avoid unemployment, the consequence for many of them is under-employment and reduced incomes. Involuntary part-time as a share of total employment has increased substantially in Ireland, Italy and Spain following the onset of the crisis (Figure 4.2). The increase has been strongest for women, where involuntary part-time reached about 14% of total employment in Italy and Spain in 2012. But also in Australia and Ireland, about 10% of women worked involuntarily in part-time jobs. For men, the share of involuntary part-time was about 5% in Ireland and Spain in 2012.

Immigrants’ employment thus seems to be more sensitive to economic conditions than that of the natives. On average, the change in employment rates for the foreign-born between 2007 and 2012 was approximately the same as for the native-born (Figure 4.3).This, however, hides large differences across countries. In those countries which experienced the sharpest drop in employment rates of the native-born (Greece, Ireland and Spain), foreign-born fared even worse than the natives. In contrast, in countries with increasing employment rates, such as Germany, there was a larger increase in the employment rates of the foreign-born than among the natives.

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.

Involuntary part-time workers are part-timers (wor-king 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

European Commission (2013), “EU Employment and Social Situation”, Quarterly Review, Brussels, March.

OECD (2013a), “Changes in Family Policies and Outcomes in EU and OECD Countries: Is There Convergence”, OECD Publishing, Paris, forthcoming.

OECD (2013b), International Migration Outlook 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/migr_outlook-2013-en.

OECD (2013c), OECD Employment Outlook 2013, OECD Publi-shing, Paris, http://dx.doi.org/10.1787/empl_outlook-2013-en.

Figure notes

Figure 4.1: Panel A: Data for the Russian Federation are annual and refer to 2012. Data for Mexico refer to Q1 2013. Panel B: Data for South Africa refer to Q1 2007.

Figure 4.2: Data for Switzerland refer to 2010 instead of 2012. Countries are ranked in increasing order of the percentage point change of the total population.

Figure 4.3: Data refer to 2008 instead of 2007 for Canada, Germany and Ireland; and to Q2 2007 for Switzerland.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

4.1. The economic crisis has had a large impact on the employment rates in many countries

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013), OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm) and International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm).

StatLink  http://dx.doi.org/10.1787/888932966371

4.2. Involuntary part-time work increased during the crisis

Percentage point change in the share of involuntary part-timers in total employment between 2007 and 2012

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013), OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm) and International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm).

StatLink  http://dx.doi.org/10.1787/888932966371

4.3. Immigrants’ employment seems to be more sensitive to economic conditions than that of the natives in some countries

Percentage point change in the employment rates of the native-born and foreign-born population between 2007 and 2012, 15-64

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013), OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm) and International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm).

StatLink  http://dx.doi.org/10.1787/888932966371

Unemployment

Record high unemployment rates in a number of countries have put stress on the benefit systems (see “Recipients of out-of-work benefits” indicator). Unemployment, and particularly long-term unemployment, may also harm career chances in the future, reduce life satisfaction and increase social costs. Establishment in the labour market for youth has become more difficult, while older unemployed often have problems re-entering the workforce.

During the second quarter of 2013, the highest unemployment rates in the OECD were in Greece and Spain – eight times higher than the lowest unemployment rate, in Korea (Figure 4.4, Panel A). The average unemployment rate of 9.1% in the OECD covers a wide diversity. Austria, Japan, Korea, Norway and Switzerland had an unemployment rate below 5%. As many as ten countries had an unemployment rate above 10%.

The economic crisis has had a strong, but varied impact on unemployment rates (Figure 4.4, Panel B). The average OECD unemployment rate increased by 3 percentage points between mid-2007 and mid-2013. Greece and Spain were hit particularly hard, seeing an increase of above 18 percentage points. Increases of more than 5 percentage points were also observed in Ireland, Italy, Portugal and Slovenia. Countries which succeeded in reducing their unemployment rates included Chile, Germany, Israel, Korea and Turkey.

In most countries, male unemployment has been more affected by the crisis than female unemployment. The gender difference is particularly strong in countries such as Ireland, Portugal and Spain, where the contraction of the construction industry is a major factor driving the increased unemployment. High representation of women in the public sector can also be one explanation why women have fared better than men during the crisis in many countries. However, women in Estonia, Luxembourg and Turkey had a stronger increase in the unemployment rates than men.

Long-term unemployment has increased in many countries. The share of people unemployed for one year or more as a percentage of the total unemployment has increased the most in Ireland, Spain and the United States (Figure 4.5), and by as much as 30 percentage points in Ireland. Mid-2013, six out of ten unemployed were out of work for one year or more in Greece, Ireland and the Slovak Republic. The share of long-term unemployed decreased by 10 percentage points or more in Germany and Poland. In spite of the positive achievements, long-term unemployment still accounts for more than 40% of total unemployment in Germany and Poland.

Youth have been hit particularly hard by the deteriorated labour market situation (see also the “NEETs’” indicator). The unemployment rate for young people aged 15-24 increased by 20 percentage points or more from mid-2007 to mid-2013 in Greece, Portugal and Spain (Figure 4.6). At the OECD level, the rate increased by 7 percentage points during the same period. Mid-2013, more than 50% of the age group was out of work in Greece and Spain. At the other end of the scale, youth unemployment rates dropped in Austria, Chile, Germany, Israel and Turkey. Germany, Japan and Switzerland had mid-2013 the lowest unemployment rate for this age group, at about 7%.

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 (15-64 years old). 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 employment rates are presented by gender and age.

Long-term unemployment is defined here as people unemployed for one year or more. For more information, see www.oecd.org/employment/database.

Further reading

OECD (2013), OECD Employment Outlook 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2013-en.

Figure note

Figure 4.4, Panel A: Data for the Russian Federation are annual and refer to 2012.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

4.4. Unemployment has increased more for men than for women

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013).

StatLink  http://dx.doi.org/10.1787/888932966390

4.5. Higher incidence of long-term unemployment since the begining of the crisis

Percentage point change in the share of people unemployed for one year or more as a percentage of total unemployment between 2007 and Q2 2013

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013).

StatLink  http://dx.doi.org/10.1787/888932966390

4.6. Unemployment hit youth hardest in most countries

Percentage point change in unemployment rates between 2007 and Q2 2013

Source: OECD calculations based on quarterly national labour force surveys, the OECD Short-Term Labour Market Statistics and the OECD Labour Force Statistics Databases (cut-off date: 8 October 2013).

StatLink  http://dx.doi.org/10.1787/888932966390

Youth neither in employment, education nor training (NEETs)

Participation in employment, education or training is important for youth to become established in the labour market and achieve self-sufficiency. Record high unemployment rates in a number of countries have hit youth especially hard. In addition, inactivity rates of youth are substantial in many countries, meaning that they are neither employed, nor registered as unemployed, in education or in training.

More than 20% of all youth aged 15/16-24 were unemployed or inactive, and neither in education nor in training (NEET) in Greece, Italy, Mexico and Turkey in the fourth quarter of 2012 (Figure 4.7, Panel A). The lowest rates were observed in Denmark, Iceland, the Netherlands and Switzerland, with rates of 6% or lower. The average NEET rate in the OECD area was about 13%.

The NEET rate has increased in most OECD countries since the onset of the economic crisis (Figure 4.7, Panel B). From the fourth quarter of 2007 to the fourth quarter of 2012, the increase was strongest in Greece, Luxembourg, Ireland, Italy and Spain. On the other hand, there were also some countries where the NEET rates dropped. The decrease was particularly strong in the Czech Republic and Turkey. The higher NEET rates in many counties can mainly be explained by increased unemployment. At the average OECD level, the inactivity rate declined by 1 percentage point, and in most countries the rate declined or increased moderately.

On average across OECD countries, the NEET rates for the broader 15-29 age group are higher for people with low education levels than for those with high education (Figure 4.8). The gap is highest in Belgium, Mexico and the United Kingdom.

The share of 15-24 year-olds who are unemployed or inactive and neither in education nor in training is higher for foreign-born than for natives (Figure 4.9). Exceptions are Hungary, Ireland and the United Kingdom. The impact of the crises on the NEET rates is relatively similar for foreign-born and natives in most countries. In the Czech Republic, Finland, Greece, Luxembourg, Norway and Slovenia, were the relative change in the rates for foreign-born larger than for natives.

The NEET rates in emerging economies are generally high (Figure 4.7, Panel A). In India, Saudi Arabia and South Africa, more than 20% of the population aged 15/16-24 were unemployed or inactive and neither in education nor in training in the fourth quarter of 2012.

Definitions and measurement

The so-called NEET population refers to youth population who is neither in employment, education nor training. Data refer to OECD estimates based on national labour force surveys. National definitions broadly conform to this generic definition, but may vary depending on national circumstances.

NEET rates are presented by status of inactivity (unemployed or inactive), completed level of education and migrant status.

Data for some countries (such as Iceland) should be interpreted carefully due to relatively small sample size.

Further reading

OECD (2013a), OECD Employment Outlook 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook-2013-en.

