UNICEF
Innocenti Research Centre
Report Card 9

The children
left behind
A league table of inequality in childwell-being in the world’s rich countries

Innocenti Report Card 9 was written by Peter Adamson.
Part 1 of the Report draws on the analysis carried out by Candace Currie,
Dorothy Currie, Leonardo Menchini, Dominic Richardson andChris Roberts, and presented in the Innocenti Working Paper 2010-19(available on the UNICEF Innocenti Research Centre (IRC) website:www.unicef-irc.org).
For Report Card 9 the Health Behaviour in School-aged Children (HBSC)
International Coordinating Centre provided the statistical results for the
analysis of inequality in children’s health. The Organisation for Economic
Co-operation and Development (OECD) provided distributional analysis ofchild well-being indicators for material well-being and educational
outcomes. HBSC and the OECD are not responsible for the interpretationof these results or other analysis in this report. The project wascoordinated by the UNICEF IRC.
The UNICEF Innocenti Research Centre would like to acknowledge
the generous support for Report Card 9 provided by the Andorran,Australian, Belgian, German, Swiss and United Kingdom National
Committees for UNICEF.
Any part of this Innocenti Report Card may be freely reproduced using
the following reference:
UNICEF (2010), ‘The Children Left Behind: A league table of inequality
in child well-being in the world’s rich countries’, Innocenti Report Card 9,
UNICEF Innocenti Research Centre, Florence.
The Report Card series is designed to monitor and compare theperformance of economically advanced countries in securing the rightsof their children.
The UNICEF Innocenti Research Centre in Florence, Italy, wasestablished in 1988 to strengthen the research capability of theUnited Nations Children’s Fund (UNICEF) and to support its advocacy
for children worldwide.
The Centre helps to identify and research current and future areas of
UNICEF’s work. Its prime objectives are to improve international
understanding of issues relating to children’s rights and to help facilitate
the full implementation of the United Nations Convention on the Rights of
the Child in all countries.
The Centre’s publications are contributions to a global debate on childrights issues and include a wide range of opinions. The views expressed
are those of the author and researchers and do not necessarily reflect thepolicies or views of UNICEF.
© United Nations Children’s Fund (UNICEF), November 2010
UNICEF Innocenti Research Centre
Piazza SS. Annunziata, 1250122 Florence, Italy
Tel: (+39) 055 2033 0
Fax: (+39) 055 2033 220
florence@unicef.org
www.unicef-irc.org

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UNICEF
Innocenti Research Centre

Whether in health, in education, or in material well-being, somechildren will always fall behind the average. The critical questionis – how far behind? Is there a point beyond which falling behind
is not inevitable but policy susceptible, not unavoidable but
unacceptable, not inequality but inequity?
There are no widely agreed theoretical answers to thesequestions. Report Card 9 seeks to stimulate debate on the issue
by introducing a common measure of ‘bottom-end inequality’.
This permits each country’s performance to be assessed according
to the standard of what the best-performing countries have beenable to achieve. Such a standard may not represent the best that
may be aspired to in theory, but in practice it suggests a level
below which ‘falling behind’ is manifestly not inevitable.
The Report Card series is premised on the belief that the truemeasure of a nation’s standing is how well it attends to its children– their health and safety, their material security, their educationand socialization, and their sense of being loved, valued, andincluded in the families and societies into which they are born.
Its common theme is that protecting children during their vital,vulnerable years of growth is both the mark of a civilized society
and the means of building a better future.
This ninth report in the series builds on previous issues byfocusing specifically on those children in all OECD countries
who are at risk of being left behind – of being neither included
nor protected – by the wealthy societies in which they live.

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T hec hildr en

lef t

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b e h in d

A league table of inequality in child well-being
in the world’s rich countries

Fig. 1a A league table of inequality in child well-being

Fig. 1b The overall record

The table summarizes the findings of Report Card 9, ranking 24 OECD countries by theirperformance in each of three dimensions of inequality in child well-being.

Figure 1b ranks each country by its overall
inequality record. Three points have been
awarded for a better than average
performance, 2 points for a performanceat or close to the OECD average, and1 point for a below average performance(see note for definitions). Countries inalphabetical order within groups.

inequality lower thanOECD averageinequality close to
OECD averageinequality higher thanOECD average

Material well-beingrank

Education well-being

rank

Health well-being

rank

Switzerland

1

Finland1

Netherlands

1

Iceland2

Ireland2

Norway2Netherlands

3

Canada3

Portugal3

Denmark

4

Denmark

4

Germany

4

France5

Poland5

Switzerland

5

Finland6

Hungary

6

Belgium

6

Austria7

Sweden7

Ireland7

Norway8Netherlands

8

Denmark

8

Sweden

9

Spain

9

Canada9Austria

Germany

10

Iceland10

Czech Republic

10

CanadaCzech Republic

11

Norway11

United Kingdom

11

Luxembourg

12

Switzerland

12

Slovakia

12

Ireland13

United Kingdom

13

Austria

13

Spain

14

Portugal

14

Sweden14

Portugal

Belgium

15

Slovakia

15

France15

Belgium

Portugal

16

Luxembourg

16

Finland16

Czech Republic

Canada17

Czech Republic

17

Iceland17

Hungary

Greece18

Greece18

Poland18

United Kingdom

19

United States

19

Luxembourg

19

Italy

20

Germany

20

Greece20

Spain

Poland

21

Italy

21

Spain

21

United Kingdom

Hungary

22

Austria22

United States

22

GreeceUnited States

23

France

23

Italy

23

Slovakia

24

Belgium

24

Hungary

24

Figs. 1a and 1b are limited to the 24 OECD countries with available data for all three
dimensions of inequality in child well-being.
Note: To compare the inequality performance of the featured countries in eachdimension of child well-being, inequality scores for the individual indicators used
are first converted to standard scores (i.e. inequality is measured in standard
deviations from the OECD unweighted average). The standardized scores are thenaveraged to arrive at an inequality score for each dimension. For purposes of Figs.
1a and 1b, ‘inequality close to average’ is defined as a score within the range ofHigher score = greater equality

8
7
6
5
3

Denmark
FinlandNetherlands
Switzerland
Iceland
Ireland
NorwaySwedenFranceGermany
PolandLuxembourg
Slovakia

Italy
United States-0.5 to +0.5 standard deviations from the OECD average. ‘inequality lower thanOECD average’ is defined as having a standard deviation score greater than +0.5
from the OECD unweighted average. ‘inequality higher than OECD average’ isdefined as having a standard score of less than -0.5 from the OECD unweighted
average.
Source: See page 30 (Data for Report Card 9: the surveys) for data sources used in
the measurement of inequality in the different dimensions of children’s well-being.

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Part 1Introduction
This Report Card presents a first
overview of inequalities in childwell-being for 24 of the world’s
richest countries.
Three dimensions of inequality are
examined: material well-being,education, and health. In each caseand for each country, the question
asked is ‘how far behind are children
being allowed to fall?’Figure 1a summarizes the results.
Within the limitations of the available
data, it represents an overview of
how well the world’s developed
nations are living up to the ideal of‘no child left behind’.
Figure 1b presents the same data froma different perspective, showing eachnation’s performance in relation to theaverage for the nations of the OECD
as a whole.
Both charts show that a small groupof countries – Denmark, Finland, theNetherlands, and Switzerland – areleading the way in promoting equality
in children’s well-being. Greece, Italy
and the United States, on the other
hand, are allowing children to fall
furthest behind.

Why inequality matters
The increase in inequality over the last
three decades – its economic causes, its
social costs, its possible remedies – isthe subject of considerable debate inOECD countries today.i On the one
hand it is argued that, after a certain
level of economic development has
been achieved, greater equality “would
increase the well-being and quality of life
for all”.ii On the other, it is maintainedthat inequality is a justifiable reflectionof differences in ability and effort andprovides incentives for further progressin all fields of human endeavour.
This is an important debate. But it is
not one that affects the premise ofthis report.
The idea that inequality is justified
as a reflection of differences in merit
cannot reasonably be applied tochildren. Few would deny thatchildren’s early circumstances arebeyond their own control. Or thatthose early circumstances have a
profound effect on their present lives
and future prospects. Or that growing
up in poverty incurs a substantiallyhigher risk of lower standards ofhealth, of reduced cognitive
development, of underachievement at
school, of lower skills and aspirations,and eventually of lower adult earnings,
so helping to perpetuate disadvantage
from one generation to the next.
None of this is the fault of the child.
Second, the question being asked here
– ‘how far behind are children being
allowed to fall?’ – requires a measure notof overall inequality but of inequality
at the bottom end of the distribution.
In other words, the metric used is not

the distance between the top and thebottom but between the median andthe bottom. The median level of childwell-being – whether in material goods,
educational outcomes, or level of health– represents what is considered normal
in a given society and falling behindthat median by more than a certain
degree carries a risk of social exclusion.
Today, ‘bottom-end inequality’ is nolonger a concern only of the politicalleft. In the United Kingdom, forexample, a Conservative Prime Minister
has argued that “We should focus on
closing the gap between the bottom and the
middle not because that is the easy thing todo, but because focusing on those who do nothave the chance of a good life is the mostimportant thing to do.”That ‘gap between the bottom and the
middle’ is the focus of Report Card 9.

Measuring inequality
It should be said from the outsetthat the data deployed for measuring
inequality, although the latest available,
are derived from surveys conducted
before the 2008 economic crisis(Box 2). Nor are the datacomprehensive. There is, for example,
very little statistical information
available on the critical early yearsof childhood.
It is also important to recognize thatwell-being has many dimensions andits measurement should also be
multidimensional to the extent thatthe data allow. “Each dimension of

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quality-of-life requires appropriate measuresof inequality with each of these measures
being significant in itself and none claiming
absolute priority over the others,” says theCommission on the Measurement of
Economic Performance and Social
Progress established in 2008 by the
President of France.*

below the national median. In thissense, a degree of falling behind isobviously inevitable. The criticalquestion is – how far behind? Is therea point beyond which falling behind
is not inevitable but policy susceptible,
not unavoidable but unacceptable,
not inequality but inequity?

Figure 1a therefore compares 24
OECD countries according to theirperformance in limiting bottom-end
inequality in three dimensions ofchildren’s well-being. Its rankings
confirm the Commission’s view thatno one indicator can stand as anadequate proxy for the others.

There are no widely agreed
theoretical answers to these questions.
But international comparison canhelp to establish practical answers bymeasuring ‘falling behind’ according
to the standard of what the bestperforming OECD countries havealready achieved. This benchmark may
not represent the very best that can be
aspired to, but it does establish a level
below which bottom-end inequality
is manifestly not inevitable.

Measuring the gap
Depending on the available data,
two different methods are proposed
for estimating ‘how far behind’
children are being allowed to fall.
The first compares the position of
the child at the 10th percentile (i.e. thechild at a lower point than 90% ofchildren in the society) with the childat the 50th percentile (the median
position). The degree of inequality ismeasured by the gap between the two,
expressed as a percentage of themedian position.
The second method (employed whensurvey data are not suitable for analysis
by percentiles) compares the level ofwell-being of the child at the medianwith the average level of all those who
fall below the median.
Different geographical and historicalcircumstances may help to explain
different degrees of inequality. And it
is of course a truism that there will
always be a bottom 10% in any country
and that 50% of children will always fallIf, for example, the gap in educational
achievement between students at
the 10th and 50th percentiles issignificantly wider in France or
Belgium than in Finland or Ireland(Figure 3d) then it seems clear thatthe children at the 10th percentile inFrench and Belgian schools are falling
further behind the median than isnecessary. The difference between thebest performing countries and the restof the OECD nations can therefore
be read as a minimum measure of theextent to which ‘falling behind’ ispolicy-susceptible – the extent towhich it is not unavoidable but unjust.
International comparison therefore sets
each nation’s performance not against
an abstract concept of equality but
against the practical benchmark of what
other nations at similar levels of
economic development have alreadyachieved. It therefore provides a realistic
measure of the scope for improvement.

* The Commission is chaired by Joseph Stiglitz, Amartya Sen and Jean-Paul Fitoussi.

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in equa l it y

The first of the three dimensions ofinequality in children’s well-being
considered here is inequality inchildren’s material well-being.
Child poverty is about more thanpoverty of income. It is also about
poverty of opportunity and expectation,of cultural and educational resources,of housing and neighbourhoods, ofparental care and time, of local services
and community resources. But fromthe child’s point of view, these different
dimensions of poverty are rarely
separate. Family circumstance,
employment and income, health andeducation systems, and the local
environment all play interacting roles
in determining well-being.

Norway, with the Nordic countries andthe Netherlands taking six of the top
eight places in the table. At the otherextreme, Italy, Canada Spain, Portugal
and Greece are seen to have the highest
levels of child income inequality. Dataon household disposable income are notavailable for the United States.*Fig. 2a Inequality in material well-being: income
The chart shows inequality at the bottom-end of the distribution in disposable income
available to children in 27 OECD countries. Calculations are based on the incomes ofhouseholds with children aged 0 to 17 (after adding benefits, deducting taxes, and making
an adjustment for the economies of scale available to larger families). For each country,
the measure of bottom-end inequality used is the gap between the income of the child at
the 50th percentile (the median level) and the income of the child at the 10th percentile(i.e. the child who is poorer than 90% of children).
The bar chart shows how far the children at the 10th percentile are falling behind(expressed as a percentage of median income in households with children).

Household income

As Figure 2a shows, household income
inequality for children is lowest in

The second measure used to compare
inequality in material well-being is‘access to basic educational resources’.
Again, the same question is asked –‘how far behind are the least advantaged
children being allowed to fall?’* Using gross (pre-tax) household income, the income available to the child at the 10th percentile in the
United States is approximately 70% below the income available to a child at the median.

No internationally comparable data
are currently available to capture thiscomplexity. But rather than relying
on income data alone, inequality inchildren’s material well-being ismeasured here by three indicators
for which suitable data are available –household incomes, access to basic educational
resources, and housing living space.

Calculations of income inequality
for children are based on the disposable
incomes of households with children
aged 0 to 17 (after adding benefits,
deducting taxes, and making an
adjustment for the economies of scale
available to larger families). To measureinequality at the bottom-end of the
distribution, the income of the childat the 50th percentile (the median)is compared with the income of thechild at the 10th percentile (i.e. poorer
than 90% of children). ‘How far behind
are the poorest children being allowed to
fall?’ is then measured by the gap
between the two.

Basic educational resources

39.4

Norway39.7

Denmark

40.0

Austria40.2

Iceland

41.2

Sweden41.5

Netherlands

41.6

France (2007)

41.9

Finland43.7

Czech Republic

44.3

Switzerland (2007)

44.6

Hungary

46.4

Luxembourg

47.4

Ireland48.1

Germany

48.9

Slovakia

50.1

United Kingdom

50.6

Belgium

51.2

Poland

54.1

Italy

55.7

Canada (2005)

56.0

Spain

56.2

Portugal

56.6

Greece46.9

OECD average
Other OECD countries

0

51.0

Australia

59.4

Republic of Korea (2007)

60.8

Chile (2006)

61.5

Mexico
10

20

30

40

50

60

Gap between the child at the 10th percentile and the child at the 50th percentile (as % of 50th percentile)
Notes: ‘Other OECD countries’ are listed separately because data limitations prevent their inclusion in theoverview tables for each dimension of child well-being. The OECD average is an unweighted average for the
23 countries included in the main league table.
Sources: EU SILC 2008. Data for France are from EU SILC 2007. See page 30 (Data for Report Card 9: the surveys)
for more detailed notes on country data including sources for Australia, Canada, Chile, Mexico, the Republic ofKorea, and Switzerland.

