EUROPEAN SEMESTER THEMATIC FICHE
SKILLS FOR THE LABOUR MARKET
Improving skills is one of the main ways to increase labour productivity in Europe. Skills are
critical for competitiveness and employability, as structural changes such as globalisation
and technological progress call for ever-higher skills for secure, quality jobs.
The Annual Growth Survey 20151 stresses the need for a skilled work force in growing
sectors such as the digital economy, green sectors and health. It particularly emphasises
vocational training, dual education systems, lifelong learning, and regional and sectorial skills
needs.
1. Assessment of the main challenges in the Member
States
1.1. Basic, transversal and specialist skills
Basic skills
In a large number of EU countries there is still a very high proportion of 15-year-olds who are
"low achievers" in basic skills.2 Only four Member States have reached the benchmark of
having fewer than 15% low achievers in mathematics (see Figure 1) — although results in
reading and science literacy are slightly better (see Tables 1 and 3 in the Annex).
1
http://ec.europa.eu/europe2020/pdf/2015/ags2015_en.pdf.
2
Low achievers are defined as those who scored below proficiency level 2 in one of the PISA test fields
(reading, mathematics or science respectively). For more details on how these levels are defined, see
http://www.oecd.org/pisa/test/.
Thematic fiches are supporting background documents prepared by the services of the Commission in
the context of the European Semester of economic policy coordination. They do not necessarily
represent the official position of the Institution.
Figure 1:
Mean score and shares of low- and top-achievers in mathematics, 2012
Score
Mean score
% low achievers (rhs)
% top performers (rhs)
%
45
520
40
35
500
30
25
480
20
460
15
10
440
5
420
0
Data source: OECD (PISA).
Under the open method of coordination in the field of Education and Training (‘ET 2020’), Member States
agreed on a benchmark where the share of low-achieving 15-year-olds in reading, mathematics and science
should be less than 15% by 2020.
The results from the OECD's programme for international student assessment (PISA) reveal
that still more than one fifth (22.1%) of the tested 15-year-olds have severe problems in
solving relatively simple mathematical tasks related to their everyday life (see tables 1 to 4 in
the Annex). The share of low-achieving boys in reading is twice that of girls (23.7% to
12.0%). It needs to be pointed out, however, that PIAAC data show that the gender
difference in reading disappears among young adults (16 to 24-year-olds).
The performance of BG, RO and CY — with more than 40% of 15-year-old low achievers in
maths — is particularly low; however, the first two have been improving. Only NL, EE, PL
and FI already meet the EU benchmark3. Broadly speaking, Member States with a greater
number of low achievers also tend to have fewer "top-performer" pupils (those at level 5 or
above in PISA maths tests); suggesting that the issue is weaknesses in the performance of
education and training systems, rather than a choice to privilege excellence over equity.
Socio-economic background remains one of the main determinants of skills acquisition in
schools. Though some progress has been made since 2003, the difference in skill levels
between those with the lowest and those with the highest socio-economic status remains
very high, and persistently so across Member States (see Figure 2 below and Table 4 in the
Annex).
3
This benchmark, based on the PISA-test of 15-year-old pupils, aims at reducing below 15 % by 2020 the
share of pupils failing to reach basic levels of performance (2 out of 5).
2
Figure 2:
Impact of socio-economic background on performance in mathematics, 2012
Source: OECD (PISA).
The failure of European education and training systems to impart the most basic skills to
20% of pupils creates a serious employability problem.4 This highlights not only the size of
the challenge to improve the performance of education and training but also the huge
potential gains in terms of increased growth and employment if this proportion were reduced.
Regarding the adult population (above age 15), in the 17 EU Member States participating in
the Programme for the International Assessment of Adult Competencies (PIAAC), 43% of the
population showed medium or high levels of literacy skills (levels 3 to 5),6 percentage points
below the OECD average (49%). Furthermore, on average, one in five adults in participating
EU countries display a low level of skills in literacy. In numeracy, it is even one in four.
When it comes to very high skills, only a handful of Member States are able to match the
performance of the best non-EU countries, such as Japan. Figure 3 shows that, at global
level, Japan outperforms all other countries with its high share of performers at levels 3 and
above and very few low performers. Other big non-European economies like Canada and the
US do not score much differently from many EU countries. However, there are considerable
differences in the distribution of skills across EU Member States. Broadly speaking, three
groups of countries can be identified: those with high shares of medium to top-performing
adults and few of low-performers (like NL, FI, SE and BE/FL, among which FI comes closest
to Japan); countries with results not significantly different from the OECD average; and finally
countries with medium to top performers below 40% and very high shares of low performers
(ES and IT). While in some countries it is mainly the older age groups that show very low
skills levels, in others younger age groups also perform rather poorly (e.g. in CY and UK).
Moreover, the survey results confirm that proficiency is very strongly related to parental
education and to migrant status, but to a very different extent across countries.
4
This is particularly true given that about a half (51%) of all EU jobs require advanced literacy skills to be
performed while 30% of all jobs in the EU job market need advanced numeracy skills, according to
Cedefop’s ESJ survey.
3
Figure 3:
Share of the population 16-65 years old at each level of proficiency in literacy,
2012
100%
90%
80%
Missing
70%
Level 5
60%
Level 4
50%
40%
Level 3
30%
Level 2
20%
Level 1
10%
Below Level 1
0%
Source: OECD (PIAAC). Note: countries ordered by share of levels 1 and below combined. Missing: not taken
the test.
PIAAC results also show considerable differences in average skills levels across countries
between people who hold comparable educational degrees. Though these differences are
smaller than those within countries, it is striking that in some countries, young people with
only an upper secondary degree (FI, NL, SE) show higher skills than those with a tertiary
degree in other countries (IT, ES, CY).
Figure 4:
Average proficiency in literacy (16-29 year-olds) by educational attainment,
2012
350
Level 4
Level 3
300
275
250
Level 2
Mean score points
325
225
Level 1
200
FI
NL
SE
BE nl
AT
EE
DE
CZ
Lower secondary
PL
DK
SK
Upper secondary
IE
IT
UK
ES
CY
EU16
Tertiary
Source: OECD (PIAAC). Note: countries are ordered by average score at tertiary education level.
4
Transversal skills
Transversal skills (e.g. linguistic, digital, entrepreneurship, working with others, problem
solving and communication) continue to grow in importance5. However, in the EU, only 42%
of teenage pupils are competent in their first foreign language and just 20% in their second
foreign language6. Concerning digital skills, PIAAC showed that at least 25% of European
adults still lack even basic abilities to use ICT for problem solving. This has serious
consequences for the employability of individuals and productivity growth.
