skills report
TRANSCRIPT
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Trend Chart Policy Workshop
"Skills for Innovation: Ensuring the competitive future of
companies"
Developing Indicators for Skills and Innovation
Edward Lorenz
IDEFI-CNRSUniversity of Nice-Sophia [email protected]
The contents of this paper have not been verified by the European Commission and donot necessarily express the position of the European Commission.
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1. Introduction
The importance of human capital and skills as drivers of innovation in the
knowledge-based economy is increasingly recognised. At the same time, as the
consultation document for the New European Innovation Action Plan observes, the
European Union is not always sufficiently prepared and adapted to the specific
needs of the knowledge economy and innovation. There is a clear need to develop
policies that support more effective investment in human resources and this
requires identifying the kinds of skills enterprises need for innovation. Developing
indicators for these skills can play an important role in this process by tracking
progress across member states and creating a common framework for dialogueamong the different stakeholders in the innovation system. It sets the stage for
defining skills-enhancing policies aimed both at increasing the innovative
capabilities of nations that lag far behind the average and sustaining the innovative
capabilities of the leaders.
Innovation depends on the skills and expertise of scientists and engineers with
third-level education, but formal science and engineering training are not the only
kinds of skills that firms require. Successful innovation also depends on skills
developed by employees on-the-job in the process of solving the technical and
production-related problems they encountered in testing, producing and marketing
new products and processes.1Developing these sorts of skills in turn depends not
just on the quality of formal education, but on having the right organisational
structures and work environments. Work environments need to be designed to
promote learning through problem solving and to effectively use these skills for
innovation. This implies that indicators of skills for innovation need to do more than
capture the quality of the available pool of skills by measuring years of education.
Indicators also need to capture how these skills are used and appropriate work
environments for their further development. Section 2 below presents a framework
developed in Jensen et al. (2004) for analysing knowledge management and skills
development for innovation that takes into account both of these aspects. Section 3
illustrates the framework with some case study evidence that goes beyond the
1
The importance of linkages and information feedback between the R&D, production and salesdepartments is one of the key points developed by Kline and Rosenberg (1985) in their well-
know chain-link model of innovation.
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simple measure of the pool of scientific and technical labour. The fourth section
identifies 12 main indicators of skills for innovation. Section 5 explores the relation
between the indicators and measures of innovative capacity at the national level.
The final section draws conclusions with a stress on policy implications.
1. A Framework for Identifying Skills for Innovation
Jensen et al. (2004) develop a distinction originally proposed by B.A. Lundvall
between two different, but complementary, modes of learning and skills
development: the STI-mode (Science, Technology and Innovation) and the DUI-
mode (Doing, Using and Interacting).2The STI-mode most obviously depends on
explicit know-why and the R&D-departments in big firms play a key role in STI-
processes. Specific R&D-projects will often be triggered by practice (problems with
a product, new user needs, problems with producing) but almost immediately
attempts will be made to restate the problem in an explicit and codified form that
potential users can understand since there is a need for interaction with and feed-
back. This mode depends on the skills of engineers, scientists and technicians with
formal university training and maintaining the absorptive capacity of the enterprise
often requires continuous renewal of their knowledge through lifelong learning.
The DUI-mode of learning and innovation (Doing, Using, Interacting) most obviously
relies on employee know-how which is tacit and often highly localized. This mode
involves building structures and organisational practices which enhance and utilize
learning by doing, using and interacting. Knowledge and skills are developed
through on-going problem-solving and when the process is complex it will involve
interaction within and between teams and it may result in new shared routines for
the organization. Learning by doing and learning by using are promoted through
decision-making autonomy that allows employees to explore new novel possibilities.
This is why such practices as self-managing teams and the delegation of authority
2The distinction between STI and DUI-mode learning was originally developed by B.A. Lundvall in
a series of workshop reports for our joint EU Accompanying Measures Project,Labour,
Organisation and National Innovation Systems(Loc Nis). See the projects web page and
notably Lundvall, et al. (2004) and Lorenz and Lundvall (2004).http://www.business.aau.dk/loc-nis/
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tend to show a positive relation to learning and innovative performance (see the
case study evidence presented in section 2 below).
Innovations can be competence-enhancing or competence-destroying (Henderson
and Clark, 1990). Competence-enhancing innovations build on a firms existing
competences and tend to be especially characteristic of innovation in more
traditional or mature technologies (textiles, autos, mechanical engineering).
Innovative activity is mostly incremental in nature and typically involves small
improvements in existing technologies or new combinations of exiting technologies.
Incumbent firms are typically well-placed to carry out such innovations eitherthrough investing in in-house skills or through recruiting workers on the labour
market with complementary knowledge and skills. This corresponds to the phase of
exploiting existing knowledge and competence in Nootebooms (2000) analysis of
innovation cycles.
Competence-destroying innovations, on the other hand, mark significant breaks in
the technological architecture or paradigm and typically require new types of skills
and knowledge. This is characteristic of innovative activity during the early phases
of development of a new technological field (e.g. biotechnology). Innovative activity
will be characterised by intensive forms of exploration that are favoured by looser,
network forms of organisation. Incumbent firms may be at a disadvantage relative
to start-up firms in part because of internal resistance to the organisational and
manpower changes required to radically reconfigure their competence-base
(Chesbrough, 1999).3
As Jensen et al. (2004) observe, the importance of the STI-mode of learning and
knowledge management is most obvious in fast moving technology fields such as
ICT, bio- and nano-technology and it is these sectors which are the focus of most
3It would be a mistake to identify innovations that are new for the market with those that are
competence destroying or, conversely, to identify innovations that are new to the firm but not to the
market with those that are competence enhancing. The development of products or processes that
are new to the market may be based on incremental improvements in existing technologies. The
diffusion of competence destroying innovations, on the other hand, can pose a major challenge to
economies precisely because incumbent firms find it difficult to undertake radical reconfigurations oftheir competence base.
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current policy efforts both at the national and the European level. This reflects the
combination of respectively path dependency in relation to science and technology
policy and the combined pressure emanating from the science community and the
big science-based firms. But other traditional industries such as food, clothing
and furniture as well as many service sectors also draw upon science when it
comes to innovating production processes, the use of materials and designing new
products. Econometric analysis for the Danish case, for example, demonstrates that
small and medium-sized firms operating in such sectors tend to be the ones that
benefit the most in terms of innovative capability from a stronger connection to
science (Vinding, 2002).
