International Labour Review, Vol. 147 (2008), No. 2–3
Monitoring quality in work:
European Employment Strategy indicators
and beyond
Lucie DAVOINE,* Christine ERHEL **
and Mathilde GUERGOAT-LARIVIERE ***
Abstract. Within the framework of the European Employment Strategy, the European Union has defined a set of indicators to monitor employment quality – the socalled Laeken indicators. This article discusses and implements these indicators.
From a theoretical perspective, it shows that the concept of work quality encompasses several dimensions, which are likely to be related to national institutions, particularly industrial relations and welfare systems. It then proceeds with a comparative
analysis of quality in work across the 27 Member States, which confirms the existence
of several models in Europe and suggests that the Laeken indicators should be supplemented by additional measures.
T
he academic study of job quality has known major developments over the
past decade, especially in the fields of economics and industrial relations.
Labour economists’ growing interest in job satisfaction data has generated a
debate about the pre-eminent factors explaining workers’ judgements on the
quality of their jobs (Clark, 2005). Besides, many studies question the trend
decline in job satisfaction observed in national and European surveys, despite
rising real wages (Green, 2006), which could be explained, among other factors,
by some kind of work intensification and its impact on work-life balance. Job
quality has also become an economic policy issue both at the international level,
through the definition of “decent work” by the ILO (1999), and at the European
* Centre d’études de l’emploi and Paris School of Economics, email: lucie.davoine@gmail.
com. ** Paris School of Economics, University Paris 1 and Centre d’études de l’emploi, email:
[email protected]. *** Paris School of Economics, University Paris 1 and Centre
d’études de l’emploi, email: [email protected]. This article is based on results that were
obtained in a report financed by the DG Employment of the European Commission.
Responsibility for opinions expressed in signed articles rests solely with their authors and
publication does not constitute an endorsement by the ILO.
Copyright © International Labour Organization 2008
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International Labour Review
level, through the inclusion of so-called “quality in work” indicators in the European Employment Strategy in 2001 (European Commission, 2001a). 1 These
definitions involve a range of dimensions – including wage level, social security
and representation rights, type of contract, and training opportunities – which
can be influenced by labour market and social policies.
Nevertheless, these international indicators are rarely used in the literature, and apart from a few empirical studies (European Commission, 2001b,
2002 and 2003) and a special issue of the International Labour Review (2003),
very little is known about job quality from a comparative perspective.
This article tries to fill this gap by implementing, discussing and completing
European indicators. The empirical enquiry is based on hypotheses derived
both from the literature on job quality, and from the findings of comparative
labour market research. The article draws policy-oriented conclusions, concerning both the European Employment Strategy, in particular the relevant indicators to monitor quality in work, and the relationships between national
institutions and the quality of employment.
European Employment Strategy (EES)
and quality in work
The emergence of job quality in the EES
The introduction of work quality in the European debate about labour market
performance and labour market policy dates back to the Lisbon Summit, which
was held in 2000 against a background of emerging cooperation between Member States in the field of employment and social policy, based on the so-called
Open Method of Coordination and on the framing of the EES. At the Nice
Council in December 2000, job quality was included in the European Social
Agenda, and became an objective of the EES. Indicators of quality in work were
then defined at the Laeken Summit in December 2001. Quality in work is still an
official goal of the new EES adopted in 2003 with the aim of promoting “full
employment”‚ “employment quality and productivity”‚ “social inclusion and
social cohesion”. These three objectives were confirmed for the period 2005–08
by a Council decision of 12 July 2005.
Nevertheless, this growing interest in quality issues in the field of employment also shows signs of weakness. For instance, the 2004 Employment in
Europe report by the Commission does not include any specific chapter
devoted to work quality, contrary to the practice adopted in the three previous
years. The 2004 report by Wim Kok on employment and labour market policies
1 “Quality in work” refers to the original English version of European texts. In this article,
we will also use the terms “job quality” and “work quality” as direct equivalents. Nevertheless, to
avoid ambiguities about the concept, we will not use the English counterpart of the French translation “qualité de l’emploi”‚ which is also used sometimes in European texts.
Monitoring quality in work
165
(entitled Jobs, jobs, jobs) focused on quantitative aspects of employment (and
especially the employment rate and incentives to work), without any consideration of quality.
This brief history of quality in work at the European level highlights the
ambiguity of the concept. On the one hand, it appears to be an innovation testifying to a will to renew the European Social Model. But on the other hand, it is
strongly embedded in economic and political contexts. Indeed, the concern for
quality was supported by left-wing governments – which were a majority in the
EU at the end of the 1990s – in a successful economic context, characterized by
growing employment. But the increase in unemployment and the weakness of
social democratic parties in the 2000s have limited the scope for quality concerns. The objective is still present in the EES, but its substance has changed:
quality is increasingly interpreted in terms of job productivity and the financial
benefits of job creation. And hesitations over the definition of work quality
reveal more general ambiguities in the EES (Erhel and Palier, 2005).
From a European policy perspective, reference to quality in work since
2000 appears to have been a political compromise, which has met with uncertain
and variable success. Nevertheless, results have been achieved in terms of indicators and monitoring.
Indeed, the political process has led to the definition of common indicators
(European Commission, 2001a). This EU definition of job quality relies on a
multi-dimensional approach, based on ten groups of indicators relating to:
intrinsic job quality; skills, life-long learning and career development; gender
equality; health and safety at work; flexibility and security; inclusion and access
to the labour market; work organization and work–life balance; social dialogue
and worker involvement; diversity and non-discrimination; overall economic
performance and productivity. According to the Commission, the first two
dimensions concern the “characteristics of the job itself”‚ whereas the other
eight dimensions concern “the work and wider labour market context”. At the
Laeken Council and in the Employment Guidelines for 2002, key indicators and
context indicators were defined for each of these dimensions, except for social
dialogue on which no political compromise could be reached. These indicators
(listed in Appendix A) are likely to be calculated on the basis of European surveys (European Community Household Panel, Labour Force Survey, etc.). In its
Employment in Europe reports (2001, 2002, 2003), the Commission undertook
to implement them and proposed some empirical analysis of the relationships
between job quality and job quantity, and job quality and flexibility.
Despite these efforts, European job quality indicators suffer from important weaknesses. First, the concept of quality in work is weakly defined, on the
basis of a political consensus rather than by theoretical analysis. For instance, as
a result of the position adopted by the United Kingdom and the Scandinavian
countries, the definition does not include wage level as a component of work
quality, whereas other countries (e.g. France) were in favour of taking this indicator into consideration. An agreement was reached by the introduction of a
wage mobility indicator (Barbier and Samba Sylla, 2004).