OECD (2013b), Education at a Glance 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2013-en.

OECD (2013c), International Migration Outlook 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/migr_outlook-2013-en.

Figure notes

Figure 4.7: Detailed data are not available for South Africa. Argentina and Brazil: Selected urban areas only. Saudi Arabia and China: May include some unemployed people who are students.

Figure 4.8: For Japan, data refer to 15-24 year-olds.

Figure 4.9: The results for NEET in Europe are overestimated because they are based on three quarters, including summertime, when under declaration of school enrolment of students is commonly observed. Data are sorted by increasing rate of unemployment for the foreign-born population.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

4.7. More young people are unemployed or inactive and not in education nor in training (NEET)

Source: OECD estimates based on national labour force surveys; OECD Short-Term Labour Market Statistics; OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm); Education at a Glance 2013 (www.oecd.org/edu/eag.htm); International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm); for European countries: Labour force surveys (Eurostat), Q1-Q3 2008, Q1-Q3 2011, Q1-Q3 2012; United States: Monthly Current Population Surveys, 2007, 2011 and 2012.

StatLink  http://dx.doi.org/10.1787/888932966409

4.8. Young people with low education are more likely to be NEET

Percentage of 15-29 year-olds not in education and not employed by completed level of education, 2011

Source: OECD estimates based on national labour force surveys; OECD Short-Term Labour Market Statistics; OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm); Education at a Glance 2013 (www.oecd.org/edu/eag.htm); International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm); for European countries: Labour force surveys (Eurostat), Q1-Q3 2008, Q1-Q3 2011, Q1-Q3 2012; United States: Monthly Current Population Surveys, 2007, 2011 and 2012.

StatLink  http://dx.doi.org/10.1787/888932966409

4.9. Immigrant youth are more likely to be NEET

NEET rates by place of birth in selected OECD countries, 2008 and 2012

Source: OECD estimates based on national labour force surveys; OECD Short-Term Labour Market Statistics; OECD Employment Outlook 2013 (www.oecd.org/els/emp/oecdemploymentoutlook.htm); Education at a Glance 2013 (www.oecd.org/edu/eag.htm); International Migration Outlook 2013 (www.oecd.org/els/mig/imo2013.htm); for European countries: Labour force surveys (Eurostat), Q1-Q3 2008, Q1-Q3 2011, Q1-Q3 2012; United States: Monthly Current Population Surveys, 2007, 2011 and 2012.

StatLink  http://dx.doi.org/10.1787/888932966409

Expected years in retirement

The duration of expected years in retirement illustrates the length of the expected remaining life expectancy from the time of average labour market exit. The indicator demonstrates how pension systems interact with labour market exit as well as the financial pressures on the pension system in the context of an ageing population. Men typically can expect to spend fewer years in retirement than women (Figure 4.10). The most recent calculations of expected years in retirement exceeded 25 years for women in Austria, Belgium, France, Italy and Luxembourg (Figure 4.10, Panel A). The period exceeded 20 years for men in Austria, Belgium, Finland, France, Greece, Italy, Luxembourg and Spain (Figure 4.10, Panel B). The number of expected years in retirement was notably low for women – under 20 years – in Chile, Iceland, Korea, Mexico, Portugal and Turkey, and for men – less than 15 years – in Estonia, Korea, Mexico and Portugal.

On average women can expect to spend almost 4.5 years longer in retirement than men (Figure 4.10). In most Eastern European countries this gap was at least six years, and also in Japan the gender gap is more than six years. Longer periods in retirement exposes women to old age poverty, resulting from the link of many pension schemes to earnings and the gender pay gap observed in all OECD countries. In addition, price indexation of pension payment in many countries means that the oldest old, predominantly women, become relatively poorer during retirement.

The duration of expected years in retirement for women in emerging countries varies from 20 years in Brazil and the Russian Federation to 15 years in South Africa (Figure 4.10, Panel A). The variation is less for men, who can expect 12 to13 years in retirement (Figure 4.10, Panel B). While the effective exit age in Brazil was more than six years lower for women than for men, the dif-ference in the Russian Federation was close to three years.

The average duration of expected years in retirement across OECD countries has increased over time. In 1970 men in the OECD countries spent on average 11 years in retirement and by 2012 this average increased to 18 years (Figure 4.11, Panel B). The duration of the expected period in retirement was longer for women; increasing from 15 years on average in 1970 to 22.5 years in 2012 (Figure 4.11, Panel A).

The increase in average duration of years in retirement from 1970 to 2012 is due both to a drop in the effective exit age from the labour force and to increased longevity. Effective age of labour force exit decreased gradually from 1970 to the late 1990s for both men and women. After some relatively stable years, the average effective exit age started to increase slowly from 2004. Life expectancy at the effective exit age from the labour force increased substantially during this period, particularly for women, and over the last two decades for men as well. Over the past few years, this increase has been fairly equal to that of the effective exit age from the labour market, and potential years in retirement have stabilised.

Definition and measurement

Expected years in retirement is a calculation of remaining life expectancy from the time of effective age of labour force exit for men and women.

The average effective age of labour force exit is calculated as a weighted average of (net) withdrawals from the labour market at different ages over a five-year period for workers initially aged 40 and over. In order to abstract from compositional effects in the age structure of the population, labour force withdrawals are estimated based on changes in labour force participation rates rather than labour force levels. These changes are calculated for each (synthetic) cohort divided into five-year age groups. For more discussion see OECD (2013).

Estimates of the number of years of additional life are calculated based from the UN World Population Prospects, the 2012 revision dataset.

Further reading

OECD (2013), Pensions at a Glance 2013: Retirement-income Systems in OECD and G20 Countries, OECD Publishing, Paris, http://dx.doi.org/10.1787/pension_glance-2013-en.

Figure note

Figure 4.10: 2011 for Brazil and 2010 for China, and 2008 for women in Turkey instead of 2012.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

4.10. Women live almost five more years in retirement than men on average

Source: Pensions at a Glance 2013 (www.oecd.org/pensions/pensionsataglance.htm): life expectancy estimates are from UN World Population Prospects, the 2012 Revision.

StatLink  http://dx.doi.org/10.1787/888932966428

4.11. Women (and men) spend 7.5 (and 8) more years in retirement in 2012 than in 1970 on average across OECD countries

Trend in age at labour market exit and years in retirement, 1970 to 2012, OECD average

Source: Pensions at a Glance 2013 (www.oecd.org/pensions/pensionsataglance.htm): life expectancy estimates are from UN World Population Prospects, the 2012 Revision.

StatLink  http://dx.doi.org/10.1787/888932966428

Education spending

On average, OECD countries spent USD 9 300 per child per year from primary through tertiary education in 2010 (Figure 4.12, Panel A). Spending was highest in the United States with just over USD 15 000 per child, followed closely by Switzerland. On the opposite end, spending was USD 5 000 or less in Chile and Mexico. Spending was also relatively low (around USD 6 000) in several Eastern European countries.

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 8% between 2008 and 2009 on average across OECD countries, it fell by 1.5% between 2009 and 2010 (Figure 4.12, Panel B). Public expenditures on educational institutions as a percentage of GDP decreased in two-thirds of those OECD countries for which data are available, most likely as a consequence of fiscal consolidation policies. Drops of more than 4% were seen in Estonia, Hungary, Iceland, Italy, Sweden, Switzerland and the United States.

On average across the OECD countries, less investment was put into early education as compared to later years, with spending per child amounting to USD 6 800 at the pre-primary level, USD 8 000 at the primary level, USD 9 000 at the secondary level and USD 13 500 at the tertiary level (Figure 4.13). These averages mask a broad range of expenditure per student by educational institutions across the OECD countries, varying by a factor of 9 at the pre-primary level, 11 at the primary level, 7 at the secondary level and 4 at the tertiary level.

In 2010, public funding accounted for 84% of all funds for educational institutions, on average across the OECD countries (Figure 4.14). It varied from around 60% in Chile and Korea to over 95% in Finland and Sweden. The share of public funding decreased from 2000 to 2010. The decline was remarkable for tertiary institutions, from 76% in 2000 to 68% in 2010. This trend is mainly influenced by non-European countries, where tuition fees are generally higher and enterprises participate more actively in providing grants to finance tertiary education.

Argentina, Brazil and Russian Federation (emerging economies for which data are available) all had education spending comparable to the low-spending OECD countries (Figure 4.12, Panel A).

Definitions 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.

Levels of education are based on the International Standard Classification of Education (ISCED 1997), which distinguishes six levels of education, classified here into four groups: pre-primary (ISCED 0 from age 3), primary (ISCED 1), secondary (ISCED 2-3-4) and tertiary (ISCED 5-6).

The public (and private) proportion of expenditure on educational institutions is the percentage of total spending originating in, or generated by, the public (and private) sector.

Further reading

OECD (2013), Education at a Glance 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/eag-2013-en.

Figure notes

Figure 4.12: Level of spending not available for Canada, Germany, Greece and Turkey.

