Human Capital Policy for Europe
Pedro Carneiro∗
University College London and Institute for Fiscal Studies
October 10, 2004
1
Low Skills - A Problem for Europe
We live in a time of turbulence.1 There are large flows of individuals, capital and information and
knowledge across the world. There are constant and rapid changes to which individuals need to
adapt everyday. In such a world the abilities to process information and to be flexible will sell at
a large premium, while inflexibility and ignorance are a recipe for a most likely failure. In such a
world, it is important to be highly skilled. In this paper I argue that there is a skill problem in
Europe and I present some basic principles that should be in the background of a human capital
strategy for Europe.
Gottschalk and Smeeding (1996) analyze trends in income inequality around the world and they
conclude that in the last twenty-five years there has been an increase in inequality in many countries
of the western world. In anglo-saxon countries, in particular in the US, the increase in inequality
is much larger than in continental Europe. Bertola (2001) argues that many continental European
∗ This
paper was prepared for the workshop on Quality and Efficiency in Education and Training organized by the
Directorate General for Economic and Financial Affairs, European Commission, Brussels, May 27th 2004. It draws on
my joint work with James Heckman (see Carneiro and Heckman, 2003). I thank Paulo Santiago, Hillary Steedman,
Jason Tsarsh and David Young for providing me with very useful references.
1 Ljunqvist and Sargent (2001) and Heckman (2001) use this term to characterize today’s labor market.
1
countries did not experience any increase in inequality. Figure 1 is taken from Bertola (2001), and
in the top panel it displays the ratio of the 50th to the 10th percentiles of the earnings’ distribution
across time for different groups of countries. The bottom panel shows 90-10 earnings differentials
across countries. There is only a consistent increase in earnings inequality across time for the set of
countries in the first graph of each panel, which are precisely the anglo-saxon countries. However,
figure 2, also from Bertola (2001), shows that the countries with the smallest increases in inequality
have on average experienced the large increases in unemployment. Bertola argues that labor market
institutions in these countries have prevented large changes in earnings inequality at the expense
of employment. Once you take this into account it is not clear whether the change in inequality in
anglo-saxon countries has been smaller or larger than the change in inequality in continental Europe,
since in figure 1 we only use individuals who are employed.2
This increase in inequality comes at a time of substantial economic growth. In the US, individuals
at the bottom ten percentile of the wage distribution have experienced losses in real wages over the
last thirty years, while those at the top benefit from large wage increases (see, for example, Juhn,
Murphy and Pierce, 1993). In the UK, individuals at the bottom end of the wage distribution have
stagnant wage growth while the those at the top experience wage increases (see Gosling, Machin
and Meghir, 2000). Rebecca Blank (1996), discussing the problem of poverty in the US, argued
that recent economic growth is very different from past economic growth. In particular, the postWWII period was a period of rapid growth both in western Europe and in the US, but its benefits
were spread across the earnings distribution. Growth was driven by the reconstruction of Europe
and the motor of economic growth was the manufacturing sector. Even low skilled workers could
experience increases in employment and earnings since good unskilled manufacturing jobs were
becoming increasingly available. In such a world, the major poverty alleviation program is economic
2 Blundell, Reed and Stoker (2003) show how accounting for unemployment can dramatically change inferences
about trends in aggregate wage growth in the UK. Accounting for the trend in unemployment is bound to also affect
any inference we make about changes in inequality.
2
growth. However, recent growth has mainly benefited skilled individuals. Machin and Van Reenen
(1998) present evidence that recent economic growth in seven OECD countries has been driven by
skill-biased technical change, with important consequences for the wage structure of these countries.
There are however countries, such as the US and the UK, where unemployment rates are low and
the low skilled can find jobs. However, such unskilled jobs tend to be in the service sector which
has grown enormously in recent years, and these are low paying jobs with slim chances of growth.
Nickell and Bell (1996) document that the difference between unemployment rates of low and high
skilled individuals has increased across OECD countries between the early and the late 1980s. The
skill premia has also generally increased across countries, and the largest increases are in the US
and the UK.
As a response to the rise in the demand, the supply of skill has increased across the western
world. Figure 3, from OECD (1997), shows cross country educational attainment for two different
cohorts of individuals in 1995. In all countries shown there has been an increase in educational
attainment of the population across cohorts. This increase was especially large in Belgium, Korea,
Greece and Spain. In contrast, it was basically zero in the US, and it was small in many other
European countries. Figure 4, from Carneiro and Heckman (2003), plots educational attainment by
cohort in the US. It shows a secular growth in educational attainment up to the cohort born in 1950.
After this cohort college participation rates and high school dropout rates become flat, in spite of
large increases in the returns to schooling across cohorts. In the UK we observe a similar pattern of
stagnation in educational attainment for the recent cohorts. Blanden and Machin (2004) show that
the age participation index is roughly flat from 1970 to the late 1980s when there is a large increase in
university participation, which is not sustained later on. Similarly, after many years of rapid growth
in rates of participation in post-compulsory education, staying on rates become flat after 1990.
