Science, technology and innovation in Europe : 2008 edition.
Date: 2008-02-27
Institutions: Eurostat
Subjects: EU research policy, competitiveness, innovation, investment, productivity, research and development, research staff, scientific research, statistics, technology

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S t a t i s t i c a l   b o o k s

Science, technology and innovation in Europe

2008 edition

an Commission
EuropEE an Commission
Europ

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00_PrØface:Preface  28/01/2008  11:27  Page 1

Democratic  societies  cannot  function  properly  without  a  solid  foundation  of  reliable  and
objective statistics. On the one hand, decision-makers at EU level, in Member States, in
local governments and in businesses need statistics to refer to when making decisions and
monitoring  their  impact.  On  the  other,  the  public  and  mass  media  need  statistics  for
drawing an accurate picture of contemporary society and evaluating the performance of
politicians and others.
The statistics and indicators presented in this 2007 edition of “Science, Technology and
Innovation in Europe” are fully in line with the strategic goals set by the European Council
in Lisbon in 2000 – the “Lisbon strategy” – and Barcelona in 2002 aiming, respectively, to
turn  the  European  Union,  by  2010,  into  the  most  competitive  and  dynamic  knowledge-
based economy in the world, capable of sustainable economic growth with more and better
jobs  and  greater  social  cohesion  and,  by  the  same  date,  to  raise  overall  research
investment in the EU from around 1.9% of GDP to approaching 3%, of which two thirds
should be financed by the business sector.

The  Lisbon  and  Barcelona  European  Councils  both  signalled  the  important  role  of  R&D
and innovation in the EU. Against this background, the 2005 initiative “Working together for
growth  and  jobs”  relaunched  the  Lisbon  strategy.  Knowledge  and  innovation  for  growth
then  became  one  of  the  three  main  areas  for  action  in  the  new  Lisbon  partnership  for
growth and jobs, which put science, technology and innovation at the heart of EU policies.
The  concept  of  a  European  Research  Area,  introduced  in  2000  as  the  contribution  by
research policy to the broader Lisbon strategy, has also been a highly successful tool for
moving research higher up the political agenda.

In this context, relevant and meaningful indicators on science, technology and innovation
are  paramount  for  informing  policy-makers  about  where  Europe  stands  on  the  path
towards more knowledge and growth. This information is also necessary to gain a better
picture  of  how  Europe  is  evolving,  compared  with  the  United  States,  Japan,  China  and
other leading economies.

This publication demonstrates, with the aid of the relevant statistics, the progress made in
recent  years  on  research,  development  and  innovation  activities.  Statistics  on  high-tech
industries and knowledge-based services, patents and human resources in science and
technology are also widely used to complete the picture.    

Considering the continuously growing importance of innovation activities at the very top of
the  political  agenda,  this  publication  puts  special  emphasis  on  the  results  of  the  latest
Community Innovation Survey (CIS) which focuses on product and process innovation, but
also looks at the effects of innovation, the sources of information for innovation activities
and expenditure on innovation and examines the factors hampering innovation and use of
intellectual property rights.

“Science, Technology  and  Innovation  in  Europe”  also  gives  a  first  insight  into  Eurostat’s
current statistical activities in important emerging domains such as the career development
of doctorate-holders or the development of new indicators in response to changing policy
and user needs in the area of science, technology and innovation.

Michel GLAUDE 
Director for Social Statistics and Information Society  

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00_PrØface:Preface  28/01/2008  11:27  Page 2

This publication was prepared by Eurostat:

- Directorate F: Social statistics and information society — Michel Glaude, Director
- Unit F-4: Education, science and culture statistics — Jean-Louis Mercy, Head of Unit

This  edition  of  the  statistical  book  was  coordinated  and  managed  by  Bernard  Félix, 
Tomas Meri, Sergiu Valentin Parvan, Veijo-Ismo Ritola and Håkan Wilén.

The  technical  work  was  carried  out  by  SOGETI  .The  texts  and  analyses,  the  layout  of  the
publication  and 
the  desktop  publishing  were  done  by  Gesina  Dierickx, 
Céline Lagrost and Sammy Sioen.

DISCLAIMER

The opinions expressed in this publication are those of the individual authors
alone  and  do  not  necessarily  reflect  the  position  of  the  European
Commission.

Data source
Eurostat  is  the  data  source  for  all  tables  and  figures  in  this  publication  unless  specified
otherwise.

Maps
GISCO, Eurostat.
©  EuroGeographics  Association  2001  for  the  administrative  boundaries,  on  behalf  of  the
national organisations responsible for official mapping in the countries concerned.

2

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- Preface............................................................................................................1

- Acknowledgments .........................................................................................2

- Overview and executive summary.............................................................14

PART 1 Investing in R&D  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Chapter 1 Government Budget Appropriations or Outlays on R&D — GBAORD  . . . . . . . . . . . .19
Chapter 1 Government Budget Appropriations or Outlays on R&D — GBAORD  . . . . . . . . . . . .19
1.1   Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

1.2   Total GBAORD  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

1.3   GBAORD by socio-economic objectives  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

 . . . . .29
Chapter 2 R&D expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29
Chapter 2 R&D expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1   Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

2.2   R&D at the national level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

- R&D intensity  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..31

- R&D expenditure in volume  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

2.3   R&D at the regional level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

PART 2 Monitoring the knowledge workers . . . . . . . . . . . . . . . . . . . . . . . .43

 . . . . .43
Chapter 3 R&D personnel  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43
Chapter 3 R&D personnel  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1   Introduction   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44

3.2   R&D personnel at the national level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

- R&D personnel as a percentage of total employment  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

- R&D personnel in full time equivalent  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

- R&D personnel in head count  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

- Researchers in full-time equivalent - FTE  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

- Researchers by gender  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52

- Researchers by economic activity  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54

- Researchers by field of science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

3.3   R&D personnel at the regional level   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56

Chapter 4 Human resources in science and technology   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59
Chapter 4 Human resources in science and technology   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59
4.1   Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60

4.2   Education inflows  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62

- Participation in tertiary education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62

- Graduation from tertiary education  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66

4.3   Stocks of human resources in science and technology  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

- HRST stocks at the national level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

- HRST stocks at the regional level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .76

4.4   Mobility  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .79

4.5   Nationality  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81

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PART 3 Productivity and competitiveness . . . . . . . . . . . . . . . . . . . . . . . . .83

. . . . . . . .83
Chapter 5 Innovation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83
Chapter 5 Innovation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
5.1   Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84

5.2   Community Innovation Survey  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84

- CIS 4  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84

- CIS – History  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85

- Innovation surveys in other countries  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86

5.3   EU-27 aggregates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87

5.4   Innovation data at the national level   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .103

- General information about the enterprises  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .103

- Product (goods or services) and process innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .110

- Innovation activity and expenditure  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .116

- Sources of information and cooperation for innovation activities  . . . . . . . . . . . . . . . . . . . . . . . . .124

- Effects of innovation during 2002-2004  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .134

- Factors hampering innovation activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .136

- Intellectual property rights  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .140

- Organisational and marketing innovations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145

5.5   Comparison between CIS 3 and CIS 4  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .150

5.6   European Innovation Scoreboard 2006 (EIS 2006)

 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .154

 . . . . . . . .157
Chapter 6 Patents  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157
Chapter 6 Patents  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.   Introduction .............................................................................................................................................158

6.2   Triadic patent families ..............................................................................................................................160

6.3   Total patent applications to the EPO and patents granted by the USPTO..............................................161

6.4   High-tech patent applications ..................................................................................................................169

Chapter 7 High-tech industries and knowledge based services..................................................175
Chapter 7 High-tech industries and knowledge based services..................................................175
7.1  Introduction ...............................................................................................................................................176

7.2   Enterprises in high-tech industries and knowledge-intensive services  ..................................................177

7.3  Venture Capital Investments .....................................................................................................................179

7.4   Trade in high-tech products .....................................................................................................................181

7.5   Employment in high-tech industries and in knowledge-intensive services..............................................183

- Performance at national level in Europe.........................................................................................183

- Performance at regional level in Europe ........................................................................................188

7.6   R&D in high-technology ...........................................................................................................................192

Chapter 8 The 2006 EU Industrial R&D Investment Scoreboard ..................................................195
Chapter 8 The 2006 EU Industrial R&D Investment Scoreboard ..................................................195

8.1.   Introduction .............................................................................................................................................196

8.2   Overview of industrial R&D investment ...................................................................................................197

- Enterprise group dynamics .............................................................................................................197

- Slight decline in R&D intensity due to relative high net sales growth  ...........................................198

- R&D concentration likely to remain high.........................................................................................198

- R&D investment in EU countries ....................................................................................................199

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8.3   Other key findings....................................................................................................................................202

- Considerable number of smaller and medium-sized EU companies 

in high R&D-intensive sectors.........................................................................................................202

- Pharmaceuticals, biotechnology and service sectors play an important role 

in R&D investment growth .............................................................................................................202

- The role of R&D for business performance ....................................................................................202

- Scoreboard webpage ......................................................................................................................202

.............203
Methodological notes........................................................................................................................203
Methodological notes...........................................................................................................

........227
Abbreviations and symbols..............................................................................................................227
Abbreviations and symbols......................................................................................................

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PART 1

Investing in R&D

Chapter 1
Chapter 1

Government Budget Appropriations or Outlays on R&D — GBAORD
Government Budget Appropriations or Outlays on R&D — GBAORD

Figure 1.1

Total GBAORD in million of 1995 constant PPS and as a percentage of GDP, 
EU-15, EU-27, Japan and United States — 1995 to 2005 .........................................................21

Figure 1.2

Total GBAORD as a percentage of GDP, EU-27 and selected countries — 2005 .....................22

Figure 1.3

Distribution of EU-27 total GBAORD, in EUR million — 2005 ....................................................22

Figure 1.4

Annual average growth rate (AAGR) of GBAORD and of GDP, 
EU-27 and selected countries — 1995 to 2000 and 2000 to 2005.............................................23

Table 1.5

Total GBAORD in EUR million and by socio-economic objectives as a % of total,
EU-27 and selected countries — 2005 .......................................................................................24

Figure 1.6

Main NABS socio-economic objectives in million 1995 constant PPS, 
EU-15 — 1995 to 2005 ...............................................................................................................25

Table 1.7

Annual average real growth rate (AAGR) of GBAORD by socio-economic objectives,
EU-27 countries and selected countries — 2000 to 2005  .........................................................26

Chapter 2
Chapter 2

R&D expenditure
R&D expenditure

Table 2.1

R&D expenditure as a percentage of GDP, by sector of performance,
EU-27 and selected countries — 2003 to 2005 ..........................................................................31

Figure 2.2

R&D as a percentage of GDP in 2005 and annual average growth rate (AAGR) 2000 - 2005,
all sectors, EU-27 and selected countries...................................................................................33

Table 2.3

R&D expenditure in EUR million and annual average growth rate (AAGR), 
by sector of performance, EU-27 and selected countries — 2000-2005....................................34

Figure 2.4

Total and business enterprise R&D expenditure by source of funds as a percentage of total,
EU-27 and selected countries – 2004.........................................................................................35

Table 2.5

Table 2.6

Table 2.7

Business enterprise R&D expenditure in EUR million, by sector of activity (NACE Rev 1.1), 
EU-27 and selected countries — 2004 .......................................................................................36

Business enterprise R&D expenditure in EUR million, by size class,
EU-27 and selected countries — 2004 .......................................................................................37

R&D expenditure in EUR million and by field of science as a percentage,
government and higher education sectors, EU-27 and selected countries — 2004 ..................38

Figure 2.8

R&D expenditure in the top 10 EU regions,
as a percentage of EU-27, all sectors — 2003 ...........................................................................39

Table 2.9

Map 2.10

Top 15 EU regions in terms of R&D expenditure,
as a percentage of GDP, all sectors — 2003..............................................................................39

Total R&D expenditure as a percentage of GDP,
all sectors, NUTS 2 — 2003 .......................................................................................................41

PART 2

Monitoring the knowledge workers

Chapter 3
Chapter 3

R&D personnel
R&D personnel

Figure 3.1

R&D personnel (HC), in all sectors and the business enterprise sector, 
as a percentage of total employment, EU-27 and selected countries — 2004 ..........................45

Table 3.2

R&D personnel (HC)  by sector of performance, as a percentage of total employment,
EU-27 and selected countries — 2002 to 2004 ..........................................................................46

Figure 3.3

R&D personnel (HC) as a percentage of total employment in 2004 and, annual average 
growth rate (AAGR) of this share 2000-2004, EU-27 and selected countries ............................47

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Table 3.4

Table 3.5

Table 3.6

Figure 3.7

Figure 3.8

Table 3.9

R&D personnel in FTE and percentage of women in 2004 and annual average growth 
rate (AAGR) 2000-2004 (1), by sector of performance, EU-27 and selected countries  ............48

R&D personnel in head count (HC), by sector of performance, 
EU-27 and selected countries — 2002 to 2004 ..........................................................................49

Researchers in FTE, by sector of performance, 
EU-27 and selected countries — 2003 to 2005 ..........................................................................50

Annual average growth rate (AAGR) of researchers in FTE, all sectors and 
business enterprise sector, EU-27 and selected countries — 2000-2005 ..................................51

Percentage of female researchers (in HC), all sectors and business enterprise sector,
EU-27 and selected countries — 2004 .......................................................................................52

Business enterprise researchers in FTE, by economic activity (NACE Rev 1.1),
EU-27 and selected countries — 2004 .......................................................................................54

Table 3.10

Researchers by field of science as a percentage, government and higher education sectors,
EU-27 and selected countries — 2004 .......................................................................................55

Figure 3.11

Top 15 regions in terms of R&D personnel in FTE and as a percentage of 
total employment (HC),all sectors — 2003 .................................................................................56

Map 3.12

Researchers as a percentage of persons employed, all sectors, 
by NUTS 2 regions — 2003 ........................................................................................................57

Chapter 4
Chapter 4

Human resources in science and technology 
Human resources in science and technology 

Figure 4.1

Definitions of Human Resources in Science and Technology (HRST) categories .....................61

Table 4.2

Figure 4.3

Students participating in tertiary education, total and in selected fields of study, 
proportion of the population aged 20-29 and proportion of female students, 
EU-27 and selected countries - 2004..........................................................................................62

Annual average growth rates of the number of students participating
in tertiary education in Science and Engineering, by gender,
EU-27 and selected countries — 1999 to 2004 ..........................................................................63

Figure 4.4

Foreign students participating in tertiary education, total and in proportion of S&E students, 
EU-27 and selected countries — 2004 .......................................................................................64

Table 4.5

Table 4.6

Doctoral students (ISCED level 6), total and in selected fields of study, 
proportion of the population aged 20-29, and proportion of female doctoral students, 
EU-27 and selected countries — 2004 .......................................................................................65

Graduates from tertiary education, total and in selected fields of study,
proportion of the population aged 20-29 and proportion of female graduates,
EU-27 and selected countries — 2004 .......................................................................................66

Figure 4.7

Annual average growth rates of graduates from tertiary education 
in Science and Engineering, by gender, EU-27 and selected countries — 1999 to 2004..........67

Table 4.8

Table 4.9

Doctoral graduates (ISCED level 6), total and in selected fields of study,
proportion of the population aged 20-29 and proportion of female doctorate graduates,
EU-27 and selected countries — 2004 .......................................................................................68

Human resources in Science and Technology (S&T) stocks, 25-64 years old,
by HRST category and proportion of females and annual average growth rate of HRSTC, 
2001 to 2006, EU-27 and selected countries — 2006 ................................................................71

Figure 4.10 Annual average growth rates of HRST, 2001 to 2006,

and their proportion of the labour force, EU-27 and selected countries — 2006 .......................72

Figure 4.11 Employed HRST with tertiary education in Science and Engineering (HRSTE), 
by selected field of occupation, as a percentage of labour force, 
EU-27 and selected countries — 2005 .......................................................................................73

Figure 4.12 Breakdown of Scientists and Engineers (SE), 25-64 years old, by gender, 

as a percentage of the total labour force, EU-27 and selected countries — 2006 .....................73

Table 4.13

HRST intensity of employed people with S&T education (HRSTE), 
as a percentage of total employment, 25-64 years old, 
in selected sectors of economic activities, EU-27 and selected countries — 2006....................74

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Figure 4.14 Unemployment rates for tertiary and non-tertiary educated population, 

25-64 years old, EU-27 and selected countries — 2006  ...........................................................75

Map 4.15

Human Resources in Science and Technology (HRST) 
as a percentage of the labour force — 2006  .............................................................................77

Table 4.16

Table 4.17

Table 4.18

The top 30 EU-27 regions ranked according to the proportion of employed human resources 
in terms of education (HRSTE), in total manufacturing and in total services, 
in thousands and as a percentage of total employment — 2006  ..............................................78

Job-to-job mobility of employed HRST, broken down by age group and by gender,
in thousands and as a percentage of employed HRST population,
EU-27 and selected countries — 2005 .......................................................................................79

Human Resources in Science and Technology (HRST), 15-74 years old, by nationality,
in thousands and as a percentage of respective labour force and 
distribution of non-nationals, EU-27 and selected countries — 2006  ........................................81

PART 3

Productivity and competitiveness

Chapter 5
Chapter 5

Innovation
Innovation

Figure 5.1

Figure 5.2

EU-27 innovative and non-innovative enterprises by size class, 
as a percentage of all enterprises — 2004 .................................................................................88

EU-27 innovative and non-innovative enterprises by selected NACE, 
as a percentage of all enterprises — 2004 .................................................................................88

Figure 5.3

EU-27 enterprises by type of innovator, as a percentage of all enterprises — 2004 .................89

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

EU-27 innovative and non-innovative enterprises by markets, 
as a percentage of all enterprises — 2004 .................................................................................90

EU-27 innovative enterprises and non-innovative enterprises by number of employees,
as a percentage of employment of all enterprises, in 2002 and 2004........................................91

EU-27 innovative enterprises, turnover of new or significantly improved products only new 
to the firm and turnover of new or significantly improved products new to the market 
by size class, as a percentage of total turnover — 2004............................................................92

EU-27 innovative enterprises engaged in intramural R&D, extramural R&D, 
acquisition of machinery, equipment and software, acquisition of other external knowledge,
training, market introduction of innovation and in other preparations, 
as a percentage of innovative enterprises — 2004 ....................................................................93

Figure 5.8

EU-27 expenditure by innovative enterprises on intramural R&D, extramural R&D, 
acquisition of machinery, equipment and software and acquisition of 
other external knowledge, as a percentage of total innovation expenditure —- 2004................95

Figure 5.9

EU-27 innovative enterprises, highly important effects of innovation, 
as a percentage of innovative enterprises — 2004 ....................................................................96

Figure 5.10 EU-27 innovative enterprises, public funding broken down by sources, 

as a percentage of all enterprises — 2004 .................................................................................97

Figure 5.11 EU-27 innovative enterprises by type of cooperation, 

as a percentage of all enterprises — 2004 .................................................................................98

Figure 5.12 EU-27 innovative enterprises by source of information, 

as a percentage of all enterprises — 2004 .................................................................................99

Figure 5.13 EU-27 innovative enterprises by highly important hampering factor, 

as a percentage of innovative enterprises – 2004 ....................................................................100

Figure 5.14 EU-27 innovative and non-innovative enterprises by protection method, 

as a percentage of all enterprises — 2004 ...............................................................................101

Figure 5.15 EU-27 innovative and non-innovative enterprises that introduced organisational 

and/or marketing innovations, organisational innovation and marketing innovation, 
as a percentage of all enterprises — 2004 ...............................................................................102

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Table 5.16

EU-27 innovative and non-innovative enterprises by highly important effect of 
organisational innovation, as a percentage of all enterprises — 2004 .....................................102

Figure 5.17

Innovative enterprises, total number and as a percentage of all enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................103

Figure 5.18

Innovative and non-innovative enterprises which are part of a group, 
as a percentage of innovative and non-innovative enterprises, by country, 
EU-27 and Norway — 2004  .....................................................................................................104

Figure 5.19

Innovative and non-innovative enterprises which are part of a group and have a head office 
in another country, as a percentage of innovative and non-innovative enterprises, 
by country, EU-27 and Norway — 2004  ..................................................................................105

Table 5.20

Innovative and non-innovative enterprises which sold goods or services during 
the three years 2002 to 2004, as a percentage of innovative and non-innovative 
enterprises, by geographic market and by country, EU-27 and Norway — 2004.....................106

Table 5.21

Innovative enterprises as a percentage of all enterprises, by size-class and by country, 
EU-27 and selected countries — 2004 .....................................................................................107

Table 5.22

Innovative enterprises, as a percentage of all enterprises, by NACE and by country, 
EU-27 and selected countries — 2004 .....................................................................................108

Table 5.23

Innovative enterprises, by number of employees in 2002 and 2004 and 
AAGR of employees and turnover, by country, EU-27 and selected countries — 2004...........109

Figure 5.24 Breakdown of innovative enterprises, by type of innovator, as a percentage of all enterprises, 

by country, EU-27 and Norway — 2004 ....................................................................................111

Figure 5.25 Breakdown of product innovators by who developed the product innovation, 

as a percentage of all enterprises, by country, EU-27 and Norway — 2004  ...........................112

Figure 5.26 Breakdown of innovative enterprises who developed the product innovation, 

as a percentage of all enterprises, by country, EU-27 and Norway — 2004  ...........................113

Figure 5.27 Breakdown of process innovators by who developed the process innovation, 

as a percentage of all enterprises, by country, EU-27 and Norway — 2004 ............................113

Table 5.28

Turnover of innovative enterprises related to new or significantly improved products 
which are new to the enterprise (but not new to the market), 
as a percentage of total turnover of innovative enterprises, by sector, 
EU-27 and selected countries — 2004 .....................................................................................114

Figure 5.29 Turnover of innovative enterprises related to new or significantly improved products 

which are new to the market, as a percentage of total turnover of innovative enterprises, 
in manufacturing and in services, by country, EU-27 and selected countries — 2004.............115

Figure 5.30

Intramural and extramural expenditure on R&D by innovative enterprises, 
as a percentage of total innovation expenditure, by country, EU-27 and Norway — 2004 ......116

Table 5.31

Breakdown of innovation expenditure into four categories, 
as a percentage of total innovation expenditure, by country, EU-27 and Norway — 2004 ......117

Figure 5.32 Breakdown of innovation expenditure into four categories, 

as a percentage of total innovation expenditure, by country, EU-27 and Norway — 2004 ......118

Figure 5.33

Innovative enterprises engaged in intramural and extramural R&D, 
as a percentage of innovative enterprises,  by country, EU-27 and Norway — 2004 ..............119

Figure 5.34 Breakdown of occasional and continuous intramural R&D, 

as a percentage of innovative enterprises, by country, EU-27 and Norway — 2004 ...............119

Table 5.35

Enterprises engaged in innovation activities, as a percentage of innovative enterprises, 
by country, EU-27 and Norway — 2004....................................................................................120

Table 5.36

Innovative enterprises which received public funding for innovation activities, 
as a percentage of innovative enterprises, by source of funds and by country, 
EU-27 and Norway — 2004 ......................................................................................................122

Table 5.37

The three most used sources of information, as a percentage of innovative enterprises, 
by country, EU-27 and Norway — 2004  ..................................................................................125

Figure 5.38

Innovative enterprises broken down into those that cooperate and those that do not,
as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................127

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Figure 5.39 Enterprises engaged in any type of innovation cooperation, 

by region of the partner, as a percentage of innovative enterprises, 
by country, EU-27 and Norway — 2004....................................................................................128

Figure 5.40 Cooperation partner: Other enterprises in the same group, 

as a percentage of innovative enterprises,  by country, 
EU-27 and selected countries — 2004 .....................................................................................129

Figure 5.41 Cooperation partner: Competitors or other enterprises in the same sector, 

as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................130

Figure 5.42 Cooperation partner: Clients or customers, as a percentage of innovative enterprises, 

by country, EU-27 and selected countries — 2004 ..................................................................130

Figure 5.43 Cooperation partner: Suppliers of equipment, materials, components or software 

by country, EU-27 and selected countries — 2004...................................................................131

Figure 5.44 Cooperation partner: Universities or other higher education institutions, 

as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................131

Figure 5.45 Cooperation partner: Government or public research institutes, 

as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................132

Figure 5.46 Cooperation partner: Consultants, commercial labs or private R&D institutes, 

as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................132

Table 5.47 Most valuable method of cooperation by partner, as a percentage of innovative enterprises, 

by country, EU-27 — 2004 ........................................................................................................133

Figure 5.48 Effects identified by enterprises as highly important for their innovation activities, 

as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2004 .....................................................................................135

Table 5.49

Highly important hampering effects, as a percentage of innovative enterprises,
by country, EU-27 and selected countries — 2004...................................................................136

Figure 5.50 Cost factors rated as highly important factors hampering innovation, as a percentage of 

innovative enterprises, by country, EU-27 and Norway — 2004 ..............................................137

Figure 5.51 Knowledge factors rated as highly important factors hampering innovation,  

as a percentage of innovative enterprises, by country, EU-27 and Norway — 2004 ...............138

Figure 5.52 Market factors rated as highly important factors hampering innovation, 

as a percentage of innovative enterprises, by country, EU-27 and Norway — 2004 ...............139

Figure 5.53 Reasons not to innovate rated as highly important factors hampering innovation, 

as a percentage of non-innovative enterprises, by country, EU-27 and Norway — 2004 ........140

Figure 5.54

Innovative enterprises that applied for a patent, as a percentage of innovative enterprises, 
by country, EU-27 and Norway — 2004....................................................................................141

Figure 5.55

Innovative enterprises that registered a trademark, as a percentage of innovative 
enterprises, by country, EU-27 and Norway — 2004................................................................142

Figure 5.56 

Innovative enterprises that registered an industrial design, as a percentage of innovative
enterprises, by country, EU-27 and Norway — 2004................................................................143

Figure 5.57

Innovative enterprises that claimed copyright, as a percentage of innovative enterprises, 
by country, EU-27 and Norway — 2004....................................................................................144

Table 5.58

Innovative and non-innovative enterprises that introduced organisational and/or marketing
innovations, as a percentage of innovative and non-innovative enterprises, by country, 
EU-27 and Norway – 2004 .......................................................................................................146

Figure 5.59 Reduced time to respond to customer or supplier needs: innovative enterprises that 

rate this as a highly important effect of organisational innovation, 
as a percentage of all enterprises, by country, EU-27 and Norway — 2004............................147

Figure 5.60

Improved quality of goods or services: innovative enterprises that rate this as 
a highly important effect of organisational innovation, as a percentage of all enterprises, 
by country, EU-27 and Norway — 2004....................................................................................148

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Figure 5.61 Reduced costs per unit output: innovative enterprises that rate this as 

a highly important effect of organisational innovation, as a percentage of all enterprises, 
by country, EU-27 and Norway — 2004....................................................................................149

Figure 5.62

Improved employee satisfaction and/or reduced rates of employee turnover: 
innovative enterprises that rate this as a highly important effect of organisational innovation, 
as a percentage of all enterprises, by country, EU-27 and Norway — 2004............................149

Figure 5.63 Enterprises with innovation activity in CIS 3 (2000) and CIS 4 (2004), 

as a percentage of all enterprises, by country, 
EU-27 and selected countries — 2000 and 2004 .....................................................................150

Table 5.64

Table 5.65

Novel innovators in CIS 3 (2000) and in CIS 4 (2004) by type of innovator, 
as a percentage of all enterprises, by country, 
EU-27 and selected countries — 2000 and 2004 .....................................................................151

Enterprises which introduced new or improved products for the market, 
as a percentage of innovative enterprises by size-class, CIS 3 (2000) and CIS 4 (2004) 
by country, EU-27 and selected countries — 2000 and 2004 ..................................................152

Figure 5.66 Enterprises with innovation activity in CIS 3 (2000) and CIS 4 (2004), 

Lack of finance from sources outside the enterprise as a highly important factor hampering
innovation activities, as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2002 and 2004 .....................................................................153

Figure 5.67 Enterprises with innovation activity in the CIS 3 (2000) and in the CIS 4 (2004), 

Lack of finance from sources outside your enterprise as high important factor of hampering
innovation activities, as a percentage of innovative enterprises, by country, 
EU-27 and selected countries — 2002 and 2004 .....................................................................153

Figure 5.68 Summary innovation Index (SII) in 2006 and average growth rate of SII, 

EU-27 and selected countries  ..................................................................................................154

Figure 5.69

Innovation gap between the EU-25 and the United States 
and between the EU-25 and Japan  .........................................................................................155

Table 5.70

EIS 2006 indicators by sub-group  ............................................................................................156

Chapter 6
Chapter 6

Patents
Patents

Figure 6.1

Figure 6.2

Table 6.3

Figure 6.4

Distribution of triadic patent families, as a percentage of total, 
EU-27, Japan, the United States and other — 2000.................................................................160

Triadic patent families per million inhabitants, 
EU-27, Japan and the United States — 1991 to 2000..............................................................161

Patent applications to the EPO: total number and as a percentage of GDP, EU-27 
and selected countries - 2003 and Patents granted by the USPTO: total number and 
as a percentage of GDP, EU-27 and selected countries — 2000.............................................162

Patent applications to the EPO per million inhabitants, EU-27 and selected countries 
(with at least 10 patent applications per million inhabitants in 2003) 
— 1993, 1998 and 2003  ..........................................................................................................163

Table 6.5

Breakdown of patent applications to the EPO by IPC section, total number and 
as a percentage of total, EU-27 and selected countries — 2003  ............................................164

Figure 6.6

Member States’ patent applications to the EPO, 
as a percentage of total EU-27 applications — 2003 ...............................................................165

Table 6.7

Breakdown of patent applications to the EPO by economic activity (NACE), 
total number and as a percentage of total, EU-27 and selected countries — 2003  ................166

Figure 6.8

Foreign ownership of domestic inventions in patent applications to the EPO, 
as a percentage of all national applications, selected countries — 2003  ...............................167

Figure 6.9

Breakdown of PCT applications designating the EPO by main countries — 2003  .................168

Table 6.10

High-tech patent applications to the EPO and annual average growth rates, 
EU-27 and selected countries —- 1993 to 2003.......................................................................170

Figure 6.11 High-tech patent applications to the EPO per million inhabitants, 

selected countries — 1993, 1998 and 2003  ............................................................................171

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Table 6.12

Breakdown of high-tech patent applications to the EPO by high-tech group, total number 
and as a percentage of total, EU-27 and selected countries — 2003  .....................................172

Figure 6.13 Breakdown of ICT patent applications to the EPO by sub-category, 

as a percentage of total, EU-27 and selected countries — 2003 .............................................173

Figure 6.14 Biotechnology patent applications to the EPO, total number and 

per million inhabitants, EU-27 and selected countries — 1993, 1998 and 2003 ......................174

Chapter 7
Chapter 7

High-tech industries and knowledge based services
High-tech industries and knowledge based services

Table 7.1

Economic statistics on high-tech sectors, EU-27— 2003 .........................................................177

Figure 7.2

Production value per enterprise in EUR million, total high-tech sectors, EU-27 — 2003.........178

Figure 7.3

Figure 7.4

Table 7.5

Venture capital at early stage as a percentage of GDP, 
EU-15 and selected countries — 2005  ....................................................................................179

Venture capital at expansion and replacement stage as a percentage of GDP, 
EU-15 and selected countries — 2005  ....................................................................................179

High-tech trade in 2005, in EUR million, as a share of total exports, 
share of extra-EU-27 trade and AAGR 2000-2005 of high-tech imports and exports, 
EU-27 and selected countries ...................................................................................................181

Figure 7.6

Distribution of employment by sector as a percentage of total, 
EU-27 and selected countries — 2006 .....................................................................................183

Table 7.7

Employment in manufacturing in 2006, by selected sectors, in thousands, 
percentage of women and AAGR 2001-2006, EU-27 and selected countries..........................184

Figure 7.8

Employment in high-tech manufacturing as a percentage of total employment in 2006 
and AAGR 2001-2006, EU-27 and selected countries .............................................................185

Table 7.9

Employment in services in 2006, by selected sectors, in thousands, 
percentage of women and AAGR 2001-2006, EU-27 and selected countries..........................186

Figure 7.10 Employment in high-tech KIS as a percentage of total employment in 2006 and 

AAGR 2001-2006, EU-27 and selected countries ....................................................................187

Figure 7.11

Top 20 leading EU-27 and EFTA regions in terms of employment in high-tech 
and medium high-tech manufacturing in 2006, in thousands, as a percentage of 
total employment and AAGR 2002-2006...................................................................................188

Map 7.12

Regional employment in high and medium high-tech manufacturing 
as a percentage of total employment — 2006 ..........................................................................189

Figure 7.13  Top 20 leading EU-27 and EFTA regions in terms of employment in 

knowledge-intensive services in 2006, in thousands, as a percentage of total employment 
and AAGR 2002-2006  ..............................................................................................................190

Map 7.14

Regional employment in knowledge-intensive services (KIS) 
as a % of total employment — 2006 .........................................................................................191

Figure 7.15 Business enterprises R&D expenditure in manufacturing by technological intensity, 

selected countries — 2003........................................................................................................192

Table 7.16

Business enterprises R&D personnel in full-time equivalent (FTE) and 
percentage of researchers in manufacturing sector by technological intensity, 
EU-27 and selected countries — 2004 .....................................................................................193

Chapter 8 
Chapter 8 

The 2006 EU industrial R&D investment scoreboard
The 2006 EU industrial R&D investment scoreboard

Table 8.1

Top 20 enterprise groups in terms of total R&D investment (EUR million) — 2005 .................197

Table 8.2

Overall performance by the companies on the Scoreboard — 2005........................................198

Table 8.3

Top 10 (sub-)sectors in terms of R&D investment, by top EU enterprise group — 2005 .........199

Table 8.4

Top 10 (sub-)sectors in terms of R&D investment, 
by top non-EU enterprise group — 2005 ..................................................................................199

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Table 8.5

Figure 8.6

Figure 8.7

Shares of R&D and sales in total for all EU enterprise groups and 
number of enterprise groups, by EU Member State — 2005....................................................200

Share of R&D investment by level of R&D intensity, EU, Japan, the United States and 
Rest of the World — 2005.........................................................................................................200

Share of R&D investment by level of R&D intensity, breakdown of 500 US, 
500 European and 200 Japanese enterprise groups by ranking step — 2005  .......................201

Figure 8.8

Breakdown of Scoreboard enterprise groups, by sector — 2005  ............................................201

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This publication presents an analysis of Science and Technology in Europe looking at the main statistical indicators in
this field. It is intended for both generalists and specialists and is divided into three main parts:

- Part 1: Investing in R&D,
- Part 2: Monitoring the knowledge workers,
- Part 3: Productivity and competitiveness.