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International Labour Review
The concept of job quality: Recent contributions
and the Laeken indicators
In labour economics, job quality was traditionally understood as being captured
by the wage level; and in some sociological or industrial relations studies, it was
related to working conditions. But recent developments in economics and socioeconomic approaches can contribute additional dimensions to the definition of
job quality.
Developments in human capital theory (Becker, 1964) recognize the heterogeneity of jobs and workers, and a first step can be made to differentiate
degrees of job quality according to the skills involved in particular jobs or the
skill-matching between workers and jobs. At the macro level, market failures
can lead to underinvestment in human capital, so that investment and participation in education and training activities could be an indicator of employment
quality.
In the recent framework of the “economics of happiness” (Layard, 2005),
the approach to job quality is enriched by the consideration of workers’ points of
view through the development of surveys on job satisfaction and workers’ wellbeing. Such surveys make it possible to determine the dimensions of job quality
by asking people what is more important to them. For instance, according to
ISSP data (Clark, 2005), “job security” and an “interesting job” are “very
important” for a majority of people and seem to be more important than items
such as “being allowed to work independently”‚ “good opportunities for
advancement”‚ and “high income”. According to such studies, it appears that
the absolute wage level is not so important. Comparison effects and habit effect
dominate: workers are unhappy if they are paid less than their colleagues or
peers (all else being equal), and wage rises have only a transitory effect (Clark,
1999). These results suggest that decent living standards, wage equity, and good
wage mobility could be taken as indicators of employment quality. A modern
definition of job quality should also take into account the impact of employment
on the other spheres of life. Indeed, the possibility of reconciliation between
work and family life appears to be a very important dimension of job quality
according to workers’ responses to the European Social Survey. This is also consistent with policy-oriented approaches, like the “transitional labour market”
perspective (Schmid and Gazier, 2002; Schmid, 2006), which stresses the importance of out-of-work quality dimensions, such as the right to training, to occupational redeployment or retraining, to a family life, and to decide one’s working
hours throughout the life cycle.
The recent framework suggested by Green (2006) integrates these results
and recognizes the multi-dimensional character of job quality. Indeed, this
author studies job quality through the evolution of different dimensions –
including skills, work effort and intensification, workers’ discretion, wages, risk
and job insecurity, and workers’ well-being – and thus takes into account the
multi-dimensional nature of job quality.
Monitoring quality in work
167
To sum up, this short review of the literature shows that employment quality is gaining prominence in the research agenda of labour economists, and that it
is preferentially treated as a multi-dimensional concept, covering the following
four main aspects:
•
socio-economic security (i.e. decent wages and secure transitions);
•
skills and training;
•
working conditions;
•
ability to combine work and family life, and promotion of gender equality.
These dimensions can be captured through a combination of objective and subjective data, and should be interpreted in both static and dynamic perspectives,
using data on transitions. A definition incorporating these four components
would match the framework proposed by the European Foundation for the
Improvement of Living and Working Conditions (2002).
The Laeken indicators also partly fit with these conclusions. Indeed, the
ten dimensions of Laeken can be related to these four synthetic components. 2
This definition also calls for self-reported data and embodies a broad concept of
job quality, involving other life spheres, especially family life. Nevertheless,
the EU definition also has important limitations if one compares it with the
academic literature or with other job quality definitions, like the ILO’s decent
work concept or the European Foundation’s approach.
First, although the Laeken definition provides a broad coverage of job
quality issues, it excludes some crucial dimensions, such as wages and work
intensity. We argue for adding a wage variable to the set of indicators used to
capture a job quality – namely, the mean wage in purchasing power parity – and
an indicator of wage “dispersion”‚ e.g. the proportion of working poor. These
wage indicators are part of the socio-economic security dimension.
Second, some dimensions are only partially covered. Concerning skills and
training, for example, the Laeken indicators focus only on the occurrence of onthe-job vocational training episodes without regard to the volume or intensity of
such activities (that can be measured through the average number of hours spent
on formal training, the cost of formal training per participant). On working conditions, the only Laeken indicator is the rate of change in the incidence of accidents at work, which gives a very limited view of working conditions. Selfreported data from the fourth European Survey on Working Conditions
(ESWS) managed by the European Foundation for the Improvement of Living
and Working Conditions can be used to construct complementary indicators,
since it gives information on physical risks and pains, stress, working hours and
working conditions.
2 Socio-economic security corresponds to Laeken dimensions 1, 5, 6 and 9; training corresponds to Laeken dimensions 2 and 10; working conditions corresponds to Laeken dimensions 4 and
8; reconciliation of work–family life and gender balance corresponds to Laeken dimensions 3 and 7.
168
International Labour Review
Beyond the Laeken indicators: A more
disaggregated approach
This section proposes a first comparative approach to job quality in Europe. It
follows the four dimensions defined above, and is based both on the Laeken
indicators and on some complementary variables. 3
Socio-economic security
This dimension aims to analyse security both in work and out of work (table 1).
The introduction of variables on wages clearly distinguishes the new Member
States from the rest: wages in new Member States are the lowest in Europe, and
these countries have the highest proportions of workers who report not being
well paid and not having good prospects for career advancement. The proportion of non-standard contracts (both part-time and fixed-term) is much lower in
these countries than in the rest of Europe, but the proportion of employed
workers at risk of poverty is high in most of them. Except in Poland and Slovakia,
the long-term unemployment rates are comparable to those of continental
countries such as Belgium, France or Germany. Continental countries, as well as
northern countries and the so-called liberal countries (namely Ireland and the
United Kingdom) are characterized by good wages. The proportions of parttime workers are high in these countries, but the proportion of employed
workers at risk of poverty is relatively small and workers have rather good prospects for career advancement.
The situation of southern countries (Greece, Italy, Portugal, Spain) is
intermediate in terms of wage levels: between that of continental countries and
that of the new Member States. These comparatively low wages may explain
why employed workers face a particularly high risk of poverty in these countries.
Fixed-term contracts are very common, especially in Spain, whereas the incidence of part-time work approximates the European average. Out-of-work
security – measured by long-term unemployment – also appears rather low in
the southern countries.
Education and training
European country performances in terms of initial education are very diverse
(table 2). On one hand, southern countries are characterized by low levels of
initial education: high proportions of early school leavers, few people with upper
secondary education. On the other hand, the new Member States and the
northern countries show high levels of initial education. Central and eastern
European countries are characterized by very high proportions (over 80 per
cent) of people who have completed upper secondary education. This feature
can be seen as a result of these countries’ communist past: the communist regime
3
Tables 1 to 4 present the main Laeken indicators (in bold) and complementary indicators.
Monitoring quality in work
Table 1.