Stagnation of educational attainment is worrisome if one believes education is an important motor
of growth, as is standard in modern growth theory (see, for example, Lucas, 1988, Becker, Murphy
3
and Tamura, 1993). Furthermore, at a time of increasing demand for skill stagnation of educational
attainment increases the vulnerability of individuals at the bottom end of the skill distribution who
are unable to benefit from economic growth. Carneiro and Heckman (2003) interpret these findings
as evidence that there is a large increase in the demand for skill and supply is not keeping up with
demand. Even in the countries where educational attainment has not reached a halt the earnings
and employment returns to schooling are rising rapidly.
Figure 3 also shows that there is an enormous degree of heterogeneity in educational attainment
across countries. In an attempt to get a better picture of differences in student quality and labor
force quality across OECD countries (and a few others) a set of literacy tests has been developed
and administered to adolescents and adults across countries. Education systems can differ across
different countries and these comparable tests may provide a better measure of the stock of skills
of a country, at least for the purpose of international comparisons.3 Hanushek and Kimko (2001)
use these tests as a measure of the quality of the labor force and argue that these are an important
determinant of economic growth. Figure 5, from OECD (2000), shows the percentage of adults in
different quantitative literacy levels in different countries. These results can be replicated for other
types of literacy, as measured by the International Adult Literacy Survey. In more than half of the
countries shown in this figure 40% or more of their labor force scores in the bottom two levels of
literacy. Figure 6 shows that there is a large gap in literacy for individuals in different levels of
education. The levels of literacy for individuals with less than secondary schooling in countries such
as the United States and Portugal is particularly worrisome. Across countries there is not much
difference in the literacy skills of those with a tertiary education. The differences across countries
emerge mostly for those who have low educational attainment. This pattern is observed even within
a younger cohort of individuals who are 20-25 years of age at the date of this test. The problem of the
3 Furthermore, such tests can be used as a measure of quality of the educational system, although one needs to
make sure these tests are adequately designed to be comparable in every country. Hanushek written extensively on
issues on school quality. For example, in Hanushek (2001) he illustrates how in the US there has been no aggregate
growth in test scores at the same time that there has been a dramatic growth in school expenditure.
4
low skilled is not less dramatic for this younger cohort. Nickell (2003) documents that the problem
of low literacy is not getting much better in the adult population across a variety of countries. In
fact, for countries such as the US and the UK it is getting worse. He also shows that there is a
strong association between inequality in literacy scores and inequality in income across countries: the
countries with the higher level of literacy inequality, such as the anglo-saxon countries and Portugal,
also have the highest levels of income inequality.
The problem of the low skilled in Europe has long been recognized. The European Commission
has sponsored the NEWSKILLS Program of Research which was developed to document and analyze
the supply and demand of low skilled workers in Europe. McIntosh and Steedman (2001) summarize
the findings of this project in a report entitled “Low Skills: A Problem for Europe”, a title I also
borrowed for this section. They describe that across countries there has been a steady decrease in the
supply of low skilled workers. At the same time there is also a sharp decrease in the demand for such
workers that surpasses the decrease in supply, generating stagnating or falling wages and increased
unemployment among the low skilled (with the exception of Portugal). Their study emphasize two
important themes of this paper, that will be further developed in the next section. First, the problem
of low skills does not consist only of a deficiency in cognitive skills, but also of a deficiency in what
they call soft skills. Several skills are important in the labor market and a broader view of what
constitutes skill is needed. Second, low skill individuals receive little or no amounts of training on
the job, either because they opt out of it when it is offered to them, or because employers choose
to offer training to workers with better skills. This is illustrated in figure 7, from OECD (2000),
which shows the proportion of people at each literacy level who receive job training. As emphasized
by Carneiro and Heckman (2003) there are strong complementarities between early human capital
investments and adult human capital investments. Low skilled workers have difficulty in benefiting
from adult training because they have a low stock of human capital on which adult investments
can build on and be productive. This says that remediation investments in adulthood may be very
5
costly and ineffective for low skilled individuals. Preventive investments that take place earlier in
the life-cycle of individuals are bound to generate much larger returns.
The recent increase in inequality and in unemployment in Europe coincides with an rise in social
unrest in several dimensions, even at a time of rapid economic growth, as illustrated in figure 8
(OECD, 2001). The percentage of children living in poverty is well above 10% for most countries in
Europe and North America. There is an upward trend in the incidence of lone parenting. There is a
rise in drug related deaths in the European Union countries, and a general rise in crime victimization
rates in the 1990s (with the exception of Canada and the US).4 The incidence of poverty and social
unrest tends to be more dramatic on the population of the unskilled. Charles Murray (1999) calls
attention to the emergence of a British underclass. This warning is echoed for the rest of Europe by
the evidence assembled in this paper. Carneiro (2002) calls for a comprehensive minimum learning
platform for all, a set of skills that not only allows each individual to participate fully in the process
of economic development, but that also promotes civic behavior and social stability.