And DUI-learning is crucial in high-technology sectors. Experience-based learning
takes place in daily production and in the implementation and use of advanced
technologies. The speed up of science-based innovation tends to run into
bottlenecks whenever the capability to absorb and efficiently use new technologies
is limited.
Empirical evidence shows not only that the DUI-mode of learning contributes
positively to innovation across sectors, but also that the most promising results are
obtained when the two modes are combined. This implies the need for some
realignment of policy at the national and European levels.
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2. Case Study Results
There exists a large HRM literature looking at the relation between enterprise
performance and the use of new managerial practices such as problem-solving
groups, enhanced autonomy in decision making and individual responsibility for
quality assessment that support DUI forms of learning. A central question raised in
this recent literature is whether there exist complementarities among the individual
HRM practices resulting in performance benefits from adopting a set or bundle of
practices simultaneously. Underlying this notion of HRM complementarities is the
idea that the core high involvement work practices (problem-solving groups,
autonomous team organisation, etc.) are more likely to be effective if they are
supported by substantial investments in training and by forms of pay linking
employees compensation to their effort and to company performance. Training can
be seen as a natural complement to work arrangements that provide increased
opportunities for employee participation in decision-making. Collective incentive
schemes, as profit sharing and gain sharing, and individual incentive schemes, as
pay for knowledge and compensation for suggestions, are seen as complementary
pay devices which encourage employees to commit themselves to the goal of
improving company performance. Such payment arrangements promise employees
a share of the increased returns from their enhanced effort (Appelbaum et al., 2000;
Becker and Gerhart, 1996; Becker and Huselid , 1998; Ichniowski et al., 1997;
Guest, 1997).
Enterprise performance in this literature has traditionally been measured as financial
performance or labour productivity and little attention has been given to innovative
performance. Yet, there are good reasons to suppose that the firms capacity for
innovation can be increased by the use of such practices as job rotation,
interdisciplinary teams, and shop or service meetings. For example, these practices
can positively contribute to the sort of interdepartmental information flows and
feedbacks which Kline and Rosenbergs (1986) chain-link model of innovation
identifies as critical to the firms capacity for technological innovation. A key idea in
the model developed by Nonaka and Takeuchi (1995) is that innovation involves a
knowledge spiral, in which tacit knowledge is converted into more explicit and
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codified forms that are then embodied in new product and services. HRM practices
such as team organisation, quality circles, suggestion schemes and shop meetings
can be used in order to provide a framework within which employees can articulate
and make more explicit their tacit knowledge.
More recently, a number of scholars interested in the relation between organisation
and innovation have drawn on nationally representative data sets to explore the
relation between HRM bundling and innovation. The results consistently show that
the likelihood of innovating is increased by the use of managerial practices that
support DUI forms of learning.
One of the first empirical studies exploring these links is that of Michie and Sheehan
(1999) using private sector establishment-level data for the UK from the 1990
Workplace and Industrial Relations Survey (WIRS3). Two indirect measures of the
firms innovative activities are used: R&D expenditures and whether the firm has
introduced advanced technological change. HRM practices are divided between 3
basic categories: practices which increase opportunities to participate in decision-
making (e.g. teamwork, flexible job assignments), practices which increase
information sharing across the enterprises (e.g. consultation, use of shop meetings)
and practices which provide incentives for participation (e.g. profit sharing, job
appraisal). Establishments are grouped into one of three HRM systems going from
traditional to modern. Modern systems are characterised by teamwork, innovative
incentive systems, implicit employment security pledges, flexible job assignments
and regular information-sharing. The regression analysis, which controls for firm size
and industrial sector, shows that firms that use more innovative work practices are
more likely to engage in R&D and to introduce technical change.
Two studies, which use data from the Danish DISKO survey, use more direct
measures of innovation to test the relation between HRM practices and innovative
performance (Laursen and Foss, 2003; Lundvall and Nielsen, 2003). The DISKO
survey identifies firms which have introduced a new product or process over the last
three years and further distinguishes between innovations that are new to the firm,
new to the Danish market and new to the world. Laursen and Foss (2003) use
principal components analysis to identify different HRM systems based on 9
measures: interdisciplinary groups, quality circles, suggestion schemes, job rotation,
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delegation of responsibility, integration of functions, performance-related pay, firm-
internal training and firm-external training. They identify two HRM systems that are
positively related to the likelihood of innovating, one that combines the two training
variables and a second that combines the other 7 organisational practices.
Moreover, their results point to important sector effects with those firms belonging to
the wholesale and ICT sectors tending to be associated with the first system and
firms belonging to the manufacturing sectors tending to be associated with the
second system.
The study by Lundvall and Nielsen (2003), in addition to exploring the relation
between innovation and HRM bundling, looks for possible effects of employee
representation and cooperation with clients, subcontractors and knowledge
institutions (universities and research institutes). Their results support those of
Laursen and Foss in showing that the likelihood of successful product innovation
increases when the firm promotes learning through the bundling of innovative work
and pay policies. There results also point to positive complementarities between
HRM practices on the one hand and the use of systems of employee representation
and cooperating with external institutions on the other.
The study by Lorenz et al. (2004) is the first internationally comparative empirical
investigation of the HRM/performance link. The analysis draws on data from two
nationally representative surveys of public and private sector establishments: the
WERS98 survey which covers UK workplaces with 10 or more employees, and the
REPONSE98 survey which covers French establishments with 20 or more
employees. The analysis is restricted to the trading sector and excludes public
services (government, health, education, etc) resulting in samples of 2086
establishments for France and 1165 establishments for the UK.4In common with the
study by Lundvall and Nielsen (2003), the econometric analysis includes measures
of employee representation as well as the more conventional HRM variables
(problem-solving groups, job rotation, teams, information sharing). Innovative
performance is measured by a question asking whether the firm has introduced a
4In both cases the samples of workplaces were arrived at through a process of stratified random
sampling using variable sampling fractions. The response rates for WERS98 and REPONSE97
were 83 and 65 per cent respectively. These rates compare well to those achieved for most US-
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new product or service over the last 3 years in the case of France and over the last 5
years in the case of the UK.