70

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Figure 2b attempts an answer bydrawing on survey data from theProgramme of International Student
Assessment (PISA).
In the 2006 PISA survey (see page 30),
a representative sample of 15-year-old
students in OECD countries was asked
which of the following were available
in their own homes:
 a desk
 a quiet place to study a computer for school work
 educational software an internet connection
 a calculator a dictionary
 school textbooks.

The resulting scores – registered ona scale of 0 to 8 – do not lendthemselves to analysis by percentile.
Inequality is therefore measured by thegap between the score of the child at
the median and the average score of allchildren who fall below the median.
The results are presented in Figure 2b.

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9inequality – on the gap between the
median score (column 2) and theaverage score below the median(column 3). Column 4 shows thedifference between the two and thechart represents the inequality gap as
a percentage of the median.
Northern European countries again
dominate the top of the table. The
lowest placed Nordic country, Norway,
posts an equality score close to theaverage for the OECD as a whole.
At the foot of the table, the United
Kingdom, Greece, and Slovakia show
the highest levels of inequality inaccess to basic educational resources.

The availability of computers andinternet access depends to some extent
on the level of economic developmentin each country; even poor children
in very wealthy countries, for example,
may have access to most or all of theitems on the ‘home educational
resources’ list. The median score
therefore differs from country tocountry. But the focus here is on

Fig. 2b Inequality in material well-being: educational resources
15-year-olds students in each country were asked which of the following were available at home: a desk, a quiet place to study,
a computer for school work, educational software, an internet connection, a calculator, a dictionary, school textbooks.
Inequality was measured by comparing each country’s median score (column 2) with the average score of those below
the median (column 3). Column 4 shows the difference between the two as an absolute number of ‘missing’ educational items.
The bar chart on the right shows the inequality gap (as a percentage of the median for each country).
Educational items(range 0-8)

MedianAverage
below themedianAverageabsolute
gap

Gap between the average below the medianand the median (as % of median)Denmark

7

6.4

0.6

7.9

Denmark

Switzerland

7

6.4

0.6

8.1

Switzerland

Netherlands

7

6.4

0.6

8.8

Netherlands

Luxembourg

7

6.3

0.7

9.5

Luxembourg

Austria

7

6.2

0.8

10.9

Austria

Finland

7

6.2

0.8

11.3

Finland

Canada

7

6.2

0.8

11.3

CanadaSweden

7

6.2

0.8

12.0

Sweden

Italy

7

6.1

0.9

13.3

Italy

Czech Republic

7

6.0

1.0

13.9

Czech Republic

Iceland

8

6.9

1.1

14.0

IcelandFrance

7

6.0

1.0

14.3

France

Spain

7

6.0

1.0

14.9

Spain

Portugal

7

5.9

1.1

16.1

Portugal

Ireland

7

5.9

1.1

16.2

Ireland

Norway

8

6.7

1.3

16.3

NorwayPoland

7

5.7

1.3

18.1

PolandGermany

8

6.5

1.5

18.5

Germany

United States

7

5.7

1.3

19.2

United States

Belgium

8

6.4

1.6

19.9

Belgium

Hungary

7

5.6

1.4

20.7

Hungary

United Kingdom

8

6.3

1.7

21.0

United Kingdom

Greece

6

4.6

1.4

22.6

GreeceSlovakia

7

5.2

1.8

25.9

Slovakia

OECD average

7.2

6.1

1.1

15.2

OECD average

Republic of Korea

7

6.0

1.0

14.2

Republic of Korea

Australia

8

6.5

1.5

18.9

Australia

Japan

6

4.8

1.2

19.9

Japan

Chile

5

3.9

1.1

21.9

Chile

Turkey

5

3.9

1.1

22.0

TurkeyNew Zealand

8

6.2

1.8

22.1

New Zealand

Mexico

5

3.9

1.1

22.5

MexicoOther OECD countries

Other OECD countries

Notes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child wellbeing. The OECD average is an unweighted average for the 24 countries includedin the main league table.
Source: PISA 2006 (see page 30).

0

5

10

15

20

25

30

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Living space
The third measure of material wellbeing is ‘living space’ – defined as thenumber of rooms per person inhouseholds with children aged 0 to
17 (not counting corridors, kitchens,
and bathrooms). Although only anapproximate measure of housing
conditions, space in the home is aconstant and important factor inyoung people’s lives. In the OECD
countries as a whole, one child inthree is estimated to be living inovercrowded conditions.iii
Figure 2c draws again on survey data
to estimate inequality in living space.
As with educational resources, the97measure used is the gap between theliving space score at the median and theaverage score of children below themedian. By this measure, Iceland,
Germany and Switzerland can be seen
to have the lowest levels of inequality inchildren’s living space (along with
Australia, which is among the countries
excluded from the main tables because
data are not available for all three
dimensions of child well-being). At thebottom of the table, inequality is highest
in the United States, Italy and Hungary.

Material inequality:
an overview
Figures 2d and 2e combine the threemeasures used – household income,access to educational resources, andliving space. For each country, andfor each indicator, the inequality
scores have been set on a common
scale in which 100 represents theOECD average and 10 represents
one standard deviation (a commonlyused measure of how spread out theitems being measured are in relation
to the average for the group as awhole). The individual indicatorscores are then averaged to provide
the overview of inequality inchildren’s material well-being
presented in Figure 2d.
Switzerland has the least inequality,
closely followed by Iceland and theNetherlands.

Fig. 2c Inequality in material well-being: housing living space
Housing living space is defined as the number of rooms per person in households with children (not counting corridors, kitchens, andbathrooms). Inequality is measured by the gap between the score at the median (column 2) and the average score of all children below themedian (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as a percentageof the median).
Living space –Rooms per person

MedianAverage
below themedianAverageabsolute
gap

Gap between the average below the medianand the median (as % of median)Iceland

1.00

0.91

0.09

8.8

IcelandGermany

1.00

0.91

0.09

8.9

Germany

Switzerland (2007)

1.00

0.91

0.09

9.1

Switzerland (2007)

Greece

0.80

0.69

0.11

14.0

Greece

Spain

1.25

1.08

0.18

14.5

Spain

France (2007)

1.00

0.85

0.15

14.5

France (2007)

Netherlands

1.25

1.03

0.23

17.6

Netherlands

Ireland

1.25

1.03

0.23

17.7

Ireland

Norway

1.20

0.97

0.23

18.8

NorwayBelgium

1.20

0.97

0.23

19.0

Belgium

Finland

1.20

0.97

0.23

19.3

Finland

Portugal

1.00

0.80

0.20

19.6

Portugal

Czech Republic

0.80

0.62

0.18

22.2

Czech Republic

Austria

1.00

0.77

0.23

22.9

Austria

Denmark

1.20

0.92

0.28

23.0

Denmark

Sweden

1.20

0.91

0.29

24.4

SwedenUnited Kingdom

1.20

0.91

0.29

24.4

United Kingdom

Slovakia

0.75

0.56

0.19

24.9

Slovakia

Luxembourg

1.25

0.93

0.33

26.2

Luxembourg

Canada (2006)

1.50

1.10

0.41

27.5

Canada (2006)

Poland

0.67

0.47

0.19

28.6

PolandUnited States (2007)

1.25

0.89

0.36

29.3

United States (2007)

Italy

1.00

0.68

0.32

31.8

Italy

Hungary

0.75

0.50

0.25

33.4

Hungary

OECD average

1.07

0.85

0.22

20.8

OECD average

Australia

1.00

0.88

0.12

11.8

Australia

Chile (2006)

0.75

0.55

0.20

26.5

Chile (2006)

Mexico

0.50

0.28

0.23

45.3

MexicoOther OECD countries

Other OECD countries

Notes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child wellbeing. The OECD average is an unweighted average for the 24 countries includedin the main league table.
Sources: EU SILC 2008. Data for France are from EU-SILC 2007. See page 30 (Datafor Report Card 9: the surveys) for more detailed notes on individual country data
including sources for Australia, Canada, Chile, Mexico, the Republic of Korea,
Switzerland and the United States.

0

10

20

30

40

50

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A second summary table (Figure 2e)shows the individual contributions of
the three indicators, allowing countries
to see their strengths and weaknesses.
Countries such as Germany, Belgium,
the United Kingdom, Greece andSlovakia, for example, are let down by
higher than average inequality inaccess to basic educational resources.

Spain, Canada, Portugal and Greece
lose ranking places by virtue of higher
than average levels of householdincome inequality.

R eportC ard

9available for the purposes ofinternational comparison. Rather thanrecording material well-being solely
by the percentage of children inhouseholds below a given income
threshold, they attempt a morerounded measure of how far behindthe least advantaged children are being
allowed to fall.

These three measures of bottom-end
inequality in children’s material wellbeing are neither ideal nor
comprehensive. But they are the best

Fig. 2d Inequality in material well-being: an overview

Fig. 2e Inequality in material well-being: a breakdown

Figure 2d combines the three measures of inequality in children’smaterial well-being (income, educational items, living space) into
an overview for the 24 OECD countries with available data. For
each country, the inequality scores of the three indicators ofmaterial inequality have been standardized, combined and placedon a common scale in which 100 represents the OECD unweighted
average and 10 is equal to one standard deviation.*

Figure 2e presents the same information as Fig 2d but shows theindividual contributions of the three inequality indicators used. For
each indicator, the length of the bar represents each country’s
distance above or below the OECD 24 average (again measured instandard deviations above or below that average). This allows
countries to see individual strengths and weaknesses.

Switzerland
Iceland90
inequality higher thanOECD-24 average

95

100
inequality close to
OECD-24 average

105

Icelandliving space

Netherlands

85

Switzerland

incomeNetherlands

educational items

Denmark

Denmark

FranceFranceFinlandFinland

Austria

Austria

NorwayNorwaySwedenSwedenGermany

Germany

Czech RepublicCzech Republic

Luxembourg

Luxembourg

Ireland

Ireland

Spain

Spain

Belgium

Belgium

PortugalPortugal

CanadaCanadaGreeceGreeceUnited Kingdom

United Kingdom

Italy

Italy

PolandPolandHungary

Hungary

United States

United States**

Slovakia

Slovakia

110
inequality lower thanOECD-24 average* A standard deviation is a measure of the spread of the distribution around
its average.
Sources: See individual Figs. 2a, 2b, 2c. See also Figure 2e for the standardized
inequality measure used for the three individual indicators of inequality inchildren’s material well-being.

115 -2.5

-2.0

-1.5

-1.0

-0.5

inequality higher thanOECD-24 average

0.0

0.5

1.0

1.5

2.0

2.5

inequality lower thanOECD-24 average** No data are available on household disposable income for the United States.
Sources: See individual Figs. 2a, 2b, and 2c.

3.0

I nnocenti

R eport

C ard

9

9

E d u cat i ona lin e q ual it y

The second dimension of inequality
considered here is inequality in young
people’s educational achievements.

compare proficiency in reading, maths
and science.

The data are drawn from theProgramme of International Student
Assessment (PISA) which regularly
tests a nationally representative sample
of 15-year-old students* in more than
40 countries. The aim is to test andAs with income, the inequality measureused is the gap between test scores at
the 10th and 50th percentiles. Figures
3a, 3b and 3c present the results.
Figure 3d combines the three measures
into an overview. Again, each country’sscore on each indicator has been set
on a common scale in which 100
represents the unweighted OECDaverage and 10 represents one standard
deviation above or below that average.
This allows each country’s
performance to be measured inrelation to both the average and thedegree of variability for the OECD
as a whole.

*The survey samples only 15-year-olds who are attending school. It may therefore not fully represent marginalized groups in some OECD countries.

Fig. 3a Inequality in reading literacy
Using PISA scores for ‘reading literacy’ of 15-year-old students, Figure 3a measures educational inequality in each country by comparing
the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of allscores). The bar chart shows the gap between the two (expressed as a percentage of the median).

Reading literacy

50thpercentile(median) score

10th
percentile
score

Absolute gap(50th percentile –10th percentile)

Gap between the child at the 10th percentile and the childat the 50th percentile (as % of 50th percentile)

FinlandDenmark
Ireland
CanadaSwitzerland
SwedenNetherlands
Spain
Hungary
PolandIcelandUnited Kingdom
Portugal
Luxembourg
NorwayAustria
FranceSlovakia
Germany
Czech Republic
Greece
Italy
Belgium
OECD average

550
499
522
534
506
513
515
468
490
513
491
501
479
487
492
499
499
473
508
489
469
478
515
500

441
378
395
402
373
378
379
343
359
374
356
359
339
344
346
348
346
326
350
335
321
325
347
359

109
121
127
132
133
135
136
125
131
139
135
142
140
143
146
151
153
147
158
154
148
153
168
141

19.9
24.2
24.2
24.8
26.1
26.2
26.5
26.6
26.8
27.0
27.6
28.4
29.2
29.3
29.7
30.3
30.7
31.0
31.1
31.4
31.5
32.1
32.5
28.1

FinlandDenmark
Ireland
CanadaSwitzerland
SwedenNetherlands
Spain
Hungary
PolandIcelandUnited Kingdom
Portugal
Luxembourg
NorwayAustria
FranceSlovakia
Germany
Czech Republic
Greece
Italy
Belgium
OECD average

563
519
450
528
505
443
415

440
388
330
381
361
310
285

123
131
120
146
144
133
130

21.8
25.1
26.8
27.9
28.6
30.0
31.3

Other OECD countries
Republic of Korea
Australia
TurkeyNew Zealand
Japan
Chile
MexicoOther OECD countries
Republic of Korea
Australia
TurkeyNew Zealand
Japan
Chile
MexicoNotes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child wellbeing. The OECD average is an unweighted average for the 23 countries includedin the main league table. Reading literacy data for the USA are missing.
Source: PISA 2006 (see page 30).

0

5

10

15

20

25

30

35

1 0

I nnocenti

R eportC ard

9Fig. 3b Inequality in maths literacy
Using PISA scores for ‘maths literacy’ of 15-year-old students, Figure 3b measures educational inequality in each country by comparing
the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of allscores). The bar chart shows the gap between the two (expressed as percentage of median).