Figure 4.13: 2009-10 change not available for Canada, Germany, Greece, Turkey, Argentina and Brazil;

Figure 4.14: Pre-primary data not available in 2010.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

4.12. Variation in per student education spending and decline in public spending in percentage of GDP between 2009 and 2010

Source: OECD (2013), Education at a Glance 2013 (www.oecd.org/edu/eag.htm).

StatLink  http://dx.doi.org/10.1787/888932966447

4.13. Spending per child increases with the level of education

Annual expenditure per student for all services, by level of education, in USD at current prices and current PPPs in 2010

Source: OECD (2013), Education at a Glance 2013 (www.oecd.org/edu/eag.htm).

StatLink  http://dx.doi.org/10.1787/888932966447

4.14. The share of education public funding has fallen, particularly for tertiary institutions

Share of public expenditure on educational institutions, by level of education, OECD average, 2010 and 2000

Source: OECD (2013), Education at a Glance 2013 (www.oecd.org/edu/eag.htm).

StatLink  http://dx.doi.org/10.1787/888932966447

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 instrumental reasons – seeing it as causing conflict, limiting co-operation or creating psychological and physical health stresses (Wilkinson and Pickett, 2009). 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 2010 (Figure 5.1, Panel A). The Gini coefficient ranges from 0.24 in Iceland to approximately twice that value in Chile and Mexico. The Nordic and central European countries have the lowest inequality in 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 almost 10 to 1 on average across OECD countries in 2010, ranging from 5 to 1 in Denmark, Iceland and Slovenia to almost six times larger (29 to 1) in Mexico.

Keeping measurement-related differences in mind, emerging countries have higher levels of income inequality than OECD countries, particularly in Brazil and South Africa. Comparable data from the early 1990s suggest that inequality increased in Asia, decreased in Latin America and remained very high in South Africa.

The distribution of income from work and capital (market income, pre-taxes and transfers) widened considerably during the first phase of the crisis. Between 2007 and 2010, market income inequality rose by 1 percentage point or more in 18 OECD countries (markers in Figure 5.1, Panel B). The increase was particularly large in Estonia, Greece, Ireland, Japan and Spain, but also in France and Slovenia. On the other hand, market income inequality fell in Poland and, to a smaller extent, in the Netherlands.

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 personal taxes. Between 2007 and 2010, the Gini coefficient for disposable income remained broadly stable in most OECD countries (bars in Figure 5.1, Panel B). It fell the most in Iceland, New Zealand, Poland and Portugal, and increased the most in France, the Slovak Republic, Spain and Sweden. Overall, the welfare state prevented inequality from going from bad to worse during the first phase of the crisis.

Income inequality increased especially at the top of the distribution: the share of pre-tax income of the top 1% earners more than doubled their share from 1985 to 2010 in the United Kingdom and the United States (Figure 5.2). In Spain and Sweden, the data show a clear upward trend albeit less marked than in English-speaking countries. The upward tendency is also less marked in France, Japan and most continental European countries. Overall, the economic 2007/08 crisis has brought about a fall in top income shares in many countries, but this fall appears to be of a temporary nature.

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 countries and the Russian Federation, on per capita incomes for all key partners except India and Indonesia for which per capita consumption was used.

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.

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

The shares of “pre-tax” income going to the top 1% earners are from the World Top Incomes Database available at http://topincomes.g-mond.parisschoolofeconomics.eu.

Further reading

OECD (2013a), “Crisis Squeezes Income and Puts Pressure on Inequality and Poverty – New results from the OECD Income Distribution Database”, www.oecd.org/social/inequality.htm.

OECD (2013b), “Trends in Top Incomes and Their Taxation”, OECD Publishing, Paris, forthcoming.

OECD (2011), Divided We Stand: Why Inequality Keeps Rising, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264119536-en.

Wilkinson, R. and K. Pickett (2009), The Spirit Level. Why Equality is Better for Everyone, Penguin Books, London.

Figure notes

Figure 5.1: Gini coefficients refer to 2009 for Hungary, Japan, New Zealand and Turkey, and 2011 for Chile instead of 2010, and to 2006 for Chile and Japan, 2008 for Australia, Finland, France, Germany, Israel, Mexico, New Zealand, Norway, Sweden and the United States instead of 2007. Data for Switzerland are not available for 2007. Latest data for key partners are for 2008/09. Gini coefficients are based on equivalised incomes for OECD countries and the Russian Federation and per capita incomes for all key partners except India and Indonesia for which per capita consumption was used.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

5.1. Large differences in levels of income inequality and market income inequality rose considerably during the first years of the crisis

Source: OECD Income Distribution Database (www.oecd.org/social/inequality.htm), except top 1% income shares from World Top Incomes Database.

StatLink  http://dx.doi.org/10.1787/888932966466

5.2. Top 1% income share differs widely over time and across OECD countries

Share of pre-tax income going to top 1% earners

Source: OECD Income Distribution Database (www.oecd.org/social/inequality.htm), except top 1% income shares from World Top Incomes Database.

StatLink  http://dx.doi.org/10.1787/888932966466

Poverty

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 generally receive more attention than income inequality measures, with greater concerns for certain groups like older people and children, since they have no or limited options for working their way out of poverty.

The average OECD relative poverty rate in 2010 was 11% for the OECD (Figure 5.3, Panel A). Poverty rates were highest at above 20% in Israel and Mexico, while poverty in the Czech Republic and Denmark affected only about one in 20 people. Anglophone and Mediterranean countries and Chile, Japan and Korea have relatively high poverty rates.

The initial phase of the crisis had a limited impact on relative income poverty (i.e. the share of people living with less than half the median income in their country annually). Between 2007 and 2010, poverty increased by more than 1 percentage point only in Italy, the Slovak Republic, Spain and Turkey (bars in Figure 5.3, Panel B). Over the same period, it fell in Chile, Estonia, Portugal and the United Kingdom, while changes were below 1 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 Estonia, Greece, Iceland, Ireland, Italy, Mexico and Spain (“diamond” symbols in Figure 5.3, Panel B). While relative poverty did not increase much or even fell in these countries, “anchored” poverty increased by 2 percentage points or more between 2007 and 2010, reflecting disposable income losses of poorer households in those countries. Only in Belgium, Germany, Israel and Poland did “anchored” poverty fall at the same time as relative poverty stagnated or increased.

Households with children and youth were hit particularly hard during the crisis. Between 2007 and 2010, average re-lative income poverty in OECD countries rose from 12.8 to 13.4% among children (0-18) and from 12.2 to 13.8% among youth (18-25). Meanwhile, relative income poverty fell from 15.1 to 12.5% among the elderly. This pattern confirms the trends described in previous OECD studies, with youth and children replacing the elderly as the group at greater risk of income poverty across the OECD  ountries.

Since 2007, child poverty increased considerably in 16 OECD countries, with increases exceeding 2 percentage points in Belgium, Hungary, Italy Slovenia, Spain and Turkey (Figure 5.4). On the other hand, child poverty fell by more than 2 percentage points in Portugal and the United Kingdom. At the same time, youth poverty increased considerably in 19 OECD countries.

In contrast to other age groups, the elderly have been relatively immune to rises in relative income poverty during the crisis. In the three years prior to 2010, poverty among the elderly fell in 20 out of 32 countries, and increased by 2 percentage points or more only in Canada, Korea, Poland and Turkey. This partly reflects the fact that old age pensions were less affected by the recession. In many countries (at least until 2010), pensions were largely exempted from the cuts implemented as part of fiscal consolidation.

Definition and measurement

Perceptions of a decent standard of living vary across countries and over time. Thus no commonly agreed measure of poverty across OECD countries exists. As with income inequality, the starting point for poverty measurement is the concept of equivalised household disposable income. Estimates are provided by national consultants (see “Definition and measurement” of the “Income inequality” indicator above).

People are classified as poor when their equivalised 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. The poverty rate is a headcount of how many people fall below the poverty line.

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 5.3, Panel B 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 (2013), “Crisis Squeezes Income and Puts Pressure on Inequality and Poverty – New results from the OECD Income Distribution Database”, www.oecd.org/social/inequality.htm.

OECD (2011), Divided We Stand: Why Inequality Keeps Rising, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264119536-en.

Figure notes

Figures 5.3 and 5.4: Data refer to 2009 for Hungary, Japan, New Zealand and Turkey, and 2011 for Chile instead of 2010, and to 2006 for Chile and Japan, 2008 for Australia, Finland, France, Germany, Israel, Mexico, New Zealand, Norway, Sweden and the United States instead of 2007. Data for Switzerland are not available for 2007. Latest data for key partners are for 2008/09, changes are not available.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

5.3. Large differences in levels of relative poverty and the evolution of poverty differs if the threshold is “anchored” at the time of the crisis

Source: OECD Income Distribution Database (www.oecd.org/social/income-distribution-database.htm).

StatLink  http://dx.doi.org/10.1787/888932966485

5.4. Poverty rose among children and youth and fell among the elderly

Percentage point changes in relative poverty rates between 2007 and 2010 by age group

Source: OECD Income Distribution Database (www.oecd.org/social/income-distribution-database.htm).