It also contains methodological notes and lists of abbreviations and symbols.

The  statistics  and  indicators  in  this  publication  focus  primarily  on  the  27  European  Union  Member  States  and,  to  a
lesser  extent,  on  the  European  Economic  Area  (EEA).  This  publication  also  looks  at  the  EU  candidate  countries,
whenever data are available. For the moment no data are available on the former Yugoslav Republic of Macedonia
(FYROM).  To  allow  high-level  international  comparison,  data  for  China,  Japan  and  the  United  States  are  in  turn
presented, where possible. There is also a regional analysis of the situation within the EU Member States. The data
reflect the information available at Eurostat on 1 January 2007.

Given  the  numerous  data  sources  involved,  the  coverage  of  the  time  series  differs  from  one  indicator  to  another.
However, the first year taken into consideration for most indicators in this publication is 1995 (except for patents). As
far as possible, this publication sets out to provide detailed and coherent time series.

Consistency  with  previous  publications  has  also  been  maintained,  adding  further  information  in  response  to  users’
requirements. All the data presented in this Statistical book are available on Eurostat’s NewCronos reference database. 

1. Government Budget Appropriations or Outlays on R&D — GBAORD

Chapter 1 shows that in 2005 budget appropriations for R&D in the United States exceeded 90 billion 1995 constant
PPS. In the European Union, the figure was almost 65 billion 1995 constant PPS, whereas in Japan it did not quite
reach the 20 billion mark. 

As  a  percentage  of  GDP,  GBAORD  in  the  EU-27,  Japan  and  the  United  States  stood  at  0.74%,  0.71%  and  1.06%
respectively. Over the period 1995 to 1999, GBAORD in the United States and in the EU-15 declined in relative terms
(as a percentage of GDP), compared with an increase over the same period in Japan. Between 1999 and 2005, the
trends were distinctly different. GBAORD, expressed as a percentage of GDP, was stable in the EU-15, but increased
slightly in Japan and even more in the United States.

Within  the  EU-27,  in  2004  Finland  had  the  highest  government  budget  spending  on  R&D  as  a  proportion  of  GDP
(1.03%). At the other end of the scale, Greece, Cyprus, Slovakia, Luxembourg, Romania, Latvia and Malta showed
GBAORD rates no higher than 0.3% of GDP.

Looking at the distribution of GBAORD by socio-economic objective, “Research financed from general university funds
(GUF)” took the lion’s share of the EU-27’s GBAORD, with 31.4% of the total. In Japan too the main socio-economic
objective was “Research financed from GUF”, which took an even higher share (33.5%). However, in the United States
over half of total GBAORD in 2005 was allocated to “Defence” (56.6%). Among the EU Member States, the distribution
by socio-economic objective varies: in 2005 “Research financed from GUF” took the largest share of total GBAORD
in 13 of the EU-27 Member States for which data are available. “Defence” was the leading socio-economic objective
in  only  the  United  Kingdom  (31.0%).  “Non-oriented  research”  was  the  top  objective  for  seven  Member  States:  the
Czech Republic (27.3%), Estonia (49.2%), Latvia (74.6%), Poland (65.1%), Romania (40.9%), Slovenia (59.7%) and
Slovakia (35.9%).

At EU-27 level, budgets increased between 2000 and 2005 for every socio-economic objective except “Production and
rational utilisation of energy” and “Exploration and exploitation of space”. “Other civil research” and “Exploration and
exploitation of the Earth” showed the highest increases.

2. R&D expenditure

Chapter 2 indicates the latest trends in R&D expenditure. In 2005 R&D expenditure as a share of GDP in the EU-27
remained stable at 1.84%. The gap with regard to R&D expenditure in Japan (3.33%) is widening, as R&D expenditure
as a share of GDP is growing in Japan. However, the gap with the United States (2.62%) was narrower.

Looking at the estimates by sector, most R&D expenditure is financed by the business enterprise sector (BES). In 2003
the BES accounted for almost 64% of R&D expenditure in the EU-27, which is below the levels in the United States
(69%) and Japan (76%).

In 2005 the leading EU-25 Member States in terms of R&D intensity were Sweden and Finland, with 3.86% and 3.48%
of GDP going to R&D expenditure, respectively. Other EU-27 countries with R&D intensity above the EU average of

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1.84% were Denmark (2.44%), Germany (2.51%), Austria (2.36%) and France (2.13%). 

In 2005 the EU-27 spent EUR 201 billion on R&D, recording an annual average growth rate of 3.3% compared with
2000. Most R&D in the EU-27 is carried out in Germany (EUR 56.4 billion), France (EUR 36.4 billion) and the United
Kingdom (EUR 30.0 billion). These three countries accounted for almost two thirds of total R&D expenditure in the 
EU-27. The highest annual average growth rates (AAGR) achieved from 2000 to 2005 were in new Member States
(from the 2004 and 2007 enlargements): Malta (32.3%), Estonia (23.0%) and Cyprus (17.3%). 

The top 15 regions in the EU-27 in terms of R&D expenditure as a percentage of GDP (R&D intensity) were mainly
located in Germany (six regions out of the top 15). In 2003 the German region Braunschweig came first with 8.70%,
which is more than four times the EU-27 average. Västsverige (SE) and Stuttgart (DE) followed with 6.03% and 4.66%
respectively.

In terms of absolute R&D expenditure, the Île de France region was well ahead, with 7.7% of the total R&D expenditure
in the EU-27, but as a ratio of GDP it was not in the top 15 regions (3.20%). 

3. R&D personnel

As documented in Chapter 3, 1.44% of total employment in the EU-27 was in R&D in 2004, with a head count (HC) of
2.96 million. Measured in full-time equivalent (FTE), EU-27 R&D personnel totalled more than 2 million, an increase of
0.50% compared with the previous year.

At  national  level,  Iceland  led  with  3.53%  of  total  employment  in  R&D,  ahead  of  Finland  (3.24%),  Sweden  (2.51%),
Denmark (2.41%) and Norway (2.27%).

In  2004,  Germany  and  France  employed  two  fifths  of  the  R&D  personnel  in  the  EU-27,  measured  in  full-time
equivalent, with 473 000 and 352 000 respectively. Germany and France were ahead in every sector, often followed
by Spain and Italy in third and fourth places.

In 2005, 1.28 million researchers, measured in FTE, were employed in the EU-27, an increase of 70 000 since 2003.
In most of the EU-27 Member States the number of researchers increased between 2003 and 2005. Most European
researchers work in Germany (271 000), France (200 000) and Spain (110 000).

Female researchers were under-represented in the EU-27 compared with males, especially in the business enterprise
sector. In 2004 they made up 28.3% of all researchers and only 18.4% of researchers in the BES. The percentage of
female  researchers  was  generally  higher  in  the  new  Member  States  (from  the  2004  and  2007  enlargements)  and
candidate countries. 

In 2004, 609 000 researchers, measured in FTE, were employed in the BES in the EU-27. The largest group of these
business  researchers  were  working  in  manufacturing  (427  000).  “Natural  sciences”  accounted  for  the  highest
proportion (28.5%) of researchers in higher education and the government sector.

With 6.5% of the EU-27 total, Île de France (FR) was the leading region in terms of R&D personnel (FTE). The leading
region in terms of the proportion of R&D personnel in total employment in 2004 was Wien (AT) with 4.5%

4. Human resources in science and technology - HRST

Chapter 4 on human resources in science and technology shows that between 1999 and 2004 the total number of
students taking tertiary education courses increased in Europe at an annual average rate of 4% for both female and
male students. In 2004 more than 18 million people in the EU were following tertiary education courses, of whom more
than  526  000  were  PhD  students.  One  student  in  four  was  following  a  course  in  either  “science,  mathematics  and
computing” or “engineering, manufacturing and construction” in 2004. Although in most countries more than half of all
students were female, engineering and, to a lesser extent, science courses attracted fewer females. Some 54.8% of
all tertiary education students in the EU were female, but only 24.0% on engineering courses and 37.5% on science
courses.

Tertiary education institutions in the EU produced more than 3.5 million new graduates in 2004. Two Member States,
namely the United Kingdom and France, turned out more than 30% of them. Comparing these new graduates with the
young  age  group,  for  every  thousand  people  aged  between  20  and  29  years  in  the  EU  there  were  around  59  new
graduates.  There  was  a  higher  proportion  of  female  graduates  (compared  with  the  female  share  of  the  student
population). On average, 58.7% of all graduates in the EU were female in 2004. Five of the six EU countries with the
highest shares of female tertiary graduates were new Member States.

The stock of human resources in S&T (HRST) is growing. Germany, the United Kingdom and France, with more than
10  million  HRST  each,  had  the  largest  HRST  populations  in  2006.  These  three  countries  combined  accounted  for
nearly half of the EU’s 85 million HRST between 25 and 64 years old.

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In terms of total employment in the same age group, employed HRST made up 36% of the total labour force in 2006.
This was combined with strong growth of 3.2% over the period 2001-2006.

In 2006 the highest proportion of scientists and engineers (SE) could be found in Belgium, where 7.9% of the labour
force declared that they were working in an occupation qualifying them as SE. In the majority of EU countries, scientists
and engineers were predominantly male. In 2006, the gender ratio in Germany, Luxembourg and the United Kingdom
was around four male scientists or engineers to one female. 

Services have far more S&T workers than manufacturing. Close to half the people working in “knowledge-intensive
services”, which include “education”, “health” and “social work”, had completed tertiary S&T education in 2006. 

In general, unemployment rates in 2006 were much lower for HRST than for non-HRST. The share of tertiary educated
unemployed averaged a low 3% in the EU-27, while the unemployment rate for non-tertiary educated was as high as
8%.

In general, the highest regional concentrations of HRST as a share of the labour force are found in capital regions, in
regions in central Europe and in the Nordic countries. At regional level, Inner London had the highest proportion of
HRST in its labour force with 57.2% in 2006. 

Looking at mobility of HRST, 25-34 year olds are more likely than older age groups to move from one job to another.
In absolute terms, the United Kingdom and France recorded the highest job-to-job mobility amongst HRST aged 25-
34, with a total of more than 830 000 people changing job during 2006. In relative terms, 7.5% of the HRST aged 25-
34  changed  jobs  in  2006,  compared  with  only  2.5%  aged  45-64.  At  the  level  of  international  mobility  of  HRST,
comparing the national with the non-national labour force, 58.7% of people that had moved to Slovakia to work were
HRST, whereas only 29.8% of employed Slovakians were HRST. In Greece, the share of HRST among non-nationals
was much smaller than among nationals, at only 13.7% compared with 30.3%. 

5. Innovation

Chapter 5 presents the results of the Fourth Community Innovation Survey (CIS 4). Roughly following the structure of
the CIS questionnaire, this chapter shows the main results at EU-27 and national levels.

In  2004  close  to  40%  of  all  EU-27  enterprises  were  “innovative”,  which  means  active  in  product  and/or  process
innovation.  The  proportion  of  innovative  enterprises  increased  with  size.  There  is  a  strong  correlation  between
innovation activity and the size of the enterprise.

The EU-27 average tells the European trends in innovation but in some cases the results at national level may be
somewhat  different.  German  enterprises  were  the  most  numerous  in  the  European  innovation  landscape,  whereas
Bulgarian firms were the least represented.

Intramural  expenditure  on  R&D  is  greater  than  extramural,  but  acquisition  of  machinery,  equipment  and  software
seems essential for many countries. Various reasons, such as the price of keeping innovations secret, may prompt
enterprises to do more R&D in-house rather than to outsource. 

Knowledge  transfer  is  made  up  of  a  combination  of  information  and  cooperation.  The  results  of  use  of  sources  of
information and the types of partners may be different than expected. It emerges that the links between the business
enterprise sector and both the government sector and higher education are rather weak.

Innovative enterprises give priority to improving the quality of goods and services. Innovation is driven primarily by
commercial considerations. Other aspects, such as the environment, are seen as collateral.

Innovation  seems  to  be  hampered  first  and  foremost  by  cost  factors.  Knowledge  and  market  factors  play  almost  a
secondary role.

CIS 4 also provides information on use of intellectual property rights and on marketing and organisational innovations.

CIS  4  and  the  previous  survey  (CIS  3)  are  not  entirely  comparable,  owing  to  changes  in  the  questionnaire,  but
comparison of the two brings out some interesting points.

The 2006 European Innovation Scoreboard (EIS) is largely based on CIS data. The core part of the EIS is calculation
of the Summary Innovation Index (SII), which makes it possible to divide the EU-27 Member States into four groups,
based on their innovation performance. 

These are:

• The innovation leaders – Sweden, Switzerland, Finland, Denmark, Japan and Germany. 
• The innovation followers – the United States, the United Kingdom, Iceland, France, the Netherlands, Belgium,

Austria and Ireland. 

• The catching-up countries – Slovenia, the Czech Republic, Lithuania, Portugal, Poland, Latvia, Greece and

Bulgaria. 

•The trailing countries – Estonia, Spain, Italy, Malta, Hungary, Croatia and Slovakia.  

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Cyprus and Romania have relatively low SII results, but seem to be catching up rapidly.
The innovation performance and trends observed for Luxembourg, Norway and Turkey are very different, so they do
not fit into any of these groups.

6. Patents

Patents statistics are widely used to generate indicators that help to measure a country’s technological output. Chapter
6  looks  at  the  data  on  triadic  patent  families,  patent  applications  to  the  European  Patent  Office  (EPO)  and  patents
granted by the United States Patent and Trademark Office (USPTO).

The data for 2000 show that the triadic patent families were highly concentrated: 34% of them were American, 32%
Japanese and 27% from the EU-27.

In 2003 a total of 62 250 patent applications to the EPO came from EU Member States, 48 768 from the United States
and 27 987 from Japan. 77 585 patents granted by the USPTO came from inventors residing in the United States, 
35 013 from Japanese residents and 23 723 from European residents. These figures show that there is a home country
advantage. Data on patent families are generally less biased, as the “home advantage” disappears to a certain extent. 

Germany was the best performing European country in terms of patent applications in 2003, not only in absolute EPO
patent applications but also in proportion to GDP and per million inhabitants.

In 2003 EU-27 inventors applied to the EPO for 10 840 high-tech patents, whereas American inventors applied for 
13 845 and Japanese for 6 834. 

In terms of absolute EPO applications, Germany is again well ahead, but in relation to population size Finland, Israel
and Sweden were the best performers in high-tech patenting.

Turning to ICT (information and communication technology) patent applications to the EPO, US inventors led in 2003
with 16 823 applications compared with 16 010 for the EU-27 and 10 507 for Japan.

For biotechnology the United States was also in the lead (3 331 patent applications), followed by the EU-27 (2 576)
and Japan (1 035).

7. High-tech industries and knowledge-based services

Chapter 7 analyses Europe’s performance in high-technology and knowledge-intensive services, looking at statistics
on enterprises (value added, labour productivity, etc.), venture capital investments, high-tech trade, employment and
R&D personnel and expenditure.

Enterprises in high-tech industries and knowledge-intensive services

In 2003 EU-27 enterprises in all high-tech sectors generated average production value of EUR 1.9 million. With EUR
7.9  million,  high-tech  enterprises  in  Ireland  generated  the  highest  production  value,  followed  by  Finland 
(EUR 4.5 million) and France (EUR 3.6 million).

Venture capital investment

In 2005 venture capital investment (VCI) was highest in Sweden, Denmark and in the United Kingdom, both at the
earliest stage and at the expansion and replacement stages.

High-tech trade

Comparing the four leading economies in terms of high-tech trade — the EU-27, China, Japan and the United States
— the EU-27 was the top importer and exporter of high-tech products. However, it also showed the largest high-tech
trade deficit.

In the case of high-tech exports, EU-27 was closely followed by the United States. While exports from the US and
Japan were on the decrease, the EU-27 remained quite stable between 1999 and 2005. On the other hand, China has
been growing rapidly, catching up with Japan in 2003 and overtaking it in 2004 and 2005.

Employment in high-tech industries and knowledge-intensive services

In  2005  more  than  140  million  people  were  employed  in  services  in  the  EU-27,  but  fewer  than  40  million  in
manufacturing. Half of the 140 million jobs in services in the EU-27 were in knowledge-intensive services (KIS) and
the other half in less knowledge-intensive services (LKIS).

Of the 39 million people employed in manufacturing, 11.9 million were working in medium-high-tech manufacturing and

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2.3  million  in  high-tech  manufacturing.  Of  the  total  manufacturing  and  services  workforce  of  180  million,  almost 
9 million people were employed in all high-tech sectors.

In  the  EU-27,  30.7%  of  all  people  employed  in  manufacturing  were  female. This  ratio  was  often  higher  in  the  new
Member  States  (from  the  2004  and  2007  enlargements). The  highest  ratio  of  female  employment  was  in  high-tech
manufacturing (34.1%). In the EU-27, 53.1% of people employed in all services were female. However, the proportion
of female employees was lower in high-tech knowledge-intensive services (KIS) with 33.1%.

In 2005, regions specialising in high-tech and medium-high-tech manufacturing were highly concentrated in Germany.
Capital regions were strong in knowledge-intensive services (KIS) and in high-tech KIS.

R&D in high technology

For the EU-27 Member States for which data are available, more than 90% of total business R&D expenditure was on
high-tech and medium-high-tech manufacturing in Germany, Hungary and in the United Kingdom. This was also the
case in Russia.

In general, the proportion of researchers among R&D personnel was higher in high-tech manufacturing than in total
manufacturing. Hungary had the highest proportion with 84.3% of researchers in high-tech manufacturing.

8. The 2006 EU Industrial R&D Investment Scoreboard

Chapter 8 (produced by the European Commission’s Directorate-General for Research) presents the main results of
the 2006 EU Industrial R&D Investment Scoreboard. The Scoreboard compares the R&D investment performance of
1 000 EU companies with 1 000 non-EU companies in 2005. 

At company level, the world top 50 R&D investors included 18 companies each from the EU and the United States, 
10 from Japan (two fewer than in 2004) and two each from Switzerland and Korea.

R&D investment is concentrated in just a very few sectors and sub-sectors. The first three sub-sectors (or sectors) in
the  EU  took  47%  of  the  total  R&D  investment  of  the  top  1  000  EU  companies.  They  were  automobiles  and  parts
(sector), pharmaceuticals (sub-sector) and telecommunications equipment (sub-sector).

But R&D investment is also highly concentrated in the EU at country level. Three countries (Germany, France and UK)
account  for  around  three  quarters  of  both  total  R&D  investment  and  sales  and  about  60%  of  the  total  number  of
companies on the EU Scoreboard.

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PART1

Chapter 1 - Government Budget Appropriations 
or Outlays on R&D  GBAORD 

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Government R&D appropriations or outlays on R&D are
broken down by socio-economic objective on the basis
of  NABS  —  Nomenclature  for  the  analysis  and
comparison  of  scientific  programmes  and  budgets,
Eurostat 1994.

Eurostat collects aggregated data at the national level;
these  are  checked,  processed,  and  compared  with
other  data  sources,  such  as  the  Main  Science  and
Technology Indicators (MSTI) published by the OECD. 

The  analysis  of  GBAORD  data  in  the  present
publication  covers  the  period  1995  to  2005  (which  is
provisional). The chapter is divided into two main parts:

- Total GBAORD,
- GBAORD by socio-economic objective.

Please note that the data presented in this publication
reflect data availability in Eurostat’s reference database
as of June 2007.

For more details on the methodologies applied, please
refer to the methodological notes.

1.1 Introduction

Government budget appropriations or outlays on R&D
— GBAORD — are one way of measuring  how  much
governments  spend  on  R&D,  in  other  words,  of
ascertaining  what  priority  governments  give  to  the
public funding of R&D. The advantage of the GBAORD
data is their timeliness, but there are some drawbacks,
such  as  data  sources  and  harmonisation,  that  should
be taken into account when using these data.

GBAORD includes all appropriations allocated to R&D
in central government or federal budgets, and therefore
refers  to  budget  provisions,  not  to  actual  expenditure.
Provincial or state government data should have been
included  when  their  input  is  significant.  Unless
otherwise stated, data include both current and capital
expenditure. They cover not only government-financed
R&D performed in government establishments, but also
government-financed  R&D  in  the  business  enterprise,
private non-profit and higher education sectors, as well
as abroad. Data on actual R&D expenditure, which are
not available in their final form until some time after the
end of the budget year concerned, may well differ from
the  original  budget  provisions.  This  and  further
methodological  information  can  be  found  in  the
'Proposed  standard  practice  for  surveys  of  research
and  experimental  development'  (Frascati  Manual,
OECD, 2002).

The  data  are  compiled  by  national  authorities  using
figures  from  public  budgets. As  data  are  not  obtained
through  surveys,  they  are  more  difficult  to  compile
because, in most countries, national budget data have
their own terminology and methodology, and therefore
often  do  not  match 
the  Eurostat/OECD
methodology set out in the Frascati Manual.

fully 

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1.2 Total GBAORD

The United States is the leading economy both in absolute and in relative
terms as regards GBAORD 
Of the three major economies, it was the United States
between  1995  and  2005 
that  allocated  most
Government  Budget  Appropriations  or  Outlays  to
Research  and  Development  (GBAORD),  both  in
absolute and in relative terms.

Indeed,  GBAORD  amounted  to  0.71%  of  GDP  in
Japan,  which  was  slightly  lower  than  the  EU-27  with
0.74%. The United States led with over 1% of GDP.

As Figure 1.1 shows, in 2005, total GBAORD from the
United  States  amounted  to  some  90  billion  in  1995
constant  PPS.  In  the  European  Union,  it  exceeded 
64  billion,  whereas  in  Japan  it  did  not  quite  reach  the 
20 billion mark.

In  relative  terms  (as  a  percentage  of  GDP)  the
differences were less significant than in absolute terms,
at  least  between  Japan  and  other  main  economies.

During the period 1995 to 1999, there was a decline in
GBAORD  in  the  United  States  and  in  the  EU-15,
expressed  as  a  percentage  of  GDP,  which  followed
similar  trends.  By  contrast,  in  Japan,  GBAORD
increased during the same period. 

Between 1999 and 2005, trends differed considerably.
EU  GBAORD  as  a  percentage  of  GDP  was  relatively
stable, whereas it increased slightly in Japan and more
so in the United States.

igure 1.1 

Total GBAORD in million of  1995 constant PPS and as a percentage of  GDP,

EU-15, EU-27, Japan and United States — 1995 to 2005

SPPtnatsnoc5991oiM

000021

PDGfo%

00009

00006

00003

0

2.1

9.0

6.0

3.0

0.0

5991

6991

7991

8991

9991

0002

1002

2002

3002

4002

5002

SPPtnatsnoc5991noillim72-UE
PDGfo%72-UE

SPPtnatsnoc5991noillim51-UE
PDGfo%51-UE

SPPtnatsnoc5991noillimPJ
PDGfo%PJ

SPPtnatsnoc5991noillimSU
PDGfo%SU

Eurostat estimations: EU-27 and EU-15.
JP: Excluding R&D in the social sciences and humanities; 2005: provional data.
JP and US: Federal or central government only.
US:  Excludes  data  for  the  R&D  content  of  general  payment  to  the  Higher  Education  sector  for  combined  education  and  research  (public  GUF)  and
excludes most or all capital expenditure.



21

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Page 22

Finland is the only Member State with GBAORD greater than 1% of GDP 

Figure 1.2 shows GBAORD expressed as a percentage
of  GDP  by  country,.  The  main  advantage  of  this
indicator is that it does not take into account the weight
of  countries,  thus  making  it  easier  to  compare
GBAORD across countries.

(1.03%). France ranked second among Member States
with  0.93%,  followed  by  Sweden  (0.89%)  and  Spain
(0.85%).  Germany  and  Switzerland  were  also  above
the  EU-27  average  (0.74%),  with  all  other  countries
ranking below it. 

In  2005,  EU-27  GBAORD  was  0.74%  of  GDP.  The 
EU-15 average was slightly higher at 0.77%.

the  European  averages  mask 

large
However, 
differences  between  countries.  In  fact,  Iceland  led  in
2005, devoting 1.44% of GDP to GBAORD. In addition
to Iceland and the United States, the only EU Member
State with a GBAORD above 1% of GDP was Finland

GBAORD  of  nine  Member  States  was  between  the
European average and 0.5% of GDP. This was also true
of Norway, Japan and Russia. 

Cyprus,  Slovakia,  Luxembourg,  Romania,  Latvia  and
Malta,  where  GBAORD  did  not  reach  0.3%  of  GDP,
came at the end of the scale. 

igure 1.2 

PDGfo%

44.1

60.1

30.1

39.0

98.0

58.0

6.1

4.1

2.1

0.1

8.0

6.0

4.0

2.0

0.0

Total GBAORD as a percentage of  GDP,

EU-27 and selected countries — 2005

77.077.0

67.0

47.0

37.0

37.0

27.0

17.0

17.017.0

17.0

56.0

36.0

06.0

75.0

55.0

24.0

14.0

14.0

63.0

13.0

13.0

92.0

82.0

82.0

22.0

02.0

91.0

SI

SU

IF

RF

ES

SE

ED

-UE
51

-UEHC
72

TP

TI

KU

ON

PJ

LN

KD

TA

UR

IS

EB

ZC

EI

UH

EE

TL

LP

LE

YC

KS

UL

OR

VL

TM

Eurostat estimations: EU-15 and EU-27.
Provisional data: BE, CZ, IE, EL, ES, FR, HU, AT, IS and JP.
National estimations: EE and HU.
Federal or central government only: AT, JP and US.
Exceptions to the reference year:  2004: PL and CH.

Five countries made up 80 % of total European GBAORD

Figure  1.3  shows  the  shares  of  the  EU-27  total
GBAORD  from  the  five  main  budgeting  countries.  In
2005,  EU-27  total  GBAORD  amounted  to  almost 
EUR 81 billion at current prices.

Germany  allocated  the  highest  budgets  to  GBAORD,
with  EUR  17.2  billion,  followed  closely  by  France  with
EUR  16  billion.  The  United  Kingdom,  Italy  and  Spain
respectively  allocated  EUR  13.0,  10.3  and  7.7  billion.
These five Member States made up approximately 80%
of EU-27 total GBAORD.

The  remaining  22  Member  States  together  granted
EUR  17.2  billion.  Of  these,  Belgium,  Denmark,  the
Netherlands,  Austria,  Portugal,  Finland  and  Sweden
each  allocated  more  than  EUR  1  billion  to  GBAORD.
This was also the case for Norway. At the other end of
the scale, six Member States each allocated less than
EUR  100  million  to  GBAORD.  They  were  Estonia,
Cyprus, Latvia, Lithuania Luxembourg and Malta (See
also Table 1.5).

igure 1.3 

Distribution of  EU-27 total GBAORD,

in EUR million — 2005

setatSrebmeMrehtO
06171

ynamreG
12271

niapS
0477

ylatI
90301

ecnarF
05951

modgniKdetinU
05921

Eurostat estimation: EU-27.
Provisional data: ES and FR.

22

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Page 23

igure 1.4 

Annual average growth rate (AAGR) of  GBAORD and of  GDP (1),
EU-27 and selected countries — 1995 to 2000 and 2000 to 2005

AAGR 1995-2000

AAGR 2000-2005

EU-27

EU-15

BE

CZ

DK

DE

EE

IE

EL

ES

FR

IT

LV

LT

LU

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

JP

RU

US

5.5

5.3

5.0

3.6

3.0

4.5

7.8

7.6

-0.8

1.3

3.7

0.9

3.4

2.8

1.4

7.0

6.7

6.7

6.8

7.0

16.3

15.3

14.4

17.5

10.5

11.7

9.6

10.5

8.9

11.8

10.8

13.9

6.0

8.0

6.5

5.8
6.0

6.5

5.5

4.7

17.9

12.5

11.9

9.7

9.9

10.2

13.4

11.5

EU-27

EU-15

BE

CZ

DK

DE

EE

IE

EL

ES

FR

IT

LV

LT

LU

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

JP

RU

US

-14.5

-2.2

3.7

4.3

3.4

4.2

3.5
3.8

3.7

2.3

1.7

1.2

10.2

11.7

3.6

2.9

3.6

9.0

5.6

7.5

7.5

6.1

6.0

8.9

10.3

10.8

12.6

17.2

16.3

15.3

15.4

24.0

2.5
2.2

3.1

4.4

5.6

4.0

-2.5

8.7

14.5

25.2

11.5

5.8

9.2

6.4

6.5

3.5

4.5

1.8

2.8

3.7

6.8

7.9

5.8

7.3

3.2

5

10

15

20

25

%

22.9

-6.2

-4.6

-1.2

-15

-10

-5

0

5

10

15

20

%

-10

-5

0

GDP

GBAORD

GDP

GBAORD

(1) AAGR is calculated in current EUR.
National estimations: SE (1995), EE (2000 and 2005), ES (2000).
Break in series: FI (1995) and US (2000).
Provisional data (2005): BE, CZ, IE, EL, ES, FR, HU, AT, IS and JP.
Exceptions to the reference period 2000-2005:

2002-2005: CZ;
2000-2004: PL and RU.