Two in three (66%) jobs in the EU require at least a moderate level of ICT skills, particularly
among workers employed in high-skilled (e.g. managers, professionals and associate
professionals) or clerical support jobs, according to data from the European Skills and Jobs
survey of the European Centre for the Development of Vocational Training. The majority of
skilled manual workers, and 4 in 10 employees in elementary jobs, also require a basic or
moderate level of ICT skills to do their jobs. However, far too many workers in lower-skilled
occupations are not required to use digital skills as part of their work life, which puts them at
an overall disadvantage also with regards to aspects of their everyday life.
Stacked bar 4 v2
Figure 5:
WhichofofICT
theskills
following
best describes
the highest
level of24-65)
ICT skills
doing
Level
required
by occupation,
adult (aged
EU for
employees,
your job?
2014
ISCO 1 Managers
10
ISCO 2 Professionals
11
ISCO 3 Technicians and associate
professionals
14
ISCO 4 Clerical support workers
15
67
32
ISCO 6 Skilled agricultural
32
% Basic ICT
2
57
25
5
23
28
35
ISCO 8 Plant and Machine operators
ISCO 9 Elementary Occupations
25
25
% Moderate ICT
10
37
26
4
2
2
% Advanced ICT
4
24
42
7
22
15
2
61
71
ISCO 5 Service and sales workers
ISCO 7 Craft and trade workers
21
37
5
36
55
% ICT skills not required
Notes: All respondents (48,676). Responses to the question: “Which of the following best describes the highest
Base:
All of
respondents
(48,676)
Source:
Cedefop
Europeandevice,
Skills Survey
level
ICT skills
required for doing your job? 1. Basic ICT (e.g. using a PC,
tablet
or mobile
email,
internet browsing); 2. Moderate ICT (e.g. Word-processing, creating documents or spreadsheets); 3. Advanced
Usedeveloping
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ICT (e.g.
software,
applications
or programming;
using–computer
statistical analysis
packages) 4. ICT skills are not required”; 5. Don’t know/no answer (not shown).
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
For more than three in four EU employees, transversal or ‘soft’ skills (e.g. problem-solving,
team-working, communication, learning to learn, planning and organisation, customer
handling) are deemed to be very or moderately important for doing their job. More than half
5
See, for example, the Public consultation on a European Area of Skills and Qualification.
6
EU Skills Panorama (2014) Foreign languages Analytical Highlight, prepared by ICF GHK and Cedefop for the
European Commission.
5
of EU employees require foreign language skills for their jobs, although such skills tend to be
specific
to abar
subset
Stacked
4 v2 of occupations.
On a6:scale from 0 to 10, where 0 means not at all important, 5 means
Figure
moderately important and 10 means essential, how important are the following
for doing your job?
Importance of transversal skills for job, adult (aged 24-65) EU employees, 2014
Problem solving skills
79
14
3 2
Team-working skills
78
15
4 1
Communication skills
77
16
4 2
74
Learning skills
Planning and
organisation skills
71
18
67
Technical skills
Customer handling
skills
Foreign language skills
18
17
30
% Very important
6
21
61
25
% Moderately important
3
8
11
26
% Not important
4 2
3
9
18
% Skill not required
Source: Cedefop European Skills Survey
Base: All respondents (48,676)
Notes: % of Use
all respondents
(48,676).
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question:
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at all important, 5 means moderately important and 10 means essential, how important are the following skills
for doing your job?”; Responses in the interval 7-10 of the importance scale have been classified as “Very
important”, 4-6 “Moderately important” and 0-3 “Not important”; “Skill not required” is a separate category.
Don’t know/no answer not shown.
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
Specialist skills
The demand for certain specialist skills, especially in areas such as Science, Technology,
Engineering and Mathematics (STEM), is growing7. According to the latest CEDEFOP
forecasts8, demand for STEM occupations9 is expected to grow by around 9.5% between
now and 2025, much higher than the average 3.2% growth forecast for all occupations.
Employment in STEM-related sectors is also expected to rise by around 6.5% between now
and 2025, although this masks big differences between different sectors. For example,
employment in computer programing and information service or architectural and
engineering is expected to rise by some 10% or 12% respectively, while the pharmaceuticals
sector is expected to see a 1% decline. However, these data do not show the breakdown by
field and there may be differences between sectors. In general, it is likely that there is almost
universal demand for IT professionals, but the demand for natural scientists or engineers
may vary considerably.
7
CEDEFOP, Rising STEMS, http://www.cedefop.europa.eu/en/publications-and-resources/statistics-andindicators/statistics-and-graphs/rising-stems.
8
http://www.cedefop.europa.eu/en/events-and-projects/projects/forecasting-skill-demand-andsupply/skills-forecasts-main-results.
9
Defined as the sum of science and engineering professionals and science and engineering associate
professionals.
6
1.2. Initial vocational education and training
Initial vocational education and training (IVET) is critically important to better link the
worlds of education and employment. More than 50% of EU students enrolled at upper
secondary level undertake vocational education and training, making IVET a key source of
new skills and competencies for EU economies.
CEDEFOP forecasts show that by 2025 almost 87% of job opportunities will require at least
medium level qualifications and substantial vocational skills. VET has a key role to play in
ensuring a steep increase in the availability of high level skills, with an increasing number of
countries setting up VET programmes at post-secondary and tertiary level. Initial VET
systems must provide adequate basic, transversal, and vocational skills that fit the needs of
employers and also equip learners to continue to learn throughout their careers, and to
manage transitions from education to employment as well as from one job to another or from
unemployment to employment.
Figure 7:
Students enrolled in vocational upper secondary education, % of all students
enrolled in upper secondary education (ISCED level 3), 2012
80
75.3
72.8
72.7
71.3
66.2
50.4
50.6
46.1
61.9
60.7
59.2
60
50
70.3 70.1
69.5
70
48.3
49.4
48.2
45.5
44.2
43.6
39.0
40
34.1
38.6
32.2 33.1
28.7
30
27.3
20
13.2
11.8
10
0
Source: Eurostat.
The mission of IVET is to prepare young people to enter the labour market by developing
skills relevant to the needs of the economy. Some countries have efficient and well
established systems of IVET (DE, AT, NL, DK) with strong involvement of employers and
social partners in planning, organising, delivering and financing. In some other countries
IVET fails to supply the relevant skills to improve productivity and innovation. The IVET
sector struggles in most EU countries with declining enrolment, a poor image - it is often
perceived by policy-makers and the public as an instrument for less able students – and is
sometimes too slow in responding to the reported skills needs of individual economic sectors.
7
Employers' representatives throughout the EU claim that there are skills shortages in
vocational professions and say they struggle to find suitable candidates for their vacancies
(see below).