169
Socio-economic security
Part-time
workers
as % of total
employment
Fixed-term
contracts
as % of total
employment
Long-term
unemployment rate
Belgium
22.2
8.7
4.2
Bulgaria
2.2
6.2
5
Czech Republic
In-work at risk
of poverty
4
Mean wage
in PPS
55
35
1 784
28
25
35
25
54
39
5
8.7
3.9
3
23.6
8.9
0.8
5
46 122
Germany
25.8
14.5
4.7
5
40 954
7.8
2.7
2.8
7
Greece
5.7
“My job
offers good
prospects
for career
advancement” (%)
35 704
Denmark
Estonia
“I am well paid
for the work
I do” (%)
58
30
34
24
27
10.7
4.8
13
16 739
32
Spain
12
34
1.9
10
19 828
48
29
France
17.2
13.5
4
6
28 847
36
37
3.4
1.4
6
56
42
13.3
13.1
3.4
9
34
24
7.7
13.1
0.9
7
19 290
55
32
3 906
Ireland
Italy
Cyprus
Latvia
6.5
7.1
2.5
9
Lithuania
9.9
4.5
2.5
10
Luxembourg
Hungary
32
28
32
23
17.1
6.1
1.4
9
4 0575
58
40
4
6.7
3.4
10
7 100
18
19
Malta
10.1
3.8
2.9
5
11 926
44
44
Netherlands
46.2
16.6
1.7
6
37 900
58
35
Austria
21.8
9
1.3
7
34 995
51
34
Poland
9.8
27.3
7.8
14
6 230
29
25
Portugal
11.3
20.6
3.8
12
14 253
29
35
Romania
9.7
1.8
4.3
24
18
Slovenia
9.2
17.3
2.9
5
35
31
Slovakia
2.8
5.1
10.2
9
5 706
25
18
Finland
14
16.4
1.9
4
31 988
35
35
Sweden
25.1
17.3
1.1
5
33 620
40
27
United Kingdom
25.5
5.8
1.2
8
41 253
54
42
Source: LFS, EWCS, Eurostat & European Commission, 2006.
guaranteed free education for everyone and thus allowed a great majority of the
population to achieve upper secondary education. However, this result should
be nuanced as it appears that the skills and competence imparted to students are
sometimes obsolete and no longer meet the needs of the labour market (Egger,
2003). Continental countries have an average position in terms of initial education: between 65 and 80 per cent of people aged 25 to 64 have completed upper
secondary education.
In terms of vocational training, the northern countries and the United
Kingdom perform better than the other countries, though the intensity of training (measured by the number of hours) and the cost per participant is lower in
the United Kingdom. Vocational training is still little developed in the southern
countries (apart from Spain) and in the new Member States. However, while the
170
Table 2.
International Labour Review
Education and training
Participation
in training
and education
Percentage
Early school
of population aged
leavers
25 to 64 years
with at least upper
secondary education
Cost of CVT
courses
per participant
(in PPS)
Hours in CVT
course
per participant
Belgium
7.5
66.9
12.6
1 644
31
Bulgaria
1.3
75.5
18
1 053
35
Czech Republic
5.6
90.3
5.5
602
25
Denmark
29.2
81.6
10.9
2 141
41
Germany
7.5
83.3
13.8
1 593
27
Estonia
6.5
88.5
13.2
1 030
31
Greece
1.9
59
15.9
1 529
39
Spain
10.4
49.4
29.9
1 514
42
France
7.5
66.9
13.1
1 625
36
Ireland
7.5
66.2
12.3
1 454
40
Italy
6.1
51.3
20.8
2 177
32
Cyprus
7.1
69.5
16
Latvia
6.9
84.5
19
729
34
Lithuania
4.9
88.3
10.3
659
41
Luxembourg
8.2
65.5
16.4
1 666
39
Hungary
3.8
78.1
12.4
1 166
38
Malta
5.5
26.5
41.7
Netherlands
15.6
72.4
12.9
2 132
37
Austria
13.1
80.3
9.6
1 160
29
Poland
4.7
85.8
5.6
598
28
Portugal
3.8
27.6
39.2
1 387
38
Romania
1.3
74.2
19
541
42
515
24
1 393
36
Slovenia
81.6
5.2
Slovakia
15
4.3
88.8
6.4
Finland
23.1
79.6
Sweden
32.1
84.1
12
1 434
31
United Kingdom
26.6
72.6
13
1 286
26
8.3
Source: LFS, CVTS, Eurostat.
proportion of people who take part in vocational training is small, this is offset in
some of these countries by a relatively higher number of hours per participant.
As with initial education, continental countries have an average performance in
terms of vocational training.
Working conditions
In most of the new Member States and in Greece, working conditions are worse
than in the rest of Europe: these countries have the lowest proportions of
workers who report being satisfied with their working conditions, and many
workers (always more than the 34 per cent EU average) say that their health is at
risk because of work (table 3). These countries are also characterized by long
Working conditions
Rate of work
accidents
(per 100,000
in employment)
Satisfied
or very satisfied
with working
conditions (%)
Health is at risk
because
of work (%)
Working to tight
deadlines (%)
Working at very
high speed (%)
Long working
days (%)
Job involves
painful/tiring
positions (%)
Repetitive
tasks
< 10 min (%)
Night work (%)
Consulted
about changes
in work organization, etc. (%)
–32
–31
–13
–17
–26
17
–29
–14
–11
6 157
2 566
24
44
24
23
18
39
51
37
23
24
28
34
49
43
30
33
35
23
23
47
31
49
46
35
24
48
19
62
54
71
69
71
60
68
55
54
57
59
69
51
58
56
59
70
61
69
56
53
62
67
55
74
72
63
60
27
60
75
72
57
73
60
50
42
67
73
40
49
57
64
59
61
72
43
51
67
75
57
78
85
47
18
24
20
15
13
19
26
14
14
22
15
14
25
20
11
20
20
13
20
21
13
36
18
21
16
15
18
39
46
30
33
46
51
66
48
53
32
49
60
49
47
44
53
44
25
50
51
57
61
52
33
45
44
31
26
40
38
47
43
38
35
48
35
28
41
45
30
46
35
26
38
45
35
26
47
42
41
32
60
38
37
14
17
21
13
15
14
15
11
16
–24
3
5
–15
–10
–15
–27
–32
–20
8
–18
3
–4
–41
–9
–20
–17
90
67
80
93
89
75
60
79
82
87
76
83
70
67
86
76
81
89
90
79
85
59
72
76
84
85
93
58
62
47
54
42
63
52
39
43
56
38
57
55
79
46
50
50
83
47
42
28
42
49
51
72
60
52
1 656
3 100
780
6 136
3 950
3 599
4 114
1 960
Source: Eurostat, national data, EWCS.