AT, JP and US: Federal or central government only.
US:  Excludes  data  for  the  R&D  content  of  general  payment  to  the  Higher  Education  sector  for  combined  education  and  research  (public  GUF)  and
excludes most or all capital expenditure.
JP: Excluding R&D in the social sciences and humanities.

Figure  1.4  compares,  by  country,  the  annual  average
growth  rates  (AAGR)  of  total  GBAORD  (expressed  in
current  prices)  between  1995  and  2000  and  between
2000 and 2005 with those of GDP.

Only  seven  Member  States,  plus  Iceland  and  Japan,
saw their GBAORD grow faster than their GDP during
this period. The AAGR even reached 17.9% and 14.4%
in Iceland and Ireland respectively.

In  EU-15,  GBAORD  expressed  in  current  terms
increased  at  a  rate  of  3.6%  between  1995  and  2000.
However,  over  the  same  period,  GDP  -  with  a  growth
rate of 5.3% - rose faster than GBAORD.

trends  differed  quite
Between  2000  and  2005, 
markedly.  In  fact,  GBAORD  in  the  European  Union
grew  faster  than  GDP.  Moreover,  GBAORD  increased
every country except Poland. 

In  contrast,  between  2000  and  2005,  GBAORD  in 
EU-15  grew  at  a  rate  of  4.2%,  higher  than  the  rate  of
growth in GDP (3.4%) over the same period.

The  EU-27  annual  average  growth  rates  of  GBAORD
and GDP between 2000 and 2005 were similar to those
of EU-15: at 4.3% and 3.7% respectively.

However, there were large differences across Member
States. Between 1995 and 2000, three Member States
saw  their  GBAORD  decrease:  Germany  (-0.8%),
Sweden (-2.2%) and Romania (-14.5%). 

The  countries  where  government  support  for  R&D
increased  most  noticeably  between  2000  and  2005
were  Romania,  Luxembourg  and  Russia  with  rates  of
increase of 25.2%, 24.0% and 22.9% respectively.

Over the same period, GBAORD growth was lower than
GDP  growth  only  in  Greece,  the  Netherlands,  Poland
and Slovakia. 

The  GBAORD  growth  rate  was  below  the  EU-27
average (3.7%) in some of the ‘older’ Member States:
Belgium,  Germany,  France,  Italy,  the  Netherlands,
Austria, Sweden, Finland and United Kingdom. 

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1.3 GBAORD by socio-economic objectives

Almost one third of European GBAORD allocated to “Research financed
from General University Funds”

Table 1.5 shows, by country, the total GBAORD in EUR
million and its distribution by socio-economic objective
of  the  NABS  –  Nomenclature  for  the  analysis  and
comparison of scientific programmes and budgets – as
a percentage of total.

As  previously  stated,  the  five  main  Member  States  in
terms  of  GBAORD  –  Germany,  France,  the  United
Kingdom, 
for
approximately 80% of total EU-27 GBAORD.

Italy  and  Spain  –  accounted 

“Research  financed  from  General  University  Funds
(GUF)” was not only the main socio-economic objective
at EU-27 level; in 2005; it also accounted for the largest
share  of  total  GBAORD  in  the  13  Member  States  for
which data by socio-economic objectives of the NABS
are available. It was also the most important objective
in Iceland, Norway, Switzerland and Japan. This socio-
economic objective covers R&D related to various fields
of  science 
(FOS),  such  as  natural  sciences,
engineering, medical sciences or social sciences.

“Non-oriented  research”  was 
the  second  most
important  socio-economic  objective  within  EU-27
overall.  It  was  also  the  main  objective  for  seven
Member  States:  Estonia  (49.2%),  Latvia  (74.6%),
Poland  (65.1%),  Slovenia  (59.7%),  Estonia  (49.2%),
Romania  (40.9%),  Slovakia  (35.9%)  and  the  Czech
Republic (27.3%). 

“Defence”  –  the  third  European  socio-economic
objective – was the leading objective only in the United
Kingdom, with 31.0% of total GBAORD. 

However, this objective also accounted for large shares
in France, Sweden and Spain, with 22.3%, 17.4% and
16.1%  respectively.  Hence,  the  fact  that  “Defence”
represented  a  substantial  share  of  total  European
GBAORD  (13.3%)  is  mainly  due  to  the  contribution
made by these four countries.

able 1.5 

Total GBAORD in EUR million and by socio-economic objectives as a % of  total,

EU-27 and selected countries — 2005

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Exceptions to the reference year: 2004: PL, CH and JP; 2003: RU.
Footnote 'i':
DE: Unrevised breakdown not adding to the revised total;
AT, CH, JP and US : Federal or central government only;
SK: Includes other classes;

JP: Defense is underestimated or based on underestimated data;
US: Total Excludes data for the R&D content of general payment to the
Higher Education sector for combined education and research (public
GUF).

24



 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
01 GBAORD:Part1_Chap1

07/12/2007

15:32

Page 25

Compared  to  the  EU-27  average  (11.0%),  some
countries  allocated  the  lion's  share  of  their  total
government  R&D  budget  to  “Industrial  production  and
technology”.  This  was  namely  the  case  of  Belgium
(30.9%), Finland (26.1%), Spain (25.2%) and Hungary
(21.5%). 

More than 10% of total GBAORD went to “Agricultural
production  and 
in  Estonia,  Cyprus,
Lithuania,  Hungary,  Slovakia  and  also  in  Iceland.
Iceland  spent  more  than  a  fifth  of  its  budget  on  this
objective. 

technology” 

In Cyprus, Lithuania, Hungary and the United Kingdom,
the Health R&D objective accounted for at least 10%.

The  areas  in  which  the  EU-27  granted  the  smallest
research”,
budgets 
in  2005  were 
“Exploration  and  exploitation  of 
the  earth”,
“Infrastructure  and  general  planning  of  land-use”,
"Control and care of the environment” and “Production
and rational utilisation of energy”. 

“Other  civil 

igure 1.6 

Main NABS socio-economic objectives in million 1995 constant PPS,

EU-15 — 1995 to 2005

5991tnatsnocSPPoiM

00002

00051

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Eurostat estimation: EU-15.

The importance of the “Defence” objective is being reduced in the EU

Figure 1.6 shows the trends in the five main European
socio-economic  objectives  expressed  in  real  terms
(1995  constant  PPS)  between  1995  and  2005  for 
EU-15.

The same two sub-periods as those highlighted for the
trends in total GBAORD (Figure 1.1) are also observed
for the main socio-economic objectives. The first period
was  from  1995  to  1999,  during  which  the  main  socio-
economic objectives were stable or actually decreased.
During the second period, from 1995 to 2005, all main
socio-economic  objectives  –  except  for  defence  –
showed an increase.

Research  financed  from  GUF,  which  was  the  main
European socio-economic objective, was also the item
that witnessed the greatest increase in absolute terms.
Indeed,  it  grew  from  16  billion  1995  constant  PPS  in
1999 to 19 billion in 2005. 

Except  in  2000,  the  second  main  EU-15  socio-
economic  objective  between  1995  and  2003  was
“Defence”’. However, budgets allocated to this objective
decreased  after  2003,  while  those    allocated  to  “non
oriented  research”  continued  to  grow.  In  fact,  non
oriented research became the second European socio-
economic objective in 2005. 

At  EU-15  level,  as  shown  in  table  1.7,  budgets
expressed  in  constant  1995  PPS  increased  between
2000 and 2005 for all socio-economic objectives except
“Production  and  rational  utilisation  of  energy”  and
“Exploration  and  exploitation  of  space”.  The  latter  two
objectives  declined,  posting  an  AAGR  of  -1.2%  and 
-0.1% respectively. 

During the same period, “Other civil research” had the
highest  growth  rate  (11.3%),  followed  by  “Exploration
and  exploitation  of  the  earth”  (7.8%)  and  “Protection
and  improvement  of  human  health”  (6.5%).  However,
these  objectives  were  among  the  least  important  at
European level (Table 1.5).



25

01 GBAORD:Part1_Chap1

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15:32

Page 26

With  the  exception  of  Italy  and  Austria,  “Research
financed from GUF” - the first socio-economic objective
in the European Union - grew in all countries between
2000  and  2005,  even  reaching  an AAGR  of  39.6%  in
Ireland.

“Defence”,  the  third  main  objective  at  European  level,
showed  considerable  variations  between  among
individual Member States, in terms of both trends and of
volume. Indeed, it increased sharply in some countries,
such  as  Italy,  Latvia,  Slovenia,  Finland  and  Sweden,
whereas it decreased in the Czech Republic, Germany,
the  Netherlands  and  Portugal.  There  was  a  similar
pattern in Switzerland. At European level, this objective
increased, although not by as much as total GBAORD.
In other words, the relative importance of the “Defence”
objective  in  European  total  GBAORD  decreased
between 2000 and 2005.

Government  R&D  budget  trends  for  “Other  civil
research”, which in the main increased at EU-15 level,
also  vary  from  country  to  country.  While  GBAORD
allocations  to  this  objective  increased  at  an AAGR  of
approximately  90%  in  Spain  and  70%  in Austria,  they
decreased  in  Slovakia,  recording  a  rate  of  -15.0%
between 2000 and 2005.

Conversely,  “Production  and  rational  utilisation  of
energy”, which decreased at EU-15 level (-1.2%), grew
between  1999  and  2004  in  twelve  Member  States,  as
well  as  in  Norway,  Switzerland,  Japan  and  the  United
States.  The  highest  growth  rates  were  recorded  in
Lithuania  and  in  the  Czech  Republic,  with  45.4%  and
18.4% respectively. 

able 1.7 

Annual average real growth rate (AAGR) of  GBAORD by socio-economic objectives (1),
EU-27 countries and selected countries — 2000 to 2005

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3.9

i7.7

i4.2

ip6.4

n
o

i
t

l

a
r
o
p
x
E

72-UE

51-UE

EB

GB

ZC

KD

ED

EE

EI

LE

SE

RF

TI

YC

VL

TL

UL

UH

TM

LN

TA

LP

TP

OR

IS

KS

IF

ES

KU

SI

ON

HC

PJ

SU

(1) AAGR is calculated in 1995 constant PPS.
Exceptions to the reference period:
2002-2005: CZ and EE;
2000-2004: PL, CH and JP.

s6.3

p3.31-

:

p0.31

0.31-

i4.6-

e9.51

p4.3

p2.0

p7.51

p4.0

5.91

:

7.2-

7.82

:

:

:

2.9

ip2.0

:

1.0-

:

5.1-

9.2-

4.5

p7.01

5.0-

p3.0

2.3

i1.5

i5.2

ip3.0

f

o

n
o

i
t

a

t
i

l

o
p
x
e

m
o
r
f

d
e
c
n
a
n

i
f

h
c
r
a
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s
e
R

F
U
G

:

e
c
a
p
s

:

d
n
a

n
o

i
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r
o
p
x
E

i

s
p
h
s
n
o

i
t

l

a
e
r

d
n
a

:

s7.2

p4.0

:

p1.13

3.9-

i5.1

s1.0-

p3.4-

:

p8.4

6.4-

i9.0

s0.2

p9.0

:

p5.5

9.4

i9.0

:

d
e

t

n
e
i
r
o
-
n
o
N

h
c
r
a
e
s
e
r

:

s3.2

p7.2

:

p8.01

4.2

i4.0

e1.3

e6.96

e0.74-

p1.4

p0.0

p2.32

p6.51-

4.11

:

3.41-

0.03

:

:

:

p:

p6.93

p1.44-

p2.63

p7.0

p9.1-

6.2

:

4.7

:

:

:

:

p6.4

p4.4

p0.3

6.1-

:

:

:

:

:

:

p7.1-

p2.43

p8.2-

5.2

:

9.14

:

:

:

:

s3.4

p1.8

:

p3.61

1.2-

i6.0

e7.2

p1.5

p4.2

p9.51

p4.1

6.1

:

3.31-

5.7-

:

:

:

l
i

i

v
c

r
e
h
O

t

h
c
r
a
e
s
e
r

:

s3.11

p5.4-

:

p4.6

5.6

i4.54

:

:

p0.2

p2.19

p5.4

:

:

:

7.1

:

:

:

e
c
n
e

f

e
D

:

s3.1

p4.4

:

p2.0-

7.7

i8.5-

:

:

p9.6

p5.0

p7.1

5.83

:

5.71

7.4-

:

:

:

l
i

i

v
c

l

a

t

o
T

D
R
O
A
B
G

D
R
O
A
B
G

l

a

t

o
T

:

s8.2

p8.1

:

p6.8

1.0-

i4.0

e6.8

2.21

p5.2

p6.31

p7.0

1.0

:

7.9

3.21

:

:

:

s6.2

s6.2

p8.1

:

p5.8

0.0

1.0

e6.01

p2.21

p5.2

p7.01

p9.0

2.3

:

8.9

2.21

6.02

:

:

0.6-

1.8-

5.0

2.1

1.1-

4.0

ip8.31

ip6.31

ip0.26

ip8.0-

ip3.2

ip6.07

2.5-

ip5.2

4.0-

ip7.2

5.0-

ip7.2

:

4.8

:

0.21

2.51-

6.1

p0.7

4.3

p0.3

3.3-

i9.611

i9.4

ip0.5-

:

2.5

:

5.21

1.02-

8.5

p4.4

2.0

p9.13

8.1

i2.02

i8.1-

ip5.21

:

5.31-

:

:

:

2.0-

5.21-

5.1

:

3.2

:

i8.8

ip8.1

:

8.6

:

:

8.6

0.3

6.4

7.5

p0.6

2.3

8.3

i4.2

:

:

5.01

:

0.5

i7.3

9.7

4.01

5.9

p6.6

4.21

:

i7.6

ip3.3

:

5.5

:

:

0.51-

:

:

0.01

:

:

i8.21-

:

:

:

9.6-

:

2.621

:

7.42

0.82

2.0

:

1.9

i8.7-

i6.9

ip9.8

:

5.5

:

7.5

:

0.3

5.4

0.5

0.2-

4.5

:

8.6

2.0-

4.3

0.7

4.3

p4.5

p4.5

3.3

8.4

i5.3

ip7.4

7.3

8.4

i8.3

ip0.7

Footnote 'i':
DE: Unrevised breakdown not adding to the revised total;
AT, CH, JP and US : Federal or central government only;
SK: Includes other classes;
JP: Defense is underestimated or based on underestimated data;
US: Total Excludes data for the R&D content of general payment to the
Higher Education sector for combined education and research (public
GUF).

26



 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Page 27

“Multiple Funding” — More than a Challenge for Austrian Universities

Graz University of Technology (TUG) will receive about €24 million over the next ten years from the MAGNA industrial
group, to build up the 'Frank Stronach Institute (FSI).' This deal is definitely different from the usual ways of financing
research  in Austrian  universities.  Moreover,  it  can  be  labeled  neither  as  typical  contract  research  nor  as  a  typical
donation,  therefore  raising  fundamental  questions  of  science–industry  cooperation  in  a  given  country.  It  is  an
altogether  remarkable  step  on  the  long  path  to  'multiple  funding',  i.e.,  a  broader  finance  base  for  the  Austrian
universities.

In  a  number  of  countries,  including  the  United  States  and  Canada,  successful  research  universities  boast  a  broad
range of income sources, from public block funding and research grants to donations, royalties and tuition fees. In
sharp  contrast  to  this  situation,  the Austrian  universities  are  nearly  exclusively  financed  by  public  institutions.  The
major part comes from the federal government in the form of General University Funds (GUF). While overall public
spending  for  R&D  is  about  the  OECD  average  (about  0.8  percent  of  GDP),  our  relative  share  of  public  university
funding (more than 0.5 percent of GDP = the R&D share of GUF) is much higher than the OECD average (less than
0.4 percent). The share of private funding of university research is estimated at about only 3 percent of total university
research funding and well below the OECD average. The private contributions generally do not come in the form of
donations but as short and mid term contracts in which industry pays for specified and applied research work. Even
in this category, Austrian universities attract less private money than average OECD counterparts. University reform
in Austria as stipulated by the Universitätsgesetz (University Act) 2002 will slowly change this situation as it includes,
inter alia, strong incentives for strategies, clearer research profiles, full costing and broadening of the financial base.

Source: Office of Science and Technology (Austria), 2007

Government Funding Mechanisms for Academic Research

Because  U.S.  universities  generally  do  not  maintain  data  on  departmental  research,  U.S.  totals  are  understated
relative to the R&D effort reported for other countries. The national totals for Europe, Canada, and Japan include the
research  component  of  general  university  fund  (GUF)  block  grants  provided  by  all  levels  of  government  to  the
academic sector. These funds can support departmental R&D programs that are not separately budgeted. The U.S.
federal  government  does  not  provide  research  support  through  a  GUF  equivalent,  preferring  instead  to  support
specific,  separately  budgeted  R&D  projects.  However,  some  state  government  funding  probably  does  support
departmental research at public universities in the United States. 

Whereas GUF block grants are reported separately for Japan, Canada, and European countries, the United States
does  not  have  an  equivalent  GUF  category.  In  the  United  States,  funds  to  the  university  sector  are  distributed  to
address the objectives of the federal agencies that provide the R&D funds. Nor is GUF equivalent to basic research.
The  treatment  of  GUF  is  one  of  the  major  areas  of  difficulty  in  making  international  R&D  comparisons.  In  many
countries, governments support academic research primarily through large block grants that are used at the discretion
of  each  individual  higher  education  institution  to  cover  administrative,  teaching,  and  research  costs.  Only  the  R&D
component  of  GUF  is  included  in  national  R&D  statistics,  but  problems  arise  in  identifying  the  amount  of  the  R&D
component and the objective of the research. Government GUF support is in addition to support provided in the form
of  earmarked,  directed,  or  project-specific  grants  and  contracts  (funds  for  which  can  be  assigned  to  specific
socioeconomic categories). In the United States, the federal government (although not necessarily state governments)
is  much  more  directly  involved  in  choosing  which  academic  research  projects  are  supported  than  are  national
governments in Europe and elsewhere. In each of the European G-7 countries, GUF accounts for 50% or more of total
government R&D to universities and for roughly 45% of the Canadian government academic R&D support. These data
indicate not only relative international funding priorities but also funding mechanisms and philosophies regarding the
best methods for financing academic research.

Source: National Science Board's Science and Engineering Indicators 2006, National Science Foundation



27

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02 R&D Expenditure:Part1_Chap2

07/12/2007

15:36

Page 29

PART1

Chapter 2 - R&D expenditure

02 R&D Expenditure:Part1_Chap2

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Page 30

2.1 Introduction

R&D  activities  are  often  considered  as  being  a  main
driver  of  economic  development,  innovation  and
growth.  They  comprise  creative  work  undertaken
systematically  with  a  view  to  increasing  the  stock  of
knowledge,  including  knowledge  of  man,  culture  and
society,  and  the  use  of  this  stock  of  knowledge  to
devise new applications. The basic statistical variables
are  R&D  expenditure  (described  in  this  chapter)  and
R&D personnel (see Chapter 3), which are measured at
both national and regional levels. 

The  European  goal  in  R&D,  as  set  by  the  Lisbon  and
Barcelona  European  Councils,  is  the  achievement  by
2010 of an R&D intensity of at least 3% of GDP in the
EU  (taking  into  account  the  different  starting  points  of
Member  States),  with  two  thirds  of  R&D  expenditure
being financed by the business enterprise sector.

R&D  expenditure  means  ‘intramural’  expenditure,  i.e.
all expenditure on R&D within a statistical unit or sector
of  the  economy  during  a  specific  period,  according  to
the  sources  of  funds.  Intramural  R&D  expenditure  is
broken  down  by  institutional  sector,  i.e.  by  sector  of
performance. 

Two  manuals  are  used  as  methodological  references
for R&D surveys: 

-  Standard  method  for  surveys  on  R&D  and
experimental  development  —  Frascati
Manual, OECD 2002;

- The regional dimension of R&D statistics and 
of  innovation  —  Regional  Manual,  Eurostat,
1996.

This  chapter  presents  the  key  indicators  of  R&D
expenditure  and  it  outlines  the  main  trends  over  the
past five years. It is divided into two sections:

- First, main trends are highlighted at national
level  by  looking  at  the  performance  of  the 
EU-27  Member  States,  Iceland,  Norway  and 
Candidate Countries. This part also considers
the  international  level  by  looking  at  data  for 
China, Japan and the United States. 

- Second, R&D expenditure is analysed at the
regional  level,  focusing  on  the  regions  of  the 
EU-27 Member States, Iceland and Norway.

In this chapter, two main indicators are used to present
R&D: 

- R&D intensity (expressed as a percentage of
GDP),
- R&D in volume (in euro).

Data  on  R&D  expenditure  are  broken  down  into  the
following institutional sectors: 

- the business enterprise sector (BES), 
- the government sector (GOV),
- the higher education sector (HES), 
- the private non-profit sector (PNP) and
- all sectors, which corresponds to the sum of
the previous four sectors.

In  addition  to  institutional  sectors,  other  breakdowns
are used to present R&D data, such as:

- the source of funds,
- the sector of activity,
- the size class,
- the field of science.

The  regional  analysis  is  carried  out  at  NUTS  2  level.
When other NUTS levels are used, this is indicated by
a  footnote.  Readers  should  also  note  that,  under  the
NUTS  classification,  the  entire  national  territory  of
Denmark,  Estonia,  Cyprus,  Latvia,  Lithuania,
Luxembourg, Malta, Slovenia and Iceland is considered
as  a  NUTS  0,  1  or  2  region,  which  means  that  these
countries as a whole may appear in rankings at NUTS
2 level.

The  analysis  covers  the  period  2000-2005. The  same
length of time series is not available for all countries. In
general,  when  data  for  2005  are  not  available  for  a
particular country, the latest available year is presented.

The  complete  R&D  expenditure  time  series  are
available in Eurostat’s reference database NewCronos.
Data for China, Japan and the United States are taken
from OECD — Main Science and Technology Indicators
(MSTI).

30



02 R&D Expenditure:Part1_Chap2

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Page 31

2.2 R&D at the national level

R&D intensity

Table  2.1  shows  R&D  expenditure  expressed  as  a
percentage of GDP — R&D intensity, by country and by
sector  of  performance.  The  main  advantage  of  this
the  economic
indicator’s 
it  neutralizes 
that 
importance  of  countries/regions, 
thus  enabling
comparison.

is 

The EU-27’s R&D intensity amounted to 1.84% in 2005.
Up  to  almost  two  thirds  of  R&D  (1.17%  of  GDP)  was
contributed  by  the  business  enterprise  sector  (BES).
The  higher  education  (HES)  and  government  (GOV)
sectors  together  accounted  for  almost  all  of  the
remaining third, i.e. 0.65% of GDP. The residual 0.02%
of  GDP  was  invested  in  the  private  non-profit  sector
(PNP).

Not  only  was  EU-27  R&D  intensity  significantly  lower
than  its  main  international  competitors  such  as  Japan
(3.33%) and the United States (2.62%); it also fell short
of the 3%-target to be achieved by 2010 as set by the
Lisbon strategy.

R&D  intensity  in  China  reached  1.34%  of  GDP,  which
was  notably  lower  than  the  EU-27  average.  However,
China’s  R&D  intensity  increased  rapidly,  whereas 
EU-27  R&D  intensity  decreased  slightly  during  the
same period.

Only  two  European  Member  States  exceeded  the  3%
target:  Sweden  (3.86%)  and  Finland  (3.48%).  Four
other Member States attained an R&D intensity of over
2%:  Germany  (2.51%),  Denmark  (2.44%),  Austria
(2.36%) and France (2.13%). This was also the case in
Iceland (2.83% in 2004). All other Member States were
below  this  threshold.  Moreover,  in  twelve  Member
States  R&D  intensity  accounted  for  less  than  1%  of
GDP.

The  BES  accounted  for  the  highest  share  of  R&D
intensity  in  a  majority  of  Member  States  and  in  other
countries. Exceptions were Bulgaria and Poland, where
GOV  was  the  main  sector  for  R&D,  and  Greece,
Cyprus,  Lithuania  and  Portugal  where  HES  took  the
largest share.

able 2.1 

R&D expenditure as a percentage of  GDP, by sector of  performance,

EU-27 and selected countries — 2003 to 2005

All sectors

Business enterprise sector

Government sector

Higher education sector

2004

2005

1.84 s

1.85 p

0.51

1.26

2.48 p

2.50

0.88

1.21 p

0.61 p

1.06

2.14

1.10

0.37

0.42

0.76

1.66

0.88 i

0.63 b

1.78 p

2.23

0.56

0.77 p

0.39

1.45

0.51

3.46

:

1.73

2.83

1.62

2.93

1.22

1.23

3.17

1.16

1.84 s

1.82 p

0.50

1.42

2.44 p

2.51 e

0.94 p

1.25 p

0.61 p

1.12 p

2.13 p

:

0.40 p

0.57

0.76

1.56 p

0.94 i

0.61 p

:

2.36 p

0.57

0.81 p

:

1.22 i

0.51

3.48

3.86

:

:

1.51 p

:

:

1.34

3.33

1.07

2003

1.19 s

1.31

0.10

0.76

1.77

1.76

0.27

0.77

0.20

0.57

1.36

0.52

0.07

0.13

0.14

1.48

0.34

0.08

1.01

:

0.15

0.25

0.22

0.84

0.32

2.42

2.93 i

1.14

1.48

0.99

:

0.44

0.71

2.40

0.88

2004

1.17 s

1.28 p

0.12

0.80

1.69

1.75

0.34

0.78 p

0.19 p

0.58

1.34

0.53

0.08

0.19

0.16

1.46

0.36

0.45 b

1.03

1.51

0.16

0.27 p

0.21

0.97

0.25

2.42

:

1.09

1.59

0.89

2.16

0.51

0.82

2.38

0.80

2005

1.17 s

1.24 p

0.11

0.92

1.67 p

1.76 e

0.42 p

0.82 p

0.18 p

0.61 p

1.32 p

0.55 p

0.09 p

0.23

0.16

1.34 p

0.41

0.42 p

1.02 p

1.60 p

0.18

0.29 p

:

0.87 p

0.25

2.46

2.92

:

:

0.82 p

:

:

0.91

2.54

0.73

2003

0.25 s

0.13

0.35

0.29

0.18

0.34 i

0.12

0.09

0.13

0.16

0.36

0.19

0.13

0.09

0.18

0.18

0.29 i

0.02

0.25 b

:

0.22

0.13

0.12

0.29

0.18 i

0.33

0.14 i

0.18

0.71

0.26

:

0.24

0.31

0.30

0.32

2004

0.24 s

0.13 p

0.34

0.27

0.17

0.34 i

0.12

0.09

0.12 p

0.17

0.37

0.20

0.13

0.08

0.19

0.18

0.26 i

0.01

0.26

0.11

0.22

0.12 p

0.13

0.29

0.16 i

0.33

:

0.18

0.60

0.25

0.03 i

0.25

0.28

0.30

0.29

2005

0.24 s

0.14 p

0.33

0.27

0.18 p

0.34 ei

0.11

0.08 p

0.13 p

0.19 p

0.37 p

0.17 p

0.13 p

0.11

0.19

0.19 p

0.26 i

0.02

0.24 p

0.12 p

0.21

0.11 p

:

0.23 i

0.15

0.33

0.12

:

:

0.24 p

:

:

0.29

0.28

0.28

2003

0.41 s

0.42

0.05

0.19

0.59

0.43

0.37

0.29 e

0.29

0.32

0.42

0.37

0.11

0.16

0.35

0.01 e

0.25

0.16

0.49

:

0.17

0.28

0.04

0.18

0.08

0.66

0.87

0.40

0.61

0.48

:

0.43

0.12

0.44

0.08

2004

0.40 s

0.41 p

0.05

0.19

0.61

0.41

0.40

0.33

0.29 p

0.31

0.41

0.36

0.13

0.15

0.41

0.02

0.22

0.17

0.50 p

0.59

0.18

0.30 p

0.04

0.19

0.10

0.68

:

0.40

0.57

0.48

0.67

0.45

0.13

0.43

0.06

2005

0.41 s

0.41 p

0.05

0.23

0.58 p

0.42 e

0.39

0.35 p

0.30 p

0.32 p

0.42 p

:

0.15 p

0.23

0.42

0.02 p

0.24

0.17

:

0.63 p

0.18

0.32 p

:

0.12 i

0.10

0.66

0.80

:

:

0.45 p

:

:

0.13

0.45

0.06

2003

1.87 s

1.89

0.50

1.25

2.56

2.52

0.79

1.16 e

0.63

1.05

2.17

1.11

0.35

0.38

0.67

1.66

0.93 i

0.26

1.76

2.21 e

0.54

0.74

0.39

1.32

0.58

3.43

3.95 i

1.79

2.86

1.73

:

1.11

1.13

3.20

1.28

2.66 i

2.58 i

2.62 pi

1.84 i

1.79 i

1.82 pi

0.33 i

0.32 i

0.31 pi

0.37 i

0.37 i

0.37 pi

EU-27

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

CN

JP

RU

US

CN, JP, RU and US: source OECD-MSTI.
Footnote 'i':
DE: Includes other classes;
HU, SK: Defence excluded (all or mostly);



SI and SE: Underestimated or based on underestimated data;
SE, CH and US: Federal or central government only;
US: Excludes most or all capital expenditure.

31

02 R&D Expenditure:Part1_Chap2

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One of Europe’s key areas: the increase and improvement of investment 
in Research and Development

The European Commission has prepared a special webpage on growth and jobs. There, the actual problem
of the lack of R&D investments is explained and solutions are put forward both at EU- and at Member State
level. It also provides key messages (see below) and examples.

Key messages

•  In  order  to  compete  internationally,  the  EU  has  to  deliver  high-quality  innovative  products  and  services. 
Research and development is needed to deliver them. 

• Eco-innovation is an economic opportunity for the EU – with a real potential for higher growth. Europe is 
strong in this area and can use this to strengthen its global competitiveness. Therefore, the Commission will
promote R&D in eco-innovation. 

• Member States and the Commission need to work towards an increase in R&D spending of 3% of GDP. If
Europe continues to invest less in R&D and to invest less efficiently, it cannot hope to attain its objective as
the most dynamic and competitive world economy. 

• Investment in R&D pays off in terms of economic and productivity growth. 

• The new Framework Programme for Research and Development will be geared towards addressing the 
problems facing EU investment in research and development. It must be properly funded if it is to succeed 
in this ambition.

More information available on: http://ec.europa.eu/growthandjobs.