I end this introduction with a note on heterogeneity. Anyone who looks at international data realizes that there is a large degree of heterogeneity across countries. Europe is no exception. Literacy
levels, educational attainment, income inequality and so many other variables are widely different
across countries. The recent debate in development economics emphasizes that this heterogeneity
is very important, and that it is wrong to think of general best practices or policies that will have
similar effects across countries. This paper will be concerned with general principles of the process
of skill formation but the application of such principles to different countries has to be moderated
by each country’s set of problems and opportunities. Furthermore, understanding the sources of
heterogeneity is likely to lead to important insights for the design of new policies. Similarly, at
a more micro level, heterogeneity has been found to be pervasive and important in all aspects of
economic life (Heckman, 2001). The recent literature on policy evaluation emphasizes that different
4 See
OECD (2001).
6
policies have different effects on different individuals, and that the effectiveness of a policy depends
dramatically on the characteristics of the target population. How to account for heterogeneity in
policy design and evaluation has to be a major theme any policy debate, whether this heterogeneity
is at the micro or macro level.
In the next section I summarize recent work by Carneiro and Heckman (2003) on human capital
accumulation throughout the life cycle. Although the evidence underlying this work is primarily
for the US, there are important general lessons we can draw on for Europe. Furthermore, similar
work is being developed in Europe and part of my own goal with this paper is to begin to assemble
similar evidence for European countries. In this section I will also review the effectiveness of some
policies that act on different stages of the life-cycle of an individual. In the third section of this
paper I review some specific european problems and in the fourth section I review what I known by
the Lisbon Strategy. The last section of this paper presents a small summary and conclusion.
2
Human Capital Policy Over the Life-Cycle
Carneiro and Heckman (2003) review the evidence on human capital policy over the life-cycle. They
analyze the effectiveness of different human capital interventions that take place a different ages of
an individual’s life, and interpret the literature in view of a life cycle model of skill formation. They
document that early childhood interventions directed towards disadvantaged children have proven
to be successful, although much of their impact is on non-cognitive skills of the treated children.
Non-cognitive skills are important not only for future engagement in risky and criminal behavior, but
also for educational attainment and labor market outcomes. Similarly, mentoring programs directed
toward underperforming teenagers and teenage parents have had important effects on their lives
primarily through their impact of their non-cognitive skills. More traditional interventions aimed at
improving school quality (such as class size reductions or increases in expenditure per pupil) have
7
not been very effective.5 The apparent reason for such policy failure is our general lack of knowledge
of the relative effectiveness of different inputs in the education production function. One exception
is the evidence on the importance of teachers, which has been recognized in the literature for more
than thirty years. Teachers are a very important determinant of quality, but it is still not well known
what are the characteristics of a teacher that we should look for or that we should promote to raise
the quality of our schools. Teacher quality is crucial for a successful educational experience but
information about teacher quality is not easily available. Local information on individual’s teacher
practices and results (information that is generally unavailable in survey data) is likely to be very
relevant for evaluating a teacher’s performance, and if that is the case, a decentralized system of
school administration and education choice that can better acquire and use such local information
is called for. With this in mind, some researchers have advocated more administrative autonomy for
schools and more choice for parents, even though the evidence on the effectiveness of either is still
weak. A movement in this direction would probably lead to a larger emphasis of the role of market
forces in education, which are almost absent in most countries education systems. Such a movement
may lead to better local incentives for teachers and schools and to an increase in private expenditure
in education. Figure 9, from OECD (2003), shows that the level of private investment in education
is very low compared to the level of public investment, especially at lower levels of education. In
a time of tight public budgets, turning to private investment is likely to be an attractive way to
increase investment in children.
As shown in the first section of this paper, several individuals reach young adulthood with a
serious lack of skills to triumph in the modern labor market. In response to this problem, governments around Europe and the US have tried to design and implement a set of remediation programs
such as publicly provided job training for the unemployed. Unfortunately, the evidence points to
the general ineffectiveness of these remediation investments, with some exceptions.
5 One
important exception has been the Literacy Hour in England, evaluated by Machin and McNally (2003).
8
Carneiro and Heckman (2003) suggest that a framework that rationalizes this finding: remediation
investments that build on a childhood and adolescence where skill formation was neglected may not
amount to anything significant, because there is very little to build on. They argue that there are
important features of the technology of skill formation that should not be neglected in policy making.
First, Human capital accumulation starts in the womb and takes place throughout the whole life.
Families, firms and schools are equal partners in the process of skill formation. Second, there are
multiple skills and multiple abilities that are relevant for individual success in life. Noncognitive
skills are as important as cognitive skills, even though they are often neglected in research and
policy. Third, these abilities are both inherited and created. The traditional debate about nature
vs. nurture is outdated and of little relevance. Finally, they emphasize two main ideas. One the is
the idea of plasticity. Individuals tend to be more plastic in the earlier years of their lives, although
the degree of plasticity varies according to the type of skill. For example, IQ is fairly stable after age
8, while many behavioral skills can be quite plastic through adolescence and adulthood. If plasticity
is an important component of the technology of skill formation, a given investment will be more
productive if done earlier rather than later in the life cycle, because the capacity to use such an
investment is higher earlier in the life cycle rather than later. The other idea they emphasize is that
investments in human capital are complementary over time. This implies that the productivity of
later investments is higher the larger the amount of early investments. Heckman (1998) summarizes
this by saying that “skill begets skill”. As a consequence, it may be very difficult and costly to
remediate at later ages the lack of early investments. If individuals do not have a solid base to build
on additional investments in them may have very low productivity. However, complementarity also
implies that early investments are not productive if they are not followed up by later investments.