Maths literacy

50thpercentile(median) score

10th
percentile
score

Absolute gap(50th percentile– 10th percentile)

Gap between the child at the 10th percentile and thechild at the 50th percentile (as % of 50th percentile)

Finland
IrelandDenmark
CanadaPolandNetherlands
IcelandUnited Kingdom
SwedenHungary
NorwaySpain
United States
Switzerland
Slovakia
Luxembourg
Portugal
Germany
Greece
Italy
FranceCzech Republic
Austria
Belgium
OECD average

550
503
514
529
495
534
507
494
503
490
490
482
472
534
494
492
468
505
461
462
499
510
511
528
501

444
396
404
416
384
412
391
381
387
377
373
366
358
401
370
368
348
375
341
341
369
376
373
381
381

106
107
110
113
111
121
116
113
116
113
117
116
114
133
124
124
120
130
120
121
130
134
138
147
120

19.3
21.2
21.4
21.5
22.4
22.8
22.8
22.9
23.1
23.1
23.9
24.1
24.2
24.8
25.0
25.2
25.5
25.8
26.1
26.1
26.2
26.2
27.0
27.9
24.1

Finland
IrelandDenmark
CanadaPolandNetherlands
IcelandUnited Kingdom
SwedenHungary
NorwaySpain
United States
Switzerland
Slovakia
Luxembourg
Portugal
Germany
Greece
Italy
FranceCzech Republic
Austria
Belgium
OECD average

Other OECD countries
Australia
Republic of Korea
Japan
New ZealandTurkeyChile
Mexico

521
550
526
522
415
408
406

406
426
404
401
316
302
299

115
124
122
122
99
106
107

22.0
22.5
23.2
23.3
23.8
25.9
26.4

Other OECD countries
Australia
Republic of Korea
Japan
New ZealandTurkeyChile
MexicoNotes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child well-being.
The OECD average is an unweighted average for the 24 countries included in themain league table.

0

5

10

15

20

25

30

Source: PISA 2006 (see page 30).

Fig. 3c Inequality in science literacy
Using PISA scores for ‘science literacy’ of 15-year-old students, Figure 3c measures educational inequality in each country by comparing
the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of allscores). The bar chart shows the gap between the two as a percentage of the median.

Science literacy

50thpercentile(median) score

10th
percentile
score

Absolute gap(50th percentile– 10th percentile)

Gap between the child at the 10th percentile and thechild at the 50th percentile (as % of 50th percentile)

Finland
Hungary
PolandCanadaIrelandSwedenSpain
Slovakia
Portugal
Denmark
Czech Republic
NorwayNetherlands
GreeceIceland
Italy
Switzerland
Austria
Germany
Luxembourg
United Kingdom
Belgium
FranceUnited States
OECD average

566
506
498
540
510
505
491
489
476
498
514
488
530
477
493
477
516
516
521
490
518
518
501
488
505

453
388
381
410
385
381
370
368
357
373
385
365
395
353
364
351
378
378
381
358
376
374
359
349
376

113
117
117
130
124
124
121
121
119
125
130
123
139
124
129
126
138
138
140
132
142
145
142
139
129

20.0
23.2
23.4
24.1
24.4
24.6
24.7
24.8
24.9
25.0
25.2
25.2
25.5
26.0
26.2
26.3
26.7
26.8
26.9
27.0
27.4
27.9
28.3
28.4
25.5

Finland
Hungary
PolandCanadaIrelandSwedenSpain
Slovakia
Portugal
Denmark
Czech Republic
NorwayNetherlands
GreeceIceland
Italy
Switzerland
Austria
Germany
Luxembourg
United Kingdom
Belgium
FranceUnited States
OECD average

Other OECD countries
TurkeyRepublic of Korea
MexicoChileAustraliaJapan
New Zealand

416
526
407
434
530
539
534

325
403
306
323
395
396
389

91
123
102
111
136
142
141

21.9
23.4
24.9
25.6
25.6
26.4
27.1

Other OECD countries
TurkeyRepublic of Korea
MexicoChileAustraliaJapan
New ZealandNotes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child well-being.
The OECD average is an unweighted average for the 24 countries included in themain league table.

0

5

10

Source: PISA 2006 (see page 30).

15

20

25

30

I nnocenti

R eportC ard

9Figure 3e breaks down this overview
in order to show how bottom-end
inequality in the three different kindsof literacy contributes to eachcountry’s overall inequality score.

1 1of opportunity in education meansthat differences in educational
outcomes are now mostly a reflectionof the distribution of natural abilities.
As Figures. 3a, 3b and 3c show,
different OECD countries have very
different patterns of bottom-end
inequality in educational outcomes;
and it is reasonable to assume that thisis the result not of differences in the
distribution of natural abilities but ofdifferences in policies which, overNo trade off
Such measurements serve to informtwo commonly contested issues.
First, they undermine the argumentthat steady progress towards equality

time, limit the extent to which lessable students fall behind. Figure 3d, forexample, shows that lower-achieving
students in Finland, Ireland andCanada are far less likely to fall along way behind their peers than are
students in Austria, France or Belgium.
The pattern of bottom-end inequality
in educational outcomes therefore
reflects more than the lottery of birthand circumstance. It may reflect

Fig. 3d Educational inequality: an overview

Fig. 3e Educational inequality: a breakdown

Figure 3d combines the three measures of inequality in children’s
educational outcomes (in reading, maths and science literacy) into
an overview for 24 OECD countries. For each country, the scoreson the three indicators have been standardized, averaged, andplaced on a common scale in which 100 represents the OECD
unweighted average and 10 is equal to one standard deviation.*

Figure 3e presents the same information as Figure 3d but shows
the individual contributions of the three inequality indicators used.
For each indicator, the length of the bar represents each country’s
distance above or below the OECD 24 average (again measured instandard deviations above or below that average). This allows
countries to see individual strengths and weaknesses.

Finland
Ireland85

90

inequality higher
than average

95

100

105

inequality close
to average

110

115

Irelandsciences

Canada80

Finlandreading

Canadamaths

Denmark

Denmark

PolandPolandHungary

Hungary

SwedenSwedenNetherlands

Netherlands

Spain

Spain

Iceland

Iceland

NorwayNorwaySwitzerland

Switzerland

United Kingdom

United Kingdom

PortugalPortugal

Slovakia

Slovakia

Luxembourg

Luxembourg

Czech RepublicCzech Republic

GreeceGreeceUnited States

United States**

Germany

Germany

Italy

Italy

Austria

Austria

FranceFranceBelgium

Belgium

120

125

inequality lower
than average* A standard deviation is a measure of the spread of the distribution around its
average.
Sources: See Figs. 3a, 3b, and 3c. See also Fig 3e for the standardized inequality
measure used for the three individual indicators of inequality in educational
well-being.

130 -2.5

-2.0

-1.5

-1.0

-0.5

inequality higher thanOECD-24 average

0.0

0.5

1.0

1.5

2.0

inequality lower thanOECD-24 average** Reading literacy data for the United States are missing.
Sources: See individual Figs. 3a, 3b, and 3c.

2.5

3.0

1 2

I nnocenti

differences in national efforts to
reduce socio-economic disadvantage.
Or it may reflect efforts to weaken
the link between socio-economic
disadvantage and school
achievement (children whose
mothers did not complete secondary
school, for example, are atsubstantially greater risk of having
low reading literacy scores, but thatrisk is two or three times greater insome countries than in others.)iv
It is likely, also, that different degrees
of inequality reflect different
degrees of policy concern, over time,
for those at risk of falling behind.

Second, international comparisons ofinequality in educational outcomes also
inform the issue of whether a trade-offmust be made between investing in
low-achieving students and maximizing
the potential of those in the higher
reaches of the ability range. Figure 3f(i)suggests an answer to this question by
showing that there is no relationshipbetween greater inequality and better
performance at the median. In fact themost unequal countries tend towards
slightly lower scores at the 50th
percentile. The two countries with thelowest bottom-end inequality in reading
literacy, Finland and South Korea, areR eportC ard

9also the two countries with the highest
median levels of educational achievement.
A child born in either of these countries
therefore has both a lower chance offalling a long way behind his or her peers
and a higher chance of scoring above theaverage reading literacy mark for theOECD as a whole.
Figure 3f(ii) shows that the point holds
when we look at performance of thehighest-achieving students. Again, thecountries with better results at the 90th
percentile of achievement tend to be
the countries with the lowest levels of
bottom-end inequality.

Fig. 3f(i) Bottom-end inequality and median achievement
The chart compares inequality in reading literacy with median level scores for reading literacy in 30 OECD countries.
580
560

Median score

540
520
500
480
460
440
420
400
15

20

25

30

35

% gap between 50th and 10th percentiles
Notes: Blue vertical and horizontal lines indicate unweighted OECD average (30 countries). Trend line obtained by linear regression. Data for the United States are missing.
For country abbreviations see page 33.
Source: PISA 2006 (see page 30).

Fig. 3f(ii) Bottom-end inequality and top-end achievement
The chart compares inequality in reading literacy with scores at the 90th percentile of achievement in 30 OECD countries.
680
660

90th percentile score

640
620
600
580
560
540
520
500
15

20

25

30

% gap between 50th and 10th percentiles
Notes: Blue vertical and horizontal lines indicate unweighted OECD average (30 countries). Trend line obtained by linear regression. Data for the United States are missing.
For country abbreviations see page 33.
Source: PISA 2006 (see page 30).

35

I nnocenti

R eportH e a lt h

C ard

9

1 3

i nequal it y

The third and last dimension of childwell-being in which the data permit
cross national measurement ofinequality is health.

behaviours of schoolchildren at ages
11, 13 and 15 in 41 countries ofEurope and North America.

 feeling bad tempered feeling nervous
 having difficulty getting to sleep
 feeling dizzy.

Again, three indicators are used:children’s self-reported healthcomplaints; healthy eating; andfrequency of vigorous physical activity.
All three are well-established markers
for children’s current and future health.
The data are derived from the 20052006 round of Health Behaviour in
School-aged Children, a World HealthOrganization collaborative study
which regularly surveys the healthSelf-reported health

The answers were transferred ontoa scale ranging from 0 (frequent
occurrences of all seven complaints)
to 28 (no health complaints).

Among many other questions,participants in the HBSC survey wereasked how often in the previous six
months they had experienced thefollowing problems:Figure 4a uses these scores to estimate
the degree of inequality in children’s
self-reported health. As before, themeasure used is the gap between eachcountry’s median score (column 2) andthe average score of all children below

 headache stomach ache feeling low feeling irritable

Fig. 4a Health inequality: self-reported health complaints
The 2005-2006 HBSC survey (see page 30) asked 11, 13 and 15-year-old students how often in the previous six months they had
experienced the following problems: headache, stomach ache, feeling low, feeling irritable, feeling bad tempered, feeling nervous,
having difficulty getting to sleep, feeling dizzy. The answers were transferred onto a scale ranging from 0 (frequent occurrences ofall seven complaints) to 28 (no health complaints).
Inequality was then measured by comparing each country’s median score (column 2) with the average score of those below themedian (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as apercentage of the median).
Health complaints(range 0-28)

MedianAverage
below themedianAverageabsolute
gap

Gap between the average below the medianand the median (as % of median)

Netherlands
Austria
Portugal
Germany
Denmark
Ireland
NorwayUnited Kingdom
Switzerland
Belgium
Czech Republic
FranceSlovakia
CanadaFinland
Spain
SwedenIceland
PolandItaly
Hungary
Luxembourg
GreeceUnited States
OECD average

25.0
25.0
25.0
23.0
24.0
23.0
23.0
22.0
22.0
23.0
21.0
21.0
20.0
22.0
22.0
23.0
22.0
21.0
22.0
19.0
21.0
22.0
22.0
22.0
22.3

20.2
19.9
19.9
18.3
19.0
18.2
18.1
17.1
17.0
17.7
16.2
16.1
15.3
16.7
16.7
17.4
16.4
15.6
16.3
14.0
15.4
16.1
16.1
15.8
17.1

4.8
5.1
5.1
4.7
5.0
4.8
4.9
4.9
5.0
5.3
4.8
4.9
4.7
5.3
5.3
5.6
5.6
5.4
5.7
5.0
5.6
5.9
5.9
6.2
5.2

19.2
20.4
20.4
20.5
20.7
20.8
21.5
22.4
22.9
23.1
23.1
23.3
23.6
24.3
24.3
24.3
25.5
25.8
25.8
26.3
26.8
26.9
27.0
28.0
23.6

Netherlands
Austria
Portugal
Germany
Denmark
Ireland
NorwayUnited Kingdom
Switzerland
Belgium
Czech Republic
FranceSlovakia
CanadaFinland
Spain
SwedenIceland
PolandItaly
Hungary
Luxembourg
GreeceUnited States
OECD average

18.0

13.0

5.0

27.8

Other OECD countries
TurkeyOther OECD countries
TurkeyNotes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child well-being.
The OECD average is an unweighted average for the 24 countries included in themain league table.

0

5

10

Source: HBSC 2005-2006 (see page 30).

15

20

25

30

1 4

I nnocenti

the median (column 3). The inequality
gap is presented both as an absolutedifference between the two scores(column 4) and as a bar chart showing
the bottom-end inequality gap as apercentage of each country’s median.
Self-reporting has limitations as anindicator of health status. Culturaldifferences, for example, may play a partin explaining differences between eachcountry’s average score (although selfreporting by children has been shown
to be a good predictor of adult healthoutcomesv). But the focus here is not
on averages but on the inequalities
revealed by comparing each country’s
median score with the average scorebelow the median.
Again it is noticeable that the countries
with the highest median levels of health– the Netherlands, Austria, and Portugal

R eportC ard

9Regular exercise in adolescencealso brings short and long-term healthbenefits and is positively associatedwith cognitive development,
emotional well-being, and evenacademic achievement.vii For children
and adolescents, the World HealthOrganization recommends
60 minutes of ‘moderate to vigorous’
physical exercise every dayviii(a recommendation that is not widely
followed; taking an unweighted
average of the 41 countries includedin the HBSC study, only 12%of 15-year-old girls and 20% of
15-year-old boys report taking anhour of moderate to vigorous
physical activity every dayix).

– are also the countries with thelowest levels of health inequality.

Healthy eating andvigorous physical activity
The second and third indicators
available for the measurement ofbottom-end inequalities in children’s
health are based on HBSC survey
data under the headings of ‘healthyeating’ and ‘vigorous physical activity’.
Healthy eating is basic to a child’s
normal growth and development andto long-term health. Unhealthyeating, by contrast, is associated witha wide range of immediate andlong-term health problems including
obesity, type 2 diabetes, and cardiovascular disease.vi A key componentof healthy eating is the inclusion offruit and vegetables in a child’s
daily diet.

In both cases, HBSC survey data
have been translated into scores for‘healthy eating’ (on a scale of 0 to 14)
and ‘frequency of vigorous physicalFig. 4b Health inequality: healthy eating
The 2005-2006 HBSC survey asked 11, 13 and 15-year-old students how often they ate fruit and vegetables. The answers were converted
into a ‘healthy eating’ score on a scale of 0 (no fruit or vegetable consumption) to 14 (daily consumption of both fruit and vegetables).
Inequality was then measured by comparing each country’s median score (column 2) with the average score of those below themedian (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as apercentage of the median).