Source: European Commission, 2007

R&D intensity at international level

For many of the G-8 countries (i.e., the G-7 countries plus Russia), the latest R&D/GDP ratio is no higher now than it
was at the start of the 1990s, which ushered in a period of slow growth or decline in their overall R&D efforts […]. The
two exceptions, Japan and Canada, both exhibit substantial increases in this indicator between 1990 and 2002. In
Japan  this  indicator  declined  in  the  early  1990s  as  a  result  of  reduced  or  level  R&D  spending  by  industry  and
government,  a  pattern  similar  to  that  exhibited  by  the  United  States.  Japan’s  R&D/GDP  ratio  subsequently  rose  to
3.1%  in  2002,  the  result  of  a  resurgence  of  industrial  R&D  in  the  mid-1990s  coupled  with  anaemic  economic
conditions. In the 5 years between 1997 and 2002, real GDP in Japan grew by only 1.8%, so relatively small increases
in R&D expenditures resulted in a rise in its R&D/GDP ratio. [In] contrast, over the same period real GDP grew by
21.8% in Canada; hence, the rise in its R&D/GDP ratio is more indicative of robust R&D growth.

Geopolitical events also affect R&D intensity indicators as evidenced by Germany and Russia. Germany’s R&D/GDP
ratio fell from 2.8% at the end of the 1980s, before reunification, to 2.2% in 1994. Its R&D/GDP has since risen to 2.5%
in 2003. The end of the Cold War and collapse of the Soviet Union had a drastic effect on Russia’s R&D intensity. R&D
performance in Russia was estimated at 2.0% of GDP in 1990; that figure dropped to 1.4% in 1991 and then dropped
further to 0.7% in 1992. The severity of this decline is compounded by the fact that Russian GDP contracted in each
of these years. Both Russia’s R&D and GDP exhibited strong growth after 1998. In the 5 years between 1998 and
2003, Russia’s R&D doubled and its R&D/GDP ratio rose from 1.0% to 1.3%. 

Overall, the United States ranked fifth among OECD countries in terms of reported R&D/GDP ratios. Israel (not an
OECD member country), devoting 4.9% of its GDP to R&D, currently leads all countries, followed by Sweden (4.3%),
Finland (3.5%), Japan (3.1%), and Iceland (3.1%). In general, nations in Southern and Eastern Europe tend to have
R&D/GDP ratios of 1.5% or lower, whereas Nordic nations and those in Western Europe report R&D spending shares
greater than 1.5%. This pattern broadly reflects the wealth and level of economic development in these regions.

Source: “Science and Engineering Indicators 2006”, National Science Board, USA

32



02 R&D Expenditure:Part1_Chap2

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igure 2.2 

5002-0002RGAA

YC

R&D as a percentage of  GDP in 2005 and annual average growth rate (AAGR) 2000 - 2005(1), 
all sectors, EU-27 and selected countries

48.1=72-UE

evitcejbo%3

EE

NC

VL

TL

RH

SE

UH

ZC

OR

RT

TP

GB

LE

LP

KS

TI

UR

EI

IS

UL

ON

LN

EB

KU

TA

RF

KD

ED

SU

HC

SI

PJ

IF

22.0-=72-UE

ES

21

9

6

3

0

3-

6-

0.0

5.0

0.1

5.1

0.2

5.2

0.3

5.3

0.4
PDGfo%saD&R

(1) Calcultated on R&D expenditure expressed as a percentage of GDP.
MT does not appear because the 2002-2005 AAGR amounts to 32%. MT's R&D intensity amounted to 0.61% of GDP in 2005. 
CN, JP, RU and US: source OECD-MSTI.
Exceptions to the reference year: 

Exceptions to the reference period:

2004: IT, NL, RO, UK, IS, CH and HR; 
2002: TR.

2000-2002: TR;
2000-2004: IT, NL, RO, UK, IS, CH and HR; 
2001-2005: EL and SE.

Figure 2.2 shows R&D intensity, together with its annual
average growth rate (AAGR). 

In the EU-27, R&D intensity in 2005 was 1.84% of GDP,
and between 2000 and 2005 the AAGR was - 0.22%.

In the group of followers, R&D intensity was below the
EU-27 average, but its AAGR was above it. This group
includes  twelve  Member  States,  such  as  Spain,  Italy,
Cyprus  and  Romania.  China,  Russia  and  Turkey  also
belonged to this group. 

Three main groups of countries can be distinguished in
terms of their R&D intensity and of their AAGR: a group
of leaders, a group of followers and a group of trailers.

In  the  group  of  leaders  R&D  intensity  and  its  AAGR
were  above  the  EU-27  average.  This  group  includes
five  Member  States  —  Finland,  Germany,  Denmark,
Austria  and  France  —  plus  Japan,  Switzerland  and
Iceland.  These  are  countries  that  keep  on  increasing
their lead.

Sweden  may  also  be  considered  as  belonging  to  the
group of leaders, though its R&D AAGR was below the
EU-27 average.

Among the leaders, Finland and Sweden were the only
Member  States  to  have  already  exceeded  the  3%-
objective  set  out  by  the  Lisbon  strategy.  For  the  other
countries  in  the  leading  group,  reaching  this  target
seems to be quite a realistic possibility, given the trends
in their R&D expenditure patterns.

Although  these  countries  have  been  lagging,  they  are
gradually  closing  the  gap  with  the  EU-27  average.
Nevertheless,  particular  efforts  seem  to  be  needed  in
order to reach the 3%-target by 2010.

In the group of trailers, both R&D intensity and AAGR
were  below  the  EU-27  average.  This  group  includes
nine  Member  States  –  for  example,  the  United
Kingdom,  Greece,  Poland  and  Bulgaria.  Norway  also
belonged to this group.

Not  only  are  these  Member  States  under  the  EU-27
average and a long way from the 3%-objective, but the
existing gaps can be expected to increase. Moreover, if
no  major  changes  take  place,  these  countries  will  not
reach the 3%-target to be achieved by 2010.



33

02 R&D Expenditure:Part1_Chap2

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R&D expenditure in volume

Table 2.3 shows that the EU-27 spent EUR 201 billion
on R&D in 2005 as compared to EUR 251 billion spent
by the United States and EUR 120 billion by Japan. As
was  stated  above,  most  R&D  expenditure  was  in  the
business enterprise sector (BES), but this phenomenon
is  more  significant  in  Japan  (75%)  and  in  the  United
States (70%) than in the EU-27 (64%).

Between  2000  and  2005,  EU-27  R&D  expenditure
increased at an AAGR of 3.3%. By comparison, Japan
and  the  United  States  saw  their  R&D  expenditure
decrease  during  the  same  period.  However,  these
decreases can be explained in part by changes in the
exchange  rate  of  their  national  currencies  against  the
euro.

Within  the  EU-27,  three  Member  States  -  Germany,
France and the United Kingdom - accounted for almost
two  thirds  of  total  EU-27  R&D  expenditure.  Germany
alone,  with  EUR  56  billion,  made  up  more  than  one
quarter of EU-27 total. France and the United Kingdom
followed  with  EUR  36  billion  and  EUR  30  billion
respectively.

Three other Member States - Italy, Sweden and Spain -
allocated  more  than  EUR  10  billion  to  R&D.  As
mentioned  earlier,  Sweden  was  the  leading  Member
State in terms of R&D intensity.

All  the  Member  States  registered  growth  in  their  R&D
expenditure  between  2000  and  2005.  The  highest
growth was generally recorded in those Member States
that  joined  the  European  Union  recently  (in  2004  and
2007) – such as Malta, Estonia and Romania.

The European leaders in R&D expenditure in absolute
terms — Germany, France and the United Kingdom —
experienced only slight growth or remained at the same
level over the period. 

Both  total  R&D  expenditure  and  business  R&D
expenditure  increased  in  all  the  Member  States.
Slovenia  was  the  only  Member  State  to  record  a
decrease  in  R&D  spending  in  its  higher  education
sector,  whereas  R&D  expenditure  in  the  government
sector  declined  in  Denmark,  Malta,  Portugal,  Slovenia
and the United Kingdom.

able 2.3 

R&D expenditure in EUR million and annual average growth rate (AAGR), by sector of  performance,

EU-27 and selected countries — 2000-2005

All sectors

Business enterprise sector

Government sector

Higher education sector

2000

2005

AAGR
2000-2005

EU-27

170 632 s

201 020 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

CN

JP

RU

US

4 964

71

744

3 892

50 619

37

1 284 e

852

5 719

30 954 b

12 460

25

38

73

364

405 i

12

7 626

4 029 e

1 197

927 e

149

297

143

4 423

10 511 i

29 070

251 e

3 037

6 852

271

1 389

153 860

2 948

5 428 p

106

1 417

5 097 p

56 356 e

104 p

2 020 p

1 112 p

10 100 p

36 396 p

15 253

54 p

73

157

458 p

838 i

27 p

8 723 p

5 784 p

1 386

1 189 p

235

338 i

194

5 474

11 109

29 956

297

3 599 p

8 486

345

:

119 748

5 473

289 917 i

251 254 pi

3.33

1.80

8.33

13.75

5.54

2.17

22.96

9.48

6.90

12.05

3.29

5.19

17.30

14.13

16.53

4.71

15.63

32.30

3.42

7.50

2.98

5.11

12.15

2.62

6.35

4.36

1.39

0.75

4.23

4.33

5.49

12.90

:

-8.02

16.73

-3.51

Footnote 'i':

DE: Includes other classes;
HU, SK: Defence excluded (all or mostly);
SI  and  SE:  Underestimated  or  based  on
underestimated data;
SE, CH and US: Federal or central government
only;
US: Excludes most or all capital expenditure.

34

2000

2005

110 472 s

128 091 s

3 589

15

446

2 596

35 600

8

900

278

3 069

19 348

6 239

5

15

16

337

180

3

4 458

2 638

432

258 e

103

167

94

3 136

8 118 i

18 884

142 e

1 814

5 065

115

465

109 181

2 087

3 705 p

23

914

3 481 p

39 406 e

47 p

1 320 p

326 p

5 491 p

22 543 p

7 293

12 p

30

32

395 p

362

19 p

5 039

3 919 p

440

430 p

130

241 p

97

3 877

8 410

18 883

167

1 944 p

6 257

144

:

89 783

3 780

AAGR
2000-2005

3.00

0.64

8.49

15.41

6.04

2.05

41.32

7.96

4.04

12.34

3.10

3.98

18.68

14.40

15.30

3.23

15.02

85.86

3.11

8.23

0.38

10.82

5.96

7.56

0.61

4.33

0.89

0.00

4.26

1.75

5.43

11.54

:

-6.31

16.02

2000

2005

23 519 s

26 447 s

AAGR
2000-2005

2.37

5.90

7.75

7.05

-5.67

2.17

6.51

5.37

4.98

13.53

3.30

3.67

9.15

10.37

5.06

16.59

17.27

-26.37

6.48

4.21

5.48

-6.08

30.17

-3.53

10.33

2.25

3.63

-4.32

-0.66

6.79

0.23

9.52

:

-9.85

17.71

0.60

2000

2005

35 285 s

44 357 s

1 005

1 237 p

7

106

770

8 146

19

280 e

383

1 694

5 804 b

3 865

6

14

27

1

97

7

2 120 b

1 135

377

348 e

17

49

14

789

2 085

5 985

41 e

780

1 566

95

839

22 354

134

11

232

1 215 p

9 300 e

43

565 p

548 p

2 888 p

7 100 p

5 005

20 p

29

86

7 p

211

8

2 430 p

1 544 p

438

465 p

24

32 i

40

1 042

2 314

7 012

60

1 078 p

1 943

129

:

16 358

299

33 221 i

34 111 pi

AAGR
2000-2005

4.68

4.24

9.69

17.01

9.56

2.68

17.30

15.05

9.41

11.26

4.11

6.67

27.46

15.86

26.29

50.72

16.69

4.01

3.47

6.36

3.01

6.01

7.95

-8.30

23.90

5.71

2.64

4.04

10.01

8.43

5.54

16.60

:

-9.89

22.16

0.66

416 p

71

265

367 p

7 650 e

12

135 p

228 p

1 707 p

6 305 p

2 722

18 p

14

39

56 p

235 i

1

1 252

297 p

504

162 p

80

64 i

58

523

343

3 078

63

577 p

91 i

72

:

11 149

1 383

30 652 pi

312

49

188

492

6 873

9

104

188

905

5 361 b

2 356

11

8

31

26

106 i

2

974 b

242

386

222 e

28

77

35

468

297 i

3 672

64 e

444

90 bi

60

86

15 217

721

216 552 i

29 926 i
Exceptions to the reference year 2000:

176 241 pi

-5.02

2001: EL, SE and NO;
2002: MT and HR.

Exceptions to the reference year 2005:

2004: IT, NL, RO, UK, IS, CH, HR, RU, US;
2003: JP.

Exceptions to the reference period 2000-2005:

2000-2003: JP;
2000-2004: IT, NL, RO, UK, IS, CH, RU, US;
2001-2005: EL, SE and NO;
2002-2004: HR;
2002-2005: MT.



02 R&D Expenditure:Part1_Chap2

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Figure  2.4  (all  sectors)  indicates  that  business
enterprises  are  the  principal  source  of  financing  for
R&D expenditure in the EU-27 as a whole (55%). This
also applies in the majority of individual Member States
and  other  countries  observed.  The  Lisbon  strategy,
however, aims to reach two-thirds financing by the BES
in Europe.

The BES already finances two-thirds or more of R&D in
Germany  (67%),  Luxembourg  (80%)  and  Finland
(69%).  The  same  is  true  of  Switzerland  and  Japan.
Denmark and Sweden posted shares of over 60%.

The  sources  of  finance  are  more  balanced  in  the
Member  States  that  recently  joined  the  EU  (2004  and
2007  enlargements),  the  Candidate  Countries  and
Russia.  With  the  exception  of  the  Czech  Republic,
Latvia and Slovenia, the government sector’s share is
far  greater  than  that  of  the  business  sector  in  these
countries.  This  may  be  explained  by  the  fact  that  the
government sector was traditionally very strong in these
countries and that the business sector still requires time
to  develop  further,  in  order  to  be  able  to  invest  more
funds in R&D.

The  remaining  sources  -  ‘abroad’  and  ‘other  national
sources’  -  are  of  minor  importance  in  the  majority  of
countries,  except  in  Estonia,  Greece,  Latvia,  Malta,
Austria and the United Kingdom, where more than 15%
of R&D expenditure is financed from ‘abroad’.

The  breakdown  by  sources  of  funds  shows  that  the
BES  has  one  principal  source,  whereas  total  R&D
expenditure  in  all  sectors  has  at  least  two  main
sources.  On  average  for  the  EU-27,  82%  of  business
R&D  expenditure  was  self-financed  and,  moreover,
business enterprises were the main source of funds in
all Member States. 

The  lowest  shares  were  found  in  Austria  (67%),
Romania  (67%)  and  the  United  Kingdom  (66%).  In
Austria and in the United Kingdom, this is explained by
the  fact  that  business  R&D  was  to  a  large  extent
financed 
the
in  Romania 
government sector contributed more to BES R&D.

from  abroad,  while 

igure 2.4 

Total and business enterprise R&D expenditure by source of  funds as a percentage of  total,

EU-27 and selected countries — 2004

All sectors

Business enterprise sector

28

28

36

37

19

20

19

55

60

53

60

67

57

48

52

46

51

47

27

32

44

38

58

69

65

70

75

44

44

49

43

41

EU-27

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

JP

RU

US

2

8

EU-27

66

44

35

24

42

27

32

3

13

1

5

2

4

3

10

0

2

17

2

9

30

2

46

4

22

41

38

5

6

2

9

64

63

80

31

52

60

0

6

12

6

11

23

1

22

11

0

8

10

36

1

11

33

1

19

65

60

49

57

3

3

5

5

2

5

30

0

11

26

0

4

1

3

23

4

7

33

40

6

17

2

14

42

47

51

23

18

31

75%

1

7

5

2

8

7

7

8

6

0

3

1

0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

JP

RU

US

31

61

63

82

82

80

97

92

86

85

87

76

82

80

75

85

77

72

77

75

82

80

89

89

67

67

82

71

95

86

8

0

10

5

0

13

15

2

0

0
0

3

1

4

12

6

0

2

4

0

10

3

0

10

4

0

19

12

0

5

9

0

14

11

11

5

0

10

3

20

3

0

25

3

0

8

4

0

18

17

0

8

3

0

15

6

0

26

17

0

3

5

0

5

0

6

27

4

0

13

27

0

2

4

0
1

6

0

8

66

10

0

23

77

81

4

0

20

10

0

9

91

94

94

98

90

53

2

1

7

0
2

5

3

1

2

1
0
1

0

9

10

38

0%

25%

50%

100%

0%

25%

50%

75%

100%

BES

GOV

Other national sources

Abroad

BES

GOV

Other national sources

Abroad

Eurostat estimation: EU-27.
IE and US: Provisional data.
HU (all sectors): Defence excluded.
US: Excludes most or all capital expenditure.

Exceptions to the reference year:

2003: BE, DK, EL, LU, NL, PT, SE, IS, NO, JP and US;
2002: MT and TR.



35

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Table  2.5  presents  an  overview  of  the  breakdown  of
business  R&D  expenditure  by  sector  of  activity  based
on  NACE  Rev  1.1  (see  methodological  notes).
‘Manufacturing’  is  by  far  the  most  important  sector  of
activity  in  the  EU-27,  accounting  for  82%  of  the  total,
followed  by  ‘services’,  with  approximately  16%.  The
other sectors add up to a mere 2%.

With an R&D expenditure of EUR 35.2 billion, Germany
was  the  leader  in  ‘manufacturing’  in  absolute  terms,
while  the  United  Kingdom  ranked  second  (EUR  15.2
billion).  The  United  Kingdom  ranked  first  in  ‘services’
with R&D expenditure of EUR 4.2 billion.

Not  only  leading  in  absolute  terms,  Germany  also
ranked  first  in  relative  terms  with  more  than  90%  of
business  R&D  expenditure  in  ‘manufacturing’.  Five
other  Member  States,  which  included  France  and  the
United Kingdom, achieved shares over 80%.

However, the distribution varied across Member States.
Specifically,  the  services  sector  accounted  for  more
business R&D expenditure than did the manufacturing
sector  in  seven  Member  States  as  well  as  in  Iceland,
Norway, Croatia and Russia.

In  Romania  and  to  a  lesser  extent  in  Poland,  other
sectors of activity, mainly agriculture, accounted for an
appreciable share of business R&D expenditure.

able 2.5 

Business enterprise R&D expenditure in EUR million, by sector of  activity (NACE Rev 1.1),

EU-27 and selected countries — 2004

Total

Agriculture,
hunting, forestry 
and fishing

Mining and 
quarrying

Manufacturing

Electricity, gas and 
water supply

Construction

Services

EU-27

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

RU

123 582 s

3 714 p

24

701

3 332

38 611

32

1 150 p

313

4 865

21 646

7 057

10

21

29

379

297

3

4 804

3 556

327

338

130

254

86

3 683

7 886 i

18 319

142

1 821

6 257

114

367

3 353

837 s

50 e

:

3

:

76

0

5 p

2

55

311

0

0

0

:

:

4

:

68

3

15

1

16

0

2

1

23

174

3

27

:

4

3

478 s

7 e

:

1

:

24

:

0 p

4

7

152

52

0

:

1

:

0

0

95

3

8

1

10

4

0

6

7

81

0

98

:

:

1

38 i

50 i

101 132 s

2 937 e

11

429

:

35 176

13

700 p

200

2 748

18 463

5 195

3

9

23

179

239

2

3 750

2 550

207

151

81

205

32

2 937

6 336

15 224

40

799

5 033

10

318

687 i

797 s

26 e

0

0

:

83

0

416 s

59 e

0

9

:

30

:

19 922 s

668 e

13

259

:

3 222

18

0 p

0 p

445 p

0

33

393

28

0

0

0

0

2

0

24

8

4

3

6

0

:

6

54

99

1

6

:

0

3

1

70

86

12

0

0

:

:

1

0

29

17

11

4

4

0

:

27

:

44

1

24

:

3

0

107

1 952

2 025

1 769

6

11

6

200

52

1

839

975

81

179

13

45

51

707

1 466

4 156

96

867

1 224

97

43

11 i

9 i

2 398 i

EU-27: Distribution by sector of activity is estimated on the basis of available Member States. 
Exceptions to the reference year:

Footnote 'i':
SE: Underestimated or based on underestimated data;
US: Excludes most or all capital expenditure.

2003: EL, FR, LU, NL, PT, SE, UK, IS, HR and RU;
2002: MT and TR.

36

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With the exception of enterprises employing 250 to 499
persons,  one  of  the  main  results  of  Table  2.6,  which
looks at R&D expenditure by size-class of enterprises,
is  that  business  R&D  expenditure  increases  with  the
size of enterprises in the EU-27 as a whole and in most
of its Member States. 

In  Germany  (87%),  Sweden  (82%)  and  the  United
Kingdom  (76%),  the  highest  share  of  business  R&D
expenditure  was  in  those  enterprises  employing  more
than 500 persons.

However,  this  rule  cannot  be  applied  strictly  to  small
countries,  probably  owing  to  the  fact  that  they  have

fewer large and very large enterprises compared to the
bigger economies.

It  also  seems  that,  in  many  countries,  enterprises
belonging  to  the  50-to-249  persons  employed  size
class invest more than those in the 250-to-499 persons
employed size class.

The  distribution  of  business  R&D  expenditure  by  size
class  was  quite  different  in  Russia.  Enterprises
employing 250 to 499 persons ranked first, followed by
enterprises  with  10  to  49  persons  employed.  Larger
enterprises  (more  than  500  persons  employed)
received the smallest share of R&D spending.

able 2.6 

Business enterprise R&D expenditure in EUR million, by size class,

EU-27 and selected countries — 2004

Total

0
person
employed

1 to 9 
persons
employed

10 to 49
persons
employed

50 to 249
persons
employed

250 to 499
persons
employed

500 and more
persons
employed

EU-27

123 582 s

34 s

1 374 s

5 872 s

14 257 s

8 700 s

93 345 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

RU

3 608

24

701

3 355

38 029

32

9

0

4

:

:

:

1 150 p

0 p

313

4 865

22 210

7 293

10

21

29

393

297

3

4 804

3 556

327

338

130

254

86

3 683

7 886 i

18 319

:

1 960

6 257

3 176

:

:

:

:

0

:

:

:

:

:

:

:

1

:

1

1

2

:

:

9

:

:

:

298

128

1

12

148

70

4

36 p

4

115

:

:

2

2

2

:

10 i

:

:

90 i

2

14

35

8

2

80 i

:

314

:

:

77

207

441

2

62

357

668

7

219 p

56

806

:

:

1

5

3

:

20

1

388

251

18

52

9

11

5

268

:

826

:

459

426

783

794

3

176

518

2 448

7

294 p

105

1 257

:

:

2

6

11

:

23

1

898

622

106

69

30

52

33

403

964

2 729

:

645

777

509

380

9

81

413

1 705

3

174 p

24

662

:

:

0

1

4

:

23

0

:

372

73

69

15

16

19

338

455

1 933

:

140

709

1 229

1 857

7

365

1 919

33 139

12

428 p

125

2 025

:

:

5

6

9

:

220

0

:

2 222

126

134

39

166

26

2 595

6 466

13 967

:

715

4 269

150

EU-27:Distribution by size class is estimated on the basis of available Member States. 
Exceptions to the reference year:

Footnote 'i':
HU, AT and FI: Includes other classes;
SE: Underestimated or based on underestimated data.

2003: BE, DK, DE, El, NL, PT, SE, UK and NO;
2002: MT and RU.



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Table  2.7  gives  an  insight  into  the  breakdown  of  R&D
expenditure  in  government  and  higher  education
sectors by fields of science (see methodological notes).

In 2004, ‘natural sciences’ received the largest share of
R&D expenditure in the EU-27 as a whole and in 16 of
its  Member  States.  This  was  also  the  main  field  of
scientific  research  in  Norway.  The  United  Kingdom
gave  absolute  priority  to  ‘natural  sciences’,  with  96%
(government sector only).

‘Engineering and technology’ was the top scientific field
in Belgium, Spain, Luxembourg, Romania, Finland and
Iceland,  while  ‘Medical  sciences’  led  in  Austria  and
Sweden.

‘Social  sciences’  was  the  main  field  only  in  Malta  and
Croatia.  However,  ‘social  sciences’  accounted  for  a
substantial  share  of  R&D  expenditure  in  Luxembourg,
Portugal and Norway.

None of the countries allocated the largest part of their
GOV and HES R&D expenditure to ‘agriculture’ or the
‘humanities’. However, ‘agriculture’ accounted for more
than  20%  of  R&D  expenditure  in  Bulgaria  (26.1%),
Cyprus (22.3%) and Iceland (22.8%).

Hungary  (14.8%)  and  Estonia  (14.0%)  devoted  the
highest  shares  of  their  R&D  expenditure  to  the
‘humanities’.

able 2.7 

R&D expenditure in EUR million and by field of  science as a percentage,

government and higher education sectors, EU-27 and selected countries — 2004

Total

Agriculture

Engineering and 
technology

Medical sciences

Natural sciences

Social sciences

Humanities

EU-27

68 366 s

6.0 s

20.6 s

18.7 s

34.9 s

11.0 s

8.8 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

HR

TR

RU

1 504

76

395

1 467

16 604

49

630

695 p

4 069

12 866

7 727

33

26

108

46 e

390 i

8

3 418

1 671

750

564

104

124

88 i

1 537

2 715 i

9 429 b

126

1 451

201

913

1 682

12.4

26.1

9.9

10.5

4.6

12.9

12.7

:

9.7

:

6.6

22.3

9.6

6.6

2.2 p

14.1 i

2.4

:

5.5

10.6

13.3

5.6

9.7

27.1

19.2

23.8

12.7

23.9

21.4

14.4

:

22.6

:

14.6

4.7

22.8

24.5

44.2 p

18.1 i

13.4

:

12.5

31.4

20.7

35.7

19.8

19.5

4.2

10.2

24.6

16.7

11.0

14.5

:

22.0

:

18.1

1.3

4.7

14.4

12.9 p

12.0 i

18.4

:

28.6

13.5

9.6

21.4

7.8

19.2

38.3

42.4

26.1

37.4

30.7

36.6

:

18.2

:

44.4

40.3

47.8

26.4

14.7 p

28.2 i

14.3

:

28.5

32.6

29.4

26.7

50.0

10.2 i

19.7 i

12.1 i

41.7 i

7.7

4.7

:

22.8

11.3

9.5

8.3

4.5

26.9

26.1

:

25.9

13.4

21.7

10.0

45.1

21.2

29.3

:

12.5

21.8

10.9

57.9

6.2

22.1

19.5

96.0

18.5

22.7

20.9

3.9

37.1

14.2

3.8

6.5

12.5

7.1

10.1

15.8

:

15.9

:

14.5

19.2

9.9

16.6

21.6 p

12.7 i

36.7

:

13.1

11.3

19.7

5.4

8.5

13.1

17.9

13.2

4.0

12.3

22.3

23.4

12.7

4.2

7.6

8.2

7.3

13.4

10.2

14.0

6.1

:

11.6

:

1.8

12.2

5.2

11.5

1.5 p

14.8 i

13.6

:

11.9

0.7

7.4

5.2

4.3

3.2

6.2

6.4

:

7.8

8.4

13.7

7.1

2.8

EU-27:Distribution by field of science is estimated on the basis of available Member States. 
Exceptions to the reference year:

Distribution by field of science:

2003: BE, DK, LU, PL, PT, SE, IS and NO;
2002: Nl and TR;
2001: UK.

Government sector only: IT and UK;
Higher education sector only: SE and TR.

Footnote 'i':
HU  and  SK:  Defence  excluded  (all  or
mostly);
SE: Federal or central government only.

38

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2.3 R&D at the regional level

igure 2.8  R&D expenditure in the top 10 EU regions,

Braunschweig

as a percentage of  EU-27, 

all sectors — 2003

)RF(ecnarFedelÎ
%7.7

)ED(nreyabrebO
%9.3

)ED(tragttutS
%2.3

)KD(kramneD
%6.2

)ED(tdatsmraD
%3.2

)RF(seplA-enôhR
%1.2
)ED(nlöK
%0.2

)ED(giewhcsnuarB
%9.1

snoiger72-UErehtO
%9.07

)ES(mlohkcotS
%7.1

)TI(aidrabmoL
%7.1

Braunschweig  unites  tradition  and  high  technology,
outstanding infrastructure and an excellent location at
the heart of northern Europe. Its spectrum ranges from
biotechnology  via 
to
transportation technology. The Technical University as
well  as  the  numerous  internationally  renowned
research  establishments  are 
for
Braunschweig  having  a  very  high  proportion  of
business set ups in high-tech sectors.

financing  all 

the  way 

reason 

the 

The Braunschweig Region of Science links the various
high-tech  core  areas  of  competence  in  an  effective
way.  Scientific 
research
organisations 
establishments as well as companies all work together
closely.

and 

Source: http://www.braunschweig.de

able 2.9 

Top 15 EU regions in terms of  

R&D expenditure, as a percentage of  GDP, 

all sectors — 2003

Figure  2.8  shows  the  shares  of  the  leading  regions  in
EU-27 total R&D expenditure (calculated in EUR), while
Table 2.9 displays the leading regions in terms of their
R&D expenditure with respect to GDP (R&D intensity).

Regions

EU-27

% of 
GDP

EUR
million

% of
EU-27

1.87 s

187 708 s

100 s

In 2003, almost 30% of R&D expenditure in the EU-27
was concentrated in ten regions. Five of these regions
were  in  Germany  and  two  were  in  France.  Denmark
(the entire country is classified at NUTS 2 level) was the
fourth  leading  region  in  absolute  terms.  The  two
remaining  regions  in  the  top  10  were  Stockholm  (SE)
and Lombardia (IT)

Île  de  France  (FR)  was  in  the  lead  with  R&D
expenditure  accounting  for  7.7%  of  the  EU-27  total,
followed by Oberbayern (DE) and Stuttgart (DE). 

The regions with the highest R&D intensity were nearly
the  same  as  those  with  the  highest  concentration  of
R&D activity in terms of volume. To be exact, Stockholm
(SE) and three of the German regions that were in the
absolute  top  10  —  Braunschweig,  Stuttgart  and
Oberbayern — also belonged to the leading regions in
relative terms (Table 2.9). Moreover, the three German
regions are among the four leading European regions in
terms of R&D intensity.

With  R&D  intensity  amounting  to  8.7%  of  GDP,
Braunschweig  (DE)  clearly  came  first.  It  was  followed
by  Västsverige  (SE),  with  approximately  6%. All  other
European  regions  had  R&D  intensities  below  5%.
However,  all  15  leading  regions  were  above  the  3%
target set in the Lisbon strategy. As can also be seen in
Map  2.10,  this  was  true  for  about  twenty  European
regions,  of  which  nine  were  German,  four  Swedish,
three  Finnish,  two  Austrian  and  two  French.  East  of
England (UK), which is classified at NUTS 1 level, was
also one of  the regions with an R&D intensity of above
3%.

Braunschweig (DE)

Västsverige (SE)

Stuttgart (DE)

Oberbayern (DE)

Pohjois-Suomi (FI)

Stockholm (SE)

Östra Mellansverige (SE)

Sydsverige (SE)

Berlin (DE)

Tübingen (DE)

East Of England (UK)

Karlsruhe (DE)

Midi-Pyrénées (FR)

Etelä-Suomi (FI)

Länsi-Suomi (FI)

8.70

6.03

4.66

4.60

4.60

4.31

4.25

4.13

3.94

3.89

3.85

3.83

3.72

3.55

3.49

3 595

3 135

5 996

7 352

726

3 276

1 632

1 490

3 096

1 908

4 595

3 166

2 283

2 933

1 139

1.9

1.7

3.2

3.9

0.4

1.7

0.9

0.8

1.6

1.0

2.4

1.7

1.2

1.6

0.6

UK: NUTS level 1.
Exception to the reference year: East Of England (UK): 1999.