11
9
14
12
9
13
19
16
20
22
19
12
20
24
17
13
21
171
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Greece
Spain
France
Ireland
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Percentage
change in rate
of occupational
accidents,
1999–2004
Monitoring quality in work
Table 3.
172
International Labour Review
working days and by high proportions of people whose work involves painful
positions. Moreover, when looking at the Laeken indicator about the evolution
of occupational accident rates, it appears that the new Member States do worse
on average than the other members. Some of them have even experienced growing rates of occupational accidents since 1999.
The northern countries perform rather well on many variables, particularly Denmark and Sweden on occupational accidents. However, they have
Europe’s highest intensity of work, measured by both indicators “working at
very high speed” and “working to tight deadlines”‚ and many workers report
that their job involves repetitive tasks.
Continental and liberal countries are characterized by rather good working conditions: consistently more than 85 per cent of workers are satisfied with
their working conditions; only around 23 per cent of them report that their
health is at risk because of work whereas the European average is 34 per cent.
Occupational accident rates are declining particularly sharply in these countries,
and their remaining indicators present average values.
In the southern countries and in France, the proportion of workers who
report being consulted about changes in work organization is lower than in the
other countries, and many of them report painful or tiring working positions.
Other indicators show average values.
Gender and work–family reconciliation
Some general patterns are observable in regard to gender and employment
across Europe (table 4). There seems to be a trade-off between women’s employment rate and labour market segregation; there is a negative correlation between
the employment impact of motherhood and the availability of childcare facilities
for children under three. More specifically, one can observe a strong positive correlation between the availability of childcare facilities offering services from one
to 29 hours per week and the proportion of women working part-time.
Empirical analysis shows huge differences between European countries.
The Mediterranean countries are in a very specific position in terms of gender
and work–family reconciliation: the gender employment gap is wide in these
countries, but segregation and the gender pay gap are generally less pronounced, as unqualified women do not enter the labour market. As a result of
women’s high rate of inactivity, the impact of maternity on employment is rather
small. The northern countries are characterized by very well developed childcare facilities, a narrow gender employment gap but rather high levels of segregation and fairly wide gender pay gaps.
The position of the new Member States and continental countries in terms
of gender and work–family reconciliation is less clear-cut. In the continental and
liberal countries, employment gaps are around the European average, as are
segregation indexes. Childcare facilities are rather well developed but the
employment impact of motherhood varies a lot across countries. Correlations
between these two indicators are pretty hard to explain as they can be affected
Gender and work–family reconciliation
Gender pay gap
Gender employment gap
(men–women)
Sectoral
segregation
Occupational
segregation
Employment
impact
of parenthood
(women), 2006
Childcare:
< 3 years old
Belgium
Bulgaria
Czech Republic
Denmark
Germany
Estonia
Greece
Spain
France
Ireland
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
7
16
19
18
22
25
9
13
12
9
9
25
17
15
14
11
4
18
18
13.9
8.2
16.9
7.8
11.3
5.7
27.2
22.9
10.8
18.4
24.2
19.1
8
5.3
18
12.7
39.6
13.2
13.4
18.3
19.6
19.1
19.4
18.2
24.3
15.9
20.4
18.1
22.7
17.8
19.6
23.8
23.1
18.3
19.9
16.5
18
19.3
26.1
28.7
28.1
27.8
26.5
31.6
22.4
27.1
26.6
26.8
23.7
29.3
29.4
29.4
26.4
28.8
24.7
25.6
25.9
0.9
21.9
40.5
3.4
26.5
25.7
4.7
8.4
9.7
18.2
5.6
4.3
19.4
4.3
5.9
33.6
11.6
8.1
17.7
42
98
100
3.5
2
73
16
12
7
39
32
20
25
19
18
11
22
17
5
40
98
99
98
97
99
99
100
99
100
100
96
97
97
100
98
100
98
7
11.5
3
12
4
4
4
5
5
4
4
12
10
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
10
9
13
8
24
20
16
20
12.7
11.9
11.6
9.3
15.1
4.1
4.8
11.5
19.4
20.4
15.5
17.8
22.8
22.7
21.6
18.6
25.5
26.5
22.8
26.8
29.9
29
26.8
25.6
10.3
–3.9
2.1
–5.5
32.8
17.5
4
2
29
70
94
87
78
61
94
95
78
91
85
66
57
63
79
55
89
69
30
64
96
99
4
4
24
3
27
53
30
77
67
76
87
100
96
83
100
96
100
12
6.5
10
16
1.5
21.3
Childcare:
compulsory
school age–
12 years old
Number of months
of maternity leave
3
4
4
173
Source: LFS, national data, Eurostat & European Commission, 2006.
Childcare:
3–compulsory
school age
Monitoring quality in work
Table 4.
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International Labour Review
by many different variables: the number of hours children are taken care of over
the week, the length of maternity leave, the proportion of women who are working part-time, but also cultural traditions.
In the new Member States, childcare facilities are less developed than in
the rest of Europe, although there are disparities between countries. The
employment impact of motherhood is very high in these countries, except in
Slovenia and Lithuania (where maternity leave is particularly long) and
in Romania. The gender pay gap, employment gap and segregation indexes are
at the European average.
This initial descriptive approach highlights the heterogeneity of European
labour markets in terms of job quality. Besides, complementary indicators matter in this overview, especially for socio-economic security, training and working
conditions. This calls for going beyond the Laeken indicators.
How many models of job quality does Europe have?
The aim of this second part of our empirical investigations is twofold: first, to
produce a taxonomy of EU Member States according to job quality indicators,
and to test its consistency with standard comparative results obtained from
studies of European labour markets; and, second, to evaluate the consequences
of introducing complementary indicators into the current EES approach.
Accordingly, the analysis proceeds in two steps. A first sub-section presents the
taxonomy of Member States based on the complete set of Laeken indicators,
and a second sub-section considers the supplementary indicators needed to
analyse particlular aspects of job quality.
An analysis based on the Laeken indicators
Figure 1 presents the results of a PCA (see box 1) based on the full set of Laeken
indicators (both key and context indicators) for the period 2005–2006. The first
(horizontal) axis explains 36.4 per cent of the total variation (in the correlation
matrix) and the second (vertical) axis, 18.9 per cent. The first axis is positively
correlated with participation in education and training, and with employment
rates. Job satisfaction and childcare services are also positively correlated with
this axis, albeit to a lesser extent. This axis is negatively correlated with the longterm and youth unemployment rates.