Healthy eating(range 0-14)

MedianAverage
below themedianAverageabsolute
gap

Gap between the average below the medianand the median (as % of median)

Netherlands
Belgium
CanadaPolandFranceSwedenCzech Republic
NorwayPortugal
United Kingdom
Switzerland
GreeceSlovakia
Luxembourg
Germany
Denmark
Austria
Spain
Italy
United States
Ireland
IcelandFinland
Hungary
OECD average

10.0
10.0
10.0
8.5
8.5
8.5
8.5
8.5
8.5
10.0
10.0
8.5
8.5
8.5
8.5
10.0
7.3
8.0
8.5
8.5
10.0
8.5
8.0
7.3
8.8

6.5
6.5
6.3
5.3
5.3
5.2
5.1
5.1
5.0
5.8
5.8
4.9
4.8
4.7
4.7
5.6
4.0
4.3
4.6
4.5
5.3
4.5
4.1
3.6
5.1

3.5
3.5
3.7
3.2
3.2
3.3
3.4
3.4
3.5
4.2
4.2
3.6
3.7
3.8
3.8
4.4
3.3
3.7
3.9
4.0
4.7
4.0
3.9
3.7
3.7

35.4
35.4
36.6
37.1
37.8
39.0
39.7
40.3
40.8
41.8
41.9
42.5
43.5
44.2
44.5
44.5
45.1
45.8
45.9
46.5
46.7
47.1
49.2
50.5
42.6

Netherlands
Belgium
CanadaPolandFranceSwedenCzech Republic
NorwayPortugal
United Kingdom
Switzerland
GreeceSlovakia
Luxembourg
Germany
Denmark
Austria
Spain
Italy
United States
Ireland
IcelandFinland
Hungary
OECD average

8.5

5.3

3.2

38.1

Other OECD countries
TurkeyOther OECD countries
TurkeyNotes: ‘Other OECD countries’ are listed separately because data limitations
prevent their inclusion in the overview tables for each dimension of child well-being.
The OECD average is an unweighted average for the 24 countries included in themain league table.

0

10

20

Source: HBSC 2005-2006 (see page 30).

30

40

50

60

I nnocenti

R eportC ard

activity’ (on a scale of 0 to 11).
Following the pattern alreadyestablished, Figs. 4b and 4c measureinequality by the gap between the
median score of each country and theaverage score for all children below
the median. The bar charts again
show each country’s inequality gap as
a percentage of the national median.
For ‘healthy eating,’ the lowest levelof inequality is to be found in the
Netherlands, Belgium and Canada
and the highest in Iceland, Finlandand Hungary.
For ‘vigorous physical activity’, theNetherlands again has least inequality,
closely followed by Switzerland andNorway. The highest levels of bottomend inequality are to be found inFrance, Italy and Spain.

9

1 5

Health: an overview

closer to the top of the table if itwere not for high levels of inequality
in ‘healthy eating’.

Figure 4d combines the three measuresof bottom-end inequality in children’s
health onto a standardized common
scale. As the bar chart shows, theNetherlands heads the table by adistance (with the lowest inequality inall three indicators). The United States,
Italy and Hungary show the highest
levels of bottom-end inequality inchildren’s health.

Statistics and children
This attempt at an international
comparison of inequality in different
dimensions of children’s well-being
is a work in progress. But its clear
overall message is that children are
falling significantly further behindin some countries than in others.
In particular, Denmark, Finland, theNetherlands, and Switzerland areleading the way in limiting how far
behind the least advantaged children
are allowed to fall.

Figure 4e breaks down this overall
performance by showing thecontributions of the three individual
indicators. It allows countries likeFrance and Poland, for example, to seethat their position in the bottom halfof the table is brought about by highlevels of inequality in ‘vigorous
physical activity’. Ireland and Finland,on the other hand, would both be

Before discussing some of theimplications of these overallfindings, two other concerns
should be acknowledged.

Fig. 4c Health inequality: vigorous physical activity
The 2005-2006 HBSC survey asked 11, 13 and 15-year-old students about their exercise habits outside school hours, converting
the answers into a score for ‘frequency of vigorous physical activity’ on a scale of 0 (no vigorous physical activity) to 11 (frequent
vigorous physical activity).
Inequality was then measured by comparing each country’s median score (column 2) with the average score of those below themedian (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as apercentage of the median).
Vigorous physical activity(range 0-11)

MedianAverage
below themedianAverageabsolute
gap

Gap between the average below the medianand the median (as % of median)

Netherlands
Switzerland
NorwayIrelandGermany
Finland
Iceland
Slovakia
Denmark
Luxembourg
United Kingdom
Czech Republic
CanadaGreeceAustria
United States
Belgium
SwedenHungary
PolandItaly
Spain
FranceOECD average

8.0
7.0
7.0
7.0
7.0
8.0
7.0
8.0
8.0
7.0
7.0
6.0
8.0
7.0
7.0
7.0
7.0
7.0
7.0
6.0
7.0
6.0
7.0
7.1

6.1
5.3
5.3
5.3
5.2
5.9
5.1
5.7
5.6
4.8
4.8
4.0
5.3
4.6
4.6
4.6
4.5
4.5
4.5
3.5
4.1
3.5
3.9
4.8

1.9
1.7
1.7
1.7
1.8
2.1
1.9
2.3
2.4
2.2
2.2
2.0
2.7
2.4
2.4
2.4
2.5
2.5
2.5
2.5
2.9
2.5
3.1
2.3

24.1
24.4
24.7
24.9
25.2
26.7
26.8
28.4
29.9
31.1
32.1
33.0
33.2
33.8
34.1
34.9
35.3
35.5
35.8
41.0
41.9
42.0
43.6
32.3

Netherlands
Switzerland
NorwayIrelandGermany
Finland
Iceland
Slovakia
Denmark
Luxembourg
United Kingdom
Czech Republic
CanadaGreeceAustria
United States
Belgium
SwedenHungary
PolandItaly
Spain
FranceOECD average

6.0

2.7

3.3

54.6

Other OECD countries
TurkeyOther OECD countries
Turkey0Notes: Data for Portugal are missing. ‘Other OECD countries’ are listed separately
because data limitations prevent their inclusion in the overview tables for eachdimension of child well-being. The OECD average is an unweighted average forthe 23 countries included in the main league table.

10

20

Source: HBSC 2005-2006 (see page 30).

30

40

50

60

1 6

I nnocentiFirst, in measuring different
dimensions of children’s well-being it
is necessary to separate outcomes thatare rarely separated in children’s lives.
Multiple disadvantage is the norm –with each dimension intimately linked
and often mutually reinforcing at thelevel of the individual child’s life.

Second, the perennial danger of all
statistics is that in offering an overview
they can seem very distant from therealities they seek to capture. And inpresenting these data, UNICEF’s plea
is that the children themselves should
as far as possible be seen not as
statistics but as individual young

R eportC ard

9people, each with a name and a face,each with needs and rights, each witha personality and a potential, eachwith a capacity to benefit from andcontribute to the societies into which
they are born, and each with a keenawareness of the norms of the societiesin which they live.

Fig. 4d Health inequality: an overview

Fig. 4e Health inequality: a breakdown

Figure 4d combines the three measures of inequality in children’s
health well-being (self-reported health complaints, healthy eating,and vigorous physical activity) into an overview for the 24 OECD
countries with available data. For each country, the inequality
scores for the three indicators of health well-being have beenstandardized, averaged and placed on a common scale in which
100 represents the OECD average and 10 is equal to one
standard deviation.*

Figure 4e presents the same information as Figure 4d but shows
the individual contributions of the three inequality indicators used.
For each indicator, the length of the bar represents each country's
distance above or below the OECD 24 average (again measured instandard deviations above or below that average). This allows
countries to see individual strengths and weaknesses.

Netherlands
Norway90
inequality higher thanOECD-24 average

95

100
inequality close to
OECD-24 average

105

110

Norwayhealth complaints

Portugal

85

Netherlands

vigorous physical activity

Portugal**

healthy eating

Germany

Germany

Switzerland

Switzerland

Belgium

Belgium

Ireland

IrelandDenmark

Denmark

CanadaCanadaCzech RepublicCzech Republic

United Kingdom

United Kingdom

Slovakia

Slovakia

Austria

Austria

SwedenSwedenFranceFranceFinlandFinland

Iceland

Iceland

PolandPolandLuxembourg

Luxembourg

GreeceGreece

Spain

Spain

United States

United States

Italy

Italy

Hungary

Hungary115

inequality lower thanOECD-24 average* A standard deviation is a measure of the spread of the distribution around
its average.
Sources: See individual Figs. 4a, 4b, and 4c. See also Fig 4e for the standardized
inequality measure used for the three individual indicators of inequality in childwell-being.

120 -2.5

-2.0

-1.5

-1.0

-0.5

inequality higher thanOECD-24 average

0.0

0.5

1.0

1.5

2.0

inequality lower thanOECD-24 average** Data on vigorous physical activity for Portugal are missing.
Sources: See individual Figs. 4a, 4b, and 4c.

2.5

3.0

I nnocenti

R eport

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9

1 7

Part 2A case has been made that national
averages are an insufficient guide tonational performance in meetingchildren’s needs. Equity measures,and in particular measures of bottom
end inequality, are also needed. (The
UNICEF global report on Progress
for Children 2010 makes a similar casefor the inclusion of equity measuresin monitoring the Millennium
Development Goals).
Secondly, it has been argued that‘falling behind’ has many mutually
reinforcing dimensions and can not
be adequately represented by any
single indicator. Policies to preventchildren from falling behind must
therefore address the different
dimensions of disadvantage
individually as well as collectively.
Such policies are largely a matter fornational research and debate. But aninternational perspective can perhaps
offer some insights into this underresearched area.

Equity in education
For the purposes of reducing bottomend inequality in children’s educational
achievement, for example, it is clearthat school admissions policies canmake a difference.
In all OECD countries where studies
have been conducted,x the averagesocio-economic level of students ina particular school has been foundto have an effect on educational
achievement that is over and above
the effects associated with thesocio-economic status of the individual
student. This finding strongly suggeststhat pupils from lower socio-economic
backgrounds benefit from attending
schools in which a wide range ofhome backgrounds are represented.
Conversely, ‘falling behind’ issignificantly more likely when students
from homes of low socio-economic
status attend schools in which theaverage socio-economic status isalso low.xi
The reasons for this ‘school
composition effect’ are many. Schools
with low socio-economic profiles may
find themselves struggling against lower
expectations on behalf of both staff andstudents; the ethos and disciplinary
climate may be less conducive tolearning; pupil-teacher relations may
be less positive; parental involvement
and support may be weaker; and thetask of attracting and retaining themost able teachers may be moredifficult. All of these are formidable
barriers to learning.
In many OECD countries there are
significant numbers of schools in which
the average socio-economic profile isbelow the 20th percentile of the socioeconomic distribution for the OECD
as a whole.xii In such cases, the school
composition effect is enlisted against
rather than in favour of those who are
already most at risk of educational
underachievement. The likely result is
an increase in bottom-end inequality.
Two obvious approaches may counter
this effect. First, the attempt can bemade to boost the performance oflow socio-economic status schools(for example by increasing theresources available to them andallowing them to offer extra incentives
to more able teachers). Second,
admission policies can be designed to
avoid the concentration of pupils fromdisadvantaged backgrounds in low
socio-economic status schools. Thismight be achieved, for example, byadmitting children in ability bands
without regard to socio-economic
background. Policies designed tomonitor and balance the socioeconomic profile of pupil intake
may also be important. As a 2006
report commissioned by UNESCO
has pointed out:
Countries with high levels of segregation
along socio-economic lines tend to havelower overall performance and greater
disparities in performance between
students from high and low socio-economic
backgrounds …
In countries with high levels of socioeconomic segregation, policies that aimed
to reduce socio-economic segregation throughcompensatory reforms would likely bringconsiderable gains in raising and levelingthe learning bar.xiii
In practice, a combination of both
approaches will be necessary incountries with high bottom end
inequality in educational outcomes(shown in Figure 3d where thebottom five countries are Belgium,
France, Austria, Italy and Germany).

1 8

Resistance to such policies is common
and is often based, at least in part, on
fears that overall educational outcomesmight be adversely affected. But theinternational comparisons set out in
Figs. 3f(i) and 3f(ii) suggest that lower
bottom-end inequality need not imply
any lowering of standards for highachieving students. As the report for
UNESCO already cited concluded:
Successful schools tend to be those thatbolster the performance of those from lessadvantaged backgrounds. Similarly, countries
that have the highest levels of performancetend to be those that are successful in not
only raising the learning bar but also
leveling it.xiv

Equity in healthHigher than average inequality inchildren’s health may also need tobe addressed by specific health sector
policies. Depending on context, suchpolicies might include: the targetingof resources and outreach programmeson those most at risk; the setting ofspecific disparity reduction targets
for key health indicators such as
obesity, exercise, healthy eating andinfant and child mortality rates; andincreasing the reach and refinement ofprevention policies designed to reducethe impact of the health behaviours
that contribute most to bottom-end
inequality in health outcomes (such as
obesity, drug and alcohol abuse, andsmoking).
But there are also clear dangers in asectoral approach to reducing bottomend inequalities in children’s health.
It is tempting to target the bottom endof the distribution with policies aimed
at specific changes in lifestyles such as
promoting exercise and healthy eatingor reducing smoking or obesity levels.
But necessary as such programmes
are,* they cannot address the fact that
inequality in health outcomes, as ineducational outcomes, is principally
driven by socio-economic status.xv
A 2010 review of health inequalities in

I nnocenti

the United Kingdom – and what canbe done about them – has this to say:
Inequalities in heath arise because of
inequalities in society – in the conditions inwhich people are born, grow, live, work, andage. So close is the link between particularsocial and economic features of society andthe distribution of health among the
population, that the magnitude of healthinequalities is a good marker of progress
towards creating a fairer society. Taking
action to reduce inequalities in health does
not require a separate health agenda, but
action across the whole of society.xvi
The significance of the social gradientin health has been demonstrated bya steady flow of research findings inmany OECD countries over recent
years.xvii Taking the three indicators ofinequality in children’s health used inPart 1 of this report, for example, thedetailed HBSC data clearly show thatchildren of more affluent families takemore regular exercise, have healthiereating habits, and report fewer healthproblems.xviii Socio-economic status,
it is worth reminding ourselves, isneither the choice nor the
responsibility of the child.
Among other studies, particularly
striking is the finding in Canada that
exposure to poverty in childhood
doubles the risk of death by age 55.xix
Similarly in the United States, socioeconomic status in childhood has beenshown to be a powerful predictor of
cardio-vascular disease in later life.xx
In Europe, the 2006 report HealthInequalities: Europe in Profile concludes
that, across the board, the poor haveshorter lives and more years of ill
health. “Socio-economic inequalities inhealth,” says the report’s author, Johan
Mackenbach of Rotterdam University’s
Medical Centre, “are unacceptable, andrepresent one of Europe’s greatest challenges
for public health.”xxi
Yet it is clear from the data presented
here and elsewhere that the relationshipbetween socio-economic status andR eportC ard

9health is not fixed. Being of low socioeconomic status clearly carries a greater
degree of risk in some countries thanin others. In most OECD countries,
for example, children born to parents
with low levels of education or into
homes with low socio-economic status
are more likely to die in the first twelve
months of life. Yet the steepness of this‘social gradient’ in infant mortality
rates varies considerably from country
to county.xxii
Some countries, therefore, are clearly
doing a better job than others either
in reducing socio-economic inequalities
or in mitigating their impact onchildren’s health and development. Andagain it is the case that the countries
with the highest median levels of health– the Netherlands, Austria, and Portugal– also have the lowest levels of healthinequality (Figure 4a). Conversely, thecountries whose children have thelowest average levels of self-reported
health all tend to have higher-thanaverage levels of health inequality.