The econometric results show positive effects of HRM bundling on innovative
performance in the both countries, while revealing important differences for the
effects of systems of employee representation. Employee representation has neutral
effects on innovative performance in France, while in the UK those firms which
combine employee representation with innovative HRM practices are more likely to
innovate. The authors account for this by differences in the regulatory environment
between the two countries, which imply that that the indicators of employeerepresentation are capturing different levels of commitment of employers to
representation. As they observe, A possible explanation of this (difference) is simply
that since representation is a legal requirement in France all or most firms will have
it, while commitment to involving employees in decision-making will vary much more
widely and in many cases may be quite low. On the other hand, where
representation is not a legal requirement, as in the UK, only those firms that are
seriously concerned to involve their employees in decision making are likely to have
it.
The above studies explore the role of the largely neglected DUI learning dynamics in
innovative performance. The first study to explicitly examine the importance of
complementarities between the science and technology mode of skills development
and the informal experience-based mode is Jensen et al. (2004). Drawing on the
DISKO data set for Danish enterprises, they use latent cluster analysis to identify
four groups of firms according to mode of skills development: those using neither the
DUI nor the STI mode, those using the DUI-mode alone, those using the STI mode
alone and those combining both modes of learning. Logit regression analysis shows
that firms that use one of the two modes alone are about twice as likely to develop a
new product or service as those using neither of the modes. Those combining both
modes are about five times as likely to innovate as a firm using neither of the modes.
The results points to strong complementarities between informal experience-based
based surveys which rarely top 25%. UK workplaces in the 10 to 19 employee size range were
excluded from the descriptive statistics and econometric analysis.
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ways of acquiring knowledge and improving skills and more formal scientifically-
based ways of getting access to and using knowledge.
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3. Indicators for skills and innovation
This section draws on the framework developed in Jensen et al. (2004) to propose
twelve skills- for-innovation indicators. The indicators fall under four different
categories:
STI-mode indicators. Four of the indicators capture the STI-mode of
learning and skills development. They are strongly overlapping with S&T
indicators used in other TrendChart reports and I explain their particular
relation to skills development.
DUI-mode indicators. Four are indicators of the DUI-mode. They are a
subset of the indicators used in Lorenz and Valeyre (2003) to develop a
taxonomy of organisational forms for the EU-15. I use the indicators that are
most clearly relevant to skills development.
Skill maintenance indicators. Two indicators capture lifelong learning that
contributes to the development and maintenance of the skills of adult
workers.
Foundation skills indicators. Two indicators capture foundation skills that are
relevant to workers capacity for lifelong learning.
Paragraph 3.5 discusses obstacles to successful development and deployment of
skills for innovation. The section concludes with a table summarising the 12
indicators, comparing the means for the EU-15 and the new member countries.
3.1. STI-mode indicators
3.1.1. HRSTC (Human Resources in Science and Technology Core)The innovative performance of enterprises depends both on the societys production
of highly trained science and technology (S&T) human resources and on the firms
capacity to integrate such human resources into innovative activities involving the
production and use new knowledge. There are a number of ways to measure such
human resources and the HRSTC measure based on the Canberra manual is used
here. HRST is defined as people who fulfil one or other of the following conditions:
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- successfully completed education at the third level in a S&T field of study5
- not formally qualified as above but employed in a S&T occupation where the above
qualifications are normally required.
A weakness of HRST as a STI-mode indicator is that it includes all people with a
third level-degree in a S&T field including those who are inactive or employed in a
non-science and technology occupation. For this reason HRSTC is chosen as the
indicator. HRSTC is restricted to those who have a third level degree and are
employed in an S&T occupation.
Figures for HRSTC are available for the EU-25 and are provided annually to
Eurostat. The figures for 2000 presented in Figure 1.1 below show that amongst the
EU-15, the Scandinavian countries, Belgium and the Netherlands are leaders.
Amongst the new member countries, Lithuania, Latvia and Cyprus are leaders.
Figure 1.1
HRSTC as per cent of e mp loyed popu lation aged 24-65
24
222 1
20
1918
18 1817 17 16
16 1515
14
1312
11
10 10 10 9 98 8
0
5
10
15
20
2 5
30
SE FI DK B E NL LU LT CY UK DE FR EE IE ES EL SI HU LV PL M T A T CZ IT PT SK
5S&T fields of study are defined to include: natural sciences, engineering and technology, medical
sciences, agricultural sciences and social sciences.
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3.1.2. BERD (Business Expenditures on R&D) as a percent of GDP
Since the publication of Cohen and Levinthals (1990) classic article on absorptive
capacity it has been appreciated that an important by-product of in-house R&D
activity is the development of the skills and competence engineers and technicians
need to access new external sources of scientific and technical knowledge for
innovation. While developing this absorptive capacity is especially important in
technologically fast-moving sectors such as pharmaceuticals and biotechnology, it
also plays a role in the ability of firms in more traditional sectors to absorb new
scientific and technical developments. As an indicator of this sort of skills
development, we use business expenditures on R&D which provides arguably thebest measure of how much in-house R&D activity is being performed by
enterprises. Figures on BERD are provided annually to Eurostat and can be found
on Newcronos. The figures for 2000, as presented in Figure 1.2, are available for all
member countries with the exception of Austria.
Figure 1.2
BERD as a pe rcen t of GDP
3,1
2,4
1,8
1,61,5 1,5
1,2
1,1
0,8 0,80, 7
0,5 0,50,5
0,40,4
0,30,2 0,2 0,2
0,1 0,10, 1 0,1
0,00
0,50
1,00
1,50
2,00
2,50
3,00
3,50
SE FI DE LU DK BE UK NL FR SI CZ IE IT ES SK HU PT PL EL LV EE LT M T CY
As in the case of indicator 1, amongst the EU-15 Sweden and Finland are the
leaders. Germany ranks third on this scale. Amongst the new member countries,
Slovenia and the Czech Republic are leaders.
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3.1.3. HRST job-to-job mobility as a percentage of employed HRST
Science and technology skills for innovation can be developed internally through
formal R&D and knowledge management activities or they can be acquired on the
labour market. HRSTC provides a measure of the degree to which those with third-
level training are employed in science and technology occupations. Firms can also
acquire science and technology skills through the mobility of mid-career scientists,
engineers and technicians and the third STI indicator provides a measure of how
active the labour market is for science and technology human resources. Such
mobility may be of especial importance in fast moving technological sectors where
tight competitive conditions call for a quick reconfiguration of the firms competencebase. Such mobility may also be important for relatively mature technological
sectors confronted with a need to incorporate new technologies (e.g. ICT) into
established products.