The second axis is positively correlated with the proportion of people who
have attained upper secondary education (ISCED level 3) and productivity
growth; and it is negatively correlated with the proportion of early school
leavers, the employment rate of low-educated people, and the male–female
employment gap.
The third axis accounts for 8.6 per cent of total variance in the data and is
mainly defined by the proportion of early school leavers, the evolution of the
rate of occupational accidents, and labour market inequalities (women, older
workers and young people).
Monitoring quality in work
175
Figure 1 maps job quality in Europe in the first two dimensions of the PCA.
Furthermore, a cluster analysis divides the 27 EU Member States into five
clusters. Figure 2 shows the position of each cluster in terms of the Laeken key
indicators.
A northern cluster includes Sweden, Denmark, Finland and the United
Kingdom. A southern cluster includes Spain, Italy, Portugal, Greece and Malta.
A continental cluster groups Germany, France, Belgium, Luxembourg, Austria,
the Netherlands, Ireland and Slovenia. Apart from Malta and Slovenia – respectively in the southern and continental clusters – the new Member States are divided
into two groups: the first is composed of Estonia, Latvia, Lithuania, Cyprus,
Czech Republic, Hungary, Bulgaria and Romania, and the second comprises
Poland and Slovakia.
In contrast to the standard results encountered in the institutionalist comparative literature (Amable, 2003, Esping-Andersen, 1990), 4 the so-called liberal model disappears: the United Kingdom is included in the northern cluster,
while Ireland joins the continental cluster. As it has already been mentioned in a
4 Esping-Andersen’s typology distinguishes three main clusters: the liberal model, the social
democratic model, and the conservative model. Amable’s typology, which is based on a larger set of
variables, identifies five models of capitalism: Liberal, Nordic, Continental, Mediterranean, and
Asian.
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Box 1. Methodology and a guide for reading the figures
Principal Components Analysis (PCA) is used in both sub-sections in order to obtain a
comparative view of job quality regimes in Europe, taking into account their different
dimensions. PCA identifies a limited number of factors or components that can account
for most of the correlation matrix of the variables considered in the analysis. The PCA is
followed by a cluster analysis. The objective of this dual approach is first to map job
quality and then to group Member States into a few distinctive clusters.
Principal Components Analysis (PCA) is a technique used to describe large correlation matrices. 1 The value of PCA lies in its ability to “reduce” large data sets to a few factors or principal components. Linear combinations of the principal components should
then be able to account for a high proportion of the total variation in the original data. A
very useful property of PCA is that the principal components are uncorrelated and can thus
be seen as representing different “statistical dimensions” of the original data set. However,
it must be stressed that PCA cannot always reduce a large number of variables to a small
number of transformed variables. In fact, a significant reduction of the “dimensionality” of
the data set can only be obtained when the original variables are strongly correlated (either
positively or negatively). PCA is of no value if the original variables are uncorrelated.
The greater the proportion of the variation in the data explained by the first two
axes, the better the graphic representation. The contribution and meaning of these axes
are detailed. The third axis is also mentioned when it provides valuable information,
though it is not represented in the figures. Figures 1 and 3 present the factor scores for
EU Member States on the first two axes. The size of each point is proportional to the
relevance of each country in defining the space represented by the first two axes.
The description of clusters analysis is given in Appendix B. The first step of the clustering – called hierarchical ascending clustering method – consists in gathering together
those individuals or classes of individuals that resemble each other the most according
to the Ward criterion (maximization of inter-class and minimization of intra-class
variance). The output of this step is a classification tree or dendrogram (see Appendix B).
In a second step, the tree is partitioned in order to get an optimal number of clusters.
Several partitions are proposed by the software according to the optimization criteria.
Generally, we have chosen an intermediate number of clusters (for example, 5 instead of
3 or 10). These clusters should be considered with care, as it appears that adding or suppressing a few variables can slightly modify the clusters. But the position of the countries
on the map exhibits only small variations.
1
The software used for these PCA is SPAD.
previous paper (Davoine and Erhel, 2008), this counter-intuitive result reflects
the existence of functional equivalences across different institutions and/or policies that are equally successful in improving job quality.
The northern cluster is on the right-hand side of figure 1, being characterized by high participation rates in education and training and high employment
rates, already close to (or even above) the EES targets for 2010. Job satisfaction
is also higher than in other countries: almost 90 per cent of workers are satisfied
with their working conditions. Childcare facilities are very well developed in
these countries compared to the rest of Europe. These characteristics are illustrated in figure 2.
The southern cluster is characterized by a high proportion of early school
leavers. These countries exhibit wide male–female employment gaps (except
Portugal) but little segregation, and narrow gender pay gaps. Their relative performance on education and training is poor, though Spain does somewhat better
in this respect.
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(continued overleaf)
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(continued overleaf)
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181
The continental cluster comes close to the EU average on most of the indicators considered. For example, the countries in this group have average values
for participation in education and training, the proportion of early school
leavers, and the proportion of people who have attained the ISCED level 3 of
education. Furthermore, this cluster is characterized by high productivity and
significant differences in employment rates between older people and the rest
of the population. This high level of inter-generational inequality can be seen on
the third axis. However, there is also some heterogeneity within this group of
countries. For example, Austria and the Netherlands tend to be closer to the
northern cluster. This can be explained by their relatively high participation
rates in education and training compared to those of the other continental
countries. Slovenia falls in the continental cluster in the PCA carried out in this
sub-section, because of its relatively good performances on employment rates,
and education and training, compared to the other new Member States.
Although Ireland and the United Kingdom have many common features –
such as low rates of long-term and youth unemployment, limited use of fixedterm contracts and high job satisfaction – they do not belong to the same group.
This is due mainly to their markedly different performances on education and
training: the United Kingdom is characterized by a high rate of participation in
training, at 26.6 per cent, as against only 7.5 per cent in Ireland.
In the new Member States, rates of participation in training are low, while
low-skilled employment rates are also rather low. Poland and Slovakia exhibit
high long-term unemployment rates and low employment rates. The other new
Member States are mainly characterized by very low levels of productivity but
high rates of productivity growth, which is typical of countries engaged in a
catching-up process. Workers in this group of countries are less satisfied than
their counterparts in other countries.
As regards initial education, the performance of the new Member States is
very good: they have a low proportion of early school leavers and a rather high
proportion of people who achieve the ISCED level 3 of education. Bulgaria and
Romania, however, perform less well on this indicator than the other countries
in the group.
All considered, this analysis confirms that there is a significant degree of
heterogeneity across the EU27 as regards job quality, which can be summarized
in terms of five distinctive models. As suggested by the institutional complementarity framework (e.g. Amable, 2003), different institutional settings can at times
lead to similar performances, i.e. there might be functional equivalence. For
instance, the United Kingdom is close to Nordic countries despite having different institutions.