StatLink  http://dx.doi.org/10.1787/888932966485

Living on benefits

Most OECD countries operate transfer programmes that aim at preventing extreme hardship and employ a low-income criterion as the central entitlement condition. These guaranteed minimum-income benefits (GMI) provide financial support for low-income families and aim 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.

In a large majority of OECD countries, incomes for the long-term unemployed are much lower than for the recently unemployed (Figure 5.6). Making GMI benefits more accessible is key to maintaining a degree of income security for the long-term unemployed. In addition, rising 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 5.5). Poverty avoidance or alleviation is 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. In a few countries there is no generally applicable GMI benefit (Greece, Italy and Turkey). For GMI recipients living in rented accommodation, housing-related cash benefits can provide significant further income assistance, bringing overall family incomes close to or somewhat above the poverty line (Denmark, Ireland, Japan and the United Kingdom). However, family incomes in these cases depend strongly on the type of housing, the rent paid and also on the family situation. In all countries, income from sources other than public transfers is needed to avoid substantial poverty risks.

On average across OECD countries, GMI benefit levels have changed little since the onset of the economic and financial crisis. The real value of these benefits was largely the same in 2011 as in 2007. Most countries, including those with significant fiscal consolidation programmes, have so far not reduced benefit levels for the poorest. However, at the same time, countries that were especially hard-hit by the crisis and where GMI were non-existent or very low, have not taken major measures to strengthen benefit adequacy (Greece, Italy, Portugal, Spain and the United States).

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 poverty thresholds of 50% or 60% of median household incomes. These income levels account for all cash benefit entitlements of a family 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 2011 expressed in 2011 prices and are adjusted for family size using the “square root of household size” equivalence scale. Similarly, net minimum cash benefits are converted to 2011 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 is the unweighted average over four family types (single person, one-earner married couple without children, lone parent and one-earner married couple with two children) and over two full-time earnings levels of 67% and 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). Calculations for families with children assume two children aged 4 and 6 and neither childcare benefits nor childcare costs are considered. 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. (2012), “Reforming the Benefit System to ‘Make Work Pay’: Options and Priorities in a Weak Labour Market”, IZA Discussion Paper, No. 50, Institute for the Study of Labour, Bonn.

Immervoll, H., S. Jenkins and S. Königs (2014), “Experiences of Minimum-income Benefit Recipients: Duration and Dynamics of Benefit Receipt”, OECD Social, Employment and Migration Working Paper, OECD Publishing, Paris, forthcoming, www.oecd.org/els/workingpapers.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

5.5. Minimum-income benefits alone cannot typically prevent income poverty

Net income level provided by cash minimum-income benefit, including housing assistance or not, in percentage of median household income

Source: OECD Tax-Benefit Models (www.oecd.org/els/social/workincentives).

StatLink  http://dx.doi.org/10.1787/888932966504

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

Overall net replacement rates: Net income while out of work in percentage of net income in work, 2011

Source: OECD Tax-Benefit Models (www.oecd.org/els/social/workincentives).

StatLink  http://dx.doi.org/10.1787/888932966504

Social spending

In 2012-13, public social spending averaged an estimated 21.9% of GDP across the 34 OECD countries (Figure 5.7, Panel A). In general, public spending is high in continental and northern European countries, while it is below the OECD average in most countries in Eastern Europe and outside Europe. Belgium, Denmark, Finland and France spent more than 30% of GDP on social expenditures. By contrast, Korea and Mexico spent less than 10% of GDP. Social spending in the emerging economies in the late 2000s was lower than the OECD average, ranging from around 2% in Indonesia to about 15-16% in Brazil and the Russian Federation (Figure 5.7, Panel A).

Public social spending in per cent of GDP increased in all OECD countries with the exception of Hungary from 2007-08 to 2012-13 (Figure 5.7, Panel B). The growth fully took place during the period 2007-08, as a response to increased unemployment and other consequences of the economic crisis. In this initial phase, Estonia and Ireland had the strongest increase in expenditure shares. From 2009-10 to 2012-13, fiscal consolidation reduced public social spending. Nearly two-thirds of the OECD countries reduced social spending in this period. The real drop in public social spending in some countries is larger than indicated by change in the shares of GDP, since the level of GDP also fell. Indeed in some countries, the rise of the ratio of public social spending in GDP is explained largely by the fact that GDP declined.

On average in the OECD, pensions, health services and income support to the working-age population and other social services each amount to roughly one-third of the total expenditures. In a majority of OECD countries, pensions are the largest expenditure area (Figure 5.8). In Anglophone countries and most other countries outside of Europe, health dominates public social expenditure. In a few countries, such as Denmark, Ireland and Norway, the largest share is devoted to income support of the working-age population.

Accounting for the impact of taxation and private social benefits (Figure 5.8) leads to a convergence of spending-to-GDP ratios across countries. Net total social spending is 22-28% of GDP in many 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.

In Europe, people seem to be most satisfied with the health care provisions and less satisfied with the pension provisions, unemployment benefits and the way inequality and poverty are addressed (Figure 5.9). Satisfaction with health care provisions is highest in Belgium, Luxembourg and the Netherlands and lowest in Greece and Poland. Satisfaction with pension provisions is highest in Austria, Luxembourg and the Netherlands and lowest in Greece and Poland. Satisfaction with how inequality and poverty are addressed is in general quite low.

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 5.7 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 are presented in Figure 5.8. Spending by lower tiers of government may be underestimated in some federal countries.

Public social spending totals reflect detailed social expenditure data for 1980-2009. Consistent with these historical series, public social expenditure totals were calculated for 2010, 2011, 2012 and estimated for 2013, based on national sources for non-European OECD countries, and/or the OECD Economic Outlook, No. 93, May 2013, and the European Union’s Annual Macroeconomic Database (AMECO).

Data on satisfaction with welfare state performance are from the Eurobarometer surveys. The latest wave of the Social Climate survey, carried out by TNS Opinion & Social network in the 27 Member States of the European Union in 2012, evaluated Europeans’ current perceptions of their own situation and of their country. Sample sizes vary between 1 000 and 1 500 depending on the country, and data should be interpreted carefully. For more information, see http://ec.europa.eu/public_opinion/index_en.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.

European Commission (2012), “Social Climate”, Special Eurobarometer No. 391.

Figure notes

Figure 5.7, Panel A: Data refer to 2009 for Turkey, 2010 for Japan, 2012 for Chile, Korea, and Mexico and to the last years available for key partners.

Figure 5.8: Income support to the working-age population refers to cash benefits towards incapacity, family, unemployment and other social policy areas. Data for Israel concern public social spending only. Total net social expenditure data are not available for Hungary, Greece, Switzerland and Turkey. Data for Switzerland refer to 2008.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

5.7. Social expenditure increased during the crisis

Source: OECD Social Expenditures Database (SOCX); OECD Employment Outlook 2013; Instituto de Pesquisa Econômica Aplicada (IPEA), Brazil; Asian Development Bank (ADB-SPI); World Health Organization (WHO); European Commission (2012) (http://ec.europa.eu/public_opinion/index_en.htm).

StatLink  http://dx.doi.org/10.1787/888932966523

5.8. Most spending goes to pensions and health

Public social spending by broad policy area and total net social spending, in 2009, in percentage of GDP

Source: OECD Social Expenditures Database (SOCX); OECD Employment Outlook 2013; Instituto de Pesquisa Econômica Aplicada (IPEA), Brazil; Asian Development Bank (ADB-SPI); World Health Organization (WHO); European Commission (2012) (http://ec.europa.eu/public_opinion/index_en.htm).

StatLink  http://dx.doi.org/10.1787/888932966523

5.9. Satisfaction with welfare state performance varies across European countries

Eurobarometer index on satisfaction for selected areas, 2012 (a neutral index is an index score close to 0 on a scale from -10 to 10)

Source: OECD Social Expenditures Database (SOCX); OECD Employment Outlook 2013; Instituto de Pesquisa Econômica Aplicada (IPEA), Brazil; Asian Development Bank (ADB-SPI); World Health Organization (WHO); European Commission (2012) (http://ec.europa.eu/public_opinion/index_en.htm).

StatLink  http://dx.doi.org/10.1787/888932966523

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 2010, the shares of working-age individuals receiving primary out-of-work benefits were highest in Iceland, France, Finland, Spain and the United States, with rates of around 5% or more (Figure 5.10, Panel A). At the other end of the spectrum, only about 1% in Japan, Korea, Slovak Republic and Chile received unemployment insurance benefits. There is no nation-wide unemployment insurance programme in Mexico and recipient data are not available for Greece and Turkey.

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. In Sweden, where unemployment insurance membership is voluntary, recipient numbers dropped despite rising unemployment. Benefit receipt increased most in Iceland, Estonia, United States, Ireland and Spain, all countries where unemployment soared during the economic crisis.