Figure 1.3
HRST M obility as a per cent of em ployed HRST
13,3
12,2
11,6
8,3 8,2
7, 4
6,86,6 6,6 6,4
6,2 6,25,8 5,7
5,5 5,5 5,5 5,4 5,24,9
4, 0
2,5
0
2
4
6
8
10
12
14
16
DK UK FI EE FR DE ES BE LV CY MT LU LT PT SE IT AT CZ PL SI HU SK
Mobility is defined as the movement of individuals aged 24 to 65 between one job
and another from one year to the next and does not include inflows into the labour
market from a situation of unemployment or inactivity. People must fulfil the
condition of belonging to HRST in both periods of time. HRST job-to-job mobility is
provided to Eurostat on an annual basis and can be found on Newcronos. The
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figures for 2000, as presented in Table 1.3, are available for all EU-member
countries with the exception of Ireland, Greece and the Netherlands. The ranking in
suggests that two types of regulatory environments are adapted to high levels of
HRST mobility: those combining strong systems of unemployment protection with
relatively low levels of employment protection (Denmark and Finland), and those
with overall weak systems of labour market regulation (the UK).
3.1.4. Computer training
This indicator measures the percent of the actively employed population using a
computer in work that has received computer training. We use this as an STImeasure, rather than figures on ICT expenditures or the level of internet access of
enterprises, because such training is arguably a prerequisite for the use of
advanced forms of ICT in the innovation process, such as computer simulation and
computer-aided design. The figures, which are taken from a November 2001
Eurobarometer survey6, are only available for the EU-15. Denmark and Finland are
leaders.
6Eurobarometer 56.0, Les Europens et les technologies de la communication et de linformation
dans le cadre de lemploi, 2001. This is a non-periodic survey. The new European survey
instrument on ICT, which will be undertaken for the first time in 2006, will contain detailedinformation on the various forms of ICT use by firms and should provide the basis for the
construction of an alternative measure of computer skills.
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Figure 1.4
Percen t of actively em ployed population using com puters in w ork having
received computer training
70
66 66 65 65
6058
5755
51
44 44
3938
36
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
DK FI IE DE SE AT UK NL LU FR EL ES PT BE IT
3.2. DUI indicators
As discussed above, there are a number of national enterprise-level surveys of
organisational innovation that can be used to develop measures of DUI forms of
learning and skills development. However, at present there are no EU-wide
harmonised survey data on organisational innovation. In order to develop measures
of informal experience-based learning and skills development, I draw here on the
results of the third European Survey on Working Conditions undertaken by the
European Foundation for the Improvement of Living and Working Conditions7. The
survey, which was carried out in March 2000, covers only the EU-15. The survey is
carried out every 5 years and the next version in 2005 will be addressed to all
member nations of the EU-25.
The survey questionnaire was directed to approximately 1500 active persons in
each country with the exception of Luxembourg with only 500 respondents. The
7The initial findings of the survey are presented in a European Foundation report by D. Merlli and
P. Paoli [2001].
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total survey population is 21703 persons, of which 17910 are salaried employees.
The four DUI-indicators are based on the responses of the 8081 salaried
employees working in establishments with at least 10 persons in both industry and
services, but excluding agriculture and fishing, public administration and social
security, education, health and social work, and private domestic employees.
The use of employee-level data presents advantages and disadvantages compared
to enterprise-level data. An advantage is that it allows for a much richer
characterisation of actual work content, the sorts of skills required and the learning
that results. A disadvantage is the lack of enterprise performance measures whichcan only be derived by matching the survey with other surveys. An arguably best-
practice approach to this problem is to use joint survey instruments addressed both
to firm representatives and to a representative sample of their employees.8
The first two indicators capture employee involvement in decision-making, the third
captures autonomy in work and the fourth is a general measure of employee
learning in work.
3.2.1. Individual responsibility for quality assessment
A key indicator of employee involvement is individual responsibility for quality
assessment. This form of employee involvement plays a central role in the
development of the sorts of skills and knowledge that contribute to the feedbacks
and knowledge flows that Kline and Rosenberg identify as fundamental to
innovative capacity. The percentage of employees engaged in such activity ranges
from a high of 86 percent in Denmark to a low of 51 percent in Greece.
8This method is applied by the French Changement Organisationnel and Informatisation (COI)
survey. See Greenan and Mareisse (1999).
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Figure 2.1
Percent of em ployee s res ponsible for quality asse ssm ent
86
82
78 7775
71 70 7068 67 67
65 65 65
51
0,00
10, 00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
DK NL FR FI UK SE IE AT BE PT DE ES IT LU EL
3.2.2. Employee involvement in problem-solving
Since the classic study by Newell and Simon (1972) cognitive scientists, and social
scientists more generally, have appreciated the close links between problem-solvingactivity and the development of new knowledge. The indicator presented in Figure
2.1 captures problem-solving emerging out of learning-by-doing and learning-by-
using in daily work activity.
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Figure 2.2
Percental of employees w hose w ork entails problem-s olving
939 1
89
8 583
8079
77 7775
73 73
6867
59
0,00
10, 00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100, 00
NL DK SE FR BE UK ES AT LU DE IT FI IE EL PT
This form of problem-solving contributes to development of the tacit forms of
knowledge that can contribute to innovation in a subsequent phase of articulation
(e.g. through off-line problem-solving groups), as developed in Nonaka and
Takeuchis (1995) model of product innovation. Amongst the EU-15, the
Netherlands and the Scandinavian countries are leaders on this indicator.
3.2.3. Autonomy in determining work methods
Autonomy contributes to innovation because it increases the scope for the
exploration of new knowledge. Greater autonomy increases the likelihood of a
creative response to unanticipated problems that will usefully add to the stock of in-
house knowledge. The importance of autonomy has been documented by a number
of authors including Lam (2003) who has analysed the role of teams in the product
development process. The case studies referred to above all find a positive relation
between innovative performance and such factors as the delegation of responsibility
or the use of autonomous team organisation. The measure used here is the
percentage of employees exercising control over their work methods.
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Figu re 2.3
Percentage of em ploye es e xercising control over their wo rk m ethods
8179
72
68
6362
60 60 6057
56
4948
4341
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
SE NL DK DE FI IT A T LU FR UK BE IE ES EL PT
3.2.4. Learning new things in work
The fourth DUI indicator provides a general measure of whether work is organised
in a manner that promotes learning.