1.3. Work-based learning / Apprenticeships
The contribution of work-based learning and apprenticeships to youth employment and
competitiveness is widely recognised. Countries with strong VET and apprenticeship
systems perform better in terms of youth employment, including for special groups such as
Early School Leavers (ESL).10
Evidence shows that the employability of young VET graduates increases with participation
in high-quality work-based learning programmes fostering skills acquisition responding to
labour market needs.11 However, while 50% of secondary school students participate in VET
programmes, only 26.5% of VET students are in work-based programmes.12
Figure 8:
IVET work-based students as % of upper secondary IVET, 2010, 2012
Source: Cedefop calculations based on Eurostat data/UOE data collection on education systems.
Mainly as a response to the high youth unemployment rates experienced as a result of the
economic crisis, a trend towards a revival of work-based learning, particularly apprenticeship
programmes, has taken place in most EU countries.13 As for work-based learning, according
to Cedefop’s ESJ survey, the majority of employees in the EU labour market who have
completed at least an upper secondary degree are more likely to have completed their
studies only within an educational institution (e.g. a school, college or university) (56%). Only
10
The average share of ESLs: 12% (2013) – EU headline target: 10% by 2020; 7.5 million young people = 13%
were NEETs (2013).
11
Cedefop (2012), From education to working life. It should be noted that the distribution of company- and
school-based training and the proportion of young people undertaking apprenticeships varies significantly
across the EU.
12
Source: Cedefop (2010), Skills Supply and Demand in Europe, Medium-Term Forecast up to 2020.
13
CEDEFOP (2015), Stronger VET for better lives: Cedefop’s monitoring report on vocational education and
training policies 2010-2014, Reference series 98, Luxembourg: Office for Official Publications.
8
for about 41% of adult workers did their studies involve some learning in a workplace in the
broad sense (e.g. through apprenticeships, internships or other forms of work-based
learning). There are strong differences across countries in the share of employees that spent
some time in a workplace during their studies. For instance, over two-thirds of respondents in
Germany have completed some form of workplace learning, while respondents in some EU
countries (e.g. Portugal, Lithuania, Ireland, Malta, Belgium and UK) have predominantly
learned in a school-based setting.
Analysis of the ESJ survey confirms that individuals whose studies involved some learning in
a workplace are more likely to make a direct transition from school to their first job, as
opposed to having experienced an intermittent spell of unemployment or inactivity. They are
also more likely to enjoy a continued ‘virtuous’ path of skill development in their jobs, as
individuals with a work-based learning experience are, on average, placed in jobs with higher
skill demands.
Nevertheless, greater policy efforts are still required to promote the goal of work-based
learning in specific educational programmes and fields of study. For instance, while over
67% of recent graduates (aged 25-34) from the fields of medicine and health-related
sciences acquired some experience in a workplace as part of their studies, only about a
quarter (25%) of graduates from the fields of humanities, languages and arts; economics,
business and law; or other social sciences did so.
Figure 9:
Average incidence of work-based learning by field of study, younger individuals
(aged 24-34) with above upper secondary qualifications, EU28, 2014
Humanities, languages and arts
Other social sciences
Economics, Business, Law and Fin
Maths and Stats
Natural sciences
Engineering sciences
Computing sciences
Agriculture and veterinary scien
Teacher training and education s
Other
Security, transport or personal
Medicine and health-related
0
.2
.4
.6
Mean % WBL
Notes: Responses to the question: “Did your study (leading to your highest educational qualification) take place
only within an educational institution (e.g. a school, college or university) or did it involve some learning in a
workplace (e.g. apprenticeships, internships, other forms of work-based learning)?”
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
9
Data from Eurostat and other international sources do not allow us to provide a
comprehensive and consistent picture of apprenticeship demand and supply in Europe.
However, there is a broad consensus that wider availability of high-quality apprenticeships
would be an effective instrument to improve sustainable transitions from school to work in
many Member States, notably by fostering skills relevant to the labour market and improving
skills matches. Efforts to persuade companies, mainly SMEs, to invest time and money in
young learners need to be intensified, by outlining more clearly the benefits of training
apprentices combined with appropriate (financial) incentives. Other challenges need to be
overcome too, including: securing sufficient availability of qualified trainers; establishing and
implementing proper quality assurance systems; and attracting/organising funding and other
types of support for cooperation arrangements between VET institutions and businesses.
1.4. Adult learning and continuing VET
Extensive participation by adults in learning activities indicates a high commitment to invest
in upgrading skills and developing competences throughout life. This is crucial to maintain a
productive workforce equipped with relevant skills; an effective response to structural
changes such as rapid technological progress, globalisation, and the implementation of
effective active ageing strategies. According to Cedefop’s ESJ survey, about a quarter (26%)
of adult employees in the EU labour market think it is moderately likely and one in five (21%)
think it is very likely that several of their skills will become outdated over the next five
years. Employees in EE and RO (42% and 39%, respectively) are the most likely to suffer
from very high levels of perceived skill obsolescence, in contrast to those in MT and BG.
More than three in ten respondents working in the information technology or communication
services industry think it is very likely to see some of their skills become redundant in the
foreseeable future, while skill obsolescence is also high in financial, insurance or real estate
services and in professional, scientific and technical services.
10
Figure 10:
Perceived skills obsolescence by EU Member State, 2014
Notes: Percentage of adult employees who think it is moderately or very likely that several of their skills will
become outdated in the next five years.
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
Despite the need for continued investment in lifelong learning, Cedefop’s ESJ survey
highlights that more than one in five (22%) EU employees experienced stagnation in the
development of their skills in their current job. Skill development is less likely to occur among
individuals that returned to the job market after experiencing a spell of unemployment, older
individuals and those employed in semi-skilled and low-skilled occupations. To retain
continued skill development within jobs and to shield susceptible workers from skill
obsolescence, it is necessary for European lifelong learning policies to maintain their
commitment to (non-formal or informal) learning and training.
Under the open method of coordination in the field of Education and Training (‘ET 2020’),
Member States agreed on a benchmark to be reached by 2020, according to which at least
15% of the adults (aged 25-64) population should participate in learning. The average level in
2014 was 10.7%.
11
Figure 11:
Participation in adult lifelong learning (population aged 25-64, %), 2014
Source: Eurostat, Labour Force Survey
RO, BG, HR, SK, EL, HU, and PL show low levels of adults undertaking any form of
learning: below 5 % (compared to the benchmark of 15 %). LT, LV, IE, CY, MT, BE, DE, IT,
CZ, PT and ES also face a challenge as they are below the EU average. Participation in
job-related training is dependent on learning opportunities supported by the employer (i.e.
during working time or paid at least partially by enterprises), which is reflected in the latest
14
Continuing Vocational Training Survey results . On average two thirds of enterprises in the
EU provided continuing vocational training in 2010, but large enterprises were much more
likely to provide training than small and medium-sized enterprises. The proportion was less
than a third in BG, EL, PL and RO. Many more enterprises provided Continuing Vocational
Training in BE, CZ, DE, ES, FR, CY, LU, NL, AT, FI, SE and UK (70% or more).