Receipt of secondary out-of-work benefits generally increased by much less between 2007 and 2010 (Figure 5.11, Panel B). Rising long-term unemployment and increasing joblessness among people without access to insurance benefits led, however, to a substantial rise in Ireland and Spain (unemployment assistance), and in the United States (Supplemental Nutrition Assistance Program, SNAP). Receipt rates dropped somewhat in the Czech Republic and in France, as well as in some countries with more favourable labour-market developments (Australia, Germany, Poland).

By 2010, receipt of secondary benefits was highest in Ireland, Mexico and the United States (Figure 5.11, Panel A) and lowest in Belgium, Israel and Japan. The composition of these safety nets differs across countries. Social assistance dominates in Mexico (Oportunidades) and the United States (SNAP and Temporary Assistance for Needy Families, TANF). Unemployment assistance is important in Ireland, Germany, Spain, Finland and the United Kingdom. Australia, Iceland and New Zealand also provide targeted income support to a large number of lone parents. In Germany, the largely unchanged number of recipients during a period of falling unemployment suggests that reducing safety-net beneficiary numbers can be difficult.

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.

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 (2007-10).

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. Jenkins and S. Königs (2014), “Experiences of Minimum-income Benefit Recipients: Duration and Dynamics of Benefit Receipt”, OECD Social, Employment and Migration Working Paper, OECD Publishing, Paris, forthcoming, www.oecd.org/els/workingpapers.

Königs, S. (2013), “The Dynamics of Social Assistance Benefit Receipt in Germany: State Dependence Before and After the Hartz Reforms”, OECD Social, Employment and Migration Working Paper, No. 136, OECD Publishing, Paris, http://dx.doi/org/10.1787/5k3xwtg6zknq-en.

Figure notes

Secondary out-of-work benefits for a number of countries are not shown due to missing information. In the United Kingdom, insured jobseekers can receive a flat-rate benefit during the first six months of unemployment, which becomes means-tested afterwards. The split between these two categories was not available and total beneficiary numbers are indicated both as primary and secondary benefits.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

5.10. Primary out-of-work benefits: A first line of defence for the unemployed

Source: Calculations based on the upcoming OECD Benefit Recipients Database (SOCR).

StatLink  http://dx.doi.org/10.1787/888932966542

5.11. Secondary out-of-work benefits: Safety nets are crucial for the poorest, but receipt rates are often low

Source: Calculations based on the upcoming OECD Benefit Recipients Database (SOCR).

StatLink  http://dx.doi.org/10.1787/888932966542

6.

Health indicators

Life expectancy

Perceived health status

Suicide

Health expenditure

Coverage for health care

Life expectancy

For the first time in history, in 2011, life expectancy at birth on average across OECD countries exceeded 80 years, an increase of ten years since 1970 (Figure 6.1). Italy, Japan and Switzerland lead a large group of over two-thirds 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 Mexico and Turkey. While life expectancy in Turkey has increased rapidly and steadily over the past four decades, the increase in Mexico has slowed down markedly since 2000.

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 behaviour among men).

In the United States, the gains in life expectancy since 1970 have also been much more modest than in most other OECD countries (Figure 6.1). While life expectancy in the United States used to be one year above the OECD average in 1970, it is now more than one year below the average. Many possible explanations have been suggested for these lower gains in life expectancy, including: 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 socio-economic conditions affecting a large segment of the US population, with higher rates of poverty and income inequality than in most other OECD countries.

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 socio-economic 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 14 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, Poland and Slovenia), where the life expectancy gap between higher and lower educated men reaches more than ten years.

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 health 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 (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 by the OECD Secretariat 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.

Further reading

National Research Council and Institute of Medicine (2013), “US Health in International Perspective: Shorter Lives, Poorer Health”, in S. Woolf and L. Aron (eds.), Panel on Understanding Cross-National Health Differences Among High-Income Countries, National Academies Press, Washington, DC.

OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2013-en.

Figure notes

Figure 6.1: 2009 for Canada and 1971 for Canada, Israel, Italy and Luxembourg.

Figure 6.2: 2009 for Italy and Netherlands, 2007 for Austria.

Figure 6.3: For life expectancy: 2009 for Canada; for health spending: 2010 for Australia, Denmark, Japan and Mexico, 2009 for Luxembourg, and 2008 for Turkey.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

6.1. Life expectancy has increased remarkably in OECD countries

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); World Bank for non-OECD countries.

StatLink  http://dx.doi.org/10.1787/888932966561

6.2. Variation in gap in life expectancy at age 30 by sex and between the highest “tertiary” level and the lowest “below upper secondary” level of education, 2010 (or nearest year)

Source: Eurostat database complemented with national data for Austria, Netherlands and Switzerland.

StatLink  http://dx.doi.org/10.1787/888932966561

6.3. Higher health spending per capita is generally associated with higher life expectancy at birth, 2011 (or nearest year)

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); World Bank for non-OECD countries.

StatLink  http://dx.doi.org/10.1787/888932966561

Perceived health status

In almost all OECD countries, a majority of the adult population reports their health as good or better (Figure 6.4, Panel A). Canada, New Zealand and the United States are the three leading countries, with about 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, which introduce 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, the Czech Republic, Estonia, Hungary and Poland, where less than 60% of adults consider themselves to be in good health.

The percentage of adults rating their health as good or very good has remained fairly stable over the past few decades in most countries, although Japan has seen some decline since the mid-1990s.

In all OECD countries, men are more likely than women to report being in good health, except in Australia where the proportion is equal. The gender gap is especially large in Chile, Portugal and Turkey (Figure 6.4, Panel B).

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 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 (see indicator “Coverage for health care”). 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.

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 (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2013-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.

Figure 6.5: Countries are ranked in descending order of perceived health status for the whole population.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

6.4. A majority of the adult population reports their health as good

Percentage of adults reporting to be in good health, 2011 (or nearest year)

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); EU-SILC for European countries.

StatLink  http://dx.doi.org/10.1787/888932966580

6.5. Perceived health status by income level, 2011 (or nearest year)

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); EU-SILC for European countries.

StatLink  http://dx.doi.org/10.1787/888932966580

Suicide

Suicide is a significant cause of death in many OECD countries, and accounted for over 150 000 deaths in 2011. 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.

Suicide rates in 2011 were lowest in Brazil, Greece, Italy, Mexico and Turkey, at six or fewer deaths per 100 000 popu-lation (Figure 6.6). In Hungary, Japan, Korea and the Russian Federation, on the other hand, more than 20 deaths per 100 000 population were caused by suicide. There is a ten-fold difference between Greece 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.

Death rates from suicide are four times greater for men than for women across OECD countries. In Greece and Poland, men are at least seven times more likely to commit suicide than women. The gap in these two countries has widened in recent years. While the gender gap is smaller in Luxembourg and the Netherlands, male suicide rates are still twice as high as those of females.

Since 1990, suicide rates have decreased by more than 20% across OECD countries, with pronounced declines of over 40% in some countries, for example Hungary (Figure 6.7). In Estonia, rates fell by nearly 50% over the 20-year period, but not before rising substantially in the mid-1990s. 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 have remained stable since. Suicide rates also rose sharply at this time in Korea, but unlike Japan, rates have continued to increase. It is now the fourth leading cause of death in Korea (Jeon, 2011).

Previous studies have shown a strong link between adverse economic conditions and higher levels of suicide (Ceccherini-Nelli et al., 2011; Classen and Dunn, 2012; Zivin et al., 2011). Figure 6.7 shows suicide rates for a number of countries that have been hard hit by the recent economic crisis. Suicide rates rose slightly at the start of the economic crisis in countries such as Ireland, but more recent data suggests that this trend did not persist. In Greece, overall suicide rates have been stable in 2009 and 2010, despite worsening economic conditions. This underlines that countries need to continue monitoring developments closely in order to be able to respond quickly, including monitoring high-risk populations such as the unemployed and those with psychiatric disorders.

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

Ceccherini-Nelli, A. and S. Priebe (2011), “Economic Factors and Suicide Rates: Associations over Time in Four Countries”, Social Psychiatry and Psychiatric Epidemiology, Vol. 46, No. 10, pp. 975-982.

Classen, T.J. and R.A. Dunn (2012), “The Effect of Job Loss and Unemployment Duration on Suicide Risk in the United States: A New Look Using Mass-Layoffs and Unemployment Duration”, Health Economics, Vol. 21, No. 3, pp. 338-350.

Jeon, Hong Jin (2011), “Depression and Suicide”, Journal of the Korean Medical Association, Vol. 54, No. 4, pp. 370-375.

OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2013-en.

Zivin, K., M. Paczkowski and S. Galea (2011), “Economic Downturns and Population Mental Health: Research Findings, Gaps, Challenges and Priorities”, Psychological Medicine, Vol. 41, No. 07, pp. 1343-1348.

Figure notes

Figure 6.6: 2009 for Belgium, Canada, Chile, France, Iceland, New Zealand; 2010 for Greece, Ireland, Israel, Italy, Mexico, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, Brazil and Russian Federation.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

6.6. Ten-fold difference between countries with highest and lowest suicide rates

Age-standardised suicide mortality rate per 100 000 persons, 2011 or nearest year

Source: OECD Health Statistics 2013, (http://dx.doi.org/10.1787/health-data-en).