The importance of income
Socio-economic status is therefore theindispensable framework for policy
analysis of bottom-end inequality forchildren. For just as inequalities inheath reflect not only the effect ofhealth services but also ‘the conditions
in which people are born, grow, live, work,and age’, so inequalities in educational
outcomes at age 15, for example, reveal
not only what happens in schools
but also the educational resources,
stimulation and encouragement thatsurrounds a child from the earliest
weeks and months of life.
Policies designed to address specificinequalities in health or education are
therefore likely to have limited impact
if they confine themselves to the healthand education sectors alone. The mostpotent fact about children who fallsignificantly behind their peers is that,by and large, they are the children offamilies at the bottom end of thesocio-economic scale.

* In England, for example, smoking accounts for approximately half the difference in average life expectancy between the lowest and highest income groups.
(Michael Marmot (chair) 2010, Fair Societies, Healthy Lives, Strategic Review of Health Inequalities in England post 2010, p 10.)

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9

1 9

Box 1 Child poverty: a relative measure

Most OECD countries have adopted national poverty
lines based on a percentage of the nation’s medianincome. The European Union, for example, draws thepoverty line at 60% of the median. The OECD uses
50% of the median.1
In some countries, the idea of relative poverty is stilla matter of dispute. Poverty, it is argued, should bemeasured by absolute rather than relative standards.
In the United States, for example, the official poverty
line is based on a multiple of the income required toensure an adequate diet.
But it could be argued that all definitions of poverty –other than the minimum required for sheer physicalsurvival – are in fact relative definitions. Absolute
poverty in the not-too-distant past, for example, meantthat life was ‘nasty, brutish and short’. Absolute poverty
in the United States today means not being able toafford a standard of living – including standards ofnutrition, water supply, sanitation, health care andtransport – far in advance of the standards enjoyed bymost of the world’s population for most of its history.

This is not to say that the idea is new. More than 200
years ago the founding father of modern economics
argued that poverty was a relative concept:
By necessaries I understand, not only the commodities
which are indispensably necessary for the support oflife, but whatever the custom of the country renders
it indecent for creditable people, even of the lowest
order, to be without. A linen shirt, for example, is,strictly speaking, not a necessary of life. … But in thepresent times, through the greater part of Europe, acreditable day-labourer would be ashamed to appear
in public without a linen shirt ... Custom, in the samemanner, has rendered leather shoes a necessary of life
in England. The poorest creditable person of either sex
would be ashamed to appear in public without them.
… Under necessaries, therefore, I comprehend, notonly those things which nature, but those things which
the established rules of decency have renderednecessary to the lowest rank of people.
Adam Smith, “An Enquiry into the Nature and Causesof the Wealth of Nations”, Book 5, Chapter 2, 1776.

In this sense, even absolute definitions of poverty arereally relative definitions that eventually have to be
updated to take account of changing standards of whatis acceptable to the society as a whole. The question
then becomes whether the definition should be updated
infrequently in an ad hoc way or whether it should be
updated regularly and systematically – for example bytying it to the national median income.
In recent times, defining income poverty in relative
terms has become widely established, especially in theEuropean Union. In the United Kingdom, for example,
The Economist magazine notes that “A decade ago, theprospect of the Conservatives accepting the idea ofrelative poverty – rather than an absolute measure ofwant, such as a basket of goods that every household
should be able to afford – would have been fanciful.
Nowadays, it is a reality.”2

1 In discussing child poverty rates, part 2 of this report follows
the method recommended by the OECD, drawing the poverty line
at 50% of national median household income. Household income
is taken to mean ‘disposable household income’, i.e. after taxes
and public transfers. This is then ‘equivalized’ to take into accountthe economies of scale available to different sizes of households(using the square root of household size). The poverty line istherefore defined as ‘half of the median national disposable
equivalized income’; the child poverty rate is then calculated on
the same basis but taking into account only households withchildren aged 0-17.
2 ‘Still with us’, The Economist, 1 July 2010.

2 0

I nnocenti

Action to prevent children from fallingbehind in different dimensions of wellbeing must therefore eventually comeface to face with the question of thesocio-economic gradient.

Income poverty
Socio-economic status is about morethan income. A family’s past savingsand future prospects, housing andneighbourhoods, levels of parental
education and expectation, and status
in relation to the mainstream orestablished ethnic or linguistic
community – all of these enter into
the socio-economic equation. Yetof the available measures, the mostimportant single guide to, andpredictor of, a family’s socio-economic
status remains its level of householdincome. Reducing bottom-end

inequality in incomes will not solve
all other problems, but it will maketheir solution easier. Climbing thesocio-economic ladder is more feasible
if the rungs are closer together.
Reviewing many studies that show
a strong and consistent association
between relative income poverty and‘falling behind’, Susan Mayer makes
the point unflinchingly:
Parental income is positively correlatedwith virtually every dimension of childwell-being that social scientists measure,and this is true in every country for which
we have data. The children of rich parents
are healthier, better behaved, happier andbetter educated during their childhood andwealthier when they have grown up thanare children from poor families.xxiii

R eportC ard

9Relative income poverty therefore
occupies a primus inter pares position
among the indicators of ‘fallingbehind’. But monitoring of income
poverty that can exert such leverageover the trajectories of children’s lives
is not simply a matter of calculating
what proportion of a nation’s children
is growing up in households whose
income falls below a given threshold.
The depth, duration and timing of thatpoverty in relation to the different
stages of a child’s development may
also be critical. A 2007 Canadianreview of research into this issue
refines the point:
Studies that measure family income overextended periods of time and include
changes in income and the depth of income
inequality in their models and analysis

Box 2 First call: children and recession

The time lag between the gathering of data throughsample surveys in different countries and the publishingof that data in internationally comparable form isapproximately 3 years. Most of the data in this report
therefore apply to the years 2006 to 2008.
Normally, such a delay is no more than frustrating.
Socio-economic data of a kind used here tend to reflect
long-term trends rather than year on year changes.
But much has changed in the world since 2008. Economic
recession has affected millions in the OECD countries.
The response of governments, whether by cuts inspending or increases in taxation, is affecting many
millions more. Across the European Union, for example,
unemployment is predicted to surpass the 10% mark by
the time this report is published. This means thatapproximately 5 million more people will be unemployedthan before the crisis began. As joblessness is a principal
driver of poverty, it is likely that the material well-being ofchildren has deteriorated in some countries since 2008.
In the European Union, youth unemployment, in particular,
has risen from a pre-crisis level of under 15% to morethan 20% today.1 Many households have seen theirincomes fall and are facing difficulties with debt
repayments. In the United States, as many as half of all
workers have taken a cut in pay or hours or suffered atleast temporary unemployment in the two half years since

the crisis struck.2 Migrant workers and those on short-term
contracts are particularly vulnerable.
There may be worse to come. According to a report by
the European Union Social Protection Committee “The full
impact of the crisis on labour markets and public finances
has yet to be faced.”3
In other words, the snapshot of inequality in children’swell-being presented in these pages is a snapshot taken
in good times.
No overall statistics are yet available to chart the impactof recession on the children of the poorest families. But a
partial glimpse may be offered by the changing demands
on charities and government special assistanceprogrammes. The International Federation of Red Cross and
Red Crescent Societies, for example, is reporting increasing
numbers of people seeking help “with the basic necessities
of life – including some who would never normally think ofseeking help from a charitable body.”4 In the United States,
the number of people receiving SNAP benefits (under the
Supplementary Nutritional Assistance Program) has risen
by almost a quarter since the crisis began (from 29.5 million
to 36.5 million people a month in the year to August 2009).
Approximately half of all SNAP beneficiaries are children.5
It is also worrying that the Eurochild report is beginning toshow increases in demands on child protection services
in a number of European countries.6

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R eportC ard

9have found that income emerges as the variable
most strongly associated with child development
outcomes, particularly in cognitive development,
behaviour and educational attainment …
… the younger the child the larger the effecton his or her development of changes in family
income.xxiv
To give one further example, the decadeof the 1990s saw sustained economic
growth in the United States leading toa significant reduction in the numberof American children living below thepoverty line. The national child poverty
rate captured this achievement. But it
did not capture the fact that the children
who remained below the poverty line
fell even further behind.xxv It is therefore
essential to ask not only ‘how many?’but ‘how far?’.

2 1

Mitigating markets
National action to prevent families
from falling into poverty has a long
history in the OECD countries. All
governments, of whatever politicalcomplexion, use a range of tax andtransfer policies – including childbenefit packages, unemployment pay,earned income tax credits, andnational and local services – to try
to put a floor under poverty.
Figure 5a presents a comparative
overview of the extent to which
they are succeeding.
The chart compares the child poverty
rates of 21 OECD countries before
and after taxes have been deducted andsocial assistance payments made.

In its work with children in the developing world, UNICEF
has long experience of what happens to the vulnerable
when economies turn down. Through the second half of the
1980s and the early 1990s, for example, many of theworld’s poorest nations entered a period of economic
adjustment which included cuts in government spending on
basic services and subsidies on which the poor were mostdependent.
Throughout that period, UNICEF urged special action toprevent the heaviest burden from falling on those least able
to bear it.
That same argument now needs to be made to some of theworld’s richest economies.
In hard times, the poorest children should be the firstto be protected, not the last to be considered. A childhas only one chance to develop normally in mind and body.
And it is a primary responsibility of governments
to protect that chance – in good times and in bad.
In practice, this means that protecting children during thecritical early years of their growth should be given a ‘firstcall’ on societies’ resources.
The economic crisis of 2008 and its continuing aftermath
will test government commitments to this principle of ‘firstcall’. As Janet Gornick and Markus Jäntti have written, “Thecurrent recession, which is affecting all industrialized

The lighter bars show the relative childpoverty rates that would theoreticallyprevail if household incomes were
determined by market forces alone. The
darker bars show actual child poverty
rates after government intervention.
For each OECD country, the difference
between the two bars stands as a broadmeasure of government commitment
and effectiveness in reducing the
proportion of children growing up
below the national poverty line.
Two features stand out. First, thechart shows that without government
intervention all of the 21 OECD
countries would have child poverty
rates of 10% or more (Iceland, witha rate of 9.6%, being the only
exception). The majority would havechild poverty rates of between 10%

countries – and diverse government responses to it – willshed light on how the interaction between labor market
characteristics and public policies either protect or fail toprotect children from shocks to the market system.”7

1 Council of the European Union (2009) ‘Second assessment by
the Social Protection Committee and the European Commission on
the social impact of the economic crisis and on policy responses’,Council of the European Union, Brussels, 24 November 2009.
2 The Economist, 4 July 2010.
3 Council of the European Union (2010) ‘Draft joint report on
Social Protection and Social Inclusion 2010’ Council of the European
Union, Brussels, 15 February 2010.
4 International Federation of Red Cross and Red Crescent Societies(2009) The Economic Crisis and its Humanitarian Impact on Europe,
International Federation of Red Cross and Red Crescent Societies,
Geneva.
5 Isaacs, J. B. (2009) The Effects of the Recession on Child Poverty:
Poverty statistics for 2008 and growth in need for 2009, First Focus,
Brookings Institution, Washington D. C.
6 Eurochild (2009) ‘Impact of Economic and Financial Crisis onChildren and Young People’, Eurochild Report, update 9 October 2009.
7 Gornick, J. C., and M. Jäntti (2010) ’Child Poverty in Upper-Income
Countries: Lessons from the Luxembourg Income Study’ in S.
Kamerman, S. Phipps and A. Ben-Arieh (eds.) From Child Welfare
to Child Well-Being: An international perspective on knowledge inthe service of policy making, Springer, New York.

2 2

I nnocentiand 15%, and three countries –Hungary, Ireland and the United
Kingdom – would have rates of morethan 25%. No comparable data are
available for the United States.

‘pre-intervention’ child poverty,
such as Ireland and Hungary, reducechild poverty rates by approximately
two thirds.

Forces of change

Figure 5a compares the child poverty rates of 21 OECD countries before and after taxes
and benefits. The lighter bars show the notional child poverty rates if household incomes
were determined by market forces alone. The darker bars show actual child poverty rates
after governments have intervened via taxes and benefits.

0

In brief, the increase in inequality
over recent decades has been driven by
three main forces. The first is long-termsocial and demographic change (forexample the aging of populations or
the rise in the number of single adulthouseholds). The second is thechanging distribution of income andemployment opportunities brought
about by technological innovation, by
the globalization of markets, by the
migration of manufacturing to countries
with rising skills and low labour costs,
and the increasing premium on
high-end abilities and qualifications(so pushing up incomes at the top of
the distribution). The third force is therange of government policies andexpenditures, including child benefit
packages that are specifically designed
to protect those at risk of disadvantage.

child poverty before taxes
and transfers

5.2
15.2
5.4
10.7
5.7
12.0
5.8
12.1
5.8
10.7
6.7
9.6
6.8
17.9
7.2
17.6
8.8
16.4
10.0
28.3
10.0
16.9
10.6
14.1
10.7
17.9
10.7
18.6
11.0
19.9
11.0
34.0
13.5
19.6
14.6
26.4
15.5
16.6
17.2
18.5
18.7
21.2

FinlandNetherlands
SwedenNorwayDenmark
IcelandFranceAustria
Czech RepublicHungary
Belgium
Switzerland

These are the shifting tectonic platesbeneath that underlie the landscape ofchild well-being; and it is the complex
interplay between them that ultimately
determines how many children fallbehind and by how far.

Slovakia
Germany
Luxembourg
Ireland
PolandUnited Kingdom
Italy
Spain
Portugal
5

10

15

20

25

30

35

Child poverty rate (% of children living in poor households)
Note: For the methodology used for the calculation of the poverty rates see Box 1.
Sources: Data on income are from EU-SILC 2008 and refer to 2007. Data for France are from the EU-SILC 2007
survey and refer to 2006. Data for Switzerland are from the 2007 Swiss Household Panel and refer to 2006.

9This strongly indicates that policies
aimed at limiting poverty in all its forms
must also confront the changes in thewider world that are tending to bringabout widening economic inequality ina large majority of OECD countries.xxvi

Fig. 5a Markets, governments, and child poverty rates

child poverty rate after taxes andtransfers (disposable income)

C ard

Nonetheless, the chart reveals an
important truth about the causes ofchild poverty and its possible solutions:differences in child poverty rates
between developed countries are a
product not only of differences ingovernment benefits and social
protection policies but of verysignificant differences in the
distribution of earned incomes.

Figure 5a is theoretical in that it does
not allow for the changes in work
and income patterns that would likely
occur in the absence of government
benefits. Neither does it reflect
government interventions designed
to affect incomes from employment –such as minimum wage and equal pay
legislation or subsidized employment
creation and training schemes.