1.5. Employability of graduates
While many factors influence employability (such as labour market regulations or the overall
economic cycle, which lie beyond the scope of education and training policies), equipping
young people with relevant knowledge, skills and attitudes eases the transition from
education to employment. Figure 12 shows the percentage of young recent graduates in
employment against the benchmark, set by the Council in 2012, according to which at least
82% of young recent graduates should be in employment by 2020.
14
The Continuing Vocational Training Survey (CVTS) reports on continuing vocational training activities which
took place in 2010.
12
Figure 12:
Employability: employment rates of recent graduates 15, 2014 (%)
all levels
100
upper secondary education
tertiary education
90
ET 2020 benchmark
80
70
60
50
40
30
20
10
0
MT
DE
NL
AT
SE
DK
LU
UK
CZ
EE
LT
BE
HU
FI
LV EU28 PL
FR
IE
SK
SI
PT
CY
RO
BG
ES
HR
IT
EL
Source: Eurostat, Labour Force Survey.
EL, IT, CY, ES, BG, RO, CY and PT face a serious challenge: in these countries, the
employment rate of young people (aged 20-34) who recently completed at least upper
secondary school and are not in education and training is below 70%. SI, SK, IE, FR and PL
are also below the European average of 75%. There is a great difference between the
employment rates of young graduates from upper secondary education and those from
tertiary education, particularly in IE, HR, BG, and LV where the difference is more than 20
percentage points.
The employment rate of recent young graduates in the EU28 as a whole increased slightly
to 76.1% in 2014, after 5 years of decreases from the 2008 peak of 82%. The increase is
entirely due to the medium-educated (in other words, upper secondary graduates), as the
rate for tertiary graduates fell marginally also in 2014. Against this background, it is positive
that 17 Member States show increasing rates.
For vocational education and training, evidence from a study by JRC16 shows that in many
EU countries, upper secondary school graduates from vocationally oriented programmes
have higher employment rates than their non-VET counterparts, as well as lower
unemployment and inactivity rates.17 OECD analysis18 confirms that at the ISCED 3 and 4
15
The indicator on which the benchmark is based is defined as the share of all young people (aged 20 – 34)
who graduated from at least upper secondary education in the period of one to three years before, who are
in employment and who are not currently enrolled in any further education or training activity.
16
JRC CRELL (2015): Education and youth labour market outcomes: the added value of VET. Technical briefing;
based on a special extraction from LFS provided by Eurostat concerning the third quarter of 2014.
17
Measured as proportion of employed individuals aged 20-34 whose highest level of education is upper
secondary or post-secondary non-tertiary (ISCED 3-4).
18
OECD (2015):The effects of vocational education and training on adult skills and wages. What can we learn
from PIAAC?
13
level, VET is associated with a higher probability of being employed, but slightly lower hourly
earnings. The differences are small however, especially when considered by gender. At the
ISCED 5 level, there is a strong advantage of academic education in terms of earnings and
employment.
2. Horizontal issues
2.1 Labour market functioning and skills mismatch
Skills mismatch – a difference between the available skills and qualifications and those
required by the labour market – if persistent, can lead to short- and long-term economic and
social losses for individuals, employers and the society. Skills mismatch can take the form of
an imbalance between aggregate labour demand and supply, thus resulting in shortages or
surpluses in particular sectors or occupations; but it can also reflect an inadequate fit
between individuals' skills and their job requirements. In the latter case, individuals may have
higher (over-qualified/over-skilled) or lower (under-qualified/under-skilled) qualifications and
skills than required19.
Structural transformation of an economy may imply that jobs are created in different sectors
or regions than those from which jobs are shed. The efficiency of matching can to some
extent be improved by better guidance and counselling and active labour market policies,
and by supporting labour mobility.
According to this logic, the EU and most of its Member States showed increasingly efficient
matching prior to the crisis, indicating a decrease in both the unemployment rate and the job
vacancy rate. Unemployment rates and job vacancy rates both increased initially; but from
2012 onwards, job vacancy rates stagnated.
However, when labour demand remains low for a long time, individuals may remain
unemployed for a long time, and suffer from skills loss. As a result, long-term
unemployment (LTU) may put workers at a high risk of alienation from the labour market. In
2014, LTU rates (as a % of the labour force) were above the EU average of 5.1% in 10
Member States (BG, IE, EL, ES, HR, IT, CY, PT, SI, SK), while countries like DK, LU, AT, FI
and SE had significantly lower LTU rates (see Table 5 in the Annex). Between 2008 and
2014 the rate of LTU doubled in the EU. In that time period LTU also rose from 38% to 47%
of unemployment, implying that close to a half of all unemployed European citizens are out of
work for more than a year.
In the majority of EU Member States, individuals with lower education and skill levels are
faced with a higher relative risk of LTU. Since 2008, the gap in LTU rates between low-skilled
workers and highly skilled and medium-skilled workers has widened significantly (see Figure
14).
19
European Commission: Employment and Social Developments in Europe 2014.
14
Figure 14:
Long-term unemployment rates by skill level (% of labour force),
EU28, 2004-2013
Source: EMPL calculations based on Eurostat data.
There has been a concern that labour outcomes have worsened for highly educated young
people as well, in particular over the crisis period. Between 2012 and 2013, the
unemployment rate for the highest level of education attained increased in the majority of the
MS. EL saw the highest incidence of graduate unemployment (20.3 % in 2013), followed by
ES (16.3 %), CY (13.3 %), PT (13.1 %), HR (10.7%), IT (7.4%), IE (7.3 %) and SK (7.3%). By
2014, however, the situation improved for most EU Member States (see Figure 15).
Figure 15:
Unemployment rates with tertiary education attained (%), 2013-2014
Source: Eurostat, Labour Force Survey.
15
Not only did unemployment rates increase for tertiary educated individuals over the crisis
period; but the share of tertiary educated individuals that considered themselves as overqualified or over-skilled for their job increased as well. Overqualified workers tend to suffer
from lower mean wages and job satisfaction and deteriorated long term job prospects, with
migrants, female and younger workers particularly affected.20
Measuring over-qualification is a complex task. One objective way of measuring overqualification that has been proposed in the literature is by crossing ISCED5-6 levels with
ISCO 4-9 occupational categories. This approach implicitly assumes that the ISCO
occupational categories 1-3 require tertiary education, 4-8 require upper secondary
education, and ISCO 9 (elementary occupation) requires less than upper secondary
education. This imposes very strict assumptions however, and abstracts from the fact that
skills requirements within these very basic ISCO categories may vary widely both within and
between countries. According to this logic, the highest numbers of overqualified workers are
found in ES, CY and IE; and these shares have increased on average over the last decade
(Figure 16).
Figure 16:
Over-qualification. High skilled in medium or low skilled jobs
(% of 2004 and 2014)
Source: Eurostat (European Commission, DG EMPL, calculations) Crossing ISCED 5-6 and ISCO 4-9 as % of
people employed with ISCED 5-6.