Figure 2.4
Percentage of em ployees th at learn new things in work
8885
79 7875 75 74
7169 68
6663
61
5149
0,00
10, 00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
FI DK NL SE BE L U UK FR AT IT IE ES DE PT EL
This indicator captures informal learning dynamics in the broadest sense and is
highly associated with the other three DUI-indicators (see the discussion of the DUI-
scale and its relation to the innovation taxonomy in Section 4 below). Netherlandsand the Scandinavian countries are leaders.
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3.3. Lifelong learning indicators
Two indicators of lifelong learning relevant to the maintenance of the skills of older
employees are included. The first, available for the EU-25 and taken from the
annual Labour Force Survey, is the percentage of the population aged 24-65 in
2001 engaged in any form of training during the four weeks prior to the survey.9
This indicator captures learning activity both on and off the job and includes
learning that though not directly related to employment could be of importance for
maintaining or improving future learning capacity and skills development. The
second, based on the 1999 Continuous Vocational Training Survey, is thepercentage of all enterprises providing training of any type in 1999. This survey,
which is undertaken every 6 years, provides a measure of the importance of
enterprise investments in the skills development of their employees. The figures,
taken from Newcronos, are available for the EU-25 with the exception of Malta,
Cyprus and the Slovak Republic.
9Although more recent data are available for indicator 3.1, figures for 2001 are used in order to
keep all indicators at roughly the same time period.
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Figure 3.1
Percen tage of the w ork ing-age population e ngaged in training, 2001
2221
2 1
20
16
9
8 88 8
7
6 66
5 55 5
4
33 3
3 3
1
0,00
5,00
10 ,00
15,0 0
20,00
25,00
SE UK DK FI NL SK A T LV IE SI B E EE CZ IT DE ES LU PL M T PT CY HU FR LT EL
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Figure 3.2
Percentage of enterprises providing training in 199996
9188 87
8279
76 7572 71 70 69
63
53
48
43
3937 36
2422
18
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
DK SE NL UK FI IE FR DE AT LU BE CZ EE LV SI LT PL HU ES IT PT EL
3.4. Foundation skills
In common with Innovation Scoreboard Report 5, indicators of foundation skills are
included based on the OECDs Programme for International Student Assessment
(PISA). The PISA survey is carried out every 3 years and the next assessment will
be in 2006. PISA measures basic literacy and numerical skills that play a central
role in the ability of individuals to continuously learn throughout their lives. PISA
aims to assess not only what students know but also their capacity to apply that
knowledge to real world issues including those at the workplace. As the OECD
report on the first results observes, PISA is based on a dynamic model of lifelong
learning in which new knowledge and skills necessary for successful adaptation to a
changing world are continuously acquired throughout life (OECD, 2000, p. 14).
Foundation skills are especially important to sustaining dispersed forms of DUI-
mode learning which concern employees at all levels of the hierarchy and across
functional services. Such forms of skills development are particularly important for
incremental innovation which draws on the full in-house knowledge base to
progressively improve product design and product quality. Enterprises operating in
countries where a large fraction of population has such foundation skills will be
better placed to sustain DUI forms of learning and skills development.
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Two PISA-based measures from 2000 are used10. The first, a measure of reading
literacy, is the percentage of 15 year olds reading at levels 4 or 5. This requires a
capacity to locate and sequence multiple pieces of deeply embedded information,
possibly in accordance with multiple criteria.
Figure 4.1
PISA rea ding literacy: pe rcentage o f 15 yr. olds r eading at levels 4 or 5
50
4140
3837
34
32
30
2827
25 2524
23 23
21
18
0,00
10,00
20,00
30,00
40,00
50,00
60,00
FI IE UK BE SE AT FR DK DE CZ ES PL IT EL HU PT LV
The second, a measure of mathematical literacy, is the percentage of 15 yr. olds
scoring 600 or above. This requires a capacity to connect and integrate more than
one piece of material and to translate and create appropriate models within
unfamiliar context. Both types of literacy are arguably important to the sorts of
problem-solving capabilities required in learning organisations. The two measures
are available for all member nations of the EU-15 with the exception of the
Netherlands and Luxembourg. Amongst the new member countries, the measure of
reading literacy is unavailable for Hungary, the Czech Republic, Latvia, and Poland
and the measure of mathematical literacy is available for Hungary, the Czech
Republic and Poland.
10The OECD released the results of PISA 2003 in early December 2004, but these results were
available too late to be incorporated into this report.
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Figure 4.2
PISA m athem atical liter acy: per centage o f 15 yr. olds s coring 600 or bette r
24
23
22
18 18
16 16
15
14
13
12
109
7
5
4 4
0,00
5,00
10,00
15,00
20,00
25,00
30,00
BE UK FI FR AT DK SE CZ DE HU IE PL ES EL IT LU PT
3.5 Obstacles to the successful development and deployment of skills for innovation
At present there exist no European-wide data that would allow us to develop
indicators of the obstacles to the development and deployment of skills for
innovation. The 3rd Community Innovation Survey asks whether the lack of
qualified personnel hampers innovative activity. However, it doesnt address the
factors which impede the development and deployment of those skills.
Some indication of the nature of the obstacles to DUI-forms of skills development
can be gained from a recent survey of 800 European firms with 50 employees or
more carried out by the European Commission, DG Employment and Social
Affairs.11The survey classified firms into three groups based on the progress they
have made towards implementing new forms of work organisation: Non-users,
Transitional or partial users, and System users. System users are defined as firms
having introduced a wide range of new working practices and account for 10
percent of the population of firms. Non-users account for 40 percent, while
Transitional or partial users account for the remaining 50 percent. The results show
11
DG Employment and Social Affairs, 2002, New Forms of Work Organisation: The Obstacles toWider Diffusion.
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that the main obstacles are linked to the attitudes of employees and management,
and more generally to resistance to major changes in the companys culture. A
change in work organisation thus requires a change in understanding as well as in
behaviour, and there is a perceived need for tools to help change the behaviour of
management as well as employees.
3.6 Summary statistics and European leaders
Table 1 summarizes the 12 indicators for skills and innovation, comparing the
means for the EU-15 and for the new member countries.