Second, the chart shows that, while
efforts by all governments achieve
substantial reductions in child poverty,
some are achieving much more thanothers. Government action in theNordic countries and the Netherlands,
for example, reduces child poverty by
about half or more. Several countries
with very high levels of

R eport40

In this context, it becomes clear thatthe slow but steady rise of bottom-end
inequality in most OECD countries
over the last three decades has not beenbrought about by governments doing
less or spending less. Most governments
are today spending a larger proportionof GDP* on family benefits and social
protection than they were two decadesago (up by about a third, on average, inR eport

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the 21 OECD countries for which
comparable data are available). xxvii
This suggests that child poverty rates
have risen, or failed to fall, because
increasing government efforts havebeen rowing upstream against powerfulcurrents in the wider economy.
In most cases, those efforts have notbeen sufficient in scope and scale toprevent child poverty rates fromgrowing. Even in periods of sustained
economic growth such as the 1990s,
the benefits have tended to accrue
to the already advantaged, leaving
those at the bottom end of the socioeconomic scale even further behind.
The 2008 report Growing Up in North
America, for example, tells a story thathas been repeated in one degree oranother in many of the world’s
developed economies.
Inequality of market and disposable income
has been increasing in Canada, Mexico,and the United States since the 1980s.
In particular, markets have disproportionately
benefited families at the top of the income
ladder – though families in every income
group have been working longer and harder.
The scale of government intervention via
public income transfers was not enough tooffset the growing gap in market incomes.xxviii

Not by benefits alone
Government policies to restrain
bottom-end inequality are therefore
unlikely to be successful if they are
limited to social protection
expenditures alone. “The only sustainable
way to reduce inequality,” says the OECD
report Growing Unequal (2008), “is tostop the underlying widening of wages andincome from capital. In particular, we have to
make sure that people are capable of being in
employment and earning wages that keepthem and their families out of poverty.”xxix
In particular, reducing bottom-end
inequality in all its dimensions will
depend on getting to grips with one of
the most disturbing aspects of changed
economic times – the fact that full-time

9

2 3

employment no longer guarantees alife lived above the poverty line.
In many countries, this concern isdirecting attention to the question of
the minimum wage. In Australia, forexample, an independent report to the
Fair Pay Commission proposes that“an increase to the minimum wage is one
method of increasing family income andreducing child poverty.”xxx Similarly, a2007 study in Japan also proposes that“to improve the economic well-being ofhouseholds with young children the firsttask is to ensure a minimum wage thatmaintains a reasonable living standard.”xxxi
In the United Kingdom, the 2010
report of the National Equity Panelargues that “the minimum wage is a
powerful tool in reducing labour market
inequality.” xxxii Most forcefully of all,a report by the U.S. National ResearchCouncil on Integrating the Science of
Early Childhood Development makes thefollowing recommendation:
Congress should assess the nation’s tax,
wage, and income support policies withregard to their adequacy in ensuring that nochild supported by the equivalent of a fulltime working adult lives in poverty …xxxiii

Social protection
The previous section has stressed an
obvious truth that is easy to lose sightof: whether a child falls unnecessarily

far behind or not depends, in the first
instance, on whether that child is partof a well-functioning family with anadequate income from employment.
But putting government efforts inthis wider context does not meanthat those efforts are not a critical
determinant of how many children fallbehind and by how far. “If governments
stop trying to offset the inequalities byeither spending less on social protection or
by making taxes and benefits less targetedto the poor,” says the 2008 OECD
report cited above, “then the growth ininequality would be much more rapid.”xxxiv
An overview of the impact ofgovernment efforts has already been
set out (Figure 5a). Across the OECD
as a whole, the effect of those efforts
is to reduce child poverty rates by morethan 40% (unweighted average).**Figure 5b shows that effect in absoluteterms for four of the most populous
countries of the European Union.
More than a million children are being
lifted above the national poverty line
in France, Germany and the United
Kingdom while Italy is making muchless of an impact. In percentage terms,
France cuts child poverty by morethan 60%, Germany and the United
Kingdom by more than 40%, and Italy
by less than 7%.

Fig. 5b Child poverty and public spending on families
The chart shows the absolute number of children living below national poverty lines
before and after government intervention via taxes and benefits.
before government intervention

after government intervention

3500
Number of poor children (in thousands)

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3000
2500
2000
1500
1000
500
0

* Even in OECD countries where social expenditure
has stagnated, falling child populations mean that
expenditure per child may have continued to increase.
** Unweighted average for the 21 OECD countries
with available data.

FranceGermany

Italy

United Kingdom

Sources: Elaboration of data on income poverty derived from EU-SILC 2008 (referring to 2007) for Germany, Italy
and United Kingdom, and from EU-SILC 2007 (referring to 2006) and data on child population derived from theUnited Nations Population Division.

2 4

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Box 3 The just society: a measure

The statistics presented in this Report Card can also
be read as a first attempt to measure nations by thestandards of a ‘just society’ as defined by the American
political philosopher John Rawls (1921 – 2002).
Rawls proposed that the just society would be one inwhich the rules were drawn up for the benefit of society
as a whole. To achieve this, he argued, the starting pointshould be ‘the original position’. By this he meant a kindof celestial ante-room in which all those waiting to beborn would draw up the rules without knowing what
position in society they themselves would occupy.
From behind this ‘veil of ignorance’, the rule-makers
would not know whether they would be born rich orpoor, male or female, with above or below averagetalents, fit or disabled, part of an ethnic minority or partof a privileged elite.
Because we would not know about our own status,
he argued, we would not be able to press for rules thatwould benefit only ourselves. Rules drawn up on thisbasis, therefore, would reflect an equal concern for allclasses and groups.
The ‘veil of ignorance’ is therefore designed to tame
the power of vested interests. And ‘the original position’is the exact opposite of the interest group model that is
so influential in today’s politics. In essence, it is similar
to the method of sharing a cake fairly between two
people by inviting one person to make the cut and theother to take first choice.
Rawls has his critics among the hundreds who havewritten books in response to his ideas. Libertarians
have objected that basic human rights such as property
rights and the right to self-ownership leave no room fora Rawlsian concept of the ‘just society’. Ronald Dworkin
has argued that hypothetical agreements about rules
drawn up from ‘the original position’ are not realagreements and therefore could not find the necessary
acceptance and authority. Amartya Sen finds the same
weakness, adding that unanimity would be unlikely tobe achieved even from ‘the original position’ and thatlack of unanimity would bring the Rawlsian thesis
crashing down. Uniting some of these criticisms,
Michael Sandel has objected that decisions about therules governing communities that have their own
traditions and histories cannot be made by reasoning
from a rootless and historically abstract position.
But the idea that the rules of society should reflect
the interests of all, and not just its dominant members,
is widely accepted in theory, even if the methods bywhich it might be achieved remain controversial.
If we assume that the end, if not the means,commands a measure of agreement, then one way ofmeasuring progress towards the aim of a just society
would be to measure the degree of disadvantage
suffered by its most disadvantaged members. That iswhat this Report Card attempts to do.
Clearly, more comprehensive data would be required
to measure degrees of disadvantage ‘in the round’,especially if, as Amartya Sen suggests, disadvantage
should be defined as “those who are least able torealise their potential and develop and exercise theircapabilities.”
Nonetheless, the data presented in these pages
represent a contribution to that process. In threedifferent dimensions of well-being – material goods,
educational level, and health – they show how far
behind the median level the least advantaged arebeing allowed to fall. And the fact that different
countries show very different patterns indicates thatsome countries are making more progress than others
towards ‘the just society’.

9I nnocenti

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As further evidence for the importanceof government policies andexpenditures, Figure 6a compares thelevel of public spending on family
benefits (in cash and tax allowances)with the reduction in child poverty
rates achieved (in percentage points)

9

2 5from the starting or ‘market’ rate ofchild poverty (see Figure 5a). As thetrend line shows, there is a significant
correlation between the two.
This comparison obviously favours
countries with a high starting or‘market’ rate of child poverty. Figure 6ashould therefore be read in conjunction
with Figure 6b which compares overall
government spending on protecting
families (including cash benefits, tax
allowances, and spending on services forfamilies) with overall child poverty rates.

Fig. 6a Reduction in child poverty rates by government family spending in cash benefits and tax allowances
The scatter plot compares the percentage point reduction in child poverty rates achieved by each country with the amount ofpublic spending on family benefits in cash and tax breaks directed towards families (as a % of GDP).
The percentage point reduction in child poverty rates is calculated by subtracting the actual rate (after all taxes and transfers)
from the starting or ‘market’ poverty rate that would theoretically prevail in the absence of government spending on families.

Size of the reduction in child poverty rates due to taxes andtransfers (percentage points of poverty reduction)

This measure of poverty reduction clearly favours countries with a high starting or market rate of child poverty. The chart should therefore
be read in conjunction with Figure 6b, comparing government spending on families with the overall level of child poverty; this shows that
achieving a low child poverty rate of close to 5% depends on both a low ‘market rate’ of child poverty in conjunction with a high level ofgovernment effort and expenditure to reduce that rate still further.
25

20

15

10

5

0
0.0

0.5

1.0

1.5

2.0

2.5

3.0

Public spending on family benefits in cash and tax breaks (as % of GDP)
Notes: Public spending data include only spending on family benefits and tax breaks. Differently from those presented in Figure 6b, they do not include expenditureon family services. Trend line obtained by linear regression. For country abbreviations see page 33.
Sources: Data for public spending are for 2007 (preliminary data from the OECD Family Database). For data on poverty see sources in Figure 2a.

Fig. 6b Child poverty rates and public spending on families

Child poverty rate (% of children living in poor households)

The scatter plot compares each country’s child poverty rate with the % of GDP spent by governments on family cash benefits,
tax allowances, and family services (see note).
20

15

10

5

0
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Public spending on family (as % of GDP)
Notes: Public spending data include only public support that is exclusively for families (e.g. child payments and allowances, parental leave benefits and childcare support).
Spending in other social policy areas such as health and housing also assists families, but not exclusively, and is therefore not included here. Blue vertical and horizontal lines
indicate unweighted OECD average (24 countries). Trend line obtained by linear regression. For country abbreviations see page 33.
Sources: Data for public spending are for 2007 (preliminary data from the OECD Family Database). For data on poverty see sources in Figure 2a.

2 6

In sum, the message is that the OECD
countries that are achieving the lowest
child poverty rates, at around 5% to 6%,
are the countries that start from a
position of low ‘market’ poverty andthen cut this rate by approximately 50%
through government intervention toprotect those still at risk.
The national and international economic
forces that tend to generate widening
gaps are still with us (though there issome evidence that inequality rose
more quickly in the decade from 1985
to 1995 than in the decade from 1995
to 2005xxxv). Reducing bottom-end
inequality – to the extent that it involves
reducing the steepness of the socioeconomic gradient in health, educationand other dimensions of child wellbeing – will therefore require renewed
government efforts to ‘row upstream’
in the years immediately ahead.
Stepping up efforts to protect those mostat risk from falling behind is even morenecessary at a time when governments
are seeking to cut public expenditure(Box 2). But it is also more difficult.
And if efforts to prevent children fromfalling avoidably behind the norms of
their societies are to be reinvigorated inchanged economic times, then a strong
case must be made.

Risks and consequences
That case is strong in principle. Fora child to suffer avoidable setbacks inthe vital, vulnerable years of growth inbody and brain is a breach of the mostbasic tenet of the Convention on the
Rights of the Child – that every childhas a right to develop to his or her
full potential. It is also a clear
contradiction of the principle ofequality of opportunity to which all
OECD countries aspire.
But the case is also strong in practice.
Allowing children to fall unnecessarily
far behind brings in its wake a long listof practical costs and consequences.
Causality is always difficult to establish,
but many hundreds of studies in manydifferent OECD countries have shown
what the costs of falling too far behind
may be. They include the greater
likelihood of:

I nnocenti

 low birthweight parental stress and lack ofparental time (Box 5) chronic stress for the child,possibly linked to long-term
health problems and reduced
memory capacityxxxvi
 food insecurity and inadequate
nutrition
 poorer health outcomes, including
obesity, diabetes, chronic asthma,anaemia, and cardio-vascular disease more frequent visits to hospitals
and emergency wards
 impaired cognitive development
 lower educational achievement
 lower rates of return on
investments in education
 reduced linguistic ability lower skills and aspirations
 lower productivity and adultearnings
 unemployment and welfare
dependence
 behavioural difficulties
 involvement with the policeand courts teenage pregnancy
 alcohol and drug dependence.
Many individual families – faced withdisadvantages of income, education,
health and housing – overcome theodds and bring up children who do
not fall into any of the above
categories. But this cannot change
the fact that children who fall behind
early in their lives, or who spend asignificant part of their early years inpoverty, are likely to find themselves ata marked and measurable disadvantage.
It bears repeating that none of this is
the fault of the child. And a societythat aspires to fairness cannot be
unconcerned that accidents of birthshould so heavily circumscribe theopportunities of life.

The costs
The practical case for a renewed effort
to prevent children from unnecessarily
falling behind is further strengthenedby the economic penalties involved.
The heaviest costs are paid by theindividual child. But the long list of
problems cited above also translates
into significant costs for society as awhole. Unnecessary bottom-end

R eportC ard

9inequality prepares a bill which isquickly presented to taxpayers in theform of increased strain on healthand hospital services, on remedial
schooling, on welfare and social
protection programmes, and on thepolice and the courts. In addition, there
is a significant cost to business and to
economies as a whole in the lower skill
levels and reduced productivity that are
the inevitable result of a large numberof children failing to develop to theirpotential. Finally, there is a cost thatmust be paid by all in the threat thatbottom-end inequality poses to social
cohesion and the quality of life inadvanced industrial economies. “Wideinequality,” says the 2010 report of theUnited Kingdom’s National Equity
Panel “is eroding the bonds of common
citizenship and recognition of humandignity across economic divides.”xxxvii
The scale of such costs, though almost
impossible to calculate, is clearlysignificant. For the European Union
as a whole, it has been estimated(2007) that health inequalities alone
account for 15% of social security
costs and 20% of health care costs.xxxviii
In Canada, the overall cost of childpoverty has been estimated (2008) at
between $4.6 and $5.9 billion a year
for the Province of Ontario alone.xxxix
In the United Kingdom, estimates by
Donald Hirsch, in a report (2006) for
the Joseph Rowntree Foundation, put
the direct costs of “services to remedy theconsequences of childhood deprivation suchas poor health, low educational attainment,
crime and anti-social behaviour” atapproximately $18 billion a year.xl
In sum, the costs of allowing children
to fall too far behind – costs to theprinciple of fairness and costs tosocial, civic and economic life – areenormous. And it is against the fullweight of these costs and consequencesthat the economic arguments for andagainst a renewed effort to protect
those most at risk should be set.