20
The expansion of higher education in Europe is not new. Since the 1970s the supply of high-skilled workers
has increased substantially. According to the theoretical and empirical evidence, what seems to be new is
that while in the past over-education was a transitory phenomenon, it has now turned into a permanent
feature. This means that anyone starting on a job below their potential capacities has fewer possibilities
than before to find work matching their educational qualifications in the future. In general, this may be
because the supply of skilled workers has increased faster than demand for them.
16
There are also subjective measures of over-qualification based on self-assessment. Based
on the ESJ survey provided by the European Centre for the Development of Vocational
Training,21 which rely on a comparison of individuals’ highest qualification level with the level
reported by themselves as necessary to actually do their current job, total qualification
mismatch affected on average 29 % of the European population (see Figure 2 in the Annex).
While workers can be well-matched by their levels of qualifications to those required by
employers during their recruitment, they may be mismatched in their levels of skills because
of heterogeneity of skill levels among individuals with the same formal credentials (e.g.
differences in non-cognitive skills). Alternatively, individuals mismatched by qualifications
may have adequate skills required for the job due to a high stock of informal skills and work
experience acquired in the labour market over time (see Figure 3 in Annex). Cedefop’s ESJ
survey reveals that in 2014 about 45% of employees in the 28 EU countries experienced skill
mismatch (both workers who feel that some of their skills are lower than needed to do their
job, and those with more skills than are needed by their job). Self-reported underskilling is
the highest in the Baltic states, while more than 40% of workers feel that their skill level is
higher than needed to do their job in AT, UK, EL, IE, DE, FI and ES.
Overskilling is a dynamic phenomenon. Workers may enter their job underskilled, become
increasingly better matched over time; and after some time even become overskilled for their
same job. More than 40% of employees in some EU countries (AT, UK, DE, EL, ES, FI and
IE) consider themselves overskilled for their job. These comprise employees who considered
themselves overskilled at the time of entering their job; but also employees who have
become overskilled over time. This happens much less often in the Baltic states, MT and RO.
21
See Employment and Social Developments in Europe 2012 Review.
17
Figure 17:
0
.2
.4
.6
Dynamic changes in skill mismatch status of EU adult employees, EU28, 2014
DE FR UK SE IT GR CZ PL NL DK HU ES AT BE IE SK FI PT EE RO LT CY SI BG LV LU MT HR
% stayed overskilled
% became overskilled although matched skills at start of job
% became overskilled although underskilled at start of job
Notes: Blue bars = percentage of adult employees who remained overskilled since the start of their current job;
Red bars = percentage of adult employees with matched skills at the start of their current job but became
overskilled over time; Green bars = percentage of adult employees who were underskilled at the start of their
current job but became overskilled over time.
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
At the same time, shortages of professionals may occur in specific sectors (e.g. healthcare,
ICT, finance)22. Forecasts23 indicate that by 2025, 46.3% of all job openings (including both
new and replacement jobs) in the EU will require high qualifications, 40.8% will be for
medium qualified and only 12.8% will be of an elementary nature. The expansion demand for
workers (i.e. new jobs) will continue to concentrate mainly on high skills.
Around 40% of employers in the EU report difficulties in finding staff with the right skills,
according to data from Eurofound’s 3rd European Company Survey.24 The incidence of skill
shortages and mismatches varies by country according to the diversity in economic structure
and growth, labour market conditions and skill matching policies in place.25 Over 60% of
establishments in AT and the Baltic states have difficulties finding suitably skilled employees,
compared to less than 25% in HR, CY, EL and ES. On average, they tend to be more
prominent in the manufacturing sectors of economies and less prevalent in the financial
22
World Economic Forum: matching skills and Labour market needs, 2014.
23
Cedefop (2015).
24
http://www.eurofound.europa.eu/surveys/ecs/2013/european-company-survey-2013.
25
CEDEFOP (2015), Skill shortages and skill gaps in EU firms, Reference series report.
18
sector. The largest recruitment difficulties are reported in the engineering, ICT and health
care sectors and for specific occupational groups (e.g. skill trades workers, welders,
technicians, software analysts, machinists etc.). The perceived skill shortages of employers
fell since the onset of the economic crisis and have remained below pre-crisis levels. While a
third to a half of the perceived shortages of employers reflect a genuine inability to find labour
with the right skills, a significant part can be attributed to poor working conditions offered and
other constraints (e.g. inefficient human resource practices, geographical barriers, lack of
attractiveness of some occupations and sectors etc.).
Figure 18:
Distribution of firms’ difficulties finding right skills by broad economic sector
and EU Member State, 2013
Source: Cedefop’s analysis (Cedefop, 2015) based on data from 3rd European Company Survey (2013).
2.2. Skills needs' assessment, anticipation and forecasting
A recent survey covering 8 EU Member States26 shows that one-third of employers report
that lack of the right skills causes major business problems, in the form of costs, quality and
time. 27% of employers indicated the lack of skills as the main reason for not being able to fill
vacancies. This is particularly problematic for small businesses which are also less likely to
invest in training for their employees after recruitment. At the same time, only 42% of young
people believe that their post-secondary studies (vocational and academic) improved their
employment opportunities and 79% are unhappy with their employment prospects.
At national level, only a third of Member States monitor the evolution of labour demand
with a further third only having partial data27. Nevertheless, initiatives to assess and
anticipate skill needs have been put in place or are currently developed and refined in most
26
McKinsey (2014), Education to Employment: Getting Europe's Youth into Work. Countries include France,
Germany, Greece, Italy, Portugal, Spain, Sweden and the UK.
27
Mapping and Analysing Bottleneck Vacancies in EU Labour, Overview report (2014).
19
EU countries.28 Approaches vary in terms of how skill needs are approximated (e.g.
qualification levels or type; occupations; sectors), their time span (short-, medium- or longterm needs) and mix of methods (e.g. quantitative, qualitative or a mix of both). 29 Usually
skills analysis is done by labour ministries, public employment services, unions, labour
market research institutes, agencies or councils and national statistics offices. Many regions
and municipalities also engage in skills analysis since labour markets are typically regional.
The outputs of skills assessment and anticipation exercises are widely used to inform a
range of skills-related policies across different policy domains (e.g. education and training,
employment and migration). Labour market intelligence should then be better exploited when
education and training courses and curricula are being designed. Skills intelligence must also
support career guidance and counselling in schools and employment services in helping
individuals choose learning and career paths that improve their employability. In doing so,
important barriers need to be overcome, including lack of funds, political support, human
resources and statistical infrastructure. Skill anticipation exercises must be linked to specific
policy objectives, feed into end-users of the information and rely on good co-ordination
across different ministries and strong stakeholder involvement.30
2.3. Visibility and recognition of skills
Work on the comparability of qualifications across Europe started a decade ago and the
European Qualifications Framework (EQF) is helping Member States to trust the quality of
each other’s qualifications and easing the mobility of learners and workers within the EU. To
date, 21 Member States have referenced their national qualifications frameworks to the 8
European levels foreseen in the European Qualifications Framework. The remaining Member
States31 are in different stages of doing so. While across the EU further steps are necessary
towards recognition, an increasing number of third countries have shown interest to align
their qualification frameworks with the European Qualifications Framework, which could
support labour migration movements and help to fill in some skills gaps.