Equipped with these ideas Carneiro and Heckman (2003) argue for strong early investments and for
continuous following investments throughout the lives of individuals.
We end this section with a provocative illustration. Figure 10, from OECD (2003), displays the
9
level of expenditure per student in different levels of schooling relative to expenditure per student
at the primary level. We realize that the prices of investment at different ages are very different
and even if the quantity invested at different ages is similar the overall expenditure will be different.
Nevertheless, this evidence may be suggestive of the current trends in schooling investments. Across
countries, expenditures per student at the university level are much higher than expenditures per
student at earlier levels of schooling. In some countries, expenditures per student at the pre-school
level are even lower than expenditures per student at the primary level. There is a lot of heterogeneity
across countries. For example, in Norway the level of expenditure per student at the pre-school
level if similar to expenditure per student at the tertiary level. In this paper we call for better
investments at earlier ages as an effective way to improve the skills of the labor force, especially for
individuals at risk of becoming low skilled. Investments at later ages are also necessary, but a better
balanced portfolio of investments may be more productive than the one we have today. Furthermore,
the pattern of investments displayed in figure 10 is highly regressive, since primary and secondary
school (where investments per student are small) is usually universal, while tertiary school (where
investments per student are high) is generally attended by students coming from richer families. A
shift of resources towards earlier ages may lead to a more efficient and more equitable allocation of
public education resources.
3
Human Capital in Europe
Human capital policy is a major concern of every economy in the modern world. In the Lisbon
European Council held in March 2000, Heads of State and Government from the EU set a goal
for 2010: “to become the most competitive and dynamic knowledge-based economy in the world,
capable of sustainable economic growth with more and better jobs and greater social cohesion”.6
6 See
Council of the European Union (2004).
10
Subsequent councils have reinforced these aspirations, and a large emphasis has been put on human
capital policy.
There has been a large concern with promoting mobility of workers across Europe. Such mobility
may be essential for achieving an efficient allocation of human resources across Europe and for the
success of economic policy set at the European level. This mobility is impaired by institutional differences and language disparities across countries. There also has been preoccupation with increasing
the skill level of the population: both endowing our economies with university educated workers
and reduce the ranks of low skilled workers in Europe, which are still of substantial size. Promoting
lifelong learning is seen as important in a setting where information flows and constant change are so
prevalent. At the same time, there is an aspiration for an increase in private investments in human
capital, by firms and families, and for a better use of public resources. All of these are very valid
goals, and they need to be tackled in a consistent and cohesive way. A message of this paper and
of the work of Carneiro and Heckman (2003) is that human capital policy involves many different
areas of policy (from health policy to education policy, from tax policy to crime prevention) and an
integrated view of an individual over the life-cycle.
For example, it will not be possible to promote tertiary education or learning on the job if
individuals do not get adequate earlier preparation in childhood and adolescence. Young adult
education and training builds on top of earlier investments. Firms, families and schools are equal
partners in the process of skill formation. To achieve a better use of resources we need better
information and common sense suggests that such information is very localized, especially in a
world where heterogeneity is so important. Therefore, more school and family autonomy in the
allocation of education resources is called for. Skill is in high demand in the modern world. Firm
investments are important and account for more than a third of the lifetime human capital acquired
by an individual. Investments in skill should be seen as investments in capital, and policies that
parallel investment policies through the use of tax credits and other instruments can be (and have
11
been) used by governments to promote investment. These are just a few examples. This paper is
very incomplete and what has been achieved is far behind what the title suggests: a human capital
strategy for Europe. But it serves as a springboard for future learning.
Above all, there is a fundamental difficulty with writing such a paper, and with thinking of global
education strategies for Europe. Europe is a composed of very different countries. The data in this
paper provides a clear illustration of this heterogeneity, and is only a part of the overall picture.
Heterogeneity means that different people react differently to the same policies. Policies need to be
designed and implemented at the local level, making use of local information. An integrated vision
of Europe is useful is important, and the principles developed in this paper are quite general, but
the implementation and design of policies needs to take into account the specificity of each country’s
problems and opportunities.
References
[1] Becker, G., K. Murphy and R. Tamura (1990), “Human Capital, Fertility and Economic
Growth”, The Journal of Political Economy, 98:5.
[2] Bertola, G. (2003), “Earnings Disparities in OECD Countries: Structural Trends and Institutional Influences”, working paper.
[3] Blanden, J. and S. Machin (2004), “Education and Family Income”, working paper.
[4] Blank, R. (1996), It Takes a Nation, Princeton University Press.
[5] Blundell, R., H. Reed and T. Stoker (2003), “Interpreting Aggregate Wage Growth: The Role
of Labor Market Participation”, American Economic Review, Vol. 3, No. 4, 1114-1131.