StatLink  http://dx.doi.org/10.1787/888932966599

6.7. The economic crisis does not appear to have led to a sharp change in overall suicide rates

Trends in age-standardised suicide mortality rate per 100 000 persons, selected OECD countries, 1990-2011

Source: OECD Health Statistics 2013, (http://dx.doi.org/10.1787/health-data-en).

StatLink  http://dx.doi.org/10.1787/888932966599

Health expenditure

How much OECD 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 2011, the United States continued to outspend all other OECD countries by a wide margin, with the equivalent of USD 8 508 per person (Figure 6.8). This level of health spending is two-and-a-half times the average of all OECD countries and 50% higher than Norway and Switzerland, which were the next biggest spending countries. Compared with large European economies such as France and Germany, the United States spends around twice as much on health care per person. Around half of the OECD countries fall within a per capita spending of between USD 3 000 and USD 4 500 (adjusted for countries’ different purchasing powers – see “Definition and measurement” below). Countries spending below USD 3 000 include most of the southern and central European members of the OECD, together with Chile and Korea. The lowest per capita spenders on health in the OECD were Mexico and Turkey with levels of less than a third of the OECD average.

Outside of the OECD, among the key emerging economies, China and India spent 13% and 4% respectively, of the OECD average on health in per capita terms in 2011.

Figure 6.8 also shows the breakdown of per capita spending on health into public and private sources. In general, the ranking according to per capita public expenditure remains comparable to that of total spending. 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 Netherlands and Norway.

Since 2009, health spending has slowed markedly or fallen in many OECD countries after years of continuous growth. However, health spending patterns across the 34 OECD countries have been affected to varying degrees. On average across the OECD, per capita health spending over the period 2000-09 is estimated to have grown, in real terms, by 4.1% annually (Figure 6.9). In stark contrast, over the subsequent two years (2009-11), average health spending across the OECD grew at only 0.2% 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, other countries outside of Europe have continued to see health spending grow, albeit in many cases at a reduced pace.

Some of the European countries hardest hit by the economic downturn saw dramatic reversals in health spending compared with the period before the crisis. Greece, for example, saw per capita health spending fall by 11% in 2010 and 2011 after a yearly growth rate of more than 5% between 2000 and 2009. Estonia and Ireland also suffered significant falls in per capita health spending after previous strong growth.

Outside of Europe, health spending growth also slowed significantly in most countries between 2009 and 2011, notably in Canada (0.8%) and the United States (1.3%). Only two OECD countries – Israel and Japan – saw the rate of health spending growth accelerate since 2009 compared with the period before. Health spending in Korea continued to grow at more than 6% per year since 2009, albeit at a slower rate than in previous years.

Definition and measurement

Total expenditure on health measures the final consumption of health goods and services (i.e. current health expenditure) plus capital investment in health care infrastructure. This includes spending by both public and private sources on medical services and goods, public health and prevention programmes and administration.

To compare spending levels between 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, in order to compare spending levels. Economy-wide (GDP) PPPs are used as the most available and reliable conversion rates.

Further reading

OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2013-en.

Figure note

Figure 6.8: Data refer to current health expenditure for Belgium and New Zealand. In the Netherlands, it is not possible to clearly distinguish the public and private share related to investments. Data for Australia, Japan and Mexico refers to 2010. Data for Turkey refers to 2008.

Figure 6.9: CPI used as deflator for Chile.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

6.8. Large differences in health spending across the OECD

Health expenditure per capita, 2011 (or nearest year)

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); WHO Global Health Expenditure Database.

StatLink  http://dx.doi.org/10.1787/888932966618

6.9. Annual average growth rate in per capita health expenditure, real terms, 2000 to 2011 (or nearest year)

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en); WHO Global Health Expenditure Database.

StatLink  http://dx.doi.org/10.1787/888932966618

Coverage for health care

Most OECD countries have achieved near-universal coverage of health care costs for a core set of services, which usually include consultations with doctors and specialists, tests and examinations, and surgical and therapeutic procedures (Figure 6.10). Two OECD countries do not have universal health coverage. In Mexico, the “Seguro Popular” voluntary health insurance scheme was introduced in 2004 to provide coverage for the poor and uninsured, and grew so rapidly that by 2011, nearly 90% of the population was covered. In the United States, coverage is provided mainly through private health insurance, and 53% of the population had this for their basic coverage in 2011. Publicly financed coverage insured 32% of the population (the elderly, people with low income or with disabilities), leaving 15% of the population without health coverage.

Basic primary health coverage, whether provided through public or private insurance, generally covers a defined “basket” of benefits, in many cases with cost-sharing. In some countries, additional health coverage can be purchased through private insurance to cover any cost-sharing left after basic coverage (complementary insurance), add additional services (supplementary insurance) or provide faster access or larger choice to providers (duplicate insurance).

The population covered by private health insurance has increased in some OECD countries over the past decade. It doubled in Belgium between 2000 and 2011 to reach 80%. It also increased in Mexico and Turkey, although it remains at a very low level. On the other hand, private health insurance coverage decreased slightly in Chile and the United States, two countries where it plays a significant role in primary coverage for health care (Figure 6.11).

Problems of access to health care can be measured by the actual utilisation of health care services and reported unmet health care needs. Any inequalities in health care utilisation and unmet care needs may result in poorer health status and increase health inequalities. A European-wide survey, conducted on an annual basis, provides information on the proportion of people reporting having some unmet needs for medical examination for different reasons. In all countries, people with low incomes are more likely to report unmet care needs than people with high incomes (Figure 6.12). The gap was particularly large in Greece, Hungary and Italy. Those with low incomes report the most common reason to be cost while high income people report more often that their unmet care needs are due to a lack of time and a willingness to wait and see if the problem may simply go away.

Definition and measurement

Coverage for health care is defined here as the share of the population receiving a core set of health care goods and services under public programmes and through private health insurance. It includes those covered in their own name and their dependents. Public coverage refers both to government programmes, generally financed by taxation, and social health insurance, generally financed by payroll taxes. Take-up of private health insurance is often voluntary, although it may be mandatory by law or compulsory for employees as part of their working conditions. Premiums are generally non-income-related, although the purchase of private coverage can be subsidised by government.

Data on unmet health care needs come from the European Union Statistics on Income and Living Conditions survey (EU-SILC). Survey respondents are asked whether there was a time in the previous 12 months when they felt they needed a medical examination but did not receive it, followed by a question as to why the need for care was unmet. The reasons include that care was too expensive, the waiting time was too long, the travelling distance to receive care was too far, a lack of time, or that they wanted to wait and see if problem got better on its own. Figures presented here cover unmet care needs for any reason.

Further reading

OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2013-en.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

6.10. Most OECD countries have achieved universal coverage for health care

Health insurance coverage for a core set of services, in percentage of total population, in 2011

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en).

StatLink  http://dx.doi.org/10.1787/888932966637

6.11. The population covered by private health insurance has increased in some OECD countries

Evolution in private health insurance coverage, in percentage of total population, 2000, 2005 and 2011

Source: OECD Health Statistics 2013 (http://dx.doi.org/10.1787/health-data-en).

StatLink  http://dx.doi.org/10.1787/888932966637

6.12. People with low incomes are more likely to report unmet care needs than people with high incomes

Percentage of unmet care needs for medical examination by income level, European countries, 2011

Source: EU-SILC 2011.

StatLink  http://dx.doi.org/10.1787/888932966637

7.

Social cohesion indicators

Life satisfaction

Tolerance

Confidence in institutions

Safety and crime

Helping others

Life satisfaction

Life satisfaction is determined not only by economic development, but also by people’s diverse experiences and living conditions. People in Norway and Switzerland are most satisfied with their lives (Figure 7.1, Panel A). The measured level in these countries was 3 steps higher than in Hungary, the country at the bottom of the 11-step ladder in 2012.

There are broad regional or cultural country groupings of life satisfaction. Four of the top five countries are Nordic. Continental Western and Eastern European OECD members are not particularly 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.

Life satisfaction deteriorated during the first years of the crisis between 2007 and 2012, particularly in European Mediterranean countries. Indeed life satisfaction dropped mostly in Greece, Italy, Portugal and Spain, followed by the United States (Figure 7.1, Panel B). On the other hand, life satisfaction improved most in non-European countries, in Chile and Mexico, and to a lesser extent in Nordic and Eastern European countries.

Life satisfaction levels for men and women across OECD countries are highly correlated (Figure 7.2). In countries where life satisfaction is high, both men and women tend to have higher life satisfaction than in countries where the levels are lower. On average across OECD countries, women report slightly higher levels of life satisfaction than men do.