Four  of  the  top  15  regions  in  terms  of  R&D  intensity
were  quite  small  in  terms  of  volume  (making  up  less
than 1% of EU-27 total): Pohjois-Suomi (FI) ranked fifth,
Östra  Mellansverige  (SE)  seventh,  Sydsverige  (SE)
eighth and Länsi-Suomi (FI) 15th.

As shown in Map 2.10, only a few countries had at least
one region with an R&D intensity of above 2% of GDP.
In addition to Germany, France, Sweden and the United
Kingdom mentioned earlier, these also included Austria,
the Netherlands and the Czech Republic. Denmark and
Iceland  (the  entire  countries  are  classified  at  NUTS  2
level) also displayed shares in excess of 2%.



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Regional differences in R&D expenditure 
Implications for regional governance

Introduction

Increasing overall EU expenditure in R&D is one of the core elements of the Lisbon Strategy, embodied in the so-
called Barcelona target (3% of GDP should be spent on R&D, of which 2%-points should be private). Within the system
of Integrated Guidelines and open co-ordination, this EU-wide target serves as a reference value at the national and
regional level. 

Current regional differences in R&D expenditure (as a share of regional income) are vast. Most regions show R&D
expenditure well below the 3% level; only 21 out of 254 regions reach the 3% target (2002 figures). These regions can
be found in Germany (11), Finland (3), France, Austria and the UK (2 each), the Netherlands, Sweden and the Czech
Republic (1 each). Braunschweig (DE) leads (7.1%) followed by Pohjois-Suomi (FI, 4.2%), East of England (UK, 3.9%)
and Středni Ĉechy (CZ, 3.5%), Vienna (AT, 3.4%) and Île de France (FR, 3.4%). We find very low income proportions
spent on R&D in southern and eastern regions. Regional disparities are considerable both within the EU as a whole
as within Member States.

The main question the presentation deals with is whether it is sensible to reproduce the Barcelona target on a
regional scale. Does it make sense to expect each and every one of 254 (NUTS-2) regions in the European Union
to  spend  3%  of  their  regional  income  on  R&D?  The  answer  is  no.  Subsequently,  the  presentation  discusses  an
alternative approach to policies to enhance regional competitiveness, based on local-global interfaces.

Implications for regional governance within the Lisbon Strategy

In short, the Barcelona target is too simple and out of touch with the more complex economic reality. Our considerable
knowledge  of  that  reality  is  insufficiently  incorporated  into  the  Lisbon  policies.  What  does  this  imply  for  regional
governance?

The  Lisbon  strategy  uses  the  open  method  of  coordination  and  rests  heavily  on  benchmarking. Although  the  open
method  of  coordination  was  introduced  to  cater  for  diversity,  its  application  has  increasingly  led  to  the  set-up  of
regional  policies  with  similar  objectives,  similar  instruments  and  similar  policy  concepts.  In  Europe,  too  many
policymakers  try  to  simply  copy  the  success  of  well-known  best  practices  and  aim  for  regional  competitiveness  by
creating  favorable  conditions  for  the  formation  of  high-tech  clusters  in  the  field  of  information  technology,
biotechnology and nanotechnology. This disregards that only a limited number of regions can be expected to succeed
as high-tech regions. Most regions in Europe are either traditional industrial regions or peripheral agricultural regions.
Traditional industrial regions face the legacy of an economic mono-structure and have problems in socio-economic
conversion.  Peripheral  agricultural  regions  have  depended  on  agriculture  for  centuries  and  face  specific  problems
such as the outward emigration of young people.

Rather than jumping on the bandwagon of investing in high-tech clusters, low-tech regions should invest in policies
that face these specific problems and make the best possible use of their own competitive advantages.

However,  traditional  regional  policies  dealing  with  the  restructuring  of  “old  economies”  have  not  always  been
successful, for a number of reasons:

- they often involve a mixture of possibly conflicting goals (restructuring, employment, environment, regional
prestige); 
- they often do not cure the underlying problems, especially due to “subsidy addiction” which maintains inertia
and does not constitute an incentive for real reorientation.

How can we avoid these pitfalls? In current research on regional policy, attention is increasingly drawn to so-called
creative global-local interfaces through which local traditions are brought in line with global trends. An example of such
an interface is the Danish region of Jutland, which has successfully combined its local tradition in furniture-making with
the global trends of lifestyle and product quality, resulting in Danish design furniture. Another example is the French
region  North  Pas-de-Calais  which  has  combined  its  traditional  local  clothing  sector  with  the  global  trend  of
convenience  shopping  by  setting-up  mail  order  services. Yet  another  example  is  the  Polish  region  around  Krakow
where traditional building and painting know-how is combined with global sustainability trends, resulting in flourishing
restoration services. 

Interestingly enough, the new combinations mentioned above were effected from the bottom-up, involving local and
regional stakeholders (local firms, residents, universities, business associations and governments) rather than by a
subsidy-based top-down policy. Moreover, such combinations do not require cutting-edge technologies; they make an
intelligent use of existing opportunities. Rather than focusing on high-tech R&D most European regions should focus
on how to organise processes by which old crafts are combined with new tricks.

More information available on:
http://ec.europa.eu/regional_policy/conferences/od2006/doc/presentations/b/groenendijk_10b02.doc.

Source: Nico Groenendijk, Jean Monnet Chair of European Economic Governance, 
Centre for European Studies, University of Twente, 2006

40

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ap 2.10 

Total R&D expenditure as a percentage of  GDP, 

all sectors, NUTS 2 — 2003

Total R&D expenditure

as a % of GDP, all sectors,

2003 - NUTS 2

EU-27: 1.87 %

<= 1%

1% - <= 2%

2% - <= 3%

> 3%

Data not available

AT and CH: 2004; UK:1999
NO, CH: country level
BE, EL, FR9, UK: NUTS 1
NL, PT: estimates
DE22, DE23: confidential data
BG: R&D expenditures only available for BG41 of the NUTS 2007

regions

Statistical Data: Eurostat Database: REGIO
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat - GISCO, 09/2007

0

600 km

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0

100

0

10

Iceland

0 100



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03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 43

PART2

Chapter 3 - R&D personnel

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 44

3.1 Introduction

As  seen  in  Chapter  2,  Research  and  Development
(R&D)  activities  are  often  regarded  as  a  catalyst  for
economic  growth,  as  they  comprise  creative  work
undertaken on a systematic basis in order to increase
the  stock  of  knowledge,  including  knowledge  of  man,
culture  and  society,  and  the  use  of  this  stock  of
knowledge to devise new applications.

The quantity of R&D personnel is one of the two basic
R&D input indicators, the other being R&D expenditure. 

As it is a key element of knowledge, S&T dissemination
and  development,  the  R&D  personnel  indicator  has
become increasingly appreciate by policymakers. R&D
personnel  data  measure  the  human  resources  going
directly into R&D activities. R&D personnel includes all
persons  employed  directly  in  R&D,  as  well  as  those
providing  direct  services,  such  as  R&D  managers,
administrators and clerical staff.

Two  manuals  are  used  as  methodological  references
for R&D surveys:

- Proposed  Standard  Practice  for  Surveys  on 
Research and Experimental Development —
Frascati Manual, OECD, 2002.

-  The  Regional  Dimension  of  R&D  and 
Innovation  Statistics  —  Regional  Manual, 
Eurostat, 1996.

This  chapter  presents 
the  key  R&D  personnel
indicators as well as the main trends during the period
2000-2005. It is divided into two sections:

-  First,  the  main  trends  are  highlighted  at
national level, by examining the performance
of the EU-27 Member States, Iceland, Norway
and  the  candidate  countries.  This  part  also
level  by  making
looks  at 
comparisons with China, Japan and Russia.
-  Second,  R&D  personnel  is  analysed  at
regional  level,  by  focusing  on  the  regions  of
the  EU-27  Member  States,  Iceland  and
Norway.

the  global 

Two populations are measured in every  section of this
chapter:

- Total R&D personnel and its sub-population
- Researchers.

‘Researchers’, as defined as professionals engaged in
the conception or creation of new knowledge, products,
the
processes,  methods  and  systems,  and 
management  of  the  projects  concerned,  Frascati

in 

Manual  (paragraph  301),  are  possibly  the  most
important population in terms of R&D activities.

As  recommended  by  the  Frascati  Manual,  R&D
personnel  data  are  expressed  in  two  units:  full-time
equivalent (FTE) and head count (HC). 

- The FTE unit corresponds to one year’s work

by one person.

-  The  HC  unit  corresponds  to  the  number  of
individuals who are employed mainly or partly
on R&D.

For  the  purposes  of  comparison  between  different
regions and periods, the derived unit based on HC ‘as
a percentage of total employment’ is frequently used in
this chapter. 

Data  concerning  R&D  personnel  are  broken  down  by
the following institutional sectors: 

- the business enterprise sector (BES), 
- the government sector (GOV),
- the higher education sector (HES), 
- the private non-profit sector (PNP), and
-  all  sectors,  which  is  equivalent  to  the  sum  of

the four sectors.

In  addition 
breakdowns can be used, such as: 

to  sectors  of  performance,  other

- sector of economic activity,
- field of science.

The regional analysis is carried out at the NUTS 2 level.
Other levels of NUTS are used in certain instances for
particular countries, and  this  is specified  in each  case
by means of a footnote. Readers should also note that,
according to the NUTS classification, the entire national
territory of Denmark, Estonia, Cyprus, Latvia, Lithuania,
Luxembourg, Malta, Slovenia and Iceland is considered
as  a  NUTS  0,  1  or  2  region,  which  means  that  those
countries  as  a  whole  may  appear  in  rankings  at  the
NUTS 2 level.

The analysis refers to the period 2000-2005 (or 2004).
The  same  length  of  time  series  does  not  cover  all
countries.  In  general,  therefore,  when  data  for  the
reference year are not available for a particular country,
the latest year available is presented.

The complete R&D personnel time series are available
on Eurostat’s reference database NewCronos. Data for
China  and  Japan  are  taken  from  OECD  —  Main
Science and Technology Indicators (MSTI).

Head count (HC) vs full time equivalent (FTE)

Headcount (HC) data are the most appropriate measure for collecting additional information about R&D personnel.

However,  R&D  may  be  the  primary  function  of  some  persons  or  it  may  be  a  secondary  function.  It  may  also  be  a
significant part-time activity. To count only persons whose primary function is R&D would result in an underestimate
of the effort devoted to R&D; to carry out a headcount of everyone spending some 

time  on  R&D  would  lead  to  an  overestimate.  The  number  of  persons  engaged  in  R&D  must,  therefore,  also  be
expressed in full-time equivalents (FTE) on R&D activities.

More information on: http://www.uis.unesco.org/TEMPLATE/pdf/S&T/Workshops/CAsia/Almaty_7.pdf

44

Source: UNESCO Institute for Statistics (UIS), 2006



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 45

3.2 R&D personnel at the national level

R&D personnel as a percentage of  total employment

As  with  R&D  intensity  (chapter  2.2),  R&D  personnel
expressed as a percentage of total employment — R&D
personnel  intensity  —  also  enables  comparisons
between countries and regions (Figure 3.1). 

In  2004,  1.44%  of  total  EU-27  employment  was
connected  to  R&D  activities,  of  which  0.63%  of  a
percentage  point  was  in  the  business  enterprises
sector.

However,  R&D  personnel  intensity  varied  significantly
across countries. At 3.53%, Iceland displayed by far the
highest  share  of  persons  employed  in  R&D.  It  was
followed by Finland, the only EU Member State having
a share above 3%. R&D personnel made up more than
2% of total employment in three other Member States:
Sweden  (2.51%),  Denmark  (2.41%)  and  Luxembourg
(2.21%).  This  was  also  the  case  for  Norway  (2.27%)
and Switzerland (2.12%).

An  analysis  of  the  contribution  of  the  business
enterprise  sector  (BES)  to  R&D  personnel  intensity
reveals that this sector was relatively most important in
Luxembourg,  with  1.89  percentage  points,  and  in
countries  from  Northern  Europe,  such  as  Finland,
Denmark  and  Iceland  where  this  sector  represented
1.72, 1.47 and 1.41 percentage points respectively. 

The  relatively  low  contribution  from  BES  in  the  new
Member States (2004 and 2007 enlargements) may be
explained  by  the  fact  that  the  government  sector  has
traditionally  been  more  important  in  terms  of  R&D  in
those countries and that the business sector still needs
time to develop, as was corroborated by R&D intensity
data (Chapter 2).

igure 3.1 

R&D personnel (HC), in all sectors and the business enterprise sector, as a percentage of  total

employment, EU-27 and selected countries — 2004

srotcesllA

rotcesesirpretnessenisuB

SI

IF

ES

KD

ON

UL

HC

TA

ED

EB

RF

PJ

SE

72-UE

EI

LE

EE

LN

ZC

UH

RH

UR

TL

TI

IS

KS

LP

TM

TP

VL

YC

GB

OR

RT

66.0

26.0

54.0

73.0

5.0

0.0

35.3

42.3

15.2

14.2

72.2

12.2

21.2

89.1

58.1

18.1

37.1

66.1

94.1

44.1

04.1

33.1

23.1

23.1

82.1

72.1

62.1

52.1

51.1

41.1

80.1

20.1

29.0

09.0

68.0

18.0

UL

IF

KD

SI

ES

TA

PJ

ON

HC

EB

ED

RF

UR

LN

EI

72-UE

ZC

SE

IS

TI

EE

TM

LE

UH

KS

RH

TP

OR

YC

LP

VL

TL

GB

RT

98.1

27.1

74.1

14.1

12.1

30.1

00.1

00.1

69.0

39.0

39.0

58.0

08.0

17.0

66.0

36.0

75.0

25.0

94.0

73.0

92.0

92.0

92.0

32.0

12.0

12.0

91.0

81.0

71.0

21.0

11.0

90.0

90.0

40.0

0.1

5.1

0.2

5.2

0.3

5.3

0.4

%

0.0

2.0

4.0

6.0

8.0

0.1

2.1

4.1

6.1

8.1

0.2

%

EU-27: Eurostat estimation.
IE: Provisional data.
MT: Break in series.
NL (all sectors): National estimation.

Exceptions to the reference year:

2003: BE, DE, EL, LU, NL, PT, SE, IS, NO and JP;
2002: TR.



45

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 46

As can be seen in Table 3.2, the BES - with 0.63% of
total employment, or 44% of all personnel employed in
R&D  -  was  the  single  most  important  sector  for  total
R&D  personnel  intensity  in  the  EU-27.  However,  the
contribution  from  this  sector  did  not  exceed  20%  in
Bulgaria, Latvia, Lithuania and Poland. The same was
also true of Croatia and Turkey.

The  higher  education  sector  (HES)  came  just  behind
the BES, as the second most important sector in terms
of  R&D  personnel.  R&D  personnel  In  the  EU-27
accounted for 0.61% of total employment in this sector.
In  other  words,  approximately  42%  of  all  persons
employed  in  R&D  were  active  in  this  sector.  In  nine
Member  States  -  which  included  Estonia,  Spain  and
Poland,  for  example  -  more  than  half  of  total  R&D
personnel worked in the higher education sector.

The  government  sector  (GOV)  made  up  only  13%  of
total  R&D  personnel  in  the  EU-27  (0.18%  of  total
the  Czech
employment).  However, 
Republic,  Cyprus,  Lithuania  and  Romania,  more  than
one in five persons engaged in work related to research
were employed in the government sector.

in  Bulgaria, 

As  a  general  rule,  with  the  exceptions  of  Luxembourg
and Russia, HES always employed a significant share
of  R&D  personnel,  although  there  was  much  greater
variation  in  the  distribution  in  BES  and  GOV  across
countries

able 3.2 

R&D personnel (HC)  by sector of  performance, as a percentage of  total employment,

EU-27 and selected countries — 2002 to 2004

All sectors

Business enterprise sector

Government sector

Higher education sector

2002

2003

2004

2002

2003

EU-27

1.44 s

1.44 s

1.44 s

0.62 s

0.62 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

JP

RU

1.80

0.61

1.13

2.27

:

1.18

1.38

:

1.40

1.72 i

1.16

0.61

0.93

0.97

:

1.26 i

0.76

1.34 e

1.77

0.89

0.81 e

0.40

1.36

0.99

3.08

:

:

:

2.25

:

1.08

0.37

1.58

1.32

1.81

0.61

1.18

2.24

1.85

1.28

1.39

1.33

1.45

1.71 i

1.13

0.64

0.79

1.01

2.21

1.24 i

0.66

1.32 e

:

0.93

0.86

0.44

1.06

0.97

3.16

2.51

:

3.53

2.27

:

1.12

:

1.66

1.30

:

0.62

1.28

2.41

:

1.32

1.40 p

:

1.49

1.73 i

1.14

0.66

0.81

1.15

:

1.27 i

0.90 b

:

1.98

0.92

:

0.45

1.08

1.02

3.24

:

:

:

:

2.12

1.26

:

:

1.25

0.92

0.07

0.47

1.39

:

0.20

0.67

0.32

0.44 b

0.84

0.39

0.16

0.24

0.04

:

0.24

0.05

0.75

0.92

0.08

0.16 e

0.20

0.59

0.26

1.65

:

:

0.99

:

0.17

0.04

0.93

0.86

0.93

0.08

0.51

1.32

0.93

0.26

0.66

0.29

0.48

0.84

0.37

0.17

0.12

0.05

1.89

0.24

0.07

0.71

:

0.11

0.19

0.19

0.48

0.21

1.70

1.21

:

1.41

1.00

:

0.15

:

1.00

0.85

Footnote 'i':
FR, HU and SK: Defence excluded (all or mostly);
CH: Federal or central government only.

2004

0.63 s

0.92 p

0.09

0.57

1.47

:

0.29

0.66 p

0.52

0.85

0.37

0.17

0.11

0.09

:

0.23

0.29 b

0.84

1.03

0.12

:

0.18

0.49

0.21

1.72

:

:

:

1.05

0.96

0.21

:

:

0.80

2002

2003

2004

2002

2003

2004

0.19 s

0.19 s

0.18 s

0.61 s

0.62 s

0.61 s

0.09

0.40

0.29

0.17 b

:

0.17

0.09

:

0.19

0.23 i

0.18

0.24

0.16

0.25

0.25

0.30 i

0.17

0.17

0.16

0.21

0.15 e

0.10

0.31

0.21 i

0.42

:

0.08

:

0.29

0.04

0.32

0.04

0.11

0.39

0.10

0.39

0.28

0.19

0.24

0.19

0.09

0.21

0.20

0.21 i

0.19

0.22

0.15

0.23

0.29

0.29 i

0.03

0.20 b

:

0.19

0.14

0.11

0.21

0.21 i

0.42

0.13

0.08

1.12

0.29

:

0.36

:

0.11

0.39

:

0.38

0.28

0.18

0.24

0.18

0.09

:

0.22

0.21 i

0.20

0.21

0.14

0.23

:

0.29 i

0.04

0.19 b

0.15

0.17

:

0.11

0.21

0.19 i

0.42

:

0.08

:

:

0.04

0.41

:

:

0.38

0.77

0.14

0.37

0.70 b

:

0.80

0.61

:

0.76

0.62

0.56

0.16

0.53

0.68

:

0.71

0.54

0.77

0.14

0.38

0.72

0.69

0.81

0.64

0.82

0.76

0.63

0.55

0.18

0.53

0.73

0.03 e

0.71

0.57

0.40 e

0.41 e

0.68

0.60

0.40 e

0.11

0.44

0.53

0.97

:

:

:

0.97

1.06

0.60

0.29

0.51

0.07

:

0.63

0.42

0.14

0.36

0.55

1.02

1.16

:

0.85

0.97

:

0.62

:

0.52

0.07

:

0.15

0.42

0.74

0.68

0.82

0.65

:

0.75

0.64

0.55

0.22

0.56

0.82

:

0.75

0.57

:

0.78

0.63

:

0.15

0.37

0.62

1.07

:

:

:

:

1.13

0.65

:

:

0.06

46



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 47

in 

R&D  personnel 
the  EU-27  expressed  as  a
percentage  of  total  employment  increased  between
2000  and  2004,  at  an  annual  average  growth  rate
(AAGR) of 1.20% — Figure 3.3.

In  comparison,  EU-27  R&D  intensity  —  i.e.  R&D
expenditure  as  a  percentage  of  GDP  —  decreased
between 2000 and 2005 (Chapter 2).

However,  six  Member  States  —  Sweden,  Greece,  the
Netherlands,  Slovakia,  Latvia  and  Slovenia  —  also
witnessed  a  relative  decline  in  R&D  personnel
compared to total employment. This was also the case
in Switzerland and Russia.

Belgium  and  Bulgaria  recorded  a  positive  annual
average growth rate of 0.59% and 0.56% respectively,
which was below the EU-27 average.

Among  the  countries  with  an  annual  average  growth
rate below that of the EU-27, only Belgium, Switzerland
and Sweden had a R&D personnel intensity higher than
the EU-27 average (1.44%).

All  other  Member  States  saw  their  shares  of  R&D
personnel  in  total  employment  grow  faster  than  the 
EU-27 average.

Moreover,  some  of  those  Member  States,  such  as
Finland, Denmark and France, also had relatively more
personnel  employed  in  R&D  than  the  EU-27  average.
This means that those countries were not only among
the  leaders  in  terms  of  R&D  personnel  intensity,  but
also  enhanced  their  leadership  during  the  period
shown. This was also the case for Norway and Japan.

Finally,  eleven  EU  Member  States  and  two  candidate
countries were below the European average in terms of
R&D personnel intensity, but, as their AAGR was higher
than the EU-27 average, these countries were catching
up.

Malta  (8.8%),  Croatia  (8.1%)  and  Romania  (6.4%)
witnessed the highest growth rates not only among the
above eleven countries, but also compared to the rest.

igure 3.3 

R&D personnel (HC) as a percentage of  total employment in 2004 and annual average growth rate

(AAGR) of  this share 2000-2004, EU-27 and selected countries

9

6

3

0

3-

6-

0.0

4002-0002RGAA

TM

44.1=72-UE

RH

OR

RT

GB

YC

TP

SE

EE

ZC

EI

TL

LP

TI

UH

PJ

RF

EB

KS

VL

LE

LN

UR

IS

TA

KD

ON

ES

HC

IF

02.1=72-UE

5.0

0.1

5.1

0.2

5.2

0.3

5.3
tnemyolpmefo%salennosrepD&R

AAGR is calcultated on R&D personnel expressed as a percentage of total employment.
Exceptions to the reference year:

Exceptions to the reference period:

2003: BE, EL, PT, SE, NO and JP;
2002: TR.

EU-27: Eurostat estimation.
MT: break in series.
IE: provisional data.
NL: national estimation.
FR: Defence excluded (all or mostly).

2000-2002: TR;
2000-2003: PT;
2001-2003: EL, SE, NO, JP;
2001-2004: ES;
2002-2003: BE and NL;
2002-2004: MT, AT and HR.



47

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 48

R&D personnel in full time equivalent

Counted  as  full  time  equivalent  (FTE),  more  than 
2  million  persons  in  the  EU  were  employed  in  R&D
activities  in  2004.  More  than  half  of  these  (1.1  million
FTE) were employed in the business enterprise sector
(BES).  Whereas  the  higher  education  sector  (HES)
counted  642  000  FTE  and  the  government  sector
(GOV) 312 000 FTE employed in R&D, The remainder,
21  000  FTE,  were  employed  in  the  private  non  profit
sector.

Germany  alone,  with  473  000  FTE,  made  up  23%  of
total  EU-27  R&D  personnel  counted  as  FTE.  German
dominance was even more noticeable in the business
enterprise  sector,  with  27%  of  EU-27's  total  FTE  in
R&D.

Whichever the sector, Germany led in absolute terms,
followed  by  France.  Generally,  Spain  and  Italy  ranked
third or fourth.

On average, only 30.6% of the FTE employed in R&D
in the EU in 2004 were female. Nevertheless, in three
Member  States  -  Bulgaria,  Latvia  and  Lithuania  -
women counted as FTE were in the majority.

With the exception of Sweden, the share of female R&D
personnel in FTE was even lower in the BES than it was
in all sectors. 

The overall trend of EU-27 R&D personnel expressed in
FTE reveals growth between 2000 and 2004 both for all
sectors and for the BES at an annual rate of 1.5%. R&D
personnel in the HES also increased, but more rapidly
(2.6%); however, it declined in the GOV (-0.7%).

able 3.4 

R&D personnel in FTE and percentage of  women in 2004 and

annual average growth rate (AAGR) 2000-2004, by sector of  performance,

EU-27 and selected countries 

All sectors

Business enterprise sector

Government sector

Higher education sector

R&D PSL
in FTE

% of
women

AAGR
2000-2004

R&D PSL
in FTE

% of
women

AAGR
2000-2004

R&D PSL
in FTE

% of
women

AAGR
2000-2004

R&D PSL
in FTE

% of
women

AAGR
2000-2004

EU-27

2 089 675 s

30.6 s

1.5 s

1 114 016 s

21.4 s

1.5 s

312 422 s

41.0 s

-0.7 s

642 266 s

41.0 s

2.6 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

JP

RU

52 256

15 647

28 765

41 607

472 533

4 735

15 713

31 849

161 933

352 485

164 026

1 017

5 103

10 557

4 318

22 826

30.3

52.7

30.6

36.8

27.2

47.5

29.5

34.8

37.4

:

33.7

39.0

56.5

52.4

:

:

717 b

26.5 b

91 594 p

42 891

78 362

25 529

33 361

7 132

14 329

58 281

72 978

:

2 940

29 748

52 250

11 162

28 964

882 414

951 569

:

23.6

28.9

45.5

46.7

36.4

44.8

:

18.1

:

38.9

:

:

49.6

31.5

:

:

-1.8

0.6

4.4

3.2

-0.7

6.3

5.3

2.6

7.6

1.9

2.2

10.6

-1.6

-2.7

4.2

-0.8

22.8

1.0

5.0

-0.2

4.0

-0.4

-4.5

-1.5

2.6

0.1

:

3.6

3.2

0.0

-7.2

3.6

-0.5

-1.4

31 375

2 158

15 064

27 230

298 072

1 083

9 650

11 608

71 123

197 223

67 519

224

881

981

3 655

6 704

22.4

47.9

21.4

32.9

18.5

29.3

22.6

19.2

28.4

:

18.5

33.5

52.4

43.5

:

:

-4.0

0.2

6.9

4.3

-1.1

26.9

2.6

1.9

10.9

2.6

1.3

11.7

-10.4

14.6

2.3

0.9

383 b

17.8 b

126.0

49 915

29 143

12 978

6 124

16 368

3 855

3 473

32 612

48 113

151 908

1 352

16 263

33 085

2 831

5 918

580 628

568 173

:

15.7

13.7

29.1

43.7

32.4

36.8

:

25.1

:

34.1

:

:

46.6

22.8

:

:

1.2

4.4

-8.6

14.7

-7.7

-1.6

-9.5

2.6

-1.6

1.1

5.6

3.1

-2.2

6.7

-0.9

-0.1

-2.5

3 757

10 384

7 422

3 439

73 867

810

1 222

5 101

27 166

51 931 i

32 401

352

1 013

3 041

512

7 595

45

13 579

2 035

19 685

4 917

9 853

1 750

32.7

57.6

43.0

43.0

37.0

63.8

38.5

41.9

48.6

:

43.0

44.1

62.4

53.3

:

:

59.6

:

40.9

25.3

58.3

52.3

44.3

3 493 i

52.5 i

7 337

3 000

20 796

775

4 985

810 i

3 634

5 502

61 893

282 422

:

33.7

37.3

38.2

:

:

51.3

23.1

:

:

1.2

-0.7

0.9

-13.2

1.8

-3.9

-4.0

4.0

4.9

-0.7

0.9

0.3

-4.0

-11.6

14.0

-1.9

-42.1

1.8

-0.6

1.1

-6.5

6.8

-9.1

-4.4

0.1

2.8

-8.5

4.1

1.5

-2.5

9.5

16.3

1.5

0.5

16 532

3 036

6 104

10 697

100 594

2 752

4 841

14 947

63 331

97 036

60 694

368

3 208

6 535

151

8 527

288

28 100 p

11 502

45 572

11 147

6 917

1 482

7 285

17 822

21 495

:

728

8 500

18 355 e

4 697

17 544

224 049

99 402

43.8

39.6

38.0

44.1

45.7

49.1

40.9

44.2

42.5

:

44.5

34.8

55.7

53.2

:

:

32.8

:

39.9

34.9

49.0

45.4

38.1

45.2

:

:

:

46.0

:

:

50.1

37.1

:

:

1.7

5.9

3.4

8.8

-1.2

4.5

16.8

2.4

6.4

1.9

2.6

28.0

2.6

1.1

60.0

-1.0

4.3

1.2

7.9

2.4

4.4

16.3

-4.0

5.6

3.6

3.4

:

0.2

4.3

4.8

-20.6

-0.6

0.0

AAGR is calcultated on R&D personnel expressed in FTE.
Exceptions to the reference year:

2003: BE, DK, DE, PT, SE, IS and JP;
2002: TR.

Footnote 'i':
FR and SK: Defence excluded (all or mostly);
CH: Federal or central government only.

Exceptions to the reference period:

2000-2002: TR;
2000-2003: IS and JP;
2001-2004: BE, SE and NO;
2002-2004: MT, AT and HR.

48



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 49

R&D personnel in head count

In  terms  of  head  count  (HC),  EU-27  R&D  personnel
reached  almost  3  million  persons,  remaining  fairly
stable between 2002 and 2004. 

The  leading  countries  in  terms  of  R&D  personnel
expressed in HC were the same as for R&D personnel
expressed  in  FTE:  namely  Germany,  followed  by
France, Italy and Spain.

With a headcount of 1.3 million, the business enterprise
sector  (BES)  again  accounted  for  the  largest  share  of
R&D personnel in the EU-27, but the higher education
sector (HES) followed more closely behind than when

R&D  personnel  is  expressed  in  full  time  equivalent
(Table  3.4).  This  indicates  that  a  larger  share  of  R&D
personnel is employed part-time in the HES than in the
BES.

The higher education sector made up more than half of
total R&D personnel in Estonia, Greece, Spain, Latvia,
Lithuania, Hungary, Malta, Poland and Slovakia.