Skills acquired outside of the formal education and training system are not often documented
or formally recognised. Identification and validation of skills is particularly relevant for
people with lower qualifications, unemployed or at risk of unemployment, and for those who
need to change their career paths, i.e. identify further training needs and access requalification opportunities. Based on the 2012 Council recommendation on the validation of
non-formal and informal learning32, pathways have been established on the validation of
skills acquired outside of the formal education and training system, e.g. through work
28
Medium-term forecasts of skill needs for all EU member states, developed by CEDEFOP under the auspices
of the EU Commission Progress program, are available at: http://www.cedefop.europa.eu/en/events-andprojects/projects/forecasting-skill-demand-and-supply/skills-forecasts-main-results.
29
Cedefop/ILO/ETF (2015) Guide to anticipating and matching skills and jobs Volume 2: Developing skills
foresights, scenarios and forecasts, Cedefop.
30
Based on information collected as part of the joint OECD/CEDEFOP/ETF/ILO ‘Anticipating and responding to
future skill needs’ questionnaire to identify effective skill governance strategies across countries,
forthcoming, 2015.
31
SE, FI, ES, EL, CY, RO, SK.
32
http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:C:2012:398:0001:0005:EN:PDF
20
experience, in-company training, digital resources, volunteering and life experience in
general. Opportunities and uptake of validation, however, vary significantly across the EU.
For instance, skills audits for particularly disadvantaged groups are available only in 13
Member States33. The possibility to acquire a national qualification or gain access to formal
education on the basis of validation is available in 15 Member States34. In line with the
Council Recommendation, Member States will report on progress on national validation
arrangements by 2018.
The 2012 Recommendation on validation of non-formal and informal learning identifies the
link to national qualification frameworks (NQFs) as an important feature of validation
arrangements across Europe. Overall, national qualification frameworks and validation are
bound together through their shared emphasis on learning outcomes. The 2012
recommendation states, that ‘the same or equivalent (learning outcomes based) standards to
those used in formal education’ should be used for validation of non-formal and informal
learning. National qualification frameworks thus provide a common reference point for
learning acquired inside as well as outside formal education and training.
33
BE (Flanders and Wallonia), NL, SE, SI, LU, EL, HU, FI, IT, PL, FR, HR, LV (Source: European Commission;
Cedefop; ICF International (2014). European inventory on validation of non-formal and informal learning
2014. Final synthesis report http://libserver.cedefop.europa.eu/vetelib/2014/87244.pdf).
34
AT, BE (Flanders), BG, CZ, DK, FR, IE, LV, LT, LU, MT, PT, SI, ES, UK (England, Scotland, Wales) (Source:
European Commission; Cedefop; ICF International (2014). European inventory on validation of non-formal
and informal learning 2014. Final synthesis report:
http://libserver.cedefop.europa.eu/vetelib/2014/87244.pdf)
21
Annex: Relevant statistics
Measuring the quantity and quality of skills and their impact on employability and productivity
is a complex task that is often constrained by data availability. The main data sources that
are currently at our disposal are presented below.
The EU Labour Force Survey (EU LFS)35, carried out by Eurostat and the national
statistical institutes in all the EU MS, collects information on a wide number of work-related
topics, including employment/unemployment and participation in lifelong learning broken
down by different categories.
The OECD Programme for International Student Assessment (PISA) 36 is a triennial
international survey which aims to evaluate education systems worldwide by testing the skills
and knowledge of 15-year-old students. The most recently published results are from the
assessment in 2012, which measured skills in reading, mathematics and science (with a
focus on mathematics) and covered all the EU MS, except Malta. The 2015 assessment will
focus on science.
The OECD Survey of Adult Skills (PIAAC)37 provides evidence about the skills of Europe's
working age population. The data informs about literacy, numeracy and problem solving in
technology rich environments of 16-65 year olds and thus allows looking into the long-term
outcomes of educational provision in terms of the skills acquired or the relation between
formal qualifications and skills levels. The 1st PIAAC round was carried out in 2008-2013 in
17 EU Member States38. The 2nd round is being carried out in 2012-2016 in 3 other MS (EL,
LT, SI).
The European Working Conditions Survey (EWCS)39 by Eurofound explores quality of
work issues and provides information on inter alia training and learning at work.
CEDEFOP's European Skills and Jobs Survey (ESJ)40 carried out in 2014 in all 28 EU MS
collected information on the match of the skills of about 49,000 EU workers (adults aged 2465) with the skill needs of their jobs. It provides a first insight of the dynamics of qualification
and skill mismatch in the EU, focusing on the interplay between changes in the (cognitive
and non-cognitive) skills of employees in their jobs as well as the changing skill needs and
complexities of their jobs. The survey also focuses on the role of European policies on initial
(e.g. work-based learning) and continuing VET (e.g. formal, non-formal and informal training)
35
http://ec.europa.eu/eurostat/web/microdata/european-union-labour-force-survey.
36
http://www.oecd.org/pisa/
37
http://www.oecd.org/site/piaac/
38
AT, BE(FL), CZ, DE, DK, EE, FI, FR, DE, IE, IT, NL, PL, SK, ES, SE, UK (England and Northern Ireland).
39
To date, Eurofound has carried out five European working conditions surveys (1991, 1995, 2000/2001, 2005
and 2010). The 6th survey to be carried out in 2015 will include all the 28 EU MS. The first results will be
available at the end of 2015.
40
http://www.cedefop.europa.eu/en/news-and-press/news/cedefop-launches-european-skills-survey-euskills.
22
and on workplace design for mitigating skill mismatch. The findings will be published in
2015.41
Other Eurostat skills and training surveys42 – Adult Education Survey (EU AES);
Continuous Vocational Training Survey (EU CVTS); Unesco-OECD-Eurostat (UOE) data
sources provide information on skills needs, training and barriers to training for individuals
and companies.