12
[6] Carneiro, Pedro and James J. Heckman, (2003). “Human Capital Policy,” Forthcoming in J.
Heckman and A. Krueger (eds.), Inequality in America: What Role for Human Capital Policy?
(Cambridge, MA: MIT Press).
[7] Carneiro, R. (2002), “Achieving a Minimum Learning Platform for All: critical queries influencing balances and policy options”, European Journal of Education, Vol. 37, No. 3, 57-63.
[8] Gosling, A., S. Machin and C. Meghir (2000), “The Changing Distribution of Male Wages in
the UK”, The Review of Economic Studies, Vol. 67, No. 4, 635-666.
[9] Gottschalk, P. and T. Smeeding (1996), “Cross-National Comparisons of Earnings and Income
Inequality”, Journal of Economic Literature, Vol. 35, No. 2, 633-687.
[10] Hanushek, E. and D. Kimko (2001), “Schooling, Labor Force Quality and the The Growth of
Nations”, American Economic Review, 90:5.
[11] Heckman, J. (2001), “Heterogeneity, Microdata and Public Policy Evaluation”, The Journal of
Political Economy, 109:4.
[12] Juhn, C., K. Murphy and B. Pierce (1993), “Wage Inequality and the Rise in Returns to Skill”,
The Journal of Political Economy, Vol. 101, No. 3, 410-442.
[13] Lucas, R. (1988), “On the Mechanics of Economic Development”, Journal of Monetary Economics, 22.
[14] Machin, S. and J. Van Reenen (1998), “Technology and Changes in Skill Structure: Evidence
from Seven OECD Countries”, The Quarterly Journal of Economics, Vol. 13, No. 4, 1215-1244.
[15] McIntosh, S. and H. Steedman (2001), Low Skills - A Problem for Europe.
[16] Murray, C. (1999), “The Underclass Revisited”, AEI Press.
[17] Nickell, S. (2003), “Poverty and Worklessness in Britain”, working paper.
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[18] Nickell, S. and B. Bell (1996), “Changes in the Distribution of Wages and Unemployment in
OECD Countries”, American Economic Review, Vol. 86, No. 2, 302-308.
[19] OECD (1998), Human Capital Investment.
[20] OECD (2001), The Well Being of Nations.
[21] OECD (2002), Education Policy Analysis - 2002.
[22] OECD (2003), Education at a Glance - 2003.
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1.95349
ctgroup==3
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4.92292
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Swe
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Fin
1.95349
1970
Fin
2000
1970
Czr
Czr
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2000
year
Graphs by ctgroup
Figure 2 : Ratio of the 90th to the 10th percentile of countries’ earnings distribution. Source: OECD.
17
Figure 2
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C o nt r o lling f o r y e a r a nd c t y e ffe c ts
Figure 8 : Vertical axis: unemployment rate (as in Figure 6) after removing country and year effects;
horizontal axis: earnings dispersion in the low portion of their distribution (as in Figure 6) after
removing country and year effects. Data points are plotted along with OLS unweighted regression line.
empl.rate, res
empl.rate, res
5.59163
USA1974
Can1973
-7.45684
Fitted values
Nl1999
Kor1996
Kor1995
Swi1991
Kor1994 Nor1987
Fin1989
Fin1983
Por1992
Kor1997
Swe1984
Fin1986
Swe1989
Swi1992
Swe1988
Por1993
Swe1983
Swi1993
S
we1986
Kor1993
Swe1985Swe1987
Can1988
Dk1986
Fin1987
Por1991
Jap1994
Swe1982
Fin1990
Swe1990
Jap1993
Nl1997
Jap1997
Fin1988