On average, the level of life satisfaction decreases with age (Figure 7.3). Beyond the OECD average, life satisfaction is “u-shaped” in some countries, increasing from about the age of 55. It is not surprising to see that on average 25-34 year-olds (entering the labour market) and 50+ (lea-ving the labour market) reported lower levels of life satisfaction in 2012 than in 2007. According to related data for Europe, groups who tended to see the greatest deterioration in incomes and labour-market prospects are more likely to have low levels of subjective well-being.

As for emerging economies, life satisfaction also varies between them, from above 6 in Argentina, Brazil and Saudi Arabia, to below 5 in India and South Africa. Between 2007 and 2012, it increased in five countries (Argentina, Brazil, China, Indonesia and the Russian Federation), and it decreased in three countries (India, Saudi Arabia and South Africa).

Definition and measurement

Data on life satisfaction comes from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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 errors, and variation in response rates; for example, data, especially for youth, should be interpreted carefully. Sample sizes vary between around 1 000 and 4 000, depending on the country. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

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. Data are also shown by gender and broad age groups.

Further reading

Boarini, R. et al. (2012), “What Makes for a Better Life? The Determinants of Subjective Well-Being in OECD Countries – Evidence from the Gallup World Poll”, OECD Statistics Working Papers, No. 2012/03, OECD Publishing, Paris, http://dx.doi.org/10.1787/5k9b9ltjm937-en.

Eurofound (2013), “Quality of life in Europe: Subjective well-being”, European Commission, Luxembourg.

OECD (2013a), How’s Life? Measuring Well-being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264121164-en.

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

Figure notes

Figure 7.1: Data refer to 2011 for Chile instead of 2012; and instead of 2007: 2006 for Slovak Republic and Slovenia, average between 2006 and 2008 for Austria, Finland, Ireland, Norway and Portugal, and 2008 for Iceland and Luxembourg.

Figures 7.2 and 7.3: Data refer to 2011 for Brazil and Chile and 2009 for Switzerland; and instead of 2007: 2006 for Slovak Republic, Slovenia and Switzerland; average between 2006 and 2008 for Austria, Finland, France, Ireland, Norway, Portugal; 2008 for Iceland and Portugal; and 2009 for Luxembourg.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

7.1. Life satisfaction varies across countries and deteriorated in several European OECD countries during the crisis

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966656

7.2. Women report slightly higher levels of life satisfaction on average across OECD countries

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966656

7.3. Life satisfaction decreases with age on average, despite an increase from 55 years and more in some countries

OECD average

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966656

Tolerance

The degree of community acceptance of minority groups is a measurable dimension of social cohesion. Acceptance of three such groups is considered here: migrants, ethnic minorities and gay and lesbian people. The level of tolerance is based on people’s assessment of the city or area where they live as a good place to live for these minority groups.

In Australia, Canada, Iceland, New Zealand and Norway at least 90% of people think that their country is a good place for immigrants to live (Figure 7.4, Panel A). On the other side of the spectrum are Estonia, Greece, Israel and Poland, where less than the half of the people think that their country is a good place for immigrants to live.

On average, people in the OECD area believe that their countries have become a slightly worse place to live for immigrants between 2007 and 2012 (Figure 7.4, Panel B). Austria and Slovenia saw a significant increase in positive sentiment on this point, whereas a large drop was noted in Greece, Israel, Mexico and Poland. This decrease was balanced out due to the small changes seen in the majority of the OECD.

Tolerance of ethnic minorities shows similar features. Overall, there is a slight decline in the share of people who think that their area is a good place to live for racial and ethnic minorities, however, a large variation across countries can be observed (Figure 7.5).

Tolerance perceptions towards gays and lesbians showed a slightly more positive change overall from 2007 to 2012 (Figure 7.6). The increase in tolerance was largest in Austria, Norway and Portugal, while the largest decline was observed in Greece, Hungary and Turkey.

There is little evidence to link changes in tolerance towards immigrants, ethnic minorities and gay and lesbian people to the economic crisis. There is, however, a tendency that the same countries listed above will be found respectively above and below the OECD average for each of the three dimensions of tolerance, indicating that diversity in general is more accepted in some countries than in others.

Large degrees of variation in acceptance of minority groups could also be found across the emerging countries. The share of people who believe that their area is a good place to live for immigrants increased substantially from 2007 to 2012 in China, while the largest decline was observed in India, the Russian Federation and South Africa. Argentina and Indonesia are the countries where the largest increase in tolerance to racial and ethnic minorities was observed, while the largest decline was seen in the Russian Federation. Regarding gays and lesbians, the tolerance increased substantially in Argentina, Brazil and South Africa, while the largest drop was measured in the Russian Federation.

Definition and measurement

Data on tolerance comes from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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. Sample sizes vary between around 1 000 and 4 000, depending on the country and data should be interpreted carefully. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

Tolerance is based on binary questions created by Gallup. For example: “Is the city or area where you live a good place or not a good place to live for immigrants from other countries? Is the city or area where you live a good place or not a good place to live for racial and ethnic minorities? Is the city or area where you live a good place or not a good place to live for gay or lesbian people?”. Rates are calculated omitting “Don’t know” and “Refused” from the denominator.

Figure notes

Figure 7.4: 2011 for Brazil, Chile, Germany, Japan, Korea, Mexico, and the United Kingdom; 2006 for Austria, Finland, Ireland, Norway, Portugal, Slovak Republic, Slovenia and Switzerland; 2008 for Iceland and Luxembourg and 2009 for China.

Figures 7.5 and 7.6: 2011 for Brazil, Chile, Germany, Japan, Korea, Mexico, and the United Kingdom; 2006 for Austria, Finland, Ireland, Norway, Portugal, Slovak Republic, Slovenia, South Africa and Switzerland; 2008 for Iceland and Luxembourg.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

7.4. Tolerance perception: large differences around a stable average

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966675

7.5. Variation in trends in tolerance perception for ethnic minorities

Percentage points variation in the share of people who think that the city or area where they live is a good place to live for racial and ethnic minorities between 2007 and 2012

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966675

7.6. Variation in trends in tolerance perception for gays and lesbians

Percentage points variation in the share of people who think that the city or area where they live is a good place to live for gay or lesbian people between 2007 and 2012

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966675

Confidence in institutions

A cohesive society is one where citizens have confidence in national-level institutions and believe that social and economic institutions are not prey to corruption. Confidence and corruption issues are dimensions which are strongly related to societal trust.

Confidence in the national government is generally high in Luxembourg, Norway, Sweden and Switzerland, while it is low in the Czech Republic, Greece and Japan. Large differences can be observed across countries (Figure 7.7, Panel A).

In a majority of OECD countries, trust in national governments declined from 2007 to 2012 (Figure 7.7, Panel B). The decline was particularly large in Greece, Ireland, Portugal and Slovenia, all countries hit hard by the crisis. However, other countries experienced a substantial increase in trust, notably Israel, the Slovak Republic and Switzerland.

Youth tended to have more trust in national governments than the total population, and their confidence declined less from 2007 to 2012. This could be the consequence of less political involvement, but also that youth are more optimistic about the future.

The economic crisis from 2008 was closely related to the crisis in the financial sector. In most OECD countries, confidence in financial institutions fell from 2007 to 2012 (Figure 7.8). Belgium, Ireland, the Netherlands, Portugal, Spain and the United States experienced the most substantial drops in confidence. Only in Iceland, Japan and Norway can a positive change be observed.

Corruption can be a sign of the degree of informality and distrust in the economy. Countries which suffered the biggest declines in GDP from 2007 to 2012 were also among those where corruption had increased (Figure 7.9). Increase in corruption was particularly high in countries such as Estonia, Greece, Ireland and Portugal. These countries also saw a stronger decline in confidence in the national government. Lower levels of corruption could be seen particularly in Australia, Germany, Japan and Mexico.

Among the emerging economies, confidence in national governments increased in Brazil, Indonesia and the Russian Federation, while it declined in India and South Africa. While confidence in financial institutions in general declined in the OECD countries, it increased in Argentina, Indonesia, the Russian Federation and Saudi Arabia.

Definition and measurement

Data on confidence in institutions comes from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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. Sample sizes vary between around 1 000 and 4 000, depending on the country. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

Data on national government confidence and financial institutions are based on questions created by Gallup. For example: “In this country, do you have confidence in each of the following, or not? In the national government? In financial institutions or banks?”. Rates are calculated omitting “Don’t know” and “Refused” from the denominator.

The corruption index is based on a binary question of whether corruption is widespread in business and government, and measures perceptions in a community about the level of corruption in business and government. The Gallup Corruption Index correlated strongly and inversely with the Transparency International Corruption Perceptions Index, which is based on experts’ rankings for the OECD countries. This is providing evidence of validity.

Further reading

OECD (2013a), Government at a Glance 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/gov_glance-2013-en.

OECD (2013b), OECD Economic Outlook 2013, No. 93, May 2013, OECD Publishing, Paris, http://dx.doi.org/10.1787/data-00655-en.

Figure notes

Figure 7.7: No data available for change in China.

Figure 7.9: No data available for change in Slovenia and Switzerland.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

7.7. Large differences and general decrease in levels of confidence in national government during the crisis

Source: Gallup World Poll (www.gallup.com); OECD Economic Outlook 2013, No. 93 (www.oecd.org/eco/outlook).