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Table 1
Summary Statistics: Skills for Innovation Indicators
Indicator
Mean:
EU-25
Mean:
EU-15
Leaders: EU-15 Mean:
New
member
countries
Leaders: New
Member
countries
STI-Mode Indicators
1.1 HRSTC as a percentageemployed population aged 24-65, 2000
14.82 16.42 SE(23.81)
FI(22.06)
12.41 LT(17.66)
CY(17.61)
1.2 BERD as a percent of GDP,2000
0.84 1.22 SE(3.12)
FI(2.41)
0.31 SI(0.81)
CZ(0.74)
1.3 HRST mobility as apercentage of HRST, 2000
6.80 7.86 DK(13.34)
UK(12.18)
5.51 EE(8.31)
LV(6.55)
1.4 Percentage of employeesusing a computer havingreceived computer training,2000
NA 54.13 DK(69.90)
FI(66.00)
NA NA NA
DUI-Mode Indicators
2.1 Percentage of employeesresponsible for qualityassessment, 2000
NA 70.52 DK(86.44)
NL(82.38)
NA NA NA
2.2 Percentage of employees
whose work involves problemsolving, 2000
NA 77.80 NL
(92.59)
DK
(91.38)
NA NA NA
2.3 Percentage of employeesexercising control of workmethods, 2000
NA 59.87 SE(80.85)
NL(78.54)
NA NA NA
2.4 Percentage of employeeswhose work involves learningnew things, 2000
NA 70.00 FI(87.71)
DK(84.5)
NA NA NA
Skill Maintenance Indicators
3.1 Percentage of the workingage population engaged intraining of any type four weekprior to survey, 2001
8.17 9.96 SE(21.6)
UK(21.1)
5.49 LV (8.2) SI (7.6)
3.2 Percentage of enterprisesoffering training of any type,1999
60.86 55.87 DK (96) SE (91) 50.29 CZ (69) EE (63)
Foundation Skills Indicators
4.1 PISA Reading literacy:percentage 15 yr. olds readingat levels 4 or 5, 2000
30.35 32.54 FI (50) IE (41) 23.251 CZ (27) PL (25)
4.2 PISA Mathematical literacy:percentage of 15 yr. oldsscoring 600 or over, 2000
13.53 13.71 BE (24) UK (23) 12.672 CZ (15) HU
(13)
1. Average for four member countries: Poland, Czech Republic, Hungary and Latvia2. Average for three member countries: Poland, Czech Republic and Hungary
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Figure 5
Skills-for-Innovation Index: EU15
0,88
0,770,74
0,59 0,56
0,200,14 0,14
0,02
-0,11 - 0,13
-0,59
-0,79
- 1,14
-1,22
-1,5000
-1,0000
-0,5000
0,0000
0,5000
1,0000
FI DK SE NL UK LU DE BE AT FR IE ES IT PT EL
Two of the indicators used in the 12-indicator Skills-for Innovation index are also
used in the construction of TrendCharts 2001 Summary Innovation Index for the
EU-15. Figure 6 below shows the relation between the Skills Index, calculated
without these two indicators, and the Summary Innovation Index. The correlation
coefficient between the two indices is 0.93 and significant at the .001 level. This
positive relationship supports the view that that the skills indicators impact positively
on innovative performance.
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Figure 7 shows that the three Scandinavian countries, which are leaders on the
scale of the 12-indicator index, are also leaders on the scale of the 5-indicator
index. Amongst the new member countries, Estonia, Cyprus and the Czech
Republic are leaders.
Figure 7
Skills-for -Innnovation Index b ase d on 5 indicators : EU-25
1.64 1.621.55
1.22
0.90
0.430.37
0.26
0.12 0.10
-0.28-0.33
-0.38 -0.38 -0.39 -0.42-0.46
-0.73-0.78 -0.81 -0.82
-0.87
-0.96 -0.98
-0.08
-1.5
-1
-0.5
0
0.5
1
1.5
2
D K FI SE UK N L B E DE LU FR IE EE A T CY CZ SI ES LV LT M T PL IT HU SK EL P T
4.3. Relating the Skills Indices to a taxonomy of modes of innovation
The relation between the Skills-for-Innovation Indices and enterprise innovative
performance can be explored further for the EU-15 and the EU-25 by using a
taxonomy of innovation modes developed by TrendChart in conjunction with
Eurostat. The taxonomy, which draws on the work of Tether (2001) and Arundel
(2003), is derived from CIS-3 data. It covers 12 member nations of the EU-15 and 7
new member countries. Four categories of innovators are distinguished: strategic
innovators, intermittent innovators, technology modifiers and technology adopters.
The two criteria upon which the classification is based are the degree of novelty of
the innovations and the creative effort that the firm expends on in-house innovative
activities. The taxonomy also distinguishes non-innovators.
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For strategic innovators, innovation is a core component of their competitive
strategy and they perform R&D on a regular basis. Intermittent innovators develop
innovations in-house when necessary or favourable but innovation is not a core
strategic activity. Technology modifiers modify existing products or processes
mainly through non-R&D based innovative activities. Technology adopters innovate
primarily by adopting innovations developed by other firms or organisations.
Table A.2 in the Annex gives the percentage distribution of enterprises for the four
innovation modes and non-innovators for the 19 EU nations for which data are
available. In order to summarise this data, cluster analysis is used to group nationsthat are close in terms of the percentage distribution of strategic innovators,
intermittent innovators, modifiers, adopter and non-innovators. Table 2 presents the
outcome of this analysis which resulted in four distinct clusters of nations.
Table 2
Percentage Distribution of the 4 Innovation Modes and
Non-innovators for Each Cluster
Countries incluster
Cluster 1
BE, DE, FR,
NL, AT, FI,
SE
Cluster 2
IT, PT, LU
Cluster 3
CZ, EE, ES,
LT
Cluster 4
EL, LV, SI,
SK, HU
EU-19
STRATEGIC 13% 6% 4% 5% 8%
INTERMIT 17% 17% 11% 9% 13%
MODIFIER 15% 17% 5% 5% 11%
ADOPTER 9% 5% 16% 6% 9%
NON-INNOVATOR
46% 55% 64% 75% 59%
The first cluster groups the Scandinavian countries, the Netherlands, France,
Belgium and Austria. This cluster is distinctive both for the over-representation of
the three innovation modes that are highest in terms of novelty requirements and in-
house creative effort and for being the only cluster in which the percentage of
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strategic innovators is above the average for the 19 nations. The second cluster
groups Italy, Portugal and Luxembourg. In this cluster both intermittent innovators
and modifiers are over-represented relative to the population average and this is the
cluster with the highest percentage of modifiers.