Early intervention
Finally, if the effort to reduce bottomend inequality in children’s well-being
is to make further progress, then it is
not just the level of government efforts

I nnocenti

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that must be increased but theireffectiveness.
Cost-effectiveness in policy is again
largely a matter for national analysis
and debate. But there is one lesson to
emerge from OECD-wide experiencethat no country can afford to ignore.
Children who fall behind begin to
do so in the very earliest stages of theirlives. And in that simple statement wecome face to face with one of themost important and least-acted-on
research findings of our times.
During pregnancy and the first few
weeks and months of life, critical stages
in the child’s mental and physicaldevelopment follow each other in
rapid succession. Each stage serves as afoundation for the next. Any falteringin early childhood therefore puts at
risk subsequent stages of growth anddevelopment. In other words,

9

2 7

disadvantage in the early phases of life
can begin to shape the neurobiologyof the developing child and initiate
a process that, once begun, has atendency to become self-reinforcing.
In particular, it is in cognitive
development that the disadvantaged
child is likely to pay the heaviest price.
By the age of two, cognitive ‘fallingbehind’ can be measured. By the ageof four, much of the potential damage
may have been done.xli, xlii, xliii
The central practical message for
efforts to reduce bottom-end
inequality in child well-being could
therefore not be clearer: the earlier theintervention, the greater the leverage.
Overall, the case both in principle andpractice for intensifying the effort toprevent children from falling behind –and for acting as early as possible inthe child’s life – has been wellBox 4 Monitoring: the need to know

The statistics presented in this report are not built on a comprehensive
consideration of what constitutes child well-being but on the moremundane foundations of data availability. In particular, an acknowledged
weakness is that almost all of the available data concern older children
and adolescents who are attending school; there is a glaring lack ofcomparable information on the critical years of early childhood.
Responding to this inadequacy of data may not seem to have much ofa claim to priority in difficult economic circumstances. But a renewed
commitment to reducing bottom-end inequalities in child well-being will
nonetheless require a renewed commitment to selective monitoring.
If limited resources are to be used effectively, then governments needto know not only how many children are falling behind. They need toknow by how much, in what ways, and for what reasons. They need toknow who and where they are. And they need to know how policy isaffecting and interacting with wider trends in the social and economic
life of the nation.
Finally, they need to have the relevant data at their disposal not once
every five or ten years but on a timescale that permits timely response
to protect those at risk. Monitoring requires resources. But it is theindispensable hand rail of cost-effective policy.

summarized by the Nobel laureate
and University of Chicago economist
James Heckman:
Investing in disadvantaged young children
is a rare public policy initiative that
promotes fairness and social justice and at
the same time promotes productivity in theeconomy and in society at large. Earlyinterventions targeted toward disadvantaged
children have much higher returns thanlater interventions such as reduced pupilteacher ratios, public job training, convict
rehabilitation programs, tuition subsidies,or expenditure on police ...xliv

Child care
Within the developed world, trends
in the way in which young children
are being brought up may now offer
a unique opportunity to put thismessage into practice. Today’s
generation of children is becoming thefirst in which a majority are spendinga significant part of early childhood insome form of out-of-home care (thesubject of Report Card 8 xlv). In theory,
this offers a large scale opportunity totake early action against the different
dimensions of disadvantage thatthreaten to become established in the
lives of very young children. Publicdemand for high-quality child carealready exists, and OECD governments
are already responding by investing infree or subsidized early childhood
services on an increasing scale.
At the heart of this opportunity is theidea that high quality early childhood
education and care can help to reducebottom-end inequality because it is the
disadvantaged child who stands to gain
the most. “Although early childhood
education and care benefits all children”,
concludes an OECD-wide child carereview by Canadian researchers
Cleveland and Krashinsky, “much of theevidence suggests that the largest benefits
flow to children from the mostdisadvantaged families …”xlvi
In practice, there is a danger that thechild care transition will contribute toa widening rather than a narrowingof bottom-end inequality. It is moreeducated parents and higher-income
homes that tend to be most aware of,2 8

and more capable of affording, childcare of the right quality. And it is thepoorer and less educated homes where
the pressures for the earliest possible
return to work are felt most acutely
and where resources for high quality
child care are least likely to beavailable. Without specific policies toaddress this issue – and to ensure theavailability and affordability of highquality early childhood services for all
children – this opportunity will
therefore be lost; ‘double disadvantage’will become the norm, and the childcare transition will likely become a
new and powerful driver of still greater
inequality in children’s well-being.
The costs of taking advantage of thischance to reduce inequalities inchildren’s well-being on a significant
scale are obviously substantial. The
costs of not taking the opportunity
will undoubtedly be even higher.
No one who has worked withdisadvantaged or at-risk children
can be in any doubt that, as James
Heckman and many others have

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9Conclusion

dimensions of children’s well-being(using the best performing countries asa minimum standard for what can beachieved). In drawing attention to thedepth of disparities revealed, and insummarizing what is known about theconsequences, it has argued that ‘fallingbehind’ is a critical issue not only for
millions of individual children today
but for the economic and social futureof their nations tomorrow.

This report began with the argumentthat children deserve the best possible
start, that early experience can cast along shadow, and that children are
not to be held responsible for thecircumstances into which they areborn. In this sense the metric used –the degree of bottom-end inequality
in child well-being – is a measure of
the progress being made towards afairer society.

In making this case, therefore, principle
and practice argue as one. For if theeffort to prevent the unnecessary
falling behind of children in the
different dimensions of their lives is
not made, then a fundamental
unfairness will continue to shame ourpretensions to equality of opportunity– and our societies will continue to
pay the price. 

argued, attempting to compensate fordisadvantage after the event is moredifficult, more costly, and less likely
to be successful. Children need to besupported and protected fromavoidable ‘falling behind’ at all stagesof their development, but the pointof greatest leverage is the point at
which the process begins.

Bringing in data from the majorityof OECD countries, the report has
attempted to show which of them areallowing children to fall behind by
more than is necessary in threeI nnocenti

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9

2 9

Box 5 Poor: in parental time

Inequality in household incomes or educational
achievement is relatively easy to measure compared
to inequality in other important dimensions of childwell-being – such as poverty of opportunity andexpectation, or of adult support and encouragement.
One of the most critical of these unmeasured
dimensions is parental time.
Clearly, the quantity and quality of the time thatparents spend interacting with their children will beaffected by many factors in addition to parents’
individual characteristics. One of those factors ishousehold income.
It might be assumed that higher-income parents work
longer hours and have less time for child care. And it istrue that some low income jobs, and most obviously
part time jobs, leave parents with more non-incomeearning time. But the majority of low-income parents
are in full-time employment, and there is no evidence
to suggest that they work fewer hours.
Nor should it be overlooked that higher income families
can more easily afford to pay others to do regular,
time-consuming non-earning jobs – for example
cleaning, cooking (including eating out), washing,ironing, shopping, car cleaning, gardening, andhousehold maintenance.
‘Poverty of parental time’ may be particularly acutein the United States. According to a 2010 study,1 lowincome American parents work longer hours thantheir equivalents in six other OECD countries studied –Austria, Belgium, Canada, Germany, Spain, and theUnited Kingdom. The study also shows that an
American mother or father at the bottom of the income
distribution scale will, on average, not only work longer
hours but also have a lower relative standard of living
than parents in the equivalent income position in theother six countries. This is especially true forhouseholds headed by single mothers.
The amount of parental time available for child care
therefore appears to be more limited in low-income
American families. And the further one goes down
the income scale, the more acute the problem
becomes. “The gap in parental time availability between
United States on the one hand, and Canada and ourEuropean country studies, on the other,” says the study,“is particularly large in the case of children in the lower
parts of the income distribution.” 2
In this way, lack of parental time adds to and interacts
with the long list of disadvantages facing children inpoor households and contributes to the complex
process by which inequality begets inequality.
If all families are included, rather than just low-income
families, then American parents spend more time with
their children than parents in most other countries forwhich data are available. The OECD report Doing
Better for Children draws on data from 15 developed
countries to show that – across the board – parents in
the United States and Norway spend the most timewith their children (and parents in France the least).
Some data are also available to show how investmentof time in parenting is divided between men andwomen. Canada, Norway, the Netherlands, and theUnited States have the most equal division ofparenting time, Austria and France the least.3
Single-mother households are particularly vulnerable
to the income-time crunch. But here too there are
inequalities between countries. Single mothers in the
United States and Canada, for example, are more
short of time than single-mothers in Sweden or theUnited Kingdom.4
Investments made by the state clearly change thecontext of this question. A country that invests inhigh-quality pre-school care, for example, may reduceparent-child time without necessarily undermining theprocess of interaction and stimulation necessary for achild‘s development. And concerns that government
services may crowd out parental time do not appear
to be supported by the available evidence. Parents in
Norway, which invests heavily in pre-school educationand out-of-school care, spend a similar amount of timewith their children as parents in the United States,
where government investment is considerably less.5

1 Burtless, G., J. Gornick, P. Fraser and T. M. Smeeding (2010),‘Income Distribution, Weekly Hours of Work, and Time for Child
Rearing: The US experience in a cross-national context’,
Luxembourg Income Study Working Paper 489 (revised version),
Luxembourg Income Study, Luxembourg.
2 Burtless, G., J. Gornick, P. Fraser and T. M. Smeeding (2010)
op. cit.
3 OECD (2009), Doing Better for Children, OECD, Paris
4 Burton, P., and S. Phipps (2009), ‘Families, Time and Money in
Canada, Germany, Sweden, the United Kingdom and the UnitedStates’, Luxembourg Income Study, Working Paper 523,
Luxembourg Income Study, Luxembourg.
5 Guryan, J., E. Hurst and M. Schettini Kearney (2008), ‘Parental
Education and Parental Time with Children’, NBER Working Paper
No. 13993, National Bureau of Economic Research, Cambridge, MA.

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9Data for Report Card 9: the surveys

Three main survey sources have been drawn uponfor Report Card 9.
OECD Programme for International Student
Assessment (PISA)

Health Behaviour in School-Aged Children (HBSC)

The 2006 PISA administered reading, maths and sciencetests to representative samples of between 4,500 and
10,000 15-year-old students in each of 57 countries
including all of the countries featured in Report Card 9.
The tests attempt to assess how well “education
systems are preparing their students to become life-longlearners and to play constructive roles as citizens insociety.” The tests are compiled by an international
expert group (including employers). Basic information is
also collected on study practices, family resources andstructures, and school environments.

HBSC is a collaborative undertaking by researchers
and academic institutions linked to the World HealthOrganization. Its participants form a network ofmultidisciplinary researchers collaborating to develop
the conceptual underpinnings of the study, identify
research questions and compile the four-yearly survey,
and work on analysis and dissemination.

In Report Card 9, data from PISA are used for the
analysis of inequality in reading, mathematics andscience literacy scores and in home access to
educational resources.
PISA results may not apply to the total population of therelevant age group in Chile, Luxembourg, Portugal andTurkey where school enrollment rates for 15 and 16
year-olds are below 90%. The survey does not include
children who, for whatever reason, are not in school.
Because of errors in implementing the 2006 PISA
survey, no data on ‘reading literacy’ are available for
the United States.
More detailed information on the OECD 2006 PISA
survey can be found at: www.oecd.org/pisa and inOECD (2007) PISA 2006: Science Competencies forTomorrow’s World, OECD, Paris.

Data from HBSC are used for the analysis of inequality
in children’s health.

The 2005/2006 HBSC survey questioned a
representative sample of approximately 1,500 students
at each of three ages (11, 13 and 15) in a total of 41
countries. The study aims to increase understandingof young people‘s health behaviours and well-being
during the key developmental stage of adolescence.
HBSC data is available for all 24 countries in the
ranking tables used in Report Card 9. This includes all
European OECD members plus the United States,
Canada and Turkey.
Data on ‘vigorous physical activity’ were not available
for Portugal.
Detailed information on HBSC can be found at:
www.hbsc.org and in Currie C. et al (2008) ‘Inequalities
in Young People’s Health: HBSC International Reportfrom the 2005/2006 Survey’, Health Policy for Children
and Adolescents, No. 5, WHO Europe and Child andAdolescent Health Research Unit, Edinburgh.

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9European Union – Statistics on Income andLiving Conditions (EU-SILC).
Data from EU-SILC are used for the analysis ofhousehold income and children’s living space.
Analysis focuses on households with children under
the age of 18.

3 1

Supplementary Sources
Income and living space data for other OECD countries
have been drawn from:
Australia
Household Income and Living Dynamics in Australia(HILDA), 2008.

EU-SILC surveys are conducted annually and cover arepresentative sample of the entire population in 22of the countries included in Report Card 9. The primary
purpose of the surveys is to monitor the indicators(the so-called Laeken Indicators) by which the EU has
agreed to measure its progress towards reducing
social exclusion. They include data on income and alimited set of non-monetary indicators of well-being.
In most cases, data on income refer to the calendar
year preceding the survey.

For income: Survey of Labour and Income Dynamics(SLID), 2005. This survey is nationally representativewith the exception of residents in the Yukon, theNorthwest Territories and Nunavut, as well as residents
of institutions and persons living on Indian reserves.
(These exclusions amount to less than 3 per cent of
Canada’s population).

For all the European Union OECD members, plus
Iceland and Norway, Report Card 9 uses EU-SILC
data for 2008 (income data refer to 2007), except data
for France which are from the 2007 survey (income
data refer to 2006).

Chile

CanadaFor housing living space: Survey of Household Spending(SHS), 2006. (The 2006 survey did not include data fromthe Northwest Territories).
National Socio-economic Characterization Survey(CASEN), 2006.
Republic of Korea
For income: Korean Labour and Income Panel Study(KLIPS), 2007. The sample is representative only of
Korean households in urban areas (excluding Jeju Island).
MexicoNational Survey of Household Incomes and Expenditures(ENIGH), 2008.
Switzerland
Swiss Household Panel (SHP), 2007.
United States
For housing living space: Panel Study on Income
Dynamics (PSID), 2007.

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9

A methodological note

Innocenti Report Card 9 focuses on inequality at thebottom-end of the distribution in different child well-being
indicators.
Country inclusion in the report
The criterion for the inclusion of countries is OECDmembership in March 2010. Of the 31 OECD member
countries at that date, only 24 had sufficient data to be
included in the final comparison (Figs. 1a and 1b). The
criterion for inclusion was the availability of suitable andcomparable data for at least 2 out of the 3 indicators used
to measure inequality in each dimension of child well-being.
Indicators and dimensions
The report assesses how far the most disadvantaged
children are allowed to fall below national norms in eachindividual country (represented by the median value).
Only indicators which return a range of values are suitable
for this kind of analysis. Nine such indicators have been
identified as also having available and comparable data fora large majority of the OECD countries.
The selected indicators are:Material well-being
 disposable household income
 possession of educational items
 housing living space
Educational well-being reading literacy
 maths literacy
 science literacy
Health well-being
 self-reported health complaints
 healthy eating
 vigorous physical activity.
Data for disposable income, and for housing living
space, refer to children aged 0-17.
Data for educational possessions, and for all three
indicators of educational well-being, refer to
15 year-old students.
Data for the three indicators included under ‘healthwell-being’ refer to students aged 11, 13 and 15.