StatLink  http://dx.doi.org/10.1787/888932966694

7.8. Confidence in financial institutions declined

Percentage points change between 2007 and 2012

Source: Gallup World Poll (www.gallup.com); OECD Economic Outlook 2013, No. 93 (www.oecd.org/eco/outlook).

StatLink  http://dx.doi.org/10.1787/888932966694

7.9. Countries which suffered the biggest declines in GDP from 2007 to 2012 were also among those where corruption had increased

Percentage points variation in the corruption index between 2007 and 2012

Source: Gallup World Poll (www.gallup.com); OECD Economic Outlook 2013, No. 93 (www.oecd.org/eco/outlook).

StatLink  http://dx.doi.org/10.1787/888932966694

Safety and crime

Safety and crime rates in the society reflect to what extent people feel that their freedom of movement and their property are protected. A high level of personal safety can promote openness, social contact and cohesion.

In most OECD countries, people’s feeling of safety while walking alone at night has changed only moderately over time (Figure 7.10). The number of countries where feeling safe while walking alone at night has increased and is larger than the number of countries where it has decreased. Feeling safe while walking alone at night is generally strong in the Nordic countries, but also in Austria, Canada, Germany and Slovenia where similar levels have been recorded. Until now few signs indicated that an effect of the economic crisis would be that walking alone at night was becoming less safe. One exception is Greece, where the feeling of safety while walking alone at night has now reached the lowest level among the OECD countries.

One hypothesis could be that the economic crisis has reduced night-life related noise and tensions, resulting in people feeling safer while walking alone at night. However, the crime rates also remained relatively stable from 2004 to 2010, measured as the OECD average (Figure 7.11). The increase was highest in Greece, Italy and Turkey, while the decline was strongest in Japan, Poland and Portugal. An ageing population as well as a development towards less use of cash and better safety technology can contribute to reduced crime rates.

In most OECD countries, the confidence in the local police is high, and has remained so during the crisis. At the OECD average, it increased slightly from 2007 to 2012 (Figure 7.12). The increase was biggest in Chile, Estonia, Greece and the Slovak Republic. Belgium, Hungary, Mexico and Norway experienced the largest decline.

Among the emerging economies, people’s safety walking alone at night increased in Argentina, Brazil, China and the Russian Federation, while it declined in India, Indonesia and South Africa. The change in confidence in local police showed a similar pattern, with an increase in Argentina, Brazil and Indonesia, while it declined in India and South Africa.

Definition and measurement

Data on trust in local police and safety comes from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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. Sample sizes vary between around 1 000 and 4 000, depending on the country and data should be interpreted carefully. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

Trust and safety are based on binary questions created by Gallup. For example: “Do you feel safe walking alone at night in the city or area where you live? In the city or area where you live, do you have confidence in the local police force, or not?”. Rates are calculated omitting “Don’t know” and “Refused” from the denominator.

Data on crime rates are based on the United Nations Office on Drugs and Crimes (UNODC) Database which is 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 brought into formal contact with the police and/or criminal justice system, all crimes taken together. “Formal contact” with the police and/or criminal justice system may include persons suspected, or arrested or cautioned. When using the figures, any cross-national comparisons 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.

Further reading

Dragolov, G., Z. Ignacz, J. Lorenz, J. Delhey and K. Boehnke (2013), Social Cohesion Radar. Measuring Common Ground, Bertelsmann Stiftung, Germany.

The Economist (20 July 2013), “The Curious Case of the Fall in Crime”.

Figure notes

Figure 7.11: “Formal contact” with the police and/or criminal justice system may include persons suspected, or arrested or cautioned. Rate per 100 000 population. Please note that when using the figures, any cross-national comparisons 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.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

7.10. Differences in feeling of safety walking alone at night

Source: Gallup World Poll (www.gallup.com); United Nations Office on Drugs and Crimes (UNODC) (www.unodc.org/).

StatLink  http://dx.doi.org/10.1787/888932966713

7.11. Crime rates stable in most countries

Total persons brought into formal contact with the police and/or criminal justice system in 2010, all crimes 2010, index 100 in 2004

Source: Gallup World Poll (www.gallup.com); United Nations Office on Drugs and Crimes (UNODC) (www.unodc.org/).

StatLink  http://dx.doi.org/10.1787/888932966713

7.12. Confidence in the local police remained high

Percentage points variation in confidence in the local police between 2007 and 2012

Source: Gallup World Poll (www.gallup.com); United Nations Office on Drugs and Crimes (UNODC) (www.unodc.org/).

StatLink  http://dx.doi.org/10.1787/888932966713

Helping others

Donations to charities, voluntary work or help to strangers as ways of showing solidarity with other people are most common in Anglophone countries. In general, the six Anglophone OECD countries rank highest when it comes to donations of money to a charity (Figure 7.13, Panel A). Only Iceland and the Netherlands are at the same level.

Low levels of donations to charities are typically found among Mediterranean, Eastern European countries and countries outside of Europe, not belonging to the Anglophone group. Countries in the middle and north of Europe are typically found to be in a position between the high and low level countries. Income levels can to some extent explain observed differences between countries, but also different traditions regarding the supporting role of the state or networks of family and friends can be of importance.

The economic crisis has put strong pressure on the welfare systems in many countries. Changes in donations, help to strangers and voluntary work are indications to what extent solidarity between citizens can alleviate the effects of the crisis. As an average across the OECD countries, the share of the population which donated to charities decreased somewhat from 2007 to 2012 (Figure 7.13, Panel B). Reduced incomes could be an explanation, leaving people with less financial room for donations. There are also indications from some countries that average donations were reduced during the crisis, limiting the role voluntary donations can have as a social safety net during a crisis.

Alternative ways of showing solidarity can be helping a stranger or doing voluntary work for an organisation. Among the OECD countries, the share of people who reported that they helped a stranger or someone they did not know, but who needed help, slowly increased from 2007 to 2012 (Figure 7.14). The increase was strongest in Finland and Italy, while Israel and Switzerland had the largest decline. The share of the population who had helped a stranger increased or was stable in the countries hit hardest by the crisis, with the exception of Greece.

The share of people who reported that they had volunteered time to an organisation remained quite stable from 2007 to 2012 (Figure 7.15). The share increased most in Italy, Korea, Mexico and Portugal, while the decline was strongest in Belgium, the Czech Republic, Ireland and Turkey.

Indonesia is the country among the emerging economies where solidarity outside the network of family and friends is most common. The level of donations to charities is at the same level as the best performing OECD countries, and increased from 2007 to 2012. In addition, the share who reported that they had helped a stranger or volunteered time to an organisation increased over the same period. Also in India, the share of people who donated money, reported helping a stranger and volunteered time to an organisation increased, albeit from a lower level. In Brazil and the Russian Federation, the share of the population showing these forms of solidarity declined during the same period.

Definition and measurement

Data on solidarity comes from the Gallup World Poll. The Gallup World Poll is conducted in more than 150 countries around the world based on a common questionnaire, translated into the predominant languages of each country. 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. Sample sizes vary between around 1 000 and 4 000, depending on the country and data should be interpreted carefully. These probability surveys are valid within a statistical margin of error, also called a 95% confidence interval. This means that if the survey is conducted 100 times using the exact same procedures, the margin of error would include the “true value” in 95 out of the 100 surveys. With a sample size of 1 000 the margin of error at 50% is ±3 percentage points. Because these surveys use a clustered sample design, the margin of error varies by question.

Solidarity is based on binary questions created by Gallup. For example: “Have you done any of the following in the past month? How about donated money to a charity? How about helped a stranger or someone you didn’t know who needed help? How about volunteered your time to an organisation?”. There are no questions about the amount of money donated or number of hours volunteered. Rates are calculated omitting “Don’t know” and “Refused” from the denominator.

Further reading

Charities Aid Foundation (2012), World Giving Index 2012 – A Global View of Giving Trends.

Reich, R. and C. Wimer (2012), Charitable Giving and the Great Recession, Recession Trends, Russell Sage Foundation and Stanford Center on Poverty and Inequality.

Figure notes

Figures 7.13., 7.14 and 7.15: 2011 for Brazil, Chile, Germany, Japan, Korea, Mexico, and the United Kingdom. 2006 for Austria, Finland, Ireland, Norway, Portugal, Slovak Republic, Slovenia and Switzerland; 2008 for Iceland and Luxembourg.

Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

7.13. Charities are most widespread in Anglophone countries and Northern Europe

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966732

7.14. The share of people who helped a stranger increased in many countries

Percentage points variation in the share of people who reported that they helped a stranger or someone they did not know who needed help between 2007 and 2012

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966732

7.15. The share of people who volunteered time to an organisation remained stable

Percentage points variation in the share of people who reported having given volunteered time to an organisation between 2007 and 2012

Source: Gallup World Poll (www.gallup.com).

StatLink  http://dx.doi.org/10.1787/888932966732

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