Cluster 3 groups together Czechoslovakia, Estonia, Spain and Lithuania. Cluster 4
groups Greece, the Slovak Republic, Latvia, Slovenia and Hungary. These two
clusters can be distinguished from the first two by the over-representation of non-
innovators and the under-representation of the three innovation modes that are
highest in terms of novelty requirements and in-house creative effort. Cluster 3 canbe distinguished from Cluster 4 by the over-representation of adopters, while cluster
4 stands out for being the one with the highest percentage of non-innovators.
In order to explore the relation between the taxonomy and DUI and STI-modes of
learning, separate DUI and STI-indices, as well as a combined DUI/STI index, are
constructed for the twelve members of the EU-15 for which the taxonomy could be
applied. As with the overall skills index, these indices are constructed by first
creating standardised variables for each indicator with mean 0 and standard
deviation 1. The standardised variables are summed and the result is divided by the
number of indicators. Table 3 shows the correlation coefficients between the modes
of innovators in percentage of all firms and the DUI, STI and combined DUI/STI
indices.
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Table 3
Correlations between Types of Innovators and Dimensions of Skills for
Innovation: EU-12
DUI/STIIndex
DUIIndex
STIIndex
STATEGIC INTERMIT MODIFIER ADOPTERNON-INNVATOR
DUI/STIIndex
1.00
DUI Index .95 1.00
STI Index .92 .76 1.00
STRAG .82 .75 .77 1.00
INTERM .59 .51 .59 .62 1.00
MODIF .21 .26 .11 .33 .73 1.00
ADOPT -.34 -.27 -.35 -.43 -.61 -.38 1.00
NON-INV -.55 -.53 -.48 -.65 -.78 -.79 .10 1.00
1. Correlations in bold are significant at the .05 level or better.
The results show positive and significant correlations between the relative
importance of strategic innovators and each of the three skills indices. Although the
correlations with adopters and non-innovators are not significant they are negative.
The positive correlation between the percentage of strategic innovators and the
STI-index is to be expected to some extent, since all strategic firms perform R&D
on a continuous basis and BERD is one of the four indicators used in the STI-index.
The positive and significant correlation with the DUI-index suggests that these very
same strategic firms draw a critical advantage for their innovative activities by
combining high levels of R&D with an emphasis on organisational forms and
practices that foster experience-based learning. Further support for the idea that the
best results are achieved where the two modes of learning are combined at a high
level is given by the larger size of the coefficient on the combined DUI/STI scale
when compared with the coefficients on the DUI or the STI-scales alone.
Table 4 shows the correlation coefficients between the 5-indicator Skills-for-
Innovation Index and the different types of innovators for all 19 EU nations for which
the innovation taxonomy could be applied.
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Table 4
Correlations between types of innovators and the 5-indicator
Skills-for-Innovation Index: 19 nations
Skillsindex: 5
indicators
STRATEGIC INTERMIT MODIFIER ADOPTER NON-INNVATOR
Skillsindex: 5indicators
1.00
STRAG .78 1.00
INTERM .65 .61 1.00
MODIF .39 .54 .74 1.00
ADOPT -.18 -.36 -.27 -.19 1.00
NON-INNOV
-.62 -.68 -.83 -.87 -.09 1.00
1. Correlations in bold are significant at the .05 level or better.
The correlations between the skills index and both the strategic and intermittent
modes of innovators are positive and significant. The higher value of the coefficient
for the strategic mode points to a positive relation between the two dimensions of
skills development captured in the index and the importance of novelty
requirements in innovative activity.
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6. Conclusion
There exist large differences in the development of skills for innovation across EU-
nations. Portugal, Greece, and to a lesser extent Italy, as well as a number of the
new member countries, show an enormous lag. The science and technology-based
perspective implies that scientifically-based ways of getting access to, producing
and utilizing knowledge dominate the innovation process.12This STI-perspective,
along with the combined pressure emanating from the science community and the
big science-based firms, accounts for the fact that such high-tech fields as ICT, bio
and nano-technology are most often the focus of current policy efforts at the
national and European levels. But other traditional industries such as food, clothing
and furniture as well as many service sectors also draw upon science when it
comes to innovating production processes, the use of materials and designing new
products.
At the same time, it would clearly be a mistake to assume that STI-mode learning is
sufficient and that DUI-mode learning will take care of itself. DUI-mode initiatives
are needed to overcome the bottlenecks high-tech faces in absorbing and using
new technologies. Low-tech needs DUI to sustain its capacity for incremental
improvements in product quality and design. Moreover, the empirical evidence
presented in this report highlights both the considerable variance in capacity for
DUI-mode learning that exists across European nations and the important
differences in this capacity that exist across firms within the same nation.13
Irrespective of whether the focus is on promoting catch-up among the nations that
lag behind, or promoting the sustained development of the leaders, this implies a
need for a realignment of policy. On the one hand, there is a need to give more
attention to developing the science and technology base of the services and low
and medium-tech manufacturing. These are the sectors which account for the bulk
of employment and growth in Europes economies. On the other hand, DUI-mode
12For a further discussion of the points raised in this concluding section, see Lundvall et al. (2004).
http://www.business.aau.dk/loc-nis/
.13See pp. 6-9 above for a discussion of detailed national survey evidence for France, the UK and
Denmark.
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learning cannot be taken for granted and initiatives are need to actively promote
this experience-based skills development in both low and high-tech sectors.
One way in which these issues can be brought to the fore in policy is through the
development of more adequate DUI-mode measures and benchmarks. There
currently exist specialised Manuals setting norms and guidelines for collecting
harmonised data at the European level on innovation (Oslo Manual), R&D (Frascatti
Manual) and HRST (Canberra Manual). A preliminary step towards developing
European-wide harmonised data on DUI-mode skills development would be a
Manual on Organisational Innovation. The European Survey on Working
Conditions, while providing some useful characterisations of individual workexperience in terms of problem-solving, responsibility for quality assessment and
degrees of autonomy, is first and foremost a survey of working conditions and it
cannot substitute for a focused survey on organisational innovation. Adequate DUI
measures would require complementary establishment-level data providing
information on the use of such collective organisational forms as problem-solving
groups, shop and department meetings and the way knowledge flows and sharing
is organised among different services and departments.