With 378 000 persons employed (HC) in R&D activities,
the  government  sector  clearly  lagged  behind  the  BES
and the HES at EU-27 level.

able 3.5 

R&D personnel in head count (HC), by sector of  performance,

EU-27 and selected countries — 2002 to 2004

All sectors

Business enterprise sector

Government sector

Higher education sector

2002

2003

2004

2002

2003

2004

2002

2003

2004

2002

2003

2004

EU-27

2 929 502 s

2 949 477 s

2 964 172 s

1 269 744 s

1 269 009 s

1 299 993 s

383 358 s

385 011 s

378 094 s

1 243 543 s

1 262 242 s

1 256 645 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

JP

RU

73 187

16 847

53 695

61 915

73 629

17 400

55 699

60 525

:

664 731

6 921

24 486

:

232 019

412 938 b

253 084

1 937

9 153

13 540

:

7 600

25 194

56 708

249 969

415 061

249 889

2 102

8 002

14 534

4 135

:

18 025

60 148

65 994

:

7 882

26 584

:

267 943

421 312

255 535

2 235

8 273

16 436

:

48 727 i

48 681 i

49 615

1 121

975

1 329 b

109 224 e

106 980 e

65 725

122 987

41 601 e

38 433

12 379

21 025

73 121

:

:

4 970

51 086

:

16 515

79 958

:

126 241

44 036

39 985

9 506

20 928

74 773

108 146

:

5 466

51 175

:

17 216

:

1 032 826

1 081 099

:

74 191

127 356

40 725

10 155

22 217

76 687

:

:

:

:

84 090

19 739

:

:

37 534

1 866

22 361

37 837

37 812

2 398

24 122

35 726

:

333 285

1 164

11 960

13 218

73 461 b

200 961

85 687

511

2 346

553

:

9 428

75

61 514

34 020

11 312

19 088

5 330

5 425

39 239

:

:

1 810

22 436

:

2 524

9 107

1 529

12 037

12 259

82 327

203 264

81 189

567

1 228

781

3 533

9 438

97

57 442

:

15 035

9 882

17 232

4 278

4 545

40 089

52 346

:

2 193

22 572

:

2 237

:

35 306

39 499

126 275

54 358 bi

50 690 i

51 284 i

148 830 b

39 343

42 610

44 061

122 358

38 112 p

2 544

26 967

40 346

:

1 735

12 800

:

92 888

206 955

81 822

571

1 135

1 309

:

8 870

3 846

11 039

13 508

4 759 b

:

980

1 609

:

31 536

3 916

10 977

13 357

5 010

84 695

1 145

1 657

9 148

:

11 053

13 220

4 882

87 586

1 099

1 609

:

750

1 580

3 504

478

724

1 472

3 301

548

705

1 443

3 330

:

11 767 i

11 474 i

11 483

428 b

68 286

38 737

16 846

251

13 924

6 010

28 543

:

7 876 e

16 601

4 638

4 642

40 674

:

:

:

23 865

37 820

3 233

:

:

9 111

2 826

4 402 i

10 064

:

23 400

1 299

6 650

1 635 i

4 858

8 644

70 342

37

15 957 b

:

25 390

7 273

9 641

1 926

4 458 i

9 903

5 521

22 761

1 740

6 642

:

5 487

:

72 367

52

15 137

5 531

23 578

10 162

2 022

4 046 i

9 943

:

22 579

:

:

1 595 i

6 398

:

:

31 205

3 913

17 577

18 929 b

31 284

3 920

17 877

19 406

:

4 338

19 725

20 348

:

246 751

242 128

32 793 e

33 581 e

4 694

10 917

:

494

5 220

9 483

:

27 532

795

25 072

83 011

10 234

4 013

11 192

23 126

:

:

1 468

22 000

41 955 e

9 133

62 207

4 813

11 500

35 088

131 725

153 131

120 736

601

5 302

10 452

54 e

27 769

841

:

85 745

21 488

12 859

3 265

11 917

24 049

49 909

:

1 323

21 961

:

9 492

:

4 894

12 175

:

135 027

155 347

123 266

757

5 694

11 797

:

29 262

849

:

29 358

86 823

:

13 739

3 450

13 442

25 298

:

:

:

:

44 675 e

10 108

:

:

:

8 352 e

:

20 300 e

870 878 i

858 470 i

839 338 i

568 628 i

558 668 i

537 473 i

257 462 i

256 098 i

258 078 i

44 135 i

43 120 i

43 414 i

609 694

653 380

331 499

335 983

Footnote’i':
FR, HU and SK: Defence excluded (all or mostly);
CH: Federal or central government only;
RU: Underestimated or based on underestimated data.



49

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 50

Researchers in full time equivalent — FTE

Among  all  the  persons  employed  (FTE)  in  R&D
activities in the EU-27 (Table 3.4), more than 60% were
classified  as  researchers  (Table  3.6).  They  accounted
for 1.28 million FTE in 2005. 

In  a  global  comparison,  the  number  of  researchers  in
Russia in the same year was 465 000 FTE and in China
and  Japan  in  2003  it  amounted  to  862  and  675  000
respectively. 

Among  the  EU  Member  States,  Germany  (with 
271 000)  ranked  first  in  terms  of  researchers  followed
by France (200 000 in 2004).

More  than  half  of  the  EU  researchers  (627 000
thousand FTE) were in 2005 employed in the business
enterprise  sector.  The  second  largest  employer  of
researchers was the higher education sector (460 000
FTE).  At  the  same  time,  the  government  sector
employed only 176 000 researchers.

able 3.6 

Researchers in FTE, by sector of  performance, 

EU-27 and selected countries — 2003 to 2005

All sectors

Business enterprise sector

Government sector

Higher education sector

2003

2004

2005

2003

2004

2005

2003

2004

2005

2003

2004

2005

EU-27

1 206 766 s

1 248 608 s

1 277 090 s

585 487 s

609 407 s

627 473 s

169 813 s

172 102 s

176 250 s

437 025 s

453 796 s

459 661 s

31 465 p

31 953 p

16 242

16 322 p

16 266 p

2 124 p

2 238 p

12 389

12 742 p

13 168 p

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

CN

JP

RU

2 026

6 113

4 833

2 337

6 168

4 661

2 287

6 076

6 113 b

2 029 p

2 193

4 318

7 669

165 019 e

38 719

42 646

40 100 e

68 243

30 917

9 589

15 809

24 882

9 827

16 300

26 167

10 053

24 169 b

28 187 p

1 225

6 558

14 734

268 942

270 649

271 119 e

161 980

3 017

10 039

15 631

92 523

192 790

70 332

490

3 203

6 606

1 949

3 369

11 010

:

100 994

200 064

72 012

583

3 324

7 356

2 031

3 331

11 487 p

17 024 p

109 753 p

:

:

644 p

3 282

7 637

2 091 p

15 180 i

14 904

15 878

505

6 012

4 295

27 581

100 646

26 866

103

464

442

1 594

4 482

1 239

7 297

15 877

162 239

661

6 300

:

32 054

106 439

27 594

108

448

484

1 546

4 309

276

37 282

:

58 595

20 242

20 965

3 775

9 627

:

48 186

:

1 917

20 989

:

5 861

:

862 108

675 330

487 477

436 b

:

25 955

60 944

442 p

:

28 207 e

62 162

20 623 p

21 003 p

21 257

4 030

10 718

41 004

48 784

:

:

21 163

25 400

7 140

:

:

:

22 958

3 834

10 921

39 582

54 175 b

:

2 155

21 653

:

:

:

:

:

477 647

464 577

51 p

199 b

19 399

:

6 829

3 794

9 920

1 516

1 914

:

28 403 i

99 352

836

23 158

16 508

8 334

3 954 p

9 092

1 657

1 815

23 397

28 295 i

96 747

:

11 480 i

11 063 i

10 692 i

:

913

:

484 164

458 845

267 850

12 640

1 015

:

:

:

:

:

:

:

:

257 621

237 959

1 157

10 353 b

17 664 p

883

6 768

4 328 p

35 521 p

:

28 297 p

130 p

468

716

1 532 p

5 008

189 p

22 666 p

17 940 e

9 412

4 114 p

10 319

1 901

1 947

21 967

95 052

1 012

478

553

2 136

15 489

24 541 i

13 976

109

517

1 686

325

4 741 i

9

7 672 b

:

486

559

:

17 151

24 779 i

14 237

104

490

1 676

342

4 693

19

7 752

1 030

3 440

6 043

1 044

2 436 i

:

9 445

467

3 258 i

:

2 158

:

191 957

33 711

146 370

6 326

1 124

2 345 i

4 200

2 345 i

9 205

3 300 i

425 i

2 420

:

:

:

474

419

2 307 p

20 240 p

:

14 428 p

107 p

589

1 805

1 974

3 474

9 072

49 196

64 403

27 774

256

2 222

4 478

383 p

30 e

4 959

28

7 034

1 119 e

7 082

1 160

2 503 i

4 374

:

501

3 449 i

:

:

:

:

:

5 957

216

10 211

:

38 455

10 062

4 941

1 178

5 273

:

:

562

6 251

:

2 790

:

185 987

172 396

71 174

13 233

12 804

12 175

3 194 p

2 948 p

36 697 bi

2 287 i

3 018 bi

17 146

2 362

4 274

7 846

65 764

2 162

4 151

:

51 616

65 498

28 226

349

2 385

5 196

143

5 902

218

:

8 281

39 716

2 607

7 575 b

8 287 p

66 000 e

1 905

4 300 p

10 251 p

53 779 p

:

:

375 p

2 224

5 116

176 p

5 911

225

:

8 999 e

40 449

10 600 p

11 138 p

5 654

1 204

6 509

13 037

17 794

:

:

6 800

12 335 e

3 705

:

:

:

5 386

742

6 458

12 879

14 210 b

:

585

7 512

:

:

:

:

:

70 844

70 494

147 896

154 827

Footnote ‘i’:
FR, HU and SK: Defence excluded (all or mostly);
SE, NO and TR: University graduates instead of reseachers;
CH: Federal or central government only.

Importance and rationale of the 'People' programme in the Seventh research Framework
Programme (FP7)

'Abundant  and  highly  trained  qualified  researchers  are  a  necessary  condition  to  advance  science  and  to  underpin
innovation, but also an important factor to attract and sustain investments in research by public and private entities.
Against the background of growing competition at world level, the development of an open European labour market
for researchers free from all forms of discrimination and the diversification of skills and career paths of researchers
are crucial to support a beneficial circulation of researchers and their knowledge, both within Europe and in a global
setting. Special measures to encourage young researchers and support early stages of scientific career, as well as
measures to reduce the 'brain drain', such as reintegration grants, will be introduced.'

More information available on: http://cordis.europa.eu/fp7/people/home_en.html

50

Source:CORDIS, 2007



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 51

As  can  be  seen  from  Table  3.6,  the  number  of
researchers counted as FTE in the EU-27 increased in
every  sector  between  2000  and  2005.  The  annual
average  growth  rates  for  all  sectors  and  the  BES
reached 2.9% and 3.8% respectively (Figure 3.7). 

In  an  international  comparison,  Japan  and  China  also
displayed  positive  rates  of  growth  in  their  numbers  of
researchers. The share of researchers even increased
more rapidly in China (7.4%) than in the EU-27 (2.9%).
Growth  in  Japan  (1.4%),  was  weaker.  Similar  trends
can be found for the BES.

Looking at the national level for all sectors, the highest
growth  rates  were  recorded  in  Malta  (17.6%),  Cyprus

(16.3%),  the  Czech  Republic  (11.8%)  and  Denmark
(9.7%). Also Spain had a relatively high growth rate of
7.4%, which was the same as for China. 

In the BES, the highest growth rates were found in the
smaller Member States such as Malta (59.0%), Estonia
(26.4%) and Lithuania (20.0%).

In  four  of  the  countries  shown,  the  number  of
researchers declined both in all sectors and in the BES.
The  greatest  decline  for  all  sectors  was  recorded  by
Croatia  with  an  AAGR  of  -8.7%  and,  for  the  BES,  in
Latvia with -14.0 %.

igure 3.7 

Annual average growth rate (AAGR) of  researchers in FTE, all sectors and

business enterprise sector, EU-27 and selected countries — 2000-2005

srotcesllA

rotcesesirpretnessenisuB

0.95

4.62

0.02

3.61

4.31

8.11

0.11

0.11

TM

YC

ZC

KD

NC

SE

EI

TA

UL

TP

EE

LE

ES

RF

SI

72-UE

LP

OR

TI

UH

RT

ON

KS

PJ

GB

ED

EB

TL

HC

UR

IS

VL

LN

RH

KU

6.71

3.61

8.11

7.9

4.7

4.7

2.6

4.5

9.4

6.4

6.4

3.4

2.4

8.3

8.3

9.2

4.2

3.2

2.2

0.2

0.2

9.1

9.1

4.1

2.1

0.1

9.0

TM

EE

TL

KD

ZC

TP

SE

YC

NC

ES

RF

IS

LE

UH

SI

TA

72-UE

EI

PJ

LN

KU

UL

TI

ED

GB

RT

EB

LP

ON

UR

OR

KS

HC

1.7

1.7

6.6

0.6

1.5

4.4

9.3

8.3

7.3

9.2

5.2

1.2

8.1

6.1

5.1

3.0

1.0-

5.0-

8.0-

4.1-

9.3-

1.4-

3.4-

1.6-

RH

0.01-

VL

0.41-

4.0-

7.0-

7.1-

4.2-

0.3-

0.4-

7.8-

01-

5-

0

5

01

51

%

01-

5-

0

5

01

51

02

52

%

EU-27: Eurostat estimation.
CZ and SE: Break in series.
DE and AT: National estimations.
BE, DK, IE, EL, CY, LU, MT and PT: Provisional data.
FI: AAGR was not calculated because data is available only for 2004-2005.

Exceptions to the reference period:

2000-2002: TR;
2000-2003: NL (all sectors), CN and JP;
2000-2004: FR, IT (all sectors) and CH;
2001-2005: DK, EL (all sectors), SE, IS and NO;
2001-2004: ES;
2002-2004: HR;
2002-2005: MT and AT.



51

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 52

Researchers by gender

Figure  3.8  shows  the  share  of  female  researchers
measured as head count (HC), both for all sectors and
for the business enterprise sector (BES).

With  28.3%  and  18.4%  of  all  sectors  and  the  BES
respectively, women still are underrepresented among
the EU-27's researchers

in  which 

the  only  country 

Latvia  was 
female
researchers,  with  a  52.8  %  share  in  all  sectors,  were
more common than male. Six other Member States —
Lithuania,  Bulgaria,  Portugal,  Portugal,  Romania  and
Estonia  —  recorded  a  share  of  female  researchers  of
over  40%.  Apart  from  Portugal,  these  were  all  new
Member  States  (2004  and  2007  enlargements).  The
share  of  female  researchers  also  exceeded  40%  in
Croatia and in Russia.

At  the  other  end  of  the  scale  are  the  Netherlands,
Luxembourg and Germany, where females account for
less than 20% of all researchers. This share was even
lower in Japan (11.5%).

The  business  enterprise  sector  displayed  a  similar
pattern.  However,  shares  of  female  researchers  were
always  lower  in  this  sector  by  comparison  with  all
sectors.  This  was  true  both  for  the  EU-27  and  for  all
individual countries for which data are shown.

For  some  countries,  the  share  of  female  researchers
was markedly lower in the BES compared to all sectors.
This  was  notably  the  case  of  Estonia,  Lithuania,
Portugal and Poland. 

igure 3.8 

Percentage of  female researchers (in HC), all sectors and business enterprise sector, 

EU-27 and selected countries — 2004

srotcesllA

rotcesesirpretnessenisuB

VL

TL

GB

TP

UR

OR

EE

KS

RH

SI

LP

LE

SE

RT

UH

IS

YC

EI

TI

ON

IF

ZC

72-UE

EB

KD

RF

HC

TA

TM

ED

UL

LN

PJ

ES

8.25

6.84

2.64

3.44

9.24

7.24

5.24

2.14

1.14

4.93

9.83

1.73

1.63

6.53

5.43

5.23

1.23

0.03

9.92

4.92

0.92

5.82

3.82

1.82

1.82

8.72

7.62

6.32

6.32

5.91

4.71

2.71

6.11

VL

GB

UR

OR

RH

LE

TL

SI

KS

TP

SE

ES

LP

RT

IS

KD

EE

UH

YC

HC

RF

EI

ON

EB

ZC

TI

TM

5.05

1.54

8.14

5.14

9.83

1.63

3.33

0.33

3.23

7.92

5.62

2.52

1.52

0.52

9.42

5.42

9.32

8.32

6.22

1.12

7.02

6.02

0.02

9.91

6.91

5.91

5.91

72-UE

IF

UL

TA

ED

LN

PJ

4.81

0.71

2.41

6.21

6.11

7.8

6.6

0

01

02

03

04

05

06

%

0

01

02

03

04

05

06

%

EU-27: Eurostat estimation.
BE (BES): Provisional data.
LU and NL (all sectors): National estimations.
MT: Break in series.
SE (BES) and NO: University graduates instead of researchers.
RU: Underestimated or based on underestimated data.

Exceptions to the reference year:

2003: BE (all sectors), DK, DE, EL, LU, NL, PT, SE, IS, NO (all sectors) and JP;
2002: TR.

52



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 53

Women in science: Under-represented and under-measured

Women account for a minority of the world’s researchers. This is particularly the case in higher-income countries. The
higher percentage of industrial research in these countries provides only a partial explanation of the low degree of
women’s participation in research. A more gender-balanced workforce is found in Eastern Europe and the CIS, Latin
America and the Caribbean, as well as some South East Asian countries.

Overall,  the  under-representation  of  women  in  research  activities  can  be  traced  back  to  education  systems,
particularly at the higher levels. Although female participation in higher education has increased globally over the last
decade, it remains weak in the most advanced degree programmes.

It is therefore of foremost importance to further analyse other aspects hindering women’s access to, continuity and
advancement in research positions. This involves issues related to stereotyping, working conditions (the “work/life”
balance), labour market conditions, governance and the role of researchers in society.

More information available on: http://www.uis.unesco.org/template/pdf/s&t/BulletinNo3_v12EN.pdf

Source: UNESCO Institute for Statistics (UIS), 2006

Women at the heart of the European research agenda

Gender equality means putting men and women on an equal footing. In an ideal world, this would mean no specific
allowances for women would need to be made in research agendas. However, given the substantial gender imbalance
in the sciences – women make up half the student population, but hold only 15% of senior academic positions – clear
allowances need to be made to promote a healthier gender equilibrium.

The current disequilibrium jeopardises Europe’s bid, in the context of its landmark Lisbon Strategy, to forge the world’s
leading knowledge-based economy. The EU is moving ahead to boost investment in R&D to 3% of its collective gross
domestic product (GDP). This is likely to involve the creation of some 700 000 new research-related jobs by 2010 –
which Europe will have trouble filling as long as half of its population remain sidelined in the S&T field.

Traditionally, research agendas have not taken the specific needs of women into account. However, if society is to
develop a better understanding and acceptance of the developments in science and technology, specific measures
must be taken to address both the under-representation of women in science, and the lack of attention paid to gender
differences within research.

More information available on: http://ec.europa.eu/research/science-society/index.cfm?fuseaction=public.topic&id=27

Source: European Commission, 2007



53

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 54

Researchers by economic activity

Table  3.9  provides  an  overview  of  the  breakdown  of
business enterprise researchers in full time equivalents
(FTE) classified by sector of economic activity (NACE).
In terms of number of researchers, manufacturing was
by far the most important sector of economic activity in
2004 in the EU-27. It made up 70.0% of the entire BES,
followed by ‘Services’ with approximately 27.5%. All the
other sectors together made up 2.4%.

With  almost  143  000  researchers,  Germany  led  in
‘Manufacturing’, while the United Kingdom ranked first
in ‘Services’ with 28 000 researchers.

Not  only  leading  in  absolute  terms  of  the  countries
shown, Germany also ranked first in relative terms ,with
88%  of  business  enterprise 
in
‘Manufacturing’. Only France also had a share of over
80%  of  BES  researchers  in  this  sector  of  economic
activity.

researchers 

However, the distribution varied across Member States.
In nine of the Member States, Norway and Croatia, the
services  sector  employed  more  researchers  than  did
the manufacturing sector.

In  Romania,  other  sectors  of  activity,  mainly
‘Agriculture’ and, to a lesser extent, ‘Mining’, accounted
for a significant share of business researchers.

able 3.9 

Business enterprise researchers in FTE, by economic activity (NACE Rev 1.1),

EU-27 and selected countries — 2004

Total

Agriculture, hunting, 
forestry and fishing

Mining and 
quarrying

Manufacturing

Electricity, gas 
and water supply

Construction

Services

EU-27

BE

BG

CZ

DK

DE

EE

IE

GR

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

CH

HR

TR

609 407

16 322 p

1 239

7 297

15 877

161 980

661

6 300

4 295

32 054

106 439

27 594

108

448

484

1 546

4 309

47

23 158

16 508

8 334

3 794

9 092

1 657

1 815

23 397

28 403 i

96 747

11 063 i

12 640

1 015

3 697

5 398

186 i

:

28

78

215

0

8

10

233

1 145

:

2

:

:

:

95

:

211

13

0

24

1 305

0

48

3

98

1 000

76

:

21

61

2 117

33 i

:

1

:

54

:

2

13

45

436

94

0

:

6

:

3

0

336

10

2

2

718

25

0

22

42 i

:

433

:

0

45

426 748

10 699 i

462

3 654

9 414

142 537

314

3 290

1 960

15 366

85 245

17 071

47

176

364

:

2 859

30

14 044

11 458

3 872

1 414

5 644

1 272

464

18 516

21 567 i

:

4 570

9 365

222

2 715

4 021

116 i

0

3

:

421

17

0

2

185

1 725

88

3

:

2

:

69

1

152

42

14

14

501

0

:

27

121 i

:

35

:

:

20

3 388

368 i

167 735 s

5 211 i

0

53

62

215

:

0

19

701

408

39

1

11

:

:

13

0

746

81

0

56

68

0

:

109

:

:

119

:

23

4

773

3 558

6 287

18 540

327

3 000

2 290

15 524

17 479

10 302

56

261

112

:

1 270

16

7 669

4 904

4 447

2 283

856

360

1 297

4 720

6 575 i

28 000

5 830

3 275

749

852

EU-27: Distribution by sector of activity is estimated on the basis of available Member States. 
Exceptions to the reference year:

2003: DE, EL, PT and SE;
2002: TR.

Footnote ‘i':
BE: Unrevised breakdown not adding to the revised total;
SE and NO: University graduates instead of researchers.

54



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 55

Researchers by field of  science

Table  3.10  provides  an  insight  into  the  breakdown  of
researchers  in  the  government  (GOV)  and  higher
education (HES) sectors by fields of science (FOS).

In  2004,  ‘Natural  sciences’  (28.5%)  accounted  for  the
largest  share  of  researchers  from  the  two  sectors  in
EU-27. This was also true for each individual country for
which  data  are  available,  with  the  exceptions  of
Denmark,  Spain,  Malta  Romania  and  Norway.  Cyprus
(44.4%) and Italy (41.4%) had the highest proportion of
researchers devoted to this FOS.

With  an  EU  average  of  20.9%,  ‘Engineering  and
technology’ was second most important FOS in the two
sectors, in terms of employment,. In Romania this field
was the leading FOS with a share of 31.9%.

Two other FOS each employed more than 15% of the
EU's  GOV  and  HES  researchers.  These  fields  were
‘Medical  sciences’  (15.8%)  and  ‘Social  sciences’
(15.6%).  In  Malta,  Denmark  and  Spain,  by  far  the
majority of researchers in these sectors were engaged
in  ‘Medical  sciences’,  with  31.7%,  23.6%  and  22.7%
respectively.  In  Hungary  and  Lithuania,  noticeable
shares  -  of  around  20  %  -  of  the  GOV  and  HES
researchers were active in Humanities.

At  EU-27 
level,  only  6.5%  of  GOV  and  HES
researchers  were  active  in  the  field  of  ‘Agriculture’.
However,  this  share  was  over  10%  in  Belgium,
Bulgaria, Denmark, Portugal and Slovenia.

able 3.10 

Researchers by field of  science as a percentage, government and higher education sectors,

EU-27 and selected countries — 2004

Total

Agriculture

Engineering and 
technology

Medical sciences

Natural sciences

Social sciences

Humanities

EU-27

625 898 s

6.5 s

20.9 s

15.8 s

28.5 s

15.6 s

12.7 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

14 416

8 530

8 935

10 133

108 410

2 648

4 710

:

68 767

90 276 i

42 463

452

2 875

6 872

485

10 595

237

17 883

9 311

52 520

13 502

11 980

2 328

8 854 i

17 237

20 139 i

:

9 509 i

10.7

12.2

8.2

10.5

4.5

5.7

7.2

:

7.5

:

6.8

6.1

6.5

4.9

:

9.7

2.0

:

4.0

8.6

10.8

3.7

12.3

6.4 i

:

:

:

8.6

20.7

25.0

24.1

13.8

23.5

19.5

16.3

:

19.2

:

16.1

6.6

17.2

18.4

:

12.1

11.7

:

15.3

21.6

19.6

31.9

20.0

18.1

7.9

9.3

23.6

11.4

6.8

15.1

:

22.7

:

20.3

1.6

5.7

11.8

:

12.8

31.7

:

20.3

15.9

9.8

20.4

11.5

22.1

33.8

36.1

23.1

37.0

35.7

34.3

:

18.5

:

41.4

44.4

36.8

26.6

:

28.2

10.0

:

31.3

24.3

31.0

25.5

39.3

21.4 i

15.8 i

32.9 i

:

:

:

:

:

:

:

:

:

17.7

10.3

11.7

13.5

10.0

15.8

17.6

:

18.3

:

13.2

27.0

19.8

18.9

:

16.0

30.1

:

16.4

18.8

20.4

11.4

10.3

17.5

:

:

:

10.6

10.7

10.5

15.4

13.6

16.5

9.5

:

13.8

:

2.2

14.3

14.1

19.4

:

21.2

13.5

:

12.7

10.8

8.4

7.1

6.7

6.0

:

:

:

11.8

20.5

22.2

26.3

10.5

EU-27: Distribution by field of science is estimated on the basis of available Member States.
Distribution by field of science:

Government sector only: IT.

Exceptions to the reference year:

2003: BE, NL, PT and NO.

Footnote 'i':
FR and SK: Defence excluded (all or mostly);
SE and NO: University graduates instead of researchers.



55

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 56

3.3. R&D personnel at the regional level

Île de France (FR)  - with more than 135 000 persons
employed in R&D - was the leading region in terms of
R&D personnel in FTE (Figure 3.11). This region on its
own  accounted  for  6.5%  of  total  R&D  personnel  in 
EU-27. 

The regions of Trøndelag (NO) and Braunschweig (DE)
are  ranked  second  and  third  with  4.12%  and  4.05%
respectively.  Iceland,  which  is  also  classified  as  a
region at NUTS level 2, is ranked eleventh as a share
of total employment.

Germany,  with  six  regions,  was  the  most  represented
country  among  the  top  leading  regions  in  absolute
terms.  Oberbayern  (DE)  and  Stuttgart  (DE)  ranked
second  and  third,  with  59  000  and  47  000FTE
respectively. 

Denmark, which is classified as a region at NUTS level
2, was ranked fourth in absolute terms. Spain and Italy
were  represented  twice  in  the  top  15,  while  Belgium
and Finland both had one region in the rankings.

Comparing the two rankings, it appears that the top 15
regions  in  terms  of  the  absolute  number  of  R&D
personnel  do  not  automatically  have  the  highest
shares. 

The  leading  region  as  regards  the  share  of  R&D
personnel  in  total  employment  was  Wien  (AT),  with
4.52%.  This  represented  approximately  17  000  FTE,
which was almost eight times lower than Île de France
FR).  Conversely,  Île  de  France  (FR)  —  the  leading
region in absolute terms — was ranked only in twelfth
place as a share of total employment (3.52%). 

One of the salient features of the top 15 leading regions
in relative terms is that seven of them are in fact capital
regions.

Map  3.12  provides  an  overview  of  the  percentage  of
researchers as a share of total employment. Only nine
European regions had more than a 2% share in 2003.
Among  them,  Trøndelag  (NO)  led  with  a  share  of
2.95%.  Iceland,  two  German  regions  —  Oberbayern
and  Bremen  (DE)  —  and  five  capital  regions  —  Wien
(AT),  Oslo  og  Akershus  (NO),  Bratislavsky  kraj  (SK),
Région  de  Bruxelles-Capitale  (BE)  and  Praha  (CZ)  —
also had a share of researchers exceeding 2% of total
employment.

All other European regions were below this threshold of
2%.  Moreover,  only  six  other  European  regions
recorded a percentage higher than 1.5%: Île de France
(FR) and five German regions.

igure 3.11 

Top 15 regions in terms of  R&D personnel in FTE and as a percentage of  total employment (HC),

all sectors — 2003

ETFnisnoigergnidaeL

latotfo%
tnemyolpme

tnemyolpmelatotfo%sasnoigergnidaeL

)RF(ecnarFedelÎ

132531

)ED(nreyabrebO

01985

)ED(tragttutS

65174

)KD(kramneD

61614

)SE(dirdaMeddadinumoC

83593

)RF(seplA-enôhR

81573

)SE(añulataC

43663

)IF(imouS-äletE

55933

)EB(tseweGsmaalV

44813

)ED(tdatsmraD

29413

)ED(nlöK

05703

)TI(oizaL

04403

)TI(aidrabmoL

82492

)ED(ehurslraK

22282

)ED(nilreB

13572

25.3

96.3

09.2

82.2

54.2

11.2

37.1

35.3

17.1

43.2

74.2

00.2

31.1

59.2

28.2

)TA(neiW

)ON(galednørT

)ED(giewhcsnuarB

)ZC(aharP

)IF(imouS-siojhoP

)ON(suhsrekAgoolsO

)ES(mlohkcotS

)ED(nreyabrebO

)EB(ennollaWnoigéR

)IF(imouS-äletE

)SI(dnalecI

)RF(ecnarFedelÎ

)ED(nemerB

)KS(jarkyksvalsitarB

)ES(dnalrroNervÖ

25.4

21.4

50.4

30.4

89.3

98.3

78.3

96.3

26.3

35.3

35.3

25.3

14.3

03.3

52.3

0

00005

000001

000051

000002

0

2

4

ETF

38371

2644

42112

23811

8777

87321

95022

01985

3637

55933

0492

132531

8645

0586

2423

Exceptions to the reference year:

2004: CZ, ES, AT, FI;
2002: FR;
1999: SE.

NUTS 1: BE.

56



03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 57

ap 3.12 

Researchers as a percentage of  persons employed,

all sectors by NUTS 2 regions — 2003

Researchers as a percentage of persons
employed, all sectors,
by NUTS 2 regions, 2003

EU-27: 0.88 (Eurostat estimate)

<= 0.5 %

0.5 %   -  <= 1 %

1 %  -  <= 2 %

> 2 %

Data not available

AT: 2004, FR: 2001
BE, DE4, EL, FR9: NUTS 1
NL, PT, SI: estimated data
DE22, DE23: confidential data
BG: total number of researchers only available for BG41 of the
NUTS 2007 regions 
FI, SE, UK, CH: total number of researchers currently not
available at regional level

Statistical data: Eurostat database: REGIO
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat — GISCO, 10/2007

0

600 km

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0

100

0

10

Iceland

0 100



57

03 R&D personnel:Part2_Chap3  28/01/2008  11:02  Page 58

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 59

PART2

Chapter 4 - Human Ressources 

in Science and Technology

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 60

4.1 Introduction

The European Union (EU) places strong emphasis on
the need to invest more in research and development
and human capital through better education and skills.
This is considered to be a key determinant of economic
growth in a knowledge-based economy.

In  2005,  new  EU  policy  lines  were  set  up  through  the
Relaunch  of  the  Lisbon  Strategy  to  focus  priorities  on
economic  growth  and  employment.  A  strong
partnership  for  jobs  and  growth  would  have  to  be
developed  based  on  the  “knowledge  for  growth”
concept,  between  the  EU,  Member  States  and  all
stakeholders.

(HRST)  contribute  significantly 

Statistics  on  Human  Resources  in  Science  and
Technology 
to
measuring this new economy and its dynamism. They
review  the  supply  of,  and  demand  for,  highly  qualified
people  in  science  and  technology.  The  aim  of  this
chapter is to examine three aspects in detail: education
inflows, stocks of HRST and HRST mobility.