USA1994Fra1973
Fra1972
USA1997
Fra1970
Swe1981
Fra1971
Dk1988
Dk1987
USA1995
Swe1980
USA1996
Jap1996
Jap1995
Kor1992
USA1998
Fra1977
Fra1975
Can1990
Pol1993
Fra1978
Fra1976
Can1994 Swe1991
Fra1974
Fin1980
USA1993
Jap1998Jap1992
USA1999
Can1986
Dk1985
Nzl1986 Aus1995
Nl1996
Nor1983
Swi1994
Fra1979
USA2000
Can1993Nzl1984
USA1989
UK1989
USA1988
Kor1991Swe1978
USA1987
Can1991
Ger1991
Fra1980
Swi1995
Ger1992
Nl1995 Pol1994
Aus1989
Dk1989USA1986
Jap1999
Ger1993
UK1975
Can1981
Aus2000
UK1973
Swi1996
Ger1994 Aus1997
USA1985
Fra1982
Nl1993
Swi1998
Swe1975
USA1992
UK1974
Can1992
USA1984
Aus1994
Fra1983
Jap1991
Jap1983
UK1997
Nl1994
Pol1995
Pol1996
Aus1999
Fra1981
Ger1995
Aus1998
Dk1984
UK1990
UK1979
Por1989
UK1998
UK1988
UK1999
Jap1984
Ire1997
Ita1986
USA1990
UK1978
UK1970
Fin1977
UK1972
UK1976
Fin1991
UK2000
Dk1990
UK1996
USA1991Kor1998
Kor1989UK1994
Aus1990
UK1977
UK1995
Ger1996
Swi1997
Jap1985
Aus1988
Ger1989
Nor1980
Kor1999
Nl1992
Swe1992
Ger1990
UK1993
UK1980
Fra1984
UK1971
Jap1982
Aus1986
Ger1988
Bel1993
Ger1987
Nzl1996
Ger1998
UK1991
Ita1987
Aus1975Kor1990
Kor1988
Kor1987 Aus1993
Ger1997
Jap1986
Bel1994
Aus1987
Ger1986
UK1992
Aus1981
Aus1976
Bel1995
Ita1992
USA1983
Ita1988
Bel1992
Aus1985
Czr1996
Jap1989
Por1987
Dk1983
USA1979
Nl1977
Aus1991
Aus1982
Nzl1995
Jap1990
Jap1987
Jap1981
Aus1977
Fra1985
Ita1991
Jap1988
Nl1978
USA1981
Ger1985 Por1985
USA1978 Aus1992
Aus1980
Nzl1997
Nl1979
Nl1980
Bel1991
UK1987
Ita1993
USA1982
Ita1989
Nl1991
Ger1984
USA1980
Dk1980
Nzl1988
Fra1986
Bel1985
Aus1984
Kor1986
Dk1982
Nor1991
Ire1994
Bel1989
Kor1985
Fra1994 UK1985
Jap1980
Bel1986
Aus1978
Bel1988
Fra1993
Czr1997
Jap1979
UK1984
Fra1995
UK1981
Bel1987
Jap1978
Nl1981
Kor1984
Nzl1994
Fra1992
Ita1990
Aus1979
Ita1994
Fra1998
Bel1990
Fra1996Fra1987
Dk1981
UK1986
Jap1977
Aus1983
Fra1989
Fra1988
Fra1991
Fra1997
USA1977
UK1982
Jap1975
UK1983
Nl1982
Jap1976
USA1973 Ita1996Ita1995
Fra1990
Nl1990
Nl1983
Aut1996
Swe1993
Nl1989
Nl1984
Nl1986
USA1976Nl1987
Nl1988
Fin1992
Fin1999 Nl1985
Spa1995 Swe1995
USA1975
Swe1994
Fin1998 Nzl1990
Swe1996
Nzl1992
Fin1997
Czr1999
Swe1998
Fin1996
Swe1997
Fin1993
Fin1995
Fin1994
-.260007
.212532
50-10 wdiff, res
Controlling for year and cty effects
Figure 9 : Vertical axis: unemployment rate, OECD Economic Outlook definitions; horizontal axis:
earnings dispersion in the low portion of their distribution, from OECD “Trends in earnings dispersion”
file. Data points are plotted along with OLS unweighted regression line.
20
Figure 3
MEASURING THE STOCK OF HUMAN CAPITAL
◆ Figure 2.2. Percentage of younger (25-34 year olds)
and older adults (45-54) with upper secondary education or higher, 1995
Aged 25-34
%
100
90
80
70
Aged 45-54
60
50
40
30
20
10
Percentage point difference
0
0
10
20
30
40
0
10
20
30
40
%
50
United States
Germany
Czech Republic
Norway
Switzerland
United Kingdom
Canada
Sweden
Poland
Austria
France
Denmark
Finland
Netherlands
New Zealand
OECD average
Australia
Belgium
Korea
Ireland
Greece
Luxembourg
Italy
Turkey
Spain
Portugal
100
90
80
70
60
50
40
30
%
Countries are ranked by percentage of 45-54 year olds
with upper secondary attainment or higher.
Data for Figure 2.2, p. 99.
Source: Labour Force Survey data (see OECD, 1997b).
20
10
0
50
%
Younger adults are more qualified,
but the generation gap varies greatly across countries.