Table 1:
Low achievers: PISA 2012 results in reading
Low achievers in reading. %
All
EU countries
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Turkey
Liechtenstein
Norway
USA
Canada
Japan
Korea
2003
19.5
17.9
:
19.3
16.5
22.3
:
11.0
25.3
21.1
17.5
:
23.9
:
18.0
:
22.7
20.5
:
11.5
20.7
16.8
21.9
:
:
24.9
5.7
13.3
:
18.5
36.8
10.4
18.1
19.4
9.5
19.0
6.8
2006
23.1
19.4
51.1
24.8
16.0
20.0
13.6
12.1
27.7
25.7
21.7
21.5
26.4
:
21.2
25.7
22.9
20.6
:
15.1
21.5
16.2
24.9
53.5
16.5
27.8
4.8
15.3
19.0
20.5
32.2
14.3
22.4
11.0
18.4
5.8
2009
19.7
17.7
41.0
23.1
15.2
18.5
13.3
17.2
21.3
19.6
19.8
22.4
21.0
:
17.6
24.4
26.0
17.6
36.3
14.3
27.6
15.0
17.6
40.4
21.2
22.2
8.1
17.4
18.4
16.8
24.5
15.7
15.0
17.6
10.3
13.6
5.8
2012
17.8
16.1
39.4
16.9
14.6
14.5
9.1
9.6
22.6
18.3
18.9
18.7
19.5
32.8
17.0
21.2
22.2
19.7
:
14.0
19.5
10.6
18.8
37.3
21.1
28.2
11.3
22.7
16.6
21.0
21.6
12.4
16.2
16.6
10.9
9.8
7.6
Boys
2012
23.7
20.8
50.9
22.8
19.2
20.1
14.2
13.0
32.2
23.4
25.5
27.6
25.9
44.5
25.7
31.9
26.6
26.9
:
17.2
26.2
16.2
25.0
46.8
30.5
35.4
17.7
31.3
19.8
29.8
30.9
14.8
22.5
22.2
15.2
13.1
10.4
Girls
2012
12.0
11.5
27.0
10.6
10.1
8.7
4.2
6.1
13.3
13.1
12.7
9.5
12.6
20.5
8.2
10.4
17.6
13.0
:
10.6
12.8
5.2
12.5
28.1
11.1
20.4
4.6
14.0
13.5
12.0
12.2
9.7
9.6
10.8
6.6
6.1
4.5
Average
score
All
2012
497
509
436
493
496
508
516
523
477
488
505
485
490
449
489
477
488
488
:
511
490
518
488
438
481
463
524
483
499
483
475
516
504
498
523
538
536
Source: OECD (PISA).
41
http://www.cedefop.europa.eu/en/events-and-projects/projects/analysing-skill-mismatch.
42
http://ec.europa.eu/eurostat/web/education-and-training.
23
Overall situation, general trends:
Since 2006, EU-level results have considerably improved, though overall progress from 2009
to 2012 was rather small with only 1.9pp. There is also a very big and persistent performance
gap between by gender, with boys showing twice the share of low achievers as girls.
Selected trends in performance:
The share of low achievers fell in all but four countries, with particularly large drops in four
countries (DE, EE, AT and PL). Conversely, it remains still very high in BG, RO, CY and SK.
EL, HU and SE worsened their position and are above the EU average, while FI also
increased, though still well below average.
Table 2:
Low achievers: PISA 2012 results in mathematics
EU 26 countries
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Turkey
Liechtenstein
Norway
USA
Canada
Japan
Korea
% low achievers in mathematics
All
Boys
Girls
2009
2012
2012
2012
22.3
22.1
21.2
23
19.1
19.0
19.3
18.5
47.1
43.8
45.1
42.3
22.3
21.0
19.3
22.7
17.1
16.8
15.1
18.6
18.6
17.7
16.8
18.7
12.6
10.5
10.6
10.4
20.8
16.9
15.2
18.7
30.3
35.7
34.5
36.9
23.7
23.6
22.1
25.1
22.5
22.4
22.3
22.4
33.2
29.9
28.8
31.0
24.9
24.7
22.8
26.7
:
:
:
:
22.6
19.9
21.5
18.3
26.3
26.0
27.7
24.3
23.9
24.3
20.1
28.7
22.3
28.1
27.6
28.5
:
:
:
:
13.4
14.8
13.9
15.8
23.2
18.7
16.1
21.2
20.5
14.4
15.0
13.8
23.7
24.9
24.0
25.9
47.0
40.8
40.4
41.2
20.3
20.1
20.4
19.8
21.0
27.5
27.6
27.3
7.8
12.3
14.1
10.4
21.1
27.1
28.2
26.0
20.2
21.8
19.7
23.8
17.0
21.5
23.2
19.7
42.1
42.0
40.8
43.2
9.5
14.1
11.2
17.3
18.2
22.3
22.6
22.0
23.4
25.8
26.5
25.2
11.5
13.8
13.4
14.3
12.5
11.1
10.9
11.2
8.1
9.1
9.2
9.1
Average scores
All
2009
2012
493
495
515
515
428
439
493
499
503
500
513
514
512
521
487
501
466
453
483
484
497
495
460
471
483
485
:
:
482
491
477
479
489
490
490
477
:
:
526
523
496
506
495
518
487
487
427
445
501
501
497
482
541
519
494
478
492
494
507
493
445
448
536
535
498
489
487
481
527
518
529
536
546
554
Source: OECD (PISA).
24
Overall situation, general trends:
Performance has virtually stagnated between 2009 and 2012 with a drop of low achievers of
only 0.2 pp. In most EU countries, boys still do slightly better than girls, but in a number of
countries girls are performing better (BE, BG, EE, CY, LV, LT, PL, SI, SK, FI and SE).
Selected trends in performance:
Countries that have improved their performance most since 2009 include RO, PL, AT, BG, IE
and HR, while in SK, SE, HU, FI and EL it worsened significantly.
Table 3:
Low achievers: PISA 2012 results in science
All
EU 27 countries
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Turkey
Liechtenstein
Norway
USA
Canada
Japan
Korea
2009
17.8
18.0
38.8
17.3
16.6
14.8
8.3
15.2
25.3
18.2
19.3
18.5
20.6
:
14.7
17.0
23.7
14.1
32.5
13.2
21.0
13.1
16.5
41.4
14.8
19.3
6.0
19.1
15.0
17.9
30.0
11.3
15.8
18.1
9.6
10.7
6.3
Share of low achievers
Boys
2012
2012
16.6
17.5
17.7
19.1
36.9
41.8
13.8
14.6
16.7
16.4
12.2
12.9
5.0
6.0
11.1
11.6
25.5
29.8
15.7
15.9
18.7
20.5
17.3
19.5
18.7
19.6
38.0
41.9
12.4
15.3
16.1
19.5
22.2
20.3
18.0
18.8
:
:
13.1
13.2
15.8
16.2
9.0
10.2
19.0
20.3
19.0
39.5
19.0
14.8
19.0
26.8
19.0
9.7
19.0
24.8
19.0
13.9
24.0
25.6
26.4
29.9
10.4
8.1
19.6
20.7
18.1
20.0
10.4
11.1
8.5
9.0
6.6
7.6
Girls
2012
15.7
16.2
31.7
12.9
17.0
11.5
4.1
10.6
21.3
15.5
17.0
15.0
17.8
34.0
9.4
12.6
24.2
17.4
:
13.0
15.4
7.9
17.7
35.3
10.8
26.9
5.6
19.6
16.0
22.4
22.7
13.0
18.5
16.2
9.7
7.9
5.6
Average scores
All
2009
2012
501
504
507
505
439
446
500
508
499
498
520
524
528
541
508
522
473
467
488
496
498
499
486
491
489
494
:
438
494
502
491
496
484
491
503
494
461
:
522
522
494
506
508
526
493
489
428
439
512
514
490
471
554
545
495
485
514
514
496
478
454
463
520
525
500
495
502
497
529
525
539
547
538
538
Source: OECD (PISA).