To  support  the  analysis  of  Human  Resources  in
Science  and Technology,  a  number  of  sub-categories,
described  in  Figure  4.1,  were  defined  in  line  with  the
recommendations  laid  down  in  the  Manual  on  the
Measurement of Human Resources devoted to Science
and Technology  (S&T)  —  the  Canberra  Manual  (1)  —
on the basis of the following internationally harmonised
standards:

- The  International  Standard  Classification  of
Education (ISCED), giving the level of formal
education achievement;

- The  International  Standard  Classification  of
Occupations  (ISCO),  detailing  the  type  of
occupation.

Human  Resources  in  Science  and  Technology  —
HRST — are defined as persons fulfilling at least one of
the following conditions:

- Human  resources  in  terms  of  education  —
HRSTE:  individuals  having  successfully
completed tertiary level education in an S&T
field of study — ISCED 97 version levels 5a,
5b or 6,

and/or

- Human  resources  in  terms  of  occupation  —
HRSTO:  individuals  working  in  an  S&T
occupation as professionals and technicians
— ISCO-88 COM codes 2 or 3.

To  define  the  S&T  field  of  study  more  precisely,
according to the Canberra Manual (§ 71), seven broad
S&T  fields  of  study  are  used:  Natural  Sciences,
Engineering  and  Technology,  Medical  Sciences,
Agricultural  Sciences,  Social  Sciences,  Humanities,
and Other Fields.

Furthermore,  even  though  the  official  definition  of
HRST as set out in the Canberra Manual contains the
terms “S&T” (Science and Technology), the definition is
not restricted by these terms: HRSTE covers all fields

of  study,  while  HRSTO  refers  to  two  specific  major
ISCO classes:

ISCO  2  ‘Professionals’  and  ISCO  3  ‘Technicians  and
associate professionals’ — see methodological notes.

An  HRST  sub-population  of  particular  interest  is
‘Scientists and Engineers’ (SE). Those more likely to be
involved  in  leading-edge  technology  professions  are
‘Physical,  mathematical  and  engineering’  occupations
(ISCO-88 COM code 21), and ‘Life science and health’
occupations (ISCO-88 COM code 22) (2). 
Data are calculated from two main sources:

- The  inflows,  which  use  data  from  Eurostat’s
education  database,  collected  via  the  joint
U n e s c o / O E C D / E u r o s t a t  —  U O E  —
questionnaire on education statistics;

- The European Union Labour Force Survey —
EU LFS — which is used for elaborating data
on stocks and mobility for HRST.
The  education  inflows  detailed  in  Chapter  4.2  are  a
useful  measure  of  the  current  and  future  supply  of
Human  Resources  in  S&T,  because  by  completing
tertiary level education the individual will move into the
stock of HRST. Inflows can be sub-divided into various
groups, each providing a different focus. Measurements
are divided into participation in tertiary education (used
to estimate potential future inflow rates into the labour
market) and graduation from tertiary education (actual
inflows).

Information  on  participation  in  tertiary  education  also
includes data on foreign students. These data give an
idea of the proportion of internationally mobile students
in Europe. Lastly, doctoral students, entering the most
highly educated section of the work force, are analysed
more closely.

Data on stocks of Human Resources in S&T in Chapter
4.3, meanwhile, provide an indication of the number of
HRST at a particular point in time. These can then be
broken down to provide information on socio-economic
categories  of  interest,  such  as  the  gender  ratio,  age
distribution,  type  of  occupation  or  the  sector  of
economic activity in which people are working.

Finally,  HRST  mobility  results  show  two  different
aspects:  the  job-to-job  mobility  of  employed  HRST  in
Chapter  4.4  and  the  international  mobility  of  HRST  in
and outside the EU in Chapter 4.5. Job-to-job mobility
illustrates the ability of HRST to move between different
jobs and is based on the length of stay with the same
employer.  The  indicator  is  built  up  by  considering  the
number of HRST employed in years t and t-1 who have
changed jobs during the past 12-month period. A high
intensity of HRST job-to-job mobility is considered as a
good stimulus for the economy of a country.

The  international  mobility  of  HRST  is  based  on  the
person’s citizenship. It is defined as the particular legal
bond between an individual and their state acquired by
birth or naturalisation.

(1) Manual on the Measurement of Human Resources devoted to S&T, Canberra Manual, OECD, Paris, 1994.
(2)  Scientists  and  engineers  differ,  however,  from  the  Frascati  Manual  definition  of  researchers,  which  includes  persons  in  ISCO-88  Major  Group  2
Professional Occupations, Research and Development Department Managers ISCO-88 1237 and members of the armed forces with similar skills who
perform R&D; Standard method for surveys on R&D and experimental development, Frascati Manual, OECD 2002, paragraph 302.

60



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 61

igure 4.1 

Definitions of  Human Resources in Science and Technology (HRST) categories

ETSRH

 −

noitacudEfosmretniTSRH

 −  

noitacudeyraitreT

noitacudeyraitretnahtrewoL

6DECSI

a5DECSI

b5DECSI

5<DECSI

OTSRH

2OCSI

slanoisseforP

 −

noitapuccOfosmretniTSRH

−

3OCSI

snaicinhceT

eroCTSRH

 −

CTSRH

noitacudeyraitrettuohtiwTSRH

1OCSI

sreganaM

9-4,0OCSI

snoitapuccorehtollA

eroc-nonTSRH

deyolpmeTSRH-noN

deyolpmenU

deyolpmenuTSRH

 −

UTSRH

deyolpmenuTSRH-noN

 −

UTSRHN

evitcanI

evitcaniTSRH

evitcaniTSRH-noN

Lisbon strategy for growth and jobs

The  meeting  of  the  European  Council  in  Lisbon  in  March  2000  launched  the  Lisbon  Strategy  aimed  at  making  the
European Union “the most competitive and dynamic knowledge-based economy in the world, capable of sustainable
economic growth with more and better jobs and greater social cohesion” and achieving full employment by 2010. This
strategy,  developed  at  subsequent  meetings  of  the  European  Council,  rests  on  three  pillars:  an  economic  pillar
preparing the ground for the transition to a competitive, dynamic, knowledge-based economy; a social pillar designed
to modernise the European social model by investing in human resources and combating social exclusion; and an
environmental  pillar  drawing  attention  to  the  fact  that  economic  growth  must  be  decoupled  from  the  use  of  natural
resources. Recognising the limited progress achieved so far towards these targets, the European Council decided in
2005 to relaunch the Lisbon Strategy to focus priorities on economic growth and employment. This revised strategy,
no longer based on all the targets set in 2000, retained only the figure of 3% of GDP for research as an objective.

As part of this process, a new set of employment guidelines for the period 2005 to 2008 was adopted by the Council
in July 2005 to reflect the renewed focus on jobs, and they form part of the Integrated Guidelines. The employment
guidelines continue to reflect the EU’s overall goal of achieving full employment, quality and productivity at work, and
social and territorial cohesion, and advocate a lifecycle approach to work that tackles the problems faced by all age
groups. The employment guidelines fall under three broad areas for action, namely to:

- Attract and retain more people in employment and modernise social protection systems;
- Improve adaptability of workers and enterprises and the flexibility of labour markets;
- Increase investment in human capital through better education and skills.

The  follow-up  of  the  Lisbon  strategy  for  growth  and  jobs  will  be  an  important  theme  for  2007.  The  consensus  on
innovation reached at the European Council in Lahti will put the spotlight on measures at both European, national and
local level to stimulate innovation in all sectors of the economy. In addition, it will be useful to assess progress made
and discuss future approaches regarding one of the core elements of the Lisbon strategy for growth and jobs – the
European  Research Area  (ERA).  Some  progress  has  been  made  since  the  concept  was  endorsed  at  the  Lisbon
European  Council  in  2000.  The  ERA  concept  combines:  a  European  “internal  market”  for  research,  where
researchers, technology and knowledge freely circulate; effective European-level coordination of national and regional
research activities, programmes and policies; and initiatives implemented and funded at European level.

Sources: Integrated guidelines for growth and jobs (2005-2008), Brussels, 12.4.2005, COM (2005) 
141 final 2005/0057 (CNS); Employment in Europe, 2006, European Commission, 
DG for Employment, Social Affairs and Equal Opportunities, Unit D1, October 2006; 
The European Research Area: New Perspectives, European Commission, 
Brussels, 4.4.2007, COM(2007) 161 final



61

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 62

4.2 Education inflows

Participation in tertiary education

In  2004,  close  to  one  seventh  of  the  total  EU  student
population  were  following  a  tertiary  education,  which
represented more than 18 million students. Comparing
these  students  to  the  total  population  aged  20-29  (as
the majority of tertiary students are in this interval) then
one  out  of  four  in  this  age  group  in  Europe  was  in
tertiary  education.  But  national  disparities  are  clearly
apparent.  In  absolute  numbers,  70%  of  the  students
participating  in  tertiary  education  are  found  in  6  EU
countries, mainly owing to the size of the countries and
the 
the
population  aged  20-29,  Finland  had  the  highest
proportion in the EU. 

large  university  network.  Compared 

to 

Looking at the specific fields of education of “Science,
mathematics  and  computing”  and  “Engineering,
manufacturing  and  construction”,  it  appears  that  one
student  in  four  was  studying  one  of  these  subjects  at
EU-27 level in 2004. These students taking a science or

engineering course accounted for nearly 7% compared
to the population aged 20-29.

Nevertheless, engineering courses were more popular
than  science.  Close  to  4%  was  studying  engineering
compared to the population aged 20-29, while less than
3%  were  on  science  courses.  This  was  reflected  in
most  EU  countries,  the  exceptions  being  Ireland,
Greece, Cyprus, the UK, Iceland and Norway. Finland,
where  there  is  close  cooperation  between  the  Finnish
educational  institutions  and  industry,  had  the  highest
proportion of students compared to the population aged
20-29  (12.4%)  studying  engineering.  Conversely,
Greece  had  the  highest  proportion  studying  science
(6.7%). In Bulgaria and Romania, the share of students
in engineering compared to the population 20-29 years
was  more  than  four  times  the  share  of  students
following a course in science.

able 4.2

Students participating in tertiary education, total and in selected fields of  study, proportion of  the

population aged 20-29 and proportion of  female students, EU-27 and selected countries — 2004

Students participating in tertiary education, 2004

In any field

In science, mathematics 
and computing

In engineering, manufacturing 
and construction

Total

% of population 
aged 20-29

% female

Total

% of population 
aged 20-29

% female

Total

% of population 
aged 20-29

EU-27

EU-25

18 234 656 s

17 320 470 s

27.6 s

28.2 s

54.8 s

54.8 s

1 711 631 s

1 665 958 s

2.8 s

2.9 s

37.5 s

37.0 s

2 367 365 s

2 171 796 s

3.8 s

3.8 s

% female

24.0 s

23.4 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

386 110

228 468

318 858

217 130

2 330 457

65 659

188 315

597 007

1 839 903

2 160 300

1 986 497

20 849

127 656

182 656

2 717

422 177

7 867

543 396

238 521

2 044 298

395 063

685 718

104 396

164 667

299 888

428 642

2 247 441

14 710

213 845

EEA30

18 463 211 s

CH

HR

195 947

:

29.8

22.6

19.8

34.3

25.8

34.5

28.2

40.3

27.7

27.1

27.7

21.7

38.8

39.0

39.0

27.8

14.4

27.6

24.3

33.9

24.9

20.1

35.1

18.0

46.4

40.0

31.9

37.6

38.1

27.7

21.8

:

53.8

52.5

51.2

57.9

49.4

61.8

55.2

51.7

53.8

55.0

56.2

47.9

62.3

60.0

60.0

57.3

55.9

50.9

53.3

57.6

56.1

54.8

56.9

54.1

53.4

59.5

57.0

64.5

59.6

35 722

11 496

30 028

19 761

347 397

6 580

23 094

99 359

241 763

:

153 683

2 623

8 833

11 280

152

24 174

468

41 224

28 528

138 839

30 968

34 177

5 358

14 903

34 816

41 379

325 026

1 351

22 184

54.8 s

1 735 166 s

44.9

:

22 656

:

2.8

1.1

1.9

3.1

3.8

3.5

3.5

6.7

3.6

:

2.1

2.7

2.7

2.4

2.4

1.6

0.9

2.1

2.9

2.3

2.0

1.0

1.8

1.6

5.4

3.9

4.6

3.5

4.0

:

2.5

:

28.5

49.6

35.4

31.8

33.8

39.9

41.2

37.9

35.9

:

48.7

33.2

33.5

36.0

36.0

33.7

33.1

19.6

34.6

40.3

49.2

57.5

30.3

34.4

41.3

41.9

36.1

35.2

32.7

44 270

50 463

65 655

22 501

360 034

7 859

20 790

90 404

324 936

:

319 739

843

12 280

35 578

267

54 406

698

44 576

30 004

272 641

85 414

145 106

17 508

28 621

80 167

71 949

180 656

980

13 874

37.5 s

2 382 219 s

26.4

:

26 622

:

281 986

3.4

5.0

4.1

3.6

4.0

4.1

3.1

6.1

4.9

:

4.5

0.9

3.7

7.6

7.6

3.6

1.3

2.3

3.1

4.5

5.4

4.3

5.9

3.1

12.4

6.7

2.6

2.5

2.5

:

3.0

:

:

22.8

32.2

20.3

33.6

18.9

26.9

16.7

28.1

27.7

:

27.1

10.1

20.9

27.8

27.8

18.6

26.9

13.5

20.6

22.5

26.7

30.2

23.7

28.7

18.5

28.2

18.9

31.1

23.8

24.0 s

13.9

:

18.9

TR

1 972 662

:
Eurostat estimations for selected fields of study without FR: EU-27, EU-25 and EEA-30.
Exception to the reference year: LU 1999.
Students of all ages participating in tertiary education are divided by the population aged 20-29 years.

144 889

41.4

40.3

:

62



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 63

Regarding  the  gender  distribution,  although  females
accounted  for  more  than  half  of  all  students  in  all
countries — with the exception of Turkey, Switzerland,
Cyprus and Germany — this was not the case when it
came to analysing the specific fields of study of science
and engineering in Table 4.2. Parity in science fields in
the  EU  was  only  achieved  in  Romania  (57.5%)  and
almost  reached  in  Bulgaria  and  Portugal  (49.6%  and
49.2% 
respectively),  countries  where  student
participation in science was well below the EU average.
At EU level, nearly four out of ten students in science in
2004 were female, while the corresponding proportion
in the Netherlands was as low as one in five. 

“Engineering, manufacturing and construction” courses
have  even  more  problems  attracting  female  students.
Denmark and Bulgaria, with 33.6% and 32.2%, had the
highest ratios of female engineering students in the EU.
At the other end of the scale, Cyprus scored the lowest
percentage  of  female  students  in  engineering,  with  a
rate of only 10.1%.

As  shown  in  Figure  4.3,  the  overall  number  of  tertiary
students in science and engineering is growing in both
genders.  Between  1999  and  2004,  the  number  of
students in tertiary education in EU-27 increased at an
annual  average  rate  of  4%  both  for  female  and  male
students.

Over  this  period,  the  highest  growth  for  scientist  and
engineer  male  students  in  the  EU  is  found  in  Cyprus
(14%). This large increase still falls below the growth of
male tertiary students in all fields (close to 18% in the
same period). Malta scored the highest EU growth for
female students in science and engineering, with 13%.
At the bottom of the scale, Austria saw a decrease in its
number  of  both  male  and  female  students  following
science  and  engineering  courses  (-7%  for  male
students  and  -3%  for  female  students).  Meanwhile,
most of the new Member States displayed growth rates
for  male  and  female  students  in  these  specific  fields
higher than or equal to the EU-27 annual growth rate,
especially among female students.

igure 4.3 

Annual average growth rates of  the number of  students participating in tertiary education 

in Science and Engineering, by gender, EU-27 and selected countries — 1999 to 2004

11

11

11

7

6

21

11

9

9

01

01

7

31

21

01

8

7

9

8

7

5

5

6

4

6

5

2

%

02

51

41

01

5

0

5-

01-

4

4

4

4

4

4

5

4

3

3

3

3

2

4

3

3

2

2

2

2

2

2

2

1

2

2

1

1

1

4

4-

3-

3-

7-

YC

TL

SI

RT

LP

LE

TM

OR

ZC

VL

HC

UH

EE

IF

IS

52-UE72-UE

TP

KS

ED

GB

KD

SE

ES

ON

EI

KU

LN

EB

TI

TA

Exceptions to the reference period: EU-27, EU-25, BE and TR 2000/2004; PT 2001/2004; EL and CH 2002/2004.
Eurostat estimations: EU-27 and EU-25.

elaM

elameF

Erasmus Mundus

The  Erasmus  Mundus  programme  is  a  cooperation  and  mobility  programme  in  the  field  of  higher  education  which
promotes the European Union as a centre of excellence in learning around the world. It supports European top-quality
Masters courses and enhances the visibility and attractiveness of European higher education in third countries. It also
provides  EU-funded  scholarships  for  third  country  nationals  participating  in  these  Masters  courses,  as  well  as
scholarships for EU nationals studying at partner universities throughout the world.

Erasmus Mundus was first introduced in July 2001. Subsequently, the Commission adopted a programme proposal,
Erasmus World (renamed Erasmus Mundus), in July 2002. On 5 December 2003, the Erasmus Mundus programme
decision  was  adopted  and  entered  into  force  on  20  January  2004.  The  Erasmus  Mundus  programme  has  earned
political support from governments, policy makers and higher education institutions all over Europe. It is seen as a
useful means to face the need to stimulate the process of the convergence of degree structures and to enhance the
attractiveness  of  European  higher  education  world-wide. These  are  themes  central  to  the  Bologna  process  and  to
national reform of higher education in Member States. Furthermore, Erasmus Mundus coincides with the European
Union’s Lisbon Strategy, a commitment to making Europe the most competitive and knowledge-based economy in the
world and a reference for high quality and excellence in education.

Source: http://ec.europa.eu/education/programmes/mundus/index_en.html



63

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 64

Student mobility

The  previously  described  national  figures  for  overall
participation  in  tertiary  education  include  also  foreign
students. A foreign student is defined according to the
citizenship  of  the  individual.  Overestimation  of  foreign
students may exist in some countries. In some cases,
for  example,  permanently  resident  second-generation
immigrants  with  foreign  nationalities  can  constitute  an
important  group  of  students.  Despite  these  limitations,
foreign  students  can  otherwise  be  interpreted  as
internationally mobile students.

Foreign students participating in tertiary education and
those choosing to study subjects related to Science and
Engineering  (S&E)  in  2004  are  shown  in  Figure  4.4.
Large disparities from one EU country to another exist
between the proportion of foreign students and the total
respective  student  population.  Cyprus,  with  a  32%
share,  was  the  leading  EU  country  with  the  highest
proportion  of  foreign  students,  followed  by  the  United
Kingdom with a share of 16.2%.

This  proportion  fell  to  as  low  as  0.4%  in  Poland  and
Lithuania. 

In Finland and Germany, respectively 40.8% and 34.0%
of  all  internationally  mobile  students  followed  science
and engineering related disciplines. These proportions
exceeded  the  popularity  of  S&E  programmes  for  the
total tertiary student population at national level found in
Table 4.2 (38.3% and 30.3% respectively).

Looking  at  the  S&E  fields  in  the  United  Kingdom  and
Cyprus,  nearly  20%  of  all  students  studying  S&E  in
2004  were  foreign  (19.9%  and  18.4%  respectively).
Furthermore,  Cyprus  also  featured  one  of  the  highest
annual  growth  rates  between  1999  and  2004,  with  an
annual increase of 24% in the number of foreign S&E
students.  The  highest  annual  growth  rate  among  the
EU  countries  was  shown  by  the  Czech  Republic  with
34%. Despite a general trend towards growth, a few EU
countries registered a loss of foreign students, such as
Latvia (-23%), Romania (-4%) and Austria (-2%).

igure 4.4 

Foreign students participating in tertiary education, total and in proportion of  S&E students, 

EU-27 and selected countries — 2004

Foreign students

In any field

In S&E

AAGR of foreign students 
in S&E
1999-2004

As % of the 
respective
student
population

9.6

3.6

4.7

7.9

Total

37 091

8 286

14 923

17 162

Total

6 889

1 602

3 808

5 123

260 314

11.2

88 541

1 090

10 201

14 361

41 734

237 587

40 641

6 679

2 390

738

:

12 913

442

21 259

33 707

8 118

16 155

9 730

1 108

1 640

7 915

36 458

364 271

489

12 392

35 705

15 298

1.7

5.6

2.4

2.3

11.0

2.0

32.0

2.0

0.4

:

3.1

5.6

3.9

14.1

0.4

4.1

1.5

1.1

1.0

2.6

8.5

16.2

3.3

5.8

18.2

0.8

:

:

:

5 703

:

8 388

638

131

163

:

2 582

29

4 175

8 079

730

4 735

923

271

328

3 227

11 879

100 612

80

2 910

10 644

3 619

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

TR

As % of the 
respective
student
population

-30

-20

-10

0

10

20

30

40 %

8.6

2.6

4.0

12.1

12.5

:

:

:

1.0

:

1.8

18.4

0.7

0.3

:

3.3

2.5

4.9

13.8

0.2

4.1

0.5

1.2

0.8

2.8

10.5

19.9

3.4

8.1

21.6

0.8

7

7

12

10

34

-23

13

11

24

7

8

0

4

-2

-4

8

9

10

8

10

10

1

13

13

29

Exceptions to the reference year: EE, IE, LV and RO 2003.
Exceptions to the reference period: PT, SK and TR 2000/2004; BE and CY 2001/2004; CH 2002/2004; LV and RO 1999/2003.

64



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 65

Doctoral students

Doctoral students are defined as students following the
second stage of tertiary education programmes (ISCED
level 6). These programmes are devoted to advanced
study  and  original  research.  They  are  not  based  on
course-work only, and lead to the award of an advanced
research  degree,  e.g.  a  doctorate  in  economics,  in
sociology or in physics (1). Therefore, indicators of the
number  of  doctoral  students  as  shown  in  Table  4.5
provide  an  idea  of  the  extent  to  which  countries  will
have  researchers  at  the  highest  level  of  education.  In
2004, even when excluding Germany, Luxembourg and
Slovenia  —  for  which  no  data  were  available  —
approximately 526 000 doctoral students were counted
in  the  EU.  In  other  words,  2.9%  of  the  total  student
tertiary  population  (excluding 
three  missing
countries) 
following  a  doctoral
programme. Almost one third of these doctoral students
was found in France and in the UK, mainly owing to the
wide  diversity  of  doctoral  programmes  and
qualifications  proposed.  In  addition,  parity  was  almost

in  2004  were 

the 

female  doctoral  students
reached  as  European 
accounted  for  almost  half  of  all  doctoral  students
(46.9%). Looking at the doctoral student participation in
specific fields of education, “Science, mathematics and
computing”  are  more  popular  than  “Engineering,
manufacturing and construction”, which is the opposite
of  the  picture  for  the  total  tertiary  student  participation
shown in Table 4.2. In the EU, Greece and Cyprus had
the highest proportion of their doctoral students taking
science  courses.  Conversely,  engineering  attracted
more doctoral students than science in many of the new
Member States and Scandinavian countries. Up to one
in three of all doctoral students in the Czech Republic
followed  engineering  courses  in  2004.  In  addition,
female  doctoral  student  preferences  largely  went  to
“Science,  mathematics  and  computing”  rather  than
“Engineering,  manufacturing  and  construction”.  While
parity  was  almost  achieved  in  science  in  the  EU  in
2004,  almost  70%  of  the  doctoral  students  in
engineering were males.

able 4.5 

Doctoral students (ISCED level 6), total and in selected fields of  study, proportion of  the population

aged 20-29 and proportion of  female doctoral students, EU-27 and selected countries — 2004

Doctoral students (ISCED level 6), 2004

In science, mathematics 
and computing

In engineering, manufacturing 
and construction

% female

46.9 s

46.7 s

Total

88 112 s

85 547 s

38.9

51.0

36.4

43.2

:

53.5

45.7

41.9

50.7

47.1

51.0

49.5

58.2

55.7

:

42.3

23.5

41.1

45.5

47.6

54.0

51.4

:

40.6

50.5

47.1

43.9

52.9

42.6

2 143

766

5 005

926

:

469

1 613

8 346

11 486

:

9 486

85

225

488

:

1 813

:

:

2 558

4 892

3 080

1 799

:

1 402

3 060

4 492

23 978

7

1 207

46.9 s

89 326 s

38.8

:

38.8

4 525

:

3 608

Per 1 000 
population
 aged 20-29

:

:

1.7

0.8

3.1

1.5

:

2.5

2.4

5.6

1.7

:

1.3

0.9

0.7

1.0

:

1.2

:

:

2.6

0.8

1.9

0.5

:

1.5

4.7

4.2

3.4

0.2

2.1

:

5.0

:

:

% female

42.5 s

42.3 s

Total

69 760 s

65 737 s

37.9

47.9

38.9

34.1

:

44.6

45.2

35.3

47.1

:

51.4

45.9

46.7

53.7

:

33.6

:

:

35.5

51.7

54.8

53.5

:

41.9

45.8

39.1

37.6

28.6

34.6

946

1 107

6 856

1 018

:

219

705

2 277

7 782

:

7 305

5

209

577

:

840

:

:

2 037

6 544

2 813

2 916

:

2 255

5 481

4 994

12 874

5

645

42.4 s

70 410 s

33.1

:

41.4

1 686

:

4 682

Per 1 000 
population
 aged 20-29

:

:

0.7

1.1

4.2

1.6

:

1.2

1.1

1.5

1.2

:

1.0

0.1

0.6

1.2

:

0.6

:

:

2.1

1.1

1.8

0.9

:

2.5

8.5

4.7

1.8

0.1

1.1

:

1.9

:

:

% female

27.7 s

26.5 s

20.3

36.6

19.6

24.9

:

35.2

24.5

31.1

29.1

:

34.2

20.0

33.5

33.4

:

25.7

:

:

21.9

28.0

33.7

50.8

:

26.3

26.5

28.7

21.2

60.0

18.8

27.6 s

19.8

:

31.5

In any field

Per 1 000 
population
 aged 20-29

8.0

8.2

5.4

4.8

14.4

8.0

:

8.7

6.5

12.8

11.6

12.7

5.2

2.1

4.3

5.6

:

5.2

0.3

3.6

15.8

5.3

11.0

5.3

:

10.2

32.8

21.0

12.7

1.3

7.8

7.9

17.6

:

:

Total

525 574 s

502 695 s

7 014

4 834

23 282

5 093

:

1 653

4 339

18 907

76 895

101 309

37 608

202

1 425

2 623

:

7 835

17

7 054

15 524

32 054

17 445

18 045

:

9 371

21 207

22 460

89 378

51

4 356

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

EEA30

529 981 s

CH

HR

TR

15 850

:

24 891

Eurostat estimations without DE, LU, SI: EU-27, EU-25 and EEA-30 and for selected fields of study also without FR, MT, NL.
Doctoral students of all ages are divided by the population aged 20-29 years.

(1) International Standard Classification of Education, ISCED 1997, UNESCO, 1997.



65

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 66

Graduation from tertiary education

Though  student  participation  rates  are  a  useful  proxy
for future expectations of the national stocks of HRST,
they  should  be  complemented  by  data  on  the  actual
number  of  people  becoming  HRST.  Data  on  tertiary
graduates measure this. 

In 2004, there were more than 3.5 million new tertiary
graduates in the European Union — see Table 4.6. Two
EU  Member  States,  the  United  Kingdom  and  France,
accounted  for  more  than  30%  of  these  tertiary
graduates.  Poland,  which  had 
reformed  and
modernised  its  tertiary  education  system,  came  next
with  a  share  of  13.6%  of  all  EU  graduated  tertiary
students in 2004.

Balancing  these  new  graduates  against  the  young
population,  for  every  thousand  persons  aged  20-29  in
the EU there were close to 59 new graduates. However,
this proportion varies from more than 80 new graduates
per  thousand  20-29  year  olds  in  the  United  Kingdom,
Ireland,  Lithuania  and  Poland,  to  just  over  30  new
graduates  in Austria. As  previously  seen  in  Table  4.2,
this country also had a participation in tertiary education
below the EU average.

The  majority  of  these  tertiary  graduates  in  the  EU  in
2004 were female (58.7%). This proportion of all female
graduates was higher than the proportion they achieved
in terms of participation (54.7%). In addition, five of the
six  EU  countries  with  the  highest  shares  of  female
tertiary graduates were new Member states. In Estonia,
more than 70% of tertiary graduates were female.

able 4.6 

Graduates from tertiary education, total and in selected fields of  study, proportion of  the population

aged 20-29 and proportion of  female graduates, EU-27 and selected countries — 2004 

Graduates from tertiary education, 2004

In any field

In science, mathematics 
and computing

In engineering, manufacturing 
and construction

Total

Per 1 000 
population
 aged 20-29

% female

Total

EU-27

EU-25

3 569 884 s

3 376 515 s

58.7 s

57.2 s

58.7 s

58.7 s

354 611 s

344 543 s

Per 1 000 
population
 aged 20-29

5.7 s

6.0 s

% female

Total

40.3 s

39.8 s

464 687 s

431 254 s

Per 1 000 
population
 aged 20-29

7.5 s

7.5 s

% female

24.3 s

23.6 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

76 996

45 957

54 341

46 726

319 791

10 235

55 852

48 135

298 448

584 849

324 505

3 547

23 852

38 095

:

68 070

2 145

96 890

30 664

486 313

68 668

147 412

14 888

35 371

38 645

53 848

595 641

2 838

32 043

EEA30

3 605 136 s

CH

HR

TR

60 342

:

258 858

59.4

45.4

33.7

73.8

35.4

53.8

83.6

32.5

44.9

79.1

45.2

36.9

72.5

81.3

:

44.9

37.3

49.3

31.3

80.6

43.3

43.3

50.1

38.6

60.3

50.3

84.6

72.6

57.1

54.0

67.1

:

:

57.1

58.3

58.0

58.8

52.7

71.6

57.0

60.9

57.7

56.6

58.1

59.7

69.2

66.5

:

63.5

57.3

56.1

50.6

65.5

65.9

57.3

60.4

56.7

62.0

61.0

57.7

66.6

60.3

6 945

2 235

4 120

4 374

32 178

879

8 290

8 292

32 816

75 894

23 871

347

1 264

1 841

:

2 668

100

6 909

2 584

24 969

7 363

7 833

558

3 310

3 083

5 156

86 732

314

2 554

58.7 s

357 503 s

44.1

:

44.0

5 968

:

24 573

5.4

2.2

2.6

6.9

3.6

4.6

12.4

5.6

4.9

10.3

3.3

3.6

3.8

3.9

:

1.8

1.7

3.5

2.6

4.1

4.6

2.3

1.9

3.6

4.8

4.8

12.3

8.0

4.5

:

6.6

:

:

Eurostat estimations: EU-27, EU-25 and EEA-30.
Exceptions to the reference year: FR, MT and FI 2003; LU 1998.
Graduates of all ages from tertiary education are divided by the population aged 20-29 years.