19
Figure 4
Schooling participation rates by year of birth
Percentage Participating
80
(a) Whites
70
60
50
40
30
20
10
0
19
20 922 924 926 928 930 932 934 936 938 940 942 944 946 948 950 952 954 956 958 960 962 964 966 968 970 972 974 976 978 980
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Year of Birth
(b) Blacks
80
Percentage Participating
70
60
50
40
30
20
10
19
20
19
22
19
24
19
26
19
28
19
30
19
32
19
34
19
36
19
38
19
40
19
42
19
44
19
46
19
48
19
50
19
52
19
54
19
56
19
58
19
60
19
62
19
64
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
0
Year of Birth
(c) Hispanics
90
80
60
50
40
30
20
10
0
19
20
19
22
19
24
19
26
19
28
19
30
19
32
19
34
19
36
19
38
19
40
19
42
19
44
19
46
19
48
19
50
19
52
19
54
19
56
19
58
19
60
19
62
19
64
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
Percentage Participating
70
Year of Birth
College enrollment
High school graduates and GEDs*
High school dropouts**
* GED hoders are known for the birth cohort 1971-1982
** Dropouts exluded GED holders
Source: Data from 2000 Current Population Survey
Literacy in the Information Age
Figure 5
FIGURE 2.2 (concluded)
COMPARATIVE DISTRIBUTION OF LITERACY LEVELS
C. Per cent of population aged 16-65 at each quantitative literacy level, 1994-1998
80
60
40
20
0
20
40
60
80
100
100
80
60
40
20
0
20
40
60
80
100
Per cent
100
Denmark
Norway
Level 1
Czech Republic
Germany
Netherlands
Switzerland (French)
Finland
Level 2
Belgium (Flanders)
Switzerland (German)
18
Canada
Australia
Switzerland (Italian)
Level 3
United States
New Zealand
United Kingdom
Hungary
Ireland
Poland
Level 4/5
Slovenia
Portugal
Chile
The charts also reflect the different performance of some countries on each of
the scales. Australia and Canada, for example, do not differ from each other on any
scale. Both have significantly higher scores on prose compared with the Czech
Republic, Ireland and the United Kingdom. And although the scores on the document
scale for Canada are higher than those for Ireland and the United Kingdom, they are
not significantly different from those for the Czech Republic. On the prose scale the
Netherlands outperforms Belgium (Flanders) but the difference between the latter
and Germany is not meaningful. Finally, on the quantitative scale, the Czech Republic
outscores both Australia and Canada, which, in turn, outscore Ireland and the United
Kingdom. However, on this scale, their scores do not differ significantly from those
in the United States.
As the information in Figure 2.3a-c suggests, many of the observed differences
between countries are meaningful, especially those at the high and low ends of the
scale. But there are other comparisons that are not really different in a statistical
sense (the dot in the grey square). Thus, in terms of literacy proficiency, the three
language groups in Switzerland do not differ significantly from each other on any of
the scales.
Finally, Figure 2.3a-c provides data on just how significant the observed
differences between country profiles really are. As in any household survey, some
degree of sampling error and measurement error is present in the IALS data. This
error must be taken into account when examining the overall differences in mean
literacy scores across countries. The multiple comparisons shown in Figure 2.3a-c
provide a tool for identifying those differences that are most likely to be a reflection
of real differences.
Countries are ranked by the proportion in Levels 3 and 4/5.
Source: International Adult Literacy Survey, 1994-1998.
Sweden
Chapter 2/ Population Distributions of Adult Literacy
FIGURE 2.4 (concluded)
EDUCATIONAL ATTAINMENT AND LITERACY PROFICIENCY
B. Mean document score on a scale with range 0-500 points, by level of educational attainment,
population aged 16-65, 1994-1998
Points
Figure 6
Chile
Poland
Hungary
Slovenia
Portugal
Switzerland (German)
New Zealand
0
United States
0
Ireland
200
Switzerland (Italian)
200
Australia
250
Netherlands
250
United Kingdom
300
Switzerland (French)
300
Belgium (Flanders)
350
Germany
350
Canada
400
Denmark
400
Czech Republic
450
Finland
450
Norway
500
Sweden
500
C. Mean quantitative score on a scale with range 0-500 points, by level of educational attainment,
population aged 16-65, 1994-1998
Countries are ranked by the mean score of those who have completed tertiary education.
Source: International Adult Literacy Survey, 1994-1998.
23
Chile
Poland
Slovenia
New Zealand
Portugal
Switzerland (German)
Hungary
0
Ireland
0
Switzerland (French)
200
United States
200
Australia
250
Switzerland (Italian)
250
United Kingdom
300
Netherlands
300
Finland
350
Canada
350
Germany
400
Denmark
400
Belgium (Flanders)
450
Czech Republic
With less than upper
secondary education
450
Norway
Completed upper
secondary education
500
Sweden
Completed tertiary
education
Points
500
Figure 7
Chapter 3/ How Literacy is Developed and Sustained
FIGURE 3.12
LITERACY AND ADULT EDUCATION PARTICIPATION
Per cent of population aged 16-65 participating in adult education and training during the year
preceding the interview at each literacy level and in total, document scale, 1994-1998
Per cent
100
100
80
80
60
60
40
40
Total
Total
participation rate
Level
Level 1 1
20
20
Level 2 2
Level
Level
Level 3 3
0
Level 4/5
Level
4/5
Poland
Portugal
Chile
Hungary
Belgium (Flanders)
Ireland
Czech Republic
Slovenia
Netherlands
Canada
Australia
United States
Switzerland
United Kingdom
New Zealand
Norway
Sweden
Denmark
Finland
0
Countries are ranked by the total participation rate.
Source: International Adult Literacy Survey, 1994-1998.
While the rates vary between countries, the data show that in each country
there are large groups outside the emerging learning society. Those outside are often
those most in need of skills enhancement, whether through formal or informal
learning. With large groups of adults possessing low literacy skills, it is particularly
important from a policy perspective to look at their readiness to engage in learning.