Complementary indicators and a second taxonomy
The set of Laeken indicators may be improved upon in order to produce a better
definition of job quality and to allow more relevant comparisons between EU
Member States. The aim of this sub-section is to propose an alternative set of job
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International Labour Review
quality indicators, and to compare the new taxonomy derived therefrom with
Laeken-based results detailed above.
The definition of this set of indicators relies on the following principles.
First, some of the Laeken indicators are redundant (e.g. total employment rates
and employment rates by age group): this new analysis does not break down
variables as finely as the Laeken definition does. Second, the weight given to
each dimension is crucial to any analysis of job quality. In this regard, the Laeken
portfolio is not satisfactory: for example, there are many indicators measuring
participation in training and education, but only one for working conditions.
This new analysis aims to give equal importance to each of the four dimensions
of job quality mentioned above. Third, some important dimensions of job
quality are not included in the Laeken definition and should be incorporated
into the set of indicators. We have therefore introduced some complementary
indicators, such as wage level, work intensity, and characteristics of training, following the results obtained above.
In this new PCA, the first axis accounts for 26.4 per cent of the total variance in the data (figure 3). On its left-hand side, this axis is defined by relatively
bad labour market performance – long-term unemployment, involuntary parttime employment, youth unemployment – but also by bad working conditions
(health at risk because of work, long working days, painful or tiring positions)
Monitoring quality in work
183
and by a high in-work risk of poverty. On the right-hand side, countries are
mainly characterized by high levels of mean wage, job satisfaction, training and
computer use, and high employment rates, but also by a high proportion of parttime workers and high productivity.
Two of the four main dimensions of job quality are thus represented on the
first axis: socio-economic security and working conditions. Indeed, bad working
conditions appear to be correlated to economic insecurity (in-work risk of poverty and long-term unemployment). The issue of work intensity is more relevant
to countries with high wages and relatively good socio-economic security. These
results confirm the synergy between quantitative and qualitative performance,
as bad working conditions and high in-work risk of poverty are correlated to the
indicators that represent the more quantitative aspect of job quality, namely,
employment and unemployment rates.
The second axis accounts for 16.5 per cent of the variance in the data. Its
positive (top) part is defined by a large male–female employment gap and low
educational attainment, but also by the cost of CVT courses per participant. The
negative part of the axis is characterized by a high proportion of people who
have attained at least upper secondary education, productivity growth, and
marked labour market segregation between men and women coupled with a
wide gender pay gap and long maternity leave. The two main aspects of job quality that are represented on this axis are gender (in)equality and initial education
(whereas vocational training is represented on the first axis).
The third axis is mainly about gender equality issues and working conditions, with variables that do not appear in the two first axes (childcare, night
work, repetitive tasks).
The clustering analysis yields very similar results to those based on the
Laeken indicators: all countries belong to the same clusters apart from Cyprus,
which now falls in the continental cluster, and the Netherlands, which shifts to
the northern cluster. Also, Poland and Slovakia are now squarely within the
group of new Member States. The following comments will therefore focus on
the impact of the new variables introduced to supplement the Laeken indicators.
This new PCA reinforces the contrast between the northern countries and
most of the new Member States in terms of working conditions and socio-economic security. The northern group is characterized by high wages and good
working conditions, but intensity at work is particularly high in these countries
compared to the rest of the EU. Higher intensity at work is usually associated
with bad working conditions. Indeed, recent studies show intensification of work
to be a factor of deterioration in working conditions. New forms of work organization play an important role in the evolution of work intensity (Green, 2006).
The new Member States, by contrast, display low socio-economic security
(low wages and high long-term unemployment rates) and rather bad working
conditions (long working days, health at risk because of work), but the intensity
of work is much lower in these countries than elsewhere.
The introduction of a new variable on social dialogue seems to confirm
that the southern countries are characterized by a lack of dialogue between
employers and workers on work organization.
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International Labour Review
This new set of job quality indicators allows for more precise and complete
specification of the clusters in terms of job quality. Further changes in the set of
job quality indicators could be considered, like suppressing all quantitative
labour market indicators, but they would deviate too far from the Laeken definition. In general, these empirical results are quite surprising by comparison with
the usual typologies as far as the liberal model is concerned. Indeed, the introduction of new variables on working conditions and socio-economic security
does not change the position of the United Kingdom in relation to that of the
northern countries. This suggests that there are two pathways to high job quality,
which is consistent with the findings of other recent analyses of labour market
performance, based on more quantitative indicators (OECD, 2006). Besides, in
order to display a distinct liberal model, comparative analysis of European
labour markets requires institutional variables, especially job protection legislation (European Commission, 2007).
Job quality and job quantity:
Is there a trade-off?
Some authors have argued that the dynamism of employment creation in the
Anglo-Saxon countries carries a cost in terms of job quality. Green (2006), for
instance, has documented an increase in work intensity in the United Kingdom,
while American studies highlight the development of “bad jobs”‚ characterized
by low pay and poor working conditions (Appelbaum, Bernhardt and Murnane,
2003). Nevertheless, at the macroeconomic level, economic theory suggests a
positive link between job quality and economic growth, and thus the absence of
any trade-off between job quality and quantity. Such a positive relationship
operates through several channels.
First, there are a number of well-known arguments linking human capital
and economic growth. Endogenous growth models show that human capital accumulation increases the growth rate (Lucas, 1988). Investment in training and
education yields increasing returns, generating positive externalities, i.e. a
higher level of education increases not only individual productivity, but also the
productivity of co-workers. There are also network effects, making a given
amount of training all the more effective as there are positive spillovers affecting
other workers in the network.
Second, there are also some links between workers’ security and economic
growth. Security must be understood here in a broad perspective, including job
protection, but also safe working conditions, fair wages and access to social protection. All these components of security in work may increase productivity.
The empirical evidence on this point includes a study by Auer, Berg and
Coulibaly (2005), who have shown that labour productivity is positively related
to job tenure. The ILO (2005) has also carried out analyses of social protection
as a productive factor. A good level of social protection increases labour productivity, because it preserves and increases human capital through health
policies, but also through unemployment insurance and active labour market
Monitoring quality in work
185
policies (Boyer, 2006). Many security mechanisms work as automatic stabilizers,
which are particularly helpful during economic downturns. Increasing economic
security in general – and that of workers, in particular – can foster productivity
growth. Thus, the various dimensions of job quality can increase workers’ productivity and have a positive influence on economic growth and employment
creation.