Finally, while the skills indices developed in this report clearly identify leading and
lagging nations, it would be a mistake to try to identify a best practice method for
creating learning organisation on the basis of these aggregate measures. Although
the indicators identify common features of learning organisations, as Lorenz and
Valeyre (2004) discuss in more detail, there are multiple ways to build them and
whats best in a particular case will depend both on sector-specific features and on
the national institutional context. This bench-marking should be interpreted in the
spirit of the open-method of coordination with the idea that each nation will develop
the organisational practices and skills that support innovation in a way that is
adapted to its distinctive cultural and institutional resources.
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Annex
Table A.1: Skills-for-Innovation Indicators and IndicesIndicators BE CZ DK DE EE EL ES FR IE
1.1 HRSTC as a percentage employedpopulation aged 24-65, 2000
20.41
9.48 21.12
16.57
15.51
14.02
14.91
16.25
154
1.2 BERD as a percent of GDP, 2000 1.48 .74 1.51 1.75 .14 .223 .5 .83 .5
1.3 HRST mobility as a percentage ofHRST, 2000
6.64 5.35 13.34
7.36 8.31 NA 6.79 8.16 NA
1.4 Percentage of employees using acomputer having received computertraining, 2000
37.7 27.0 69.9 64.8 NA 44.3 43.7 51.1 65
2.1 Percentage of employeesresponsible for quality assessment,2000
68.3 NA 86.4 66.6 NA 50.9 65.3 77.9 70
2.2 Percentage of employees whosework involves problem solving, 2000
83.0 NA 91.4 74.7 NA 66.8 79.0 85.0 68
2.3 Percentage of employees exercisingcontrol of work methods, 2000
55.7 NA 72.0 67.7 NA 43.0 48.1 59.5 49
2.4 Percentage of employees whosework involves learning new things, 2000
74.9 NA 84.8 61.3 NA 49.1 62.8 71.2 65
3.1 Percentage of the working agepopulation engaged in training of any
type four week prior to survey, 2001
6.8 5.9 20.8 5.2 6.0 1.1 5.1 2.8 7.
3.2 Percentage of enterprises offeringtraining of any type, 1999
70 69 96 75 63 18 36 76 79
4.1 PISA Reading literacy: percentage15 yr. olds reading at levels 4 or 5, 2000
38 15 30 28 NA 23 25 32 41
4.2 PISA Mathematical literacy:percentage of 15 yr. olds scoring 600 or
24 NA 16 14 NA 7 9 18 12
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over, 2000
Indices
12 Indicator skills index .14 NA .77 .14 NA -.12 -.59 -.11 -.1
5 Indicator skills index .43 -.38 1.64 .37 -.08 -.96 -.39 .12 .1
Table A.1 (contd) Skills-for-Innovation Indicators and Indices
Indicators LU HU MT NL AT PL PT SI
1.1 HRSTC as a percentage employed
population aged 24-65, 2000
18.19 12.17 9.80 19.13 9.8 9.93 8.48 12.58
1.2 BERD as a percent of GDP, 2000 1.58 .35 .07 1.11 NA .24 .29 .811
1.3 HRST mobility as a percentage ofHRST, 2000
6.17 3.99 6.20 NA 5.49 5.22 5.71 4.90
1.4 Percentage of employees using acomputer having received computertraining, 2000
55.33 NA NA 56.7 59.6 NA 39.1 NA
2.1 Percentage of employeesresponsible for quality assessment,2000
65.1 NA NA 82.4 69.8 NA 66.7 NA
2.2 Percentage of employees whose
work involves problem solving, 2000
76.9 NA NA 92.6 77.0 NA 58.5 NA
2.3 Percentage of employees exercisingcontrol of work methods, 2000
60.0 NA NA 78.5 60.3 NA 41.0 NA
2.4 Percentage of employees whosework involves learning new things, 2000
74.5 NA NA 78.7 68.7 NA 50.8 NA
3.1 Percentage of the working agepopulation engaged in training of any
4.8 33.1 4.4 15.6 8.3 4.8 3.4 7.6
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type four week prior to survey, 2001
3.2 Percentage of enterprises offeringtraining of any type, 1999
71 37 NA 88 72 39 22 48
4.1 PISA Reading literacy: percentage15 yr. olds reading at levels 4 or 5, 2000
NA 23 NA NA 34 25 21 NA
4.2 PISA Mathematical literacy:percentage of 15 yr. olds scoring 600 orover, 2000
4 13 NA NA 18 10 4 NA
Indices
12 Indicator skills index .20 NA NA .58 .02 NA -1.1 NA
5 Indicator skills index .26 -.82 -.73 .90 -.28 -.78 -.98 -.38
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Table A.2
Taxonomy of Innovation Modes1
(percentage of all enterprises)StrategicInnovators
IntermittentInnovators
Modifiers Adopters Non-innovators
BE 11.0 16.0 17.0 15.0 41.0 100.0
DE 13.0 21.0 22.0 10.0 34.0 100.0
EL 4.0 8.0 5.0 10.0 73.0 100.0
ES 3.0 7.0 6.0 22.0 62.0 100.0
FR 12.0 14.0 10.0 11.0 53.0 100.0
IT 7.0 13.0 17.0 4.0 59.0 100.0
LU 7.0 22.0 18.0 3.0 50.0 100.0
NL 12.0 19.0 16.0 8.0 45.0 100.0
AT 13.0 15.0 18.0 8.0 46.0 100.0
PT 3.0 15.0 17.0 10.0 55.0 100.0
FI 17.0 20.0 11.0 2.0 50.0 100.0
SE 12.0 15.0 15.0 6.0 52.0 100.0
CZ 5.0 8.0 3.0 16.0 68.0 100.0
EE 5.0 13.0 9.0 13.0 60.0 100.0
HU 4.0 6.0 7.0 6.0 77.0 100.0
LT 2.0 16.0 4.0 14.0 64.0 100.0
LV 3.0 10.0 3.0 7.0 77.0 100.0
SI 11.0 11.0 5.0 1.0 72.0 100.0
SK 4.0 8.0 6.0 5.0 77.0 100.0
1. Based on CIS-3 data.