Figure 3.12 indicates that participation in adult education increases gradually by
level of literacy. Those with low literacy skills receive the least adult education. The
level of inequality – although large – is relatively smaller in Denmark, New Zealand
and Sweden, countries with high overall participation rates.8
As discussed previously, much of the variation in mean literacy levels observed
in Figure 3.12 can be attributed to the “literate culture” in which a person grew up,
and the effect this has had on educational attainment. The “long arm of the family”
is further extended through the way in which educational credentials, to a large
extent, determine entry into the labour market and the early stages of a person’s
occupational career (Tuijnman et al., 1988).
8. Relative inequality is lowest in the Czech Republic, a country with a relatively low participation rate.
43
The Well-being of Nations
Figure 8
Appendix B
SOME TRENDS IN THE SOCIAL AND ECONOMIC ENVIRONMENTS
Figure B.1. Real gross domestic product per capita, in constant prices,
average based on selected OECD countries, 1966-99
In 1998 US$ PPP
25 000
In 1998 US$ PPP
25 000
20 000
20 000
15 000
15 000
10 000
10 000
5 000
5 000
0
0
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
Selected countries include: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan,
Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.
Source: OECD. Real GDP deflated using 1998 US dollar. Based on Purchasing Power Parity.
80
© OECD 2001
CHAPTER B Financial and human resources invested in education
Figure 9
Chart B3.1
Distribution of public and private expenditure on educational institutions, by level of education (2000)
Norway
Finland
Sweden
Norway3
Finland
Sweden
Portugal
Norway
Finland
Sweden
Portugal
Denmark2
Denmark2
Denmark2
Italy
Italy
Italy
Slovak Republic
Slovak Republic
Slovak Republic
Ireland
Austria
Ireland
Ireland
Netherlands
Belgium
Spain
France
Hungary
Canada1
Japan2
Czech Republic
United States1
United Kingdom
Mexico
Australia
Germany
Iceland
Austria
Iceland
Austria
Netherlands
Belgium
Spain
France
Hungary
Canada1
Greece
Japan2
Czech Republic
United States1
Switzerland
United Kingdom
Mexico
Australia
Germany
Korea
Primary, secondary and post-secondary non-tertiary education
%
100
90
80
70
60
50
40
30
20
10
0
Turkey
Netherlands
Belgium
Spain
France
Hungary
Canada1
Greece
Japan2
Czech Republic
United States1
United Kingdom
Mexico
Australia
Germany
Tertiary education
%
100
90
80
70
60
50
40
30
20
10
0
Korea
B3
%
100
90
80
70
60
50
40
30
20
10
0
Korea
Private expenditure on educational institutions, excluding public subsidies to households and other private entities
Total public subsidies to households and other private entities, excluding public subsidies for student living costs
Public expenditure on educational institutions
Pre-primary education
1. Post-secondary non-tertiary included in tertiary education.
2. Post-secondary non-tertiary included in both upper secondary and tertiary education.
3. Total public subsidies to households may be included in private payments.
Countries are ranked in ascending order of the proportion of direct public expenditure in primary, secondary and post-secondary non-tertiary education.
Source: OECD. Table B3.2. See Annex 3 for notes (www.oecd.org/edu/eag2003).
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CHAPTER B Financial and human resources invested in education
Figure 10
Chart B1.3
Differences in expenditure on educational institutions per student relative to primary education (2000)
Ratio of expenditure on educational institutions per student at various levels of education to expenditure on
educational institutions per student in primary education, multiplied by 100
Pre-primary education
Tertiary education
Secondary education
Ratio x 100
500
450
400
350
300
250
200
150
100
Greece1
Portugal
Italy1
Iceland1
Poland1
Austria
Spain
Denmark
France
Finland
Korea
Japan
Norway1
Sweden
United Kingdom
Belgium
Australia
Germany
Netherlands
Switzerland1
United States2
Czech Republic
Ireland
Hungary1
0
Mexico
50
Slovak Republic
B1
Notes: A ratio of 500 for tertiary education means that expenditure on educational institutions per tertiary student in a
particular country is 5 times the expenditure on educational institutions per primary student.
A ratio of 50 for pre-primary education means that expenditure on educational institutions per pre-primary student in a
particular country is half the expenditure on educational institutions per primary student.
1. Public institutions only.
2. Public and independent private institutions only.
Countries are ranked in descending order of expenditure on educational institutions per student in tertiary education relative to expenditure
on educational institutions per student in primary education.
Source: OECD. Table B1.1. See Annex 3 for notes (www.oecd.org/edu/eag2003).
Educational expenditure per student over the average duration of
tertiary studies
Annual expenditure on
education per student
does not always reflect
the full cost of tertiary
studies.
Since both the typical duration and the intensity of tertiary education vary
between OECD countries, the differences between countries in annual
expenditure on education per student on educational services as shown in
Chart B1.2 do not necessarily reflect the variation in the total cost of educating
the typical tertiary student.
Students can choose
from a range of
institutions and
enrolment options.
Today, students can choose from a range of institutions and enrolment options
in order to find the best fit between their degree objectives, abilities and personal interests. Many students enrol on a part-time basis while others work
while studying, or attend more than one institution before graduating. These
varying enrolment patterns can affect the interpretability of expenditure on
education per student.
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