The inequality measures
Inequality at the bottom-end of the distribution isassessed using two different measures according to
the nature of the indicator. In all cases, the inequality
is measured in relation to the median (representing theminimum value recorded by the better performing halfof the child population).
For disposable income and for education achievements,
inequality is measured by the gap between the child at
the median position and the child at the 10th percentile(representing an income or performance lower than
90% of children).
The limited variability of the survey values for theother five indicators means that the bottom-end of
the distribution cannot be adequately represented by
the score of the 10th percentile. In this case, inequality
has been measured by the gap between the median
position and the average for all children below
the median.
In both cases, the gap measures relative inequality
and is expressed as a percentage of the median.
Between-country comparisons
In Figs. 2a-2c, 3a-3c and 4a-4c, countries are ranked inorder of progress towards greater bottom-end equality(i.e. the countries at the top of the table have thesmallest inequality gaps).
Given the nature of the data, especially when derived
from sample surveys, small differences between
countries in inequality results may be not statisticallysignificant. Country rankings (both for single indicatorsand for aggregated overviews) should be read with thislimitation in mind.
More detailed statistics, including the confidence
interval of the results, are available in the background
paper to Report Card 9 by Currie, C., D. Currie, L.
Menchini, D. Richardson and C. Roberts (2010).
The OECD average reported in the Figures refer toonly the countries with enough data to be included in
the overall comparison (Figs. 1a and 1b). In all cases,
the average is unweighted (i.e. it does not take into
account the size of the relevant child population ineach country).

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9

3 3

Country abbreviations

Comparison by dimension

Australia

AU

AustriaAT

BelgiumBE

Canada

CA

Chile

CL

Czech Republic

CZ

Denmark

DK

FinlandFI

FranceFR

GermanyDE

GreeceGR

Hungary

HU

IcelandIS

Ireland

IE

Italy

IT

Japan

JP

Korea

KR

Luxembourg

LU

The overall league tables of inequality in child well-being(Figs. 1a and 1b) are derived from these dimension level
results. A dimension score of between 95 and 105 (i.e.
between –0.5 standard deviation and +0.5 standard
deviation around the OECD average) qualifies as ‘close
to the OECD average’. Countries with dimension scores
lower than 95 are regarded as having significantly greater
inequality than the OECD average. Those with a dimension
score higher than 105 are classified as having significantly
less inequality than the OECD average.

MexicoMX

Netherlands

NL

New Zealand

NZ

NorwayNO

PolandPL

PortugalPT

Slovakia

SK

In all bar charts and rankings the countries at the top arethose with less bottom-end inequality.

Spain

ES

SwedenSE

Switzerland

CH

TurkeyTR

United Kingdom

UK

United States

US

To aggregate the indicators used into an overview ofbottom-end inequality in each dimension of child wellbeing, the inequality gaps for each indicator have beenstandardized with reference to the OECD unweighted
average and variability between countries. In each case,the degree of inequality is then expressed as the numberof standard deviations from the OECD unweighted
average. The standard deviation is a commonly-usedmeasure of how spread out the items being measured are
in relation to the average for the group as a whole. Thesestandardized inequality results for the different indicators
are reported in Figures 2f, 3f and 4f. In these charts thelength of the bar to the right of the vertical (representing
the unweighted OECD average) implies a positive value(i.e. lower bottom-end inequality compared to the OECDaverage). To the left of the vertical, the length of the bar isassociated with a negative value (i.e. higher bottom-end
inequality compared to the OECD average).
To obtain the overview of inequality, for all three
dimensions of well-being, the standardized inequality
results for each indicator have been averaged. It is thisaverage that is shown in Figures 2d, 3d and 4d, withcountries ranked in order of decreasing equality. To
facilitate the reading of these summary charts, thestandardized inequality data have been rescaled to set theOECD average at 100 and one standard deviation at 10.

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9

R EFE R E N C E SiOECD (2008) Growing Unequal? Income
distribution and poverty in OECD
countries, OECD, Paris.

xii

Douglas Willms, J. (2006) op. cit., p. 68.

xiii

Douglas Willms, J. (2006) op. cit., p. 68.

ii

xiv

Douglas Willms, J., (2006) op. cit., p. 67.

Wilkinson, R. and K. Pickett (2009) The
Spirit Level, Allen Lane, Penguin Books,
London, p. 33.
iii

OECD (2009) Doing Better for Children,
OECD, Paris.

iv

UNICEF (2002) ‘A League Table of
Educational Disadvantage in Rich
Nations’, Innocenti Report Card No. 4,
UNICEF Innocenti Research Centre,
Florence.

v

Currie, C. et al.. (2008) ‘Inequalities inYoung People’s Health, Health Behaviourin School-Aged Children, International
Report from the 2005/2006 Survey’,
Health Policy for Children andAdolescents, No. 5, WHO Europe andChild and Adolescent Health Research
Unit, Edinburg, p. 59.
vi

Currie, C., D. Currie, L. Menchini, D.
Richardson and C. Roberts (2010)‘Comparing Inequality in the Well-Beingof Children in Economically Advanced
Countries: a methodology’, Innocenti
Working Paper, 2010-19, UNICEF Innocenti
Research Centre, Florence.
vii

Currie, C., D. Currie, L. Menchini, D.
Richardson and C. Roberts (2010) op. cit.

viii

World Health Organization (2010),
Global Recommendations on Physical
Activity for Health, WHO, Geneva, p. 7.
ix

Currie, C. et al. (2008) op. cit., p.106-107.

x

Douglas Willms, J. (2006), Learning
Divides: Ten policy questions about theperformance and equity of schools andschooling systems, UNESCO Institute for
Statistics, Montreal, p. 68.

xi

Hutmacher, W., D. Cochrane and N.
Bottani (eds.) (2001) In Pursuit of Equity in
Education: Using international indicators
to compare equity policies, Kluwer
Academic Publishers, Dordecht, p. 135.

xv

World Health Organization (2008),
Closing the Gap in One Generation, WHO,
Geneva.

xxiv

Smythe, S. (2007) ‘Child and Youth
Development and Income Inequality: A
review of selected literature’, First Call,
British Columbia Child and Youth Advocacy
Coalition (funded by the Government of
Canada Social Development Partnerships
Program), Vancouver.
xxv

Siegrist J., and M. Marmot (eds.) (2006)
Social Inequalities in Health: New
evidence and policy implications, Oxford
University Press, Oxford.

Scott, K. (principal author) (2008)
Growing Up in North America: The
economic well-being of children in
Canada, the United States, and Mexico,Children in North America project(Canadian Council on Social Development,
The Annie E. Casey Foundation, Red por
los Derechos de la Infancia en Mexico,
United States – Population Reference
Bureau), The Annie E. Casey Foundation,
Baltimore, p. 15.

xviii

xxvi

xvi

Marmot, M. (chair) (2010), ‘Fair Societies,
Healthy Lives, Strategic Review of HealthInequalities in England post 2010’, The
Marmot Review, Executive Summary, p. 10.

xvii

Currie, C. et al (2008), op. cit., p. 4, 65, 91.

xix

Larson, C., (2007), ‘Poverty during
Pregnancy: Its effects on child healthoutcomes’, Paediatric Child Health,Vol. 12, No. 8.

xxviiOECD, Social Expenditure Database,www.oecd.org/els/social/expenditure(consulted September 2010).

xxviiiScott, K. (2008) op. cit. p. 15.

xxix

OECD (2008) op. cit. p. 16.

xx

Cohen, S., D. Janicki-Deverts, E. Chen
and K. Matthews (2010), ‘Childhood
Socioeconomic Status and Adult Health’,
The Biology of Disadvantage, Annals of the
New York Academy of Sciences, 1186
(2010), New York Academy of Sciences,
New York, p. 37.
xxi

Mackenbach, J. (2006) HealthInequalities: Europe in profile, University
Medical Centre, Rotterdam.

xxii

OECD (2008) op. cit. p. 27.

xxx

Commission for Children and Young
People and Child Guardian, State ofQueensland Australia (2006), Document on
Minimum Wage and Child Poverty
Submitted to the Australian Fair Pay
Commission on the Federal Minimum
Wage, July 2006, mimeo. (The Australian
Fair Pay Commission is now named Fair
Work Australia).

Mathews, M.S. and M. F. Macdorman(2010) ‘Infant Mortality Statistics from the2006 Period Linked Birth/Infant Data Set’, in
National Vital Statistics Reports, Vol 58,
No. 17, National Center for Health
Statistics, Hyattsville, MD.

Shirahase, S. (2007) ‘Cross National
Comparison of Economic Inequality among
Households with Children’, Luxembourg
Income Study Working Paper No. 461,
Luxembourg Income Study, Luxembourg.

xxiii

xxxii

Mayer, S. (2002) ‘Parental Income andChildren’s Outcomes’, Ministry of Social
Development, Wellington, NZ. Cited in J.
Micklewright (2003) ‘Child Poverty in
English-Speaking Countries’, Innocenti
Working Paper, No. 94, UNICEF Innocenti
Research Centre, Florence.

xxxi

Hills, J., (chair) (2010) An Anatomy of
Economic Inequality in the UK, Report of
the National Equality Panel, Government
Equalities Office and Centre for Analysis ofSocial Exclusion of the London School of
Economics and Political Science, London,
p. 34, 35.

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xxxiiiShonkoff, J. P. and D. A. Phillips (eds.)(2000) From Neurons to Neighborhoods:
The science of early childhood
development, Committee on Integrating
the Science of Early Childhood
Development, National Research Council
and Institute of Medicine, Washington,
D. C., p. 396.
xxxiv

OECD (2008) op. cit. p. 16.

xxxv

OECD (2008) op. cit. p. 27.

xxxvi

Evans, G. and M. A. Schamberg(2009) ‘Childhood Poverty, Chronic Stress,
and Adult Working Memory’, Proceedingsof the National Academy of Sciences of
the United States, Vol. 106, No. 16.

xxxvii

Hills, J., (chair) (2010) op. cit.

xxxviiMackenbach, J. P., W. J. Meerding
and A. E. Kunst (2007) Economic
Implications of Socio-economic
Inequalities in Health in the European
Union, European Commission,
Luxembourg.
xxxix

Laurie, N. (2008) The Cost of Poverty:
An analysis of the economic cost ofpoverty in Ontario, Ontario Association ofFood Banks, Ontario.
xl

Blanden, J., and S. Gibbons (2006)
The Persistence of Poverty acrossGenerations: A review from two Britishcohorts, Joseph Rowntree Foundation,
The Policy Press, Bristol.
xli

Feinstein, L. (2003) ‘Inequality in the
Early Cognitive Development of British
Children in the 1970 Cohort’, Economica,Vol. 70, No. 1.

xlii

Duncan, G., K. Telle, K. Ziol-Guest and
A. Kalil (2009), Long-run Impacts of Early
Childhood Poverty: Comparative evidence
from Norwegian registry data and the U.S.
PSID, paper prepared for conference The
long-run impact of early life events,
National Poverty Center, University ofMichigan, March 12-13 2009, Michigan.

9

3 5

xliii

Johnson, R., and R. Schoeni (2007)‘The Influence of Early-Life Events on
Human Capital, Health Status, and Labor
Market Outcomes over the Life Course’,
Working Paper Series, Institute for
Research on Labor and Employment,
UC Berkeley.
xliv

Heckman, J. J. (2006) ‘Skill Formationand the Economics of Investing in
Disadvantaged Children’, Science,Vol. 312, No. 5782.
xlv

UNICEF (2008) ‘The Child Care
Transition: A league table of earlychildhood education and care ineconomically advanced countries’,
Innocenti Report Card No. 8, UNICEF
Innocenti Research Centre, Florence.
xlvi

Cleveland, G. and M. Krashinsky(2003) ‘Financing ECEC Services in OECDCountries’, ECEC Thematic Workshops
and Documents, OECD, Paris.

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A cknow l e d g e m e nts

The Report Card 9 project wascoordinated by the UNICEF Innocenti
Research Centre and assisted by aninternational panel of advisors andreviewers (see below). Research wascompleted at the end of August 2010.

UNICEF advisors

The full text and a background paper tothis report can be downloaded from the
UNICEF Innocenti Research Centre
website at www.unicef-irc.org.

Chris De Neubourg(Chief, Social and Economic Policy Unit,
UNICEF IRC)

Research and data analysis
Peter Adamson(independent consultant to UNICEF IRC)
Dominic Richardson (OECD)
Dorothy Currie and Candace Currie (HBSC
International Coordinating Centre, andChild and Adolescent Health Research
Unit at the University of Edinburgh)
Chris Roberts(Social Research Division, Welsh
Assembly Government)
Leonardo Menchini (UNICEF IRC)

Support in data preparation
Nabil Ali, Anna D‘Addio,
Maciej Jakubowski and Maxime Ladaique(OECD)
Luca Tiberti (UNICEF IRC)

Support on individual
data sources
Sami Bibi(Université Laval, Québec), who kindly
provided the statistical results on
disposable income and living spacefor Canada.
Ursina Kuhn
(FORS, Swiss Centre of Expertise in theSocial Sciences), who kindly provided
the constructed variables on income
from the Swiss Household Panel.

Gordon Alexander(Director a.i., UNICEF IRC)

Administrative and technical support at
the UNICEF Innocenti Research Centre
was provided by Cinzia Iusco Bruschi.

David A. Parker(former Deputy Director, UNICEF IRC)James Elder(Chief, Communication Unit, UNICEF IRC)
Mehr Khan Williams(Special Adviser to the UNICEF Deputy
Executive Director)
Jens Matthes(Chief, Child Rights Advocacy and
Education, UNICEF PFP, Geneva)
Leonardo Menchini(Programme Specialist, UNICEF IRC)

External advisors andpeer reviewers
Jonathan Bradshaw(University of York)
Giorgina Brown(ISTAT, National Institute of Statistics, Italy)
Ferran Casas(University of Girona)
Hugh Frazer(National University of Ireland, Maynooth)Albert Motivans(UNESCO Institute for Statistics, Montreal)
Stefano Rosignoli(IRPET, Tuscan Regional Institute for
Economic Planning, Florence)Peter Gordon Saunders(Social Policy Research Centre, Universityof New South Wales, Sydney)
Erio Ziglio(World Health Organization, European
Office for Investment for Health andDevelopment, Venice)

Design and layout: MCC Design, UK(www.mccdesign.com)

9

Previous issues in this series:
Innocenti Report Card 1
A league table of child poverty in rich nations
Innocenti Report Card 2
A league table of child deaths by injury in rich nations
Innocenti Report Card 3
A league table of teenage births in rich nations
Innocenti Report Card 4
A league table of educational disadvantage in rich nations
Innocenti Report Card 5
A league table of child maltreatment deaths in rich nations
Innocenti Report Card 6
Child poverty in rich countries 2005
Innocenti Report Card 7
Child poverty in perspective: An overview of childwell-being in rich countries
Innocenti Report Card 8
The child care transition: A league table of early childhood
education and care in economically advanced countries

Graphics: MCC Design, UK
Printed by: Nuova Grafica Fiorentina srl, Florence, Italy

Innocenti Report Card 9, 2010
The children left behind
A league table of inequality in childwell-being in the world’s rich countries

UNICEF Innocenti Research Centre
Piazza SS. Annunziata, 1250122 Florence, Italy
Tel: (+39) 055 20 330
Fax: (+39) 055 2033 220
florence@unicef.org
www.unicef-irc.org
ISSN 1605-7317© The United Nations Children’s Fund (UNICEF)
November 2010
USD 20.00
ISBN 978-88-89129-99-9

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