25
Overall situation, general trends:
There was a slight improvement in performance by 1.2 pp in the period 2009-2012. The
share of low achievers among girls is lower than those of boys, at EU level and in the large
majority of Member States (all except DK, LU and SK).
Selected trends in performance:
The share of low-achievers in science rose most in SK, HU, SE and PT while it fell
considerably in AT, RO, IE and CZ. CY recorded the worst rate in 2012, at more than double
the EU-average.
Table 4:
Low achievers: PISA results in mathematics, by socio-economic status
Average scores
Students according to socioDifference in performance between
economic status 2012
bottom and top quarter
Bottom quarter
EU countries
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Liechtenstein
Norway
USA
Canada
Japan
Korea
Top quarter
:
469
384
450
460
467
496
462
413
442
442
438
447
:
453
439
438
422
:
484
458
473
441
407
458
416
488
442
458
464
490
549
442
486
500
516
:
567
501
552
545
569
558
545
502
533
561
517
522
:
532
522
546
539
:
565
552
571
548
501
552
545
555
518
545
526
563
522
532
558
575
595
2003
2012
:
-133
:
-105
-98
-123
:
-88
-96
-80
-105
:
-86
:
-76
:
-100
-126
:
-99
-90
-96
-98
:
:
-119
-69
-90
:
:
:
:
:
:
:
:
:
-98
-117
-102
-85
-102
-62
-83
-89
-91
-119
-79
-75
:
-79
-83
-108
-117
:
-81
-94
-98
-107
-94
-94
-129
-67
-76
-87
-62
-73
27
-90
-72
-75
-79
Source: OECD (PISA).
26
Overall situation, general trends:
Students from the top quarter of the OECD's Socio-economic status index show significantly
higher skills levels across all countries than those from the bottom quarter. Though the gap
varies, it is in no country below 67 score points (almost two years of education), reaching 129
points in SK (equalling roughly 3.5 years of education). Since 2003, the gap has narrowed
considerably in BE, DK, DE, IE, EL, IT, HU, NL and SE, but still increased in ES, FR, LV, LU,
AT, PL, PT, SK.
Selected trends in performance:
Countries with a relatively small performance gap between migrant students and native
students include UK, CZ, HR, LV, LU and IE. In HU and SK migrants outperform native
students.
Countries with a large performance gap between natives and migrants include BE, NL, FR,
ES, IT and the Nordic countries.
27
Table 5:
Long-term unemployment (% of active population)
GEO/TIME
2008
2009
2010
2011
2012
2013
2014
EU 28
2.6
3.0
3.9
4.2
4.7
5.2
5.1
Belgium
3.3
3.5
4.1
3.5
3.4
3.9
4.3
Bulgaria
2.9
3.0
4.8
6.3
6.8
7.4
6.9
Czech Republic
2.2
2.0
3.0
2.7
3.0
3.0
2.7
Denmark
0.5
0.6
1.5
1.8
2.1
1.8
1.7
Germany
3.9
3.5
3.3
2.8
2.4
2.3
2.2
Estonia
1.7
3.7
7.6
7.1
5.5
3.8
3.3
Ireland
1.7
3.5
6.8
8.7
9.1
7.9
6.7
Greece
3.7
3.9
5.7
8.8
14.5
18.5
19.5
Spain
2.0
4.3
7.3
8.9
11.0
13.0
12.9
France
2.8
3.2
3.7
3.8
4.0
4.2
4.4
Croatia
5.3
5.1
6.6
8.4
10.2
11.0
10.1
Italy
3.1
3.5
4.1
4.3
5.7
6.9
7.8
Cyprus
0.5
0.6
1.3
1.6
3.6
6.1
7.7
Latvia
1.9
4.5
8.8
8.8
7.8
5.8
4.7
Lithuania
1.3
3.3
7.4
8.0
6.6
5.1
4.8
Luxembourg
1.6
1.2
1.3
1.4
1.6
1.8
1.6
Hungary
3.6
4.2
5.5
5.2
5.0
4.9
3.7
Malta
2.5
2.9
3.1
3.1
3.1
2.9
2.7
Netherlands
1.3
1.1
1.4
1.7
2.0
2.6
3.0
Austria
1.0
1.2
1.2
1.2
1.2
1.3
1.5
Poland
2.4
2.5
3.0
3.6
4.1
4.4
3.8
Portugal
4.1
4.7
6.3
6.2
7.7
9.3
8.4
Romania
2.3
2.1
2.4
2.9
3.0
3.2
2.8
Slovenia
1.9
1.8
3.2
3.6
4.3
5.2
5.3
Slovakia
6.7
6.5
9.3
9.3
9.4
10.0
9.3
Finland
1.2
1.4
2.0
1.7
1.6
1.7
1.9
Sweden
0.8
1.1
1.6
1.5
1.5
1.5
1.5
United Kingdom
1.4
1.9
2.5
2.7
2.7
2.7
2.2
Source: Eurostat.
28
Figure 1:
Beveridge curves of Member States43
43
Source: Eurostat Labour Force Survey and European Commission, EU Business and Consumer Surveys. Data
seasonally adjusted. The data used are the following: unemployment rate (UR, %); labour shortage indicator
(LSI, %), derived from EU business survey results (% of manufacturing firms pointing to labour shortage as a
factor limiting production).
29
30
31
32
33
34
Figure 2:
0
20
40
60
80
100
Average incidence of qualification mismatch among 25-65 year olds,
% of employees, 2014, EU-28
DE FR UK SE IT GR CZ PL NL DKHU ES AT BE IE SK FI PT EERO LT CY SI BG LV LU MTHR
Overqualified
Underqualified
Notes: Based on comparisons of an individual’s highest level of educational qualification with the (selfperceived) level of educational qualifications actually needed to do his/her current job.
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
Figure 3:
Qualification mismatch versus skill mismatch, % of adult employees, 2014,
EU28
45
41
40
35
30
26
25
Overskilled
20
Matched skills
Underskilled
15
10
10
5
8
6
4
4
1
1
0
Overqualified
Matched qualifications
Underqualified
Source: Cedefop’s European Skills and Jobs (ESJ) survey.
35