Our empirical results tend to validate this positive view of the link between
job quality and quantity. At this stage, these results suggest that there is no tradeoff between the two. Among our clusters, we observed correlation between
quality indicators and employment levels. For instance, Denmark, Sweden and
the United Kingdom exhibit good outcomes in terms of employment quality as
well as high employment rates. Southern countries and new Member States are
characterized by both low employment rates and lower values for the main job
quality indicators. Besides, the correlation between employment rate and
employment quality indicators is positive and significant when longitudinal data
are used (see table 5). In particular, the employment rate is correlated with participation in education and training through the life cycle, as well with a small differential between male and female employment rates. Some econometric
results, taking into account other determinants of job quality (employment and
GDP shares of manufacturing) and including country effects, suggest a strong
correlation between job quality and employment rate within the EU15 level
(Davoine, 2007).
Table 5.
Correlations between quality indicators and the employment rate
for EU countries, 1983–2004
Correlation with employment rate
Training rate
0.67
Part-time rate
0.59
Temporary employment rate
Long-term unemployment rate
0.03
–0.14
Percentage of the population with secondary level education
0.45
Shift work rate
0.02
Evening work rate
0.07
Night work rate
Saturday work rate
Sunday work rate
0.25
–0.44
0.33
Occupational segregation
0.40
Senior employment gap
0.13
Gender employment gap
0.77
Employment quality index
0.74
Source: LFS 1983–2004, authors’ calculations, 138 observations (annual data, number of countries included
depending on data availability, 6 in 1983 to 21 in 2004).
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International Labour Review
Conclusion
A comparative analysis of employment quality in the EU reveals the heterogeneity of national situations not only by reference to the EES indicators, but
also in the light of complementary variables introduced to reflect four fundamental dimensions of job quality, i.e. socio-economic security, education and
training, working conditions, and gender equality. The results suggest that existing differences are related to institutions and national policies.
From a policy perspective, theoretical and empirical analysis shows that
the Laeken indicators offer a good starting point for analysing the quality of
employment, but that they present major limitations. Indeed, they overlook crucial dimensions, especially wage level and inequality. These limitations call for
the introduction of complementary indicators in the European benchmarking
process, concerning wages, training intensity, and working conditions. Despite
the political decline of concern for quality in the framework of the EES, which
might be temporary, these results also call for further investigation: for instance,
the dynamics of employment quality and their relationships with labour market
performance, economic growth, or policies, should be further explored.
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Appendix A. Description of the databases
I. Comparative database
1. PCA on Laeken indicators
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Key indicators
Participation in education and training. 2006. Source: LFS (European Commission, 2006).
Difference between men’s and women’s average gross hourly earnings as percentage of men’s average hourly earnings (for paid employees at work). 2001.
Source: National sources and ECHP, Eurostat (European Commission, 2006).
Change in rate of serious occupational accidents per 100,000 persons in employment between 1999 and 2004. Source: ESAW (European Commission, 2006).
Part-time employment as a percentage of total employment. 2006 (Eurostat web
site).
Fixed-term contracts as a percentage of total employment. 2006 (Eurostat web
site).
Growth in labour productivity (GDP per hour worked). 2004. Source: Eurostat
(European Commission, 2006).
Growth in labour productivity (GDP per person at work). 2004. Source: Eurostat
(European Commission, 2006).
Context indicators
Job satisfaction: percentage of workers who report that they are satisfied or very
satisfied with their working conditions. 2006. Source: Fourth European Working
Conditions Survey (Eurofound web site).
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Women’s participation in education and training. 2006. Source: LFS (European
Commission, 2006).
Men’s participation in education and training. 2006. Source: LFS (European
Commission, 2006).
Participation in education and training (25–34 years old). 2006. Source: LFS
(European Commission, 2006).
Participation in education and training (35–44 years old). 2006. Source: LFS
(European Commission, 2006).
Participation in education and training (45–54 years old). 2006. Source: LFS
(European Commission, 2006).
Participation in education and training (55–64 years old). 2006. Source: LFS
(European Commission, 2006).
Participation in education and training (low educational attainment). 2006.
Source: LFS (European Commission, 2006).
Participation in education and training (intermediate educational attainment).
2006. Source: LFS (European Commission, 2006).
Participation in education and training (high educational attainment). 2006.
Source: LFS (European Commission, 2006).
Participation in education and training (employed). 2006. Source: LFS (European Commission, 2006).
Participation in education and training (unemployed). 2006. Source: LFS (European Commission, 2006).
Participation in education and training (inactive). 2006. Source: LFS (European
Commission, 2006).
Share of the workforce working with computers (PCs, network, mainframe).
2006. Source: Fourth European Working Conditions Survey (Eurofound web
site).
Employment gap between men and women. 2006. Source: LFS (Eurostat web
site).
Gender unemployment gap. 2006. Source: LFS (Eurostat web site).
Occupational segregation. 2006. Source: LFS (European Commission, 2006).
Sectorial segregation. 2006. Source: LFS (European Commission, 2006).
Involuntary part-time as a percentage of part-time employment. 2006. (Eurostat
web site).
Involuntary fixed-term contracts as a percentage of fixed-term contracts. 2006.
(Eurostat web site).
15–64 years old employment rate. 2006. Source: LFS (Eurostat web site).
15–24 years old employment rate. 2006. Source: LFS (Eurostat web site).
25–54 years old employment rate. 2006. Source: LFS (Eurostat web site).
55–64 years old employment rate. 2006. Source: LFS (Eurostat web site).
Employment rate of people who have achieved ISCED level 0–2 of education.
2006. Source: LFS (Eurostat).
Employment rate of people who have achieved ISCED level 3–4 of education.
2006. Source: LFS (Eurostat).
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Employment rate of people who have achieved ISCED level 5–6 of education.
2006. Source: LFS (Eurostat).
Long-term unemployment rate. 2006. Source: LFS (Eurostat web site).
Women’s long-term unemployment rate. 2006. Source: LFS (Eurostat web site).
Men’s long-term unemployment rate. 2006. Source: LFS (Eurostat web site).
Early school leavers (defined as the percentage of the population aged 18–24 with
at most lower secondary education (ISCED level 2) and not in further education
or training. 2006. Source: LFS (European Commission, 2006).
Early school leavers (men) (defined as the percentage of the male population
aged 18–24 with at most lower secondary education (ISCED level 2) and not in
further education or training. 2006. Source: LFS (European Commission, 2006).
Early school leavers (women) (defined as the percentage of the female population
aged 18–24 with at most lower secondary education (ISCED level 2) and not in further education or training. 2006. Source: LFS (European Commission, 2006).
Youth unemployment ratio: total unemployed young people (15–24 years) as a
share of total population in the same age group. 2006. Source: LFS (European
Commission, 2006).
Employment impact of parenthood for men: percentage point difference between
employment rates without the presence of any children and with the presence of a
child aged 0–6. 2006. Source: LFS (European Commission, 2006).
Employment impact of parenthood for women: percentage point difference
between employment rates without the presence of any children and with the
presence of a child aged 0–6. 2006. Source: LFS (European Commission, 2006).
Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children in the same age group (< 3 years old). 2006. Source:
national sources (European Commission, 2006).
Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children in the same age group (from 3 years old to compulsory
school age). 2006. Source: national sources (European Commission, 2006).
Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children in the same age group (from compulsory school age
to 12). 2006. Source: national sources (European Commission, 2006).
Inactive people not seeking employment, who would nevertheless like to have
work, but who are not searching due to personal or family responsibilities. 2005.
Source: LFS (European Commission, 2006).
Difference in employment rates between 55–64 years old and 15–64 years old.
2006. Source: LFS (Eurostat web site).
Productivity (GDP per hour worked). 2005. Source: Eurostat (European Commission, 2006).
Productivity (GDP per person employed). 2005. Source: Eurostat (European
Commission, 2006).
Percentage of the population aged 25 to 64 having completed at least upper secondary education (ISCED level 3). 2006. Source: Eurostat.
Percentage of the male population aged 25 to 64 having completed at least upper
secondary education. (ISCED level 3). 2006. Source: Eurostat.
Percentage of the female population aged 25 to 64 having completed at least
upper secondary education. (ISCED level 3). 2006. Source: Eurostat.
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Job satisfaction: percentage of workers who report that they are satisfied or very
satisfied with their working conditions. 2006. Source: Fourth European Working
Conditions Survey (Eurofound web site).
Participation in education and training. 2006. Source: LFS (European Commission, 2006).
Participation in education and training (55–64 years old). 2006. Source: LFS (European Commission, 2006).
Participation in education and training (unemployed). 2006. Source: LFS (European Commission, 2006).
Share of the workforce working with computers (PCs, network, mainframe).
2006. Source: Fourth European Working Conditions Survey (Eurofound web
site).
Difference between men’s and women’s average gross hourly earnings as a percentage of men’s average hourly earnings (for paid employees at work). 2001.
Source: national sources and ECHP, Eurostat (European Commission, 2006).
Employment gap between men and women. 2006. Source: LFS (Eurostat web site).
Gender unemployment gap. 2006. Source: LFS (Eurostat web site).
Occupational segregation. 2006. Source: LFS (European Commission, 2006).
Sectorial segregation. 2006. Source: LFS (European Commission, 2006).
Change in the rate of serious occupational accidents per 100,000 persons in employment between 1999 and 2004. Source: ESAW (European Commission, 2006).
Part-time employment as a percentage of total employment. 2006 (Eurostat web
site).
Fixed-term contracts as a percentage of total employment. 2006 (Eurostat web
site).
Involuntary part-time as a percentage of part-time employment. 2006. (Eurostat
web site).
Involuntary fixed-term contracts as a percentage of fixed-term contracts. 2006.
(Eurostat web site).
15–64 years old employment rate. 2006. Source: LFS (Eurostat web site).
Long-term unemployment rate. 2006. Source: LFS (Eurostat web site).
Early school leavers, defined as the percentage of the population aged 18–24 with
at most lower secondary education (ISCED level 2) and not in further education
or training. 2006. Source: LFS (European Commission, 2006).
Youth unemployment ratio: total unemployed young people (15–24 years) as a
share of total population in the same age group. 2006. Source: LFS (European
Commission, 2006).
Employment impact of parenthood for men: percentage point difference between
employment rates without the presence of any children and with the presence of a
child aged 0–6. 2006. Source: LFS (European Commission, 2006).
Employment impact of parenthood for women: percentage point difference
between employment rates without the presence of any children and with the
presence of a child aged 0–6. 2006. Source: LFS (European Commission, 2006).
Monitoring quality in work
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Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children of the same age group (< 3 years old). 2006. Source:
national sources (European Commission, 2006).
Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children of the same age group (from 3 years old to compulsory
school age). 2006. Source: national sources (European Commission, 2006).
Childcare: children cared for (through formal arrangements other than family) as
a proportion of all children of the same age group (from compulsory school age
to 12). 2006. Source: national sources (European Commission, 2006).
Inactive people not seeking employment, who would nevertheless like to have
work, but who are not searching due to personal or family responsibilities. 2005.
Source: LFS (European Commission, 2006).
Difference in employment rates between 55–64 years old and 15–64 years old.
2006. Source: LFS (Eurostat web site).
Productivity (GDP per hour worked). 2005. Source: Eurostat (European Commission, 2006).
Productivity (GDP per person employed). 2005. Source: Eurostat (European
Commission, 2006).
Growth in labour productivity (GDP per hour worked). 2004. Source: Eurostat
(European Commission, 2006).
Growth in labour productivity (GDP per person worked). 2004. Source: Eurostat
(European Commission, 2006).
Percentage of the population aged 25 to 64 having completed at least upper secondary education (ISCED level 3). 2006. Source: Eurostat.
Complementary indicators
Length of maternity leave in months (with benefits replacing at least 2/3 of salary).
2005. Source: Eurostat (European Commission, 2006).
Short repetitive tasks of < 10 min. 2006. Source: Fourth European Working Conditions Survey (Eurofound web site).
Job involves painful/tiring positions. 2006. Source: Fourth European Working
Conditions Survey (Eurofound web site).
“My health is at risk because of work”. 2006. Source: Fourth European Working
Conditions Survey (Eurofound web site).
Working at very high speed. 2006. Source: Fourth European Working Conditions
Survey (Eurofound web site).
Working to tight deadlines. 2006. Source: Fourth European Working Conditions
Survey (Eurofound web site).
Consulted about changes in work organization, etc. 2006. Source: Fourth European Working Conditions Survey (Eurofound web site).
Working at night. 2006. Source: Fourth European Working Conditions Survey
(Eurofound web site).
Percentage working long working days. 2006. Source: Fourth European Working
Conditions Survey (Eurofound web site).
“I am well paid for the work I do”. 2006. Source: Fourth European Working Conditions Survey (Eurofound web site).
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“My job offers good prospects for career advancement”. 2006. Source: Fourth
European Working Conditions Survey (Eurofound web site).
Mean wage in PPS. 2004 (2003 for EL and FR). Source: Eurostat.
In work at risk of poverty. 2005. Source: Eurostat.
Hours of CVT courses per participant. 1999. Source: Continual Vocational Training Survey 2 (CVTS2).
Cost of CVT courses per participant. 1999. Source: Continual Vocational Training Survey 2 (CVTS2).
Monitoring quality in work
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