30.3

56.4

39.5

33.5

34.9

47.9

43.0

41.9

37.2

41.0

53.7

42.9

39.3

43.9

:

37.6

30.0

24.1

35.7

41.1

50.8

58.8

40.0

41.1

48.8

45.9

37.4

42.0

26.2

7 630

7 418

8 018

4 695

53 725

854

7 061

4 864

50 368

95 481

49 744

119

1 845

6 489

:

5 301

112

8 693

6 281

34 144

10 008

26 015

2 219

5 220

8 154

11 945

48 284

145

2 559

40.2 s

467 445 s

21.8

:

45.1

7 214

:

49 910

5.9

7.3

5.0

7.4

5.9

4.5

10.6

3.3

7.6

12.9

6.9

1.2

5.6

13.9

:

3.5

1.9

4.4

6.4

5.7

6.3

7.6

7.5

5.7

12.7

11.2

6.9

3.7

4.6

:

8.0

:

:

20.8

37.2

24.2

31.1

17.1

33.1

17.5

38.0

25.8

21.7

28.7

20.2

28.2

33.3

:

23.7

31.3

15.9

17.2

27.6

33.9

32.4

21.2

31.6

21.8

28.6

20.1

29.7

22.7

24.3 s

11.4

:

23.2

66



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 67

Looking at the graduate distribution by specific fields of
study,  close  to  one  out  of  four  EU  graduates  received
their  diploma  in  science  or  engineering  related
disciplines.  As  previously  seen  in  Table  4.2,  the
engineering  fields  of  study  were  the  most  popular  in
most EU countries. But national specificities exist. For
example,  Lithuania  had  almost  14  new  tertiary
graduates  in  engineering  for  every  thousand  persons
aged 20-29 and 3.5 times less in science. Conversely,
the United Kingdom, with one of the highest shares of
tertiary  graduates 
the
population  aged  20-29  (12.3‰),  reached  only  a
proportion  of  7  tertiary  graduates  in  engineering  for
every thousand persons aged 20-29.

in  science  compared 

to 

Whilst the female proportion was close to parity in the
science fields of study, accounting for 40.3% of science
graduates  in  the  European  Union,  the  corresponding
percentage for females in engineering was much lower,
with a share of only 24.3%.

In  science,  five  EU  countries  had  a  share  of  female
graduates  in  tertiary  education  above  50%,  of  which
Bulgaria  and  Romania,  the  two  new  Member  States,
were in top position. In engineering, females were much
under-represented. The highest share of female tertiary
graduates  in  this  field  is  scored  by  Greece  with  only
38%.

igure 4.7 

Annual average growth rates of  graduates from tertiary education in Science and Engineering, 

by gender, EU-27 and selected countries — 1999 to 2004

91

41

31

21

21

11

11

11

01

01

9

%

52

12

02

41

51

01

5

0

5-

31

11

21

9

9

8

6

8

6

7

7

7

6

6

6

6

5

4

6

5

4

6

4

2

7

7

3

3

3

3

1

3

3

2

2

5

3

1

4

3

3

2-

1-

1-

1-

1-

21

1

2-

OR

LP

TP

KS

SI

TI

VL

KD

TL

ES

RT

ZC

SE

YC

EE

GB

72-UE

EI

52-UE

LN

RF

EB

ON

TA

IS

KU

ED

UH

IF

HC

TM

Exceptions to the reference period: FR, MT and FI 2003/1999; CH 2002/2004; BE 2000/2004.
Eurostat estimations: EU-27 and EU-25.

elaM

elameF

The  annual  average  growth  rates  of  graduates  from
tertiary education in Science and Engineering (SE) by
gender  for  each  EU  country  and  other  selected
countries  are  illustrated  in  Figure  4.7.  In  most  of  the
countries, the number of new SE graduates in tertiary
education is increasing mainly because students have
become  more  aware  of  the  economic  and  social
benefits of tertiary education. 

In  the  EU,  Romania  had  the  highest  growth  rates  for
both  genders  between  1999  and  2004,  with  14%  for
male  and  21%  for  female  graduates. This  is  the  main
result  of  the  major  reform  initiated  by  the  Romanian
higher  education  institutions  themselves  since  1990.
New curricula as well as efficient new universities have
been  set  up  under  the  reform  programme  and  the
in  various  study
number  of  students  enrolled 
programmes  available  in  Romania  has  been  steadily
rising.

The  EU  average  increase  ranges  from  5%  for  male
graduates  to  6%  for  female  graduates.  Looking  at  the
new  Member  States,  seven  out  of  twelve  had  annual
average  growth  rates  above  the  EU  average  for  both
genders. 

Europe  is  progressing  towards  a  knowledge  economy
with  regard  to  human  resources  as  most  of  the  EU
countries  recorded  an  increase  in  the  number  of  SE
graduates  in  tertiary  education.  Five  countries  are
nevertheless exceptions to this trend with a decline for
male  graduates  between  1999  and  2004.  Ireland  was
the only country scoring a negative annual growth rate
for female graduates with -2%.



67

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 68

Doctoral Graduates

Of  the  3.5  million  new  EU  tertiary  graduates  in  2004,
more than 93 thousand graduated with a doctorate —
Table 4.8. This is two times more than the United States
and  six  times  more  than  Japan.  In  Europe,  Germany
was  the  leading  EU  country  in  terms  of  the  absolute
number of doctoral graduates — as around one in four
doctoral graduates in Europe graduated in Germany —
followed by the United Kingdom (with a total of 15 257
doctoral  graduates  in  2004).  Compared  to  the  total
population aged 20-29, Sweden had the highest share
of  new  doctorates  with  3.6‰.  Germany  was  next,
followed  by  Austria  and  Portugal.  In  these  countries,
compared  to  1 000  persons  aged  20-29  years, 
2.5 persons obtained a doctorate in 2006.

Again,  Sweden  is  the  EU  country  scoring  the  highest
proportion  of  doctorates  compared  to  the  population
aged 20-29 in science and engineering. Close to one in

a  thousand  persons  aged  20-29  achieved  a  doctoral
diploma  in  these  fields  of  study.  Overall,  science,
mathematics  and  computing  were  more  popular
doctoral fields of study than engineering, manufacturing
and construction.

In 2004, 43.4% of the EU doctorates graduated in 2004
were female. Proportions of over 60% were registered
in Estonia and Cyprus. 

Disparities exist between the specific fields of study of
science and engineering. In science, there were seven
EU countries with more female doctoral graduates than
male. For engineering, the proportion of female doctoral
graduates  in  all  EU  countries  is  way  below  50%.  The
closest  to  parity  was  in  Bulgaria,  where  39.2%  of  all
doctoral  graduates  in  engineering  were  females.  But
this  fell  to  as  low  as  18.6%  in  Austria  or  11.8%  in
Germany.

able 4.8 

Doctoral graduates (ISCED level 6), total and in selected fields of  study, proportion of  the population

aged 20-29 and proportion of  female doctorate graduates, EU-27 and selected countries — 2004 

Doctoral graduates (ISCED 6 level), 2004

In any field

Per 1 000 
population
 aged 20-29

% female

1.5 s

1.6 s

43.4 s

43.2 s

In science, mathematics 
and computing

In engineering, manufacturing 
and construction

Total

26 117 s

25 889 s

Per 1 000 
population
 aged 20-29

0.4 s

0.4 s

% female

39.1 s

39.0 s

Total

13 000 s

12 236 s

Per 1 000 
population
 aged 20-29

0.2 s

0.2 s

% female

23.6 s

23.2 s

1.1

0.4

1.1

1.2

2.6

1.1

1.0

0.9

1.2

1.1

0.9

0.1

0.3

0.6

:

0.6

0.1

1.4

2.5

0.9

2.5

0.8

1.2

0.9

0.3

3.6

2.2

0.3

1.3

:

3.3

:

:

33.9

50.8

35.6

35.9

39.0

62.2

45.7

38.1

47.5

41.7

50.9

61.5

58.3

57.5

:

42.9

20.0

39.4

40.5

46.9

54.7

49.3

40.6

45.0

48.7

42.6

43.1

50.0

39.8

658

77

410

100

6 025

50

265

711

2 249

4 042

1 931

6

15

70

:

171

:

499

444

867

1 013

151

93

177

306

944

4 843

4

:

43.4 s

26 121 s

36.9

:

38.0

791

:

368

0.5

0.1

0.3

0.2

0.7

0.3

0.4

0.5

0.3

0.5

0.3

0.1

0.0

0.1

:

0.1

:

0.3

0.5

0.1

0.6

0.0

0.3

0.2

0.5

0.9

0.7

0.1

:

:

0.9

:

:

28.9

55.8

34.9

26.0

29.5

44.0

45.3

32.3

48.9

38.4

54.0

83.3

53.3

61.4

:

32.7

:

37.7

35.1

52.9

51.5

45.7

40.9

46.3

43.1

39.1

37.9

50.0

:

89

74

468

376

2 107

16

108

119

603

779

1 177

:

13

62

:

36

:

483

397

908

579

690

86

155

361

1 096

2 218

:

6

39.1 s

13 006 s

32.7

:

37.8

319

:

418

0.1

0.1

0.3

0.6

0.2

0.1

0.2

0.1

0.1

0.1

0.2

:

0.0

0.1

:

0.0

:

0.2

0.4

0.2

0.4

0.2

0.3

0.2

0.6

1.0

0.3

:

:

:

0.4

:

:

20.2

39.2

21.2

27.9

11.8

37.5

28.7

21.0

27.9

25.9

31.2

:

38.5

33.9

:

33.3

:

23.4

18.6

24.1

35.6

28.7

25.6

29.7

25.5

25.9

21.2

:

50.0

23.6 s

20.4

:

34.9

Total

93 235 s

90 163 s

1 479

392

1 732

788

23 138

209

683

1 295

8 168

8 420

6 351

13

84

301

:

893

5

2 679

2 443

5 460

3 963

2 680

355

854

1 759

3 834

15 257

10

756

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

EEA30

94 001 s

CH

HR

TR

2 952

:

2 680

Eurostat estimations: EU-27, EU-25 and EEA-30.
Exception to the reference year: FR, MT and FI 2003.
Doctoral graduates of all ages are divided with the population aged 20-29 years.

68



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 69

Statistics on the Careers of Doctorate Holders (CDH statistics)

It has been increasingly perceived over the years that the provision of sufficient and well-trained human resources in
R&D and beyond, will be a challenge for EU countries. The Lisbon and the Barcelona European Council Conclusions
as  well  as  the  EU  strategy  for  growth  and  employment,  emphasized  the  needs  for  boosting  the  overall  R&D  and
innovation efforts in the EU. These strategies require the mobilization of a very high qualified workforce of doctorate
holders working in research or in other areas of the economy. Indeed, doctoral graduates are at the same time the
most qualified people in terms of educational attainment and those who are trained and most predisposed for research
careers. They are expected to contribute to the advancement and diffusion of knowledge and technologies.

In the recent years, the user needs for measuring the career development and mobility of the highest skilled part of
the labour force were discussed and identified. The “Availability and Characteristics of Surveys on the Destination of
Doctorate Recipients in OECD Countries” inventory in 2003 showed that many national surveys existed and provided
valuable  information  for  the  understanding  of  career  patterns  and  mobility  of  the  target  population.  However,  such
surveys had been developed to serve national statistical needs and were not harmonised internationally. 

For  this  reason,  the  OECD  launched  in  2004  a  collaborative  project  to  improve  countries’  capability  to  survey
recipients of highly advanced degrees. The objective was to develop an internationally comparable production system
of indicators on their careers and mobility. The UNESCO’s Institute for Statistics (UIS) and Eurostat, joined rapidly this
project  on  “Statistics  on  the  Careers  of  Doctorate  Holders”  (CDH).  These  efforts  also  led  to  the  creation  of  an
international Expert Group bringing together more than 40 countries from a wide variety of regions world-wide. The
CDH project measure personnel characteristics, the career development, mobility or other characteristics linked to the
highest  skilled  workforce.  The  overall  objective  is  to  develop  international  statistics  of  high  quality  on  mobility  and
career paths of the highest educated part of the work force. The project focuses on doctorate holders as this small
group is considered most likely to contribute to the advancement and diffusion of knowledge and technologies.

Based on the work of the Expert Group, a set of project components were elaborated to be used for the compilation
and production of harmonised and high quality CDH statistics, including: 

•  Output  tabulation  program:  This  template  used  by  countries  when  delivering  the  requested  data  to  the
international  organisations,  currently  consists  of  around  30  tables,  dealing  with  different  aspects  of  the
doctorate holders and their career paths.
•  Definitions of variables used in tabulations
•  Methodological  guidelines:  These  guidelines  are  building  on  best  practices  and  aiming  at  further
strengthening the quality of the output. The guidelines also define and structure the target population and the
sources to be used are also described and some aspects of data collection, data processing and estimation of
results are taken up.
• Core model questionnaire: all questions helping to complete the output tabulation. 
• A structure for national data compilation methodologies to be delivered together with the output tables.

Three sub groups worked in parallel on the project components: developing a set of tables for data compilation led by
the OECD, harmonization of methodology led by Eurostat and developing a model questionnaire led by UIS.

The  implementation  of  this  package  of  project  components  started  at  European  level  in  2007  and  an  updated
methodology  template  was  made  available  to  countries  together  with  a  technically  improved  template  of  the  CDH
statistics questionnaire in the autumn of 2007. 

A majority of EU countries engaged efforts in the building up of the necessary national infrastructure for CDH statistics
and for the implementation of the national CDH statistics surveys. The CDH statistics should be compiled at national
level every two years. Countries will compile the CDH statistics at national level in 2007, based on the reference year
2006. Eurostat will start the data treatment when the first data is incoming (most probably during the spring 2008).

A  broad  evaluation  of  the  CDH  statistics  2006  is  due  to  take  place  in  2008.  . The  detailed  assessment  reports  on
tables, indicators and methodology used, provided by countries early 2008, will be used to assess the 2006 data and
metadata  submissions,  in  order  to  establish  an  improved  version  of  the  CDH  package  in  view  of  the  next  data
collection exercises.

Sources: Workshop on Statistics on the Careers Development of Doctorate Holders (CDH statistics), Luxembourg,
26-27 April 2007; Working Group Meeting on Statistics on science, Technology and Innovation, Luxembourg,
05/06 November 2007; Mapping careers and mobility of doctorate holders: draft guidelines, model questionnaire
and indicators, the OECD/Eurostat/ UNESCO careers of doctorate holders (CDH) project



69

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 70

4.3 Stocks of  human resources in science and
technology

The  global  dimension  to  the  demand  for  Human
Resources  in  Science  and  Technology  (HRST)  and
access  to  international  sources  of  S&T  personnel  is
becoming  more  and  more  important.  After  having
analysed the supply of human resources in science and
technology  through  the  inflow  provided  by  tertiary
education,  this  section  looks  at  the  demand  side  of
HRST  by  analysing  the  labour  markets  in  the  EU
Member States. In general, the HRST supply increased
as  inflows  of  graduates  from  tertiary  education  also
increased.

HRST stocks at national level

“HRST 

The measurement of stocks of HRST and of its various
terms  of
sub-categories  —  named 
occupation“  (HRSTO),  “HRST  in  terms  of  education”
(HRSTE),  “HRST  core”  (HRSTC)  and  “Scientists  and
Engineers”  (SE)  —  provides  broad  indicators  on  the
state  of  the  labour  markets  for  knowledge  workers  in
European countries.

in 

Table  4.9  shows  the  stocks  of  human  resources  in
science and technology (HRST) in 2006 and the growth
in the number of persons employed in S&T over time.
Germany,  the  United  Kingdom  and  France,  with  more
than 10 million HRST in each country, had the largest
HRST populations in 2006. In other words, nearly half
of the EU’s 85 million total HRST were found in these
three countries.

Even  if  national  disparities  exist  in  terms  of  gender
distribution, the EU-27 average in 2006 reached parity,
with  50.1%  female  HRST.  Proportions  of  over  50%
were  registered  in  16  out  of  the  27  EU  countries.  In
Latvia, the proportion of female HRST was the highest
within  the  EU,  at  64.5%. Two  other  countries,  Estonia
and  Lithuania,  followed  closely  with  proportions  over
60%. Conversely, this figure was only 42.2% in Malta.

Human resources in science and technology in Australia

HRST by occupation 
(HRSTO): 1869 000 persons

HRST Core = 
HRSTO∩HRSTE:
1 243 000 persons

HRST by education
(HRSTE): 2 428000 persons

Scientists and 
Engineers: 560 000

The number of Australians with an advanced diploma, bachelor degree or higher degree (HRSTE) and/or employed
as specialist managers or professionals (HRSTO) was more than 3 million in August 2001. This represented 21% of
the population aged 15 years and over. The HRSTE increased from 11% in 1991 to 16% in 2001, while the share of
HRSTO increased from 10% in 1991 to 13% in 2001. Australia’s stock of scientists and engineers increased by 17.6%
over the five-year period. Of the HRST in 2001, 34% were aged 15-34 years, 49% were aged 35-54 years and 17%
were aged 55 years and over. Females accounted for 51% of the persons who were HRST in 2001. 

Of  the  persons  with  selected  qualifications  and/or  employed  in  selected  occupations  (HRST)  in  2001,  11%  were
persons born overseas who had not taken out Australian citizenship, with the main countries of origin being the United
Kingdom (64 000), New Zealand (42 000) and India (19 000). By state and territory, HRST ranged from 16% to 22%
of the population in 2001, with the exception of the Australian Capital Territory where the percentage was 36%.  

Australian HRST as a percentage of total Australian population ranked fifth in comparison with ten European countries
for which similar data are available. Australia was ranked below Finland, Belgium, Denmark and the Netherlands but
above Ireland, Germany, France, Spain, Italy and Portugal.

Source: Human Resources by selected qualifications and occupations, 
in Australia, 2001, Australian Bureau of Statistics, May 2003

70



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 71

Focusing  on  the  sub-groups  of  HRST,  40%  of  HRST
were tertiary educated and employed in S&T (HRSTC).
In  addition,  in  all  EU  countries  the  number  of  HRSTC
increased between 2001 and 2006, even though male
HRSTC decreased marginally in Bulgaria and Finland.
Slovenia had the highest growth rate in the number of
tertiary educated males working in S&T occupations as
well  as  the  second  highest  for  females  (12.3%  and
10.8%  respectively).  Looking  at  the  growth  rates  of
tertiary educated females employed in S&T, the annual
average growth rate of Luxembourg was above that of
Slovenia, at 15.0%. 

In addition, growth in the number of HRSTC was higher
for females than for males in most of the EU countries
(21 countries out of 27). The EU-27 average showed a
growth of 4.7% for females against 2.9% for males. This
is mainly due to the efforts made by many EU countries
to  institute  positive  actions  and  measures  to  support
women in science and engineering fields and promote
gender equality.

able 4.9 

Human resources in Science and Technology (S&T) stocks, 25-64 years old, by HRST category and

proportion of  females and annual average growth rate of  HRSTC, 2001 to 2006, 

EU-27 and selected countries — 2006 

HRST
Human resources in S&T

HRSTC
Human resources in S&T core

HRSTE
Human resources in S&T
in terms of education
excluding HRSTC

HRSTO
Human resources in S&T 
in terms of occupation
excluding HRSTC

Annual average growth 
rate of HRSTC
2001-2006

1 000s

% female

1 000s

% female

1 000s

% female

1 000s

% female

% male

% female

EU-27

EU-25

84 674 s

81 511 s

50.1 s

49.8 s

34 036 s

32 592 s

51.3 s

51.1 s

26 316 s

25 463 s

48.4 s

48.4 s

24 322 s

23 457 s

50.0 s

49.7 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

2 137

1 055

1 740

1 328

16 737

276

772

1 484

8 081

10 744

8 373

141

363

591

90

1 409

45

3 781

1 426

5 005

1 101

2 108

375

791

1 234

2 092

11 395

62

1 059

49.6

59.4

51.9

51.4

47.1

61.6

52.7

48.5

48.6

50.3

49.1

47.5

64.5

61.3

46.7

58.3

42.2

48.0

44.5

58.5

53.2

53.7

54.1

55.8

54.5

51.6

47.9

54.8

50.9

907

491

536

673

6 337

104

324

747

3 383

4 365

2 636

64

138

242

48

571

17

1 657

446

2 180

523

953

166

270

550

1 004

4 704

31

545

52.5

68.0

45.9

56.6

42.9

71.2

54.0

49.5

50.5

52.0

51.5

48.4

71.7

72.3

43.8

57.4

47.1

47.6

46.4

59.6

61.4

52.5

60.2

49.6

58.9

59.4

51.8

54.8

55.0

849

422

266

358

4 503

127

353

520

3 719

3 685

1 513

57

123

245

19

419

9

1 028

356

1 454

257

431

80

163

445

455

4 460

13

268

49.8

54.7

45.1

45.0

39.2

50.4

53.3

47.7

49.8

55.5

55.9

50.9

60.2

45.7

47.4

52.5

55.6

43.9

36.8

52.9

54.1

40.1

46.3

44.8

54.6

51.6

46.9

53.8

51.5

381

141

938

297

5 897

45

95

217

979

2 694

4 224

20

102

104

23

420

18

1 096

623

1 371

321

724

130

359

239

633

2 231

18

245

EEA30

85 795 s

50.1 s

34 612 s

51.4 s

26 597 s

48.4 s

24 585 s

CH

HR

1 817

:

42.0

:

733

:

TR

:
Eurostat estimations: EU-27, EU-25 and EEA-30.
Exceptions to the reference year: LU, IS and CH 2005.
Exceptions to the reference period: LU, IS and CH 2001/2005.

:

:

35.2

464

34.7

620

:

:

:

:

:

:

:

:

42.3

43.3

57.2

47.5

57.6

71.1

45.3

46.5

37.2

40.4

45.1

30.0

58.8

73.1

52.2

65.2

33.3

52.6

47.4

62.5

39.3

63.4

50.8

65.2

44.4

39.2

42.0

55.6

41.6

50.0 s

55.8

:

:

2.9 s

2.9 s

2.0

-0.1

3.7

2.7

1.4

3.7

6.1

4.7

5.2

1.1

3.0

4.0

4.0

0.6

9.2

4.7

0.0

3.8

5.7

9.4

6.6

4.3

12.3

9.1

-0.2

3.0

2.0

8.8

0.0

2.9 s

1.9

:

:

4.7 s

4.7 s

3.3

2.6

5.4

3.8

2.9

3.6

8.9

6.7

7.3

2.5

6.9

6.2

4.9

3.3

15.0

6.2

9.9

6.5

4.5

7.9

6.2

6.1

10.8

5.0

2.4

4.0

3.9

9.1

2.4

4.7 s

7.2

:

:



71

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 72

Employment of Human Resources in S&T – some features

The  evolution  of  the  HRST  stock  between  2001  and
2006  as  well  as  its  share  in  the  total  labour  force  are
illustrated in Figure 4.10. In the EU average, the HRST
stock  accounted  for  36%  of  the  total  labour  force  in
2006. This proportion is combined with positive growth
of 3.2% over the period 2001-2006. Nevertheless, large
differences between the countries are apparent. 

Luxembourg  and  Ireland  had  the  largest  average
growth in HRST, with around 7.5%. These two countries
combine  large  growth  with  a  relatively  high  share  of
HRST  among  the  labour  force  (41.9%  and  37.4%
respectively).  By  comparison,  Portugal,  which  had  a
2001  to  2006  average  HRST  growth  of  6.3%,
accounted  for  the  smallest  share  of  HRST  among  the
labour force in the EU, with 20.2%.

At  the  other  end  of  the  scale,  Bulgaria  registered  a
decrease in its number of HRST during the same period
with an annual average reduction close to -1%. Looking
at  HRST  as  a  proportion  of  the  total  labour  force,
Romania and Portugal (as mentioned before) featured
low  percentages  (around  20%),  while  Belgium,
Denmark,  Estonia,  the  Netherlands,  Finland,  Sweden,
Norway  and  Switzerland  reached  proportions  close  to
45%.  Nevertheless,  the  growth  of  HRST  stock  was
nearly nil in Norway and Finland whereas for the rest of
these  countries,  it  reached  a  growth  of  over  2%.  One
explanation  of  these  proportions  could  be  the  cross-
border  movement  of  highly  skilled  workers  resulting
from the promotion of intra-EU mobility.

igure 4.10 

Annual average growth rates of  HRST, 2001 to 2006, and their proportion of  the labour force,

EU-27 and selected countries — 2006

kcotSTSRH
)%(6002-1002RGAA

EI

UL

TP

OR

TM

LE

LP

KS

IS

SE

YC

SI
KU

TL

RF

TI

TA

UH

72-UE

VL

ZC

0

5

01

51

02

52

03

GB

53

04

EE

KD

LN

EB

ES

HC

ED

IF
ON

54

05

ecroFruobaLehtfo%saTSRH

8

6

4

2

0

2-

Exceptions to the reference period: LU, IS and CH 2001/2005.

Highly qualified persons employed in S&T by occupation

To  enrich  the  information  given  in  Figure  4.10,  Figure
4.11 details the type of occupation of tertiary educated
persons  employed 
in  science  and  engineering
(HRSTE) in the EU and other countries and relates it to
the labour force. 

than  as 

In  most  of  the  countries,  HRSTE  were  more  likely  to
work  as  professionals 
technicians.
Professionals  conduct  research,  improve  or  develop
concepts,  theories  and  operational  methods,  or  apply
knowledge  relating  to  different  areas  of  science.
Technicians  and  associate  professionals  perform
mostly  technical  and  related  tasks  connected  with
research  and  the  application  of  scientific  and  artistic
concepts and operational methods, and government or
business regulations, and teach at certain educational
levels.

The EU average of employed HRSTE as a percentage
of  the  labour  force  in  2006  was  25%  in  technician
occupations and up to 30% in professional occupations.
Romania had the highest share of HRSTE occupied as
professionals  among  the  labour  force  (44%),  followed
by France and Finland (37% and 36% respectively). 

Moreover, Denmark is the EU country having the lowest
share  of  HRSTE  working  as  technicians  in  terms  of
labour force, with 16%.

Notable  exceptions  to  this  trend  are Austria,  Bulgaria,
the Czech Republic, Slovakia and Belgium, where the
share  of  HRSTE  occupied  as  technicians  was  much
higher than that of professionals. In the Czech Republic
and  Slovakia  more  than  two  out  of  five  employed
technicians were HRSTE. 

72



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 73

igure 4.11 

Employed HRST with tertiary education in Science and Engineering (HRSTE), by selected field of

occupation, as a percentage of  labour force, EU-27 and selected countries — 2005

24

34

73

63

03

43

43

53

23

13

32

12

91

22

12

03

03

03

03

03

03

03

82

13

92

52

42

52

13

23

82

72

82

82

42

82

72

72

72

72

52

62

52

52

52

42

52

42

32

71

61

91

02

71

02

81

81

61

9

83

43

43

%

05

54

44

04

53

03

52

02

51

01

5

0

OR

RF

IF

TP

KU

EI

TI

KS

72-UE

TL

03AEE

HC

52-UE

ZC

ED

YC

LP

EE

KD

VL

UL

LE

SE

IS

TM

ES

SI

GB

UH

EB

TA

ON

LN

Eurostat estimations: EU-27, EU-25 and EEA-30.
Exceptions to the reference year: ES 2004; CZ 2003.
Unreliable data: LT, SI and IS for Technicians; EE and MT for Professionals and Technicians.

slanoisseforP

slanoisseforpetaicossadnasnaicinhceT

Scientists and engineers

Scientists  and  Engineers  —  SE  —  are  an  HRST  sub-
set of particular interest. By definition, it encompasses
all  people  working  in  specific  occupations  listed  in
‘Physical,  mathematical  and  engineering’  occupations
(ISCO-88  COM  code  21)  as  mathematicians  or  civil
engineers and in ‘Life science and health’ occupations
(ISCO-88  COM  code  22)  as  biologists  or  medical
doctors (see more in methodological notes).

Figure  4.12  illustrates  the  gender  distribution  of
Scientists  and  Engineers  as  a  percentage  of  the  total
labour  force  in  2006.  Clearly,  scientists  and  engineers
were more likely to be male than female in 2006, and
this in most of the countries. The male proportion was
especially high in Switzerland. Notable exceptions were
Latvia,  Poland  and  Lithuania,  where  scientists  and
engineers were more likely to be female. 

In 2006, the gender ratio in Germany, Luxembourg and
the United Kingdom was around four male scientists or
engineers  to  one  female.  Ireland  was  the  only  EU
country which achieved gender parity in the distribution
of  male  and  female  SE  in  2006,  and  this  with  a  high
proportion of scientists and engineers among the labour
force (6.8%).

The  highest  proportion  of  scientists  and  engineers  in
2006  was  found  in  Belgium,  where  almost  8%  of  the
labour  force  declared  that  they  had  an  occupation
qualifying them as scientists or engineers. At the other
end  of  the  scale  is  Portugal,  where  the  proportion  of
scientists  and  engineers  fell  to  under  3%  of  the  total
labour force.

igure 4.12 

Breakdown of  Scientists and Engineers (SE), 25-64 years old, by gender, 

as a percentage of  the total labour force, EU-27 and selected countries — 2006

%

1.4

8

6

4

2

8.3

0

4.3

4.3

7.5

1.1

0.4

1.3

9.4

2.4

9.3

5.4

6.4

4.2

5.3

9.3

7.3

1.3

5.2

8.1

8.1

7.1

2.1

0.1

8.2

0.2

0.1

1.1

9.2

9.1

3.3

3.3

3.3

8.2

0.3

5.2

8.2

9.1

2.2

6.2

6.2

4.1

5.1

5.1

5.1

8.1

3.1

7.1

4.1

2.2

8.1

0.2

4.1

3.1

3.2

1.2

6.1

3.2

0.2

5.1

0.1

0.1

4.1

0.1

2.1

7.0

EB

HC

EI

IF

ES

SI

KD

ED

LN

UL

IS

LP

KU

RF

72-UE52-UE03AEEON

SE

LE

YC

UH

TL

EE

OR

TM

VL

ZC

TI

GB

TA

KS

TP

elameF

elaM

Exceptions to the reference year: LU, IS and CH 2005.
Eurostat estimations: EU-27, EU-25 and EEA-30.
Unreliable data: EE for Female and Male; MT for Female and LT for Male.



73

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 74

HRST intensity by sector of economic activity

able 4.13 

HRST intensity of  employed people with S&T education (HRSTE),

in selected sectors of  economic activities, EU-27 and selected countries — 2006

as a percentage of  total employment, 25-64 years old, 

HRST intensity — share of employed 25-64 years old HRSTE of total employment —
in sectors of economic activity

Agriculture, hunting, 
forestry, fishing, mining 
and quarrying

Utilities and 
construction

Manufacturing

Services

High and 
Medium high-tech

Medium low and 
Low-tech

Knowledge-
intensive services 
(KIS)

Less knowledge-
intensive services 
(LKIS)

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

EEA30

CH

HR

TR

7.3

8.7

14.9

4.8

6.8

14.0

18.6

14.1

14.2

17.2

11.4

9.6

15.9

23.2

25.9

26.5

33.2

18.2

12.1

36.2

29.4

: u

25.8 u

: u

11.5

2.4

11.0

11.7

3.0

: u

9.2

6.9 u

: u

8.7

: u

12.1

10.8

3.5

2.1

2.4

15.2

6.6

17.3

11.6

3.4

14.6

11.6

18.7 u

4.6 u

9.3

: u

13.7

14.4

12.7

4.4

12.9

5.9 u

12.7 u

6.8

18.4

13.9

24.2

: u

19.0

7.4

17.1

:

:

9.1

21.8

10.1

17.6

12.9

12.1

14.0

19.7

:

:

40.9

25.6

41.9

30.7

9.9

31.9 u

: u

24.7 u

25.8 u

13.2

: u

32.5

20.8

21.4

12.8

13.0

20.6

10.8

43.5

22.7

33.3

: u

24.9

25.9

36.9

:

:

13.8

14.4

21.0

10.4

6.4

21.2

16.4

22.8

19.9

11.8

23.0

16.8

5.2

13.3

12.9

20.5

17.2

8.5

: u

18.3

16.2

10.8

4.2

7.5

9.8

7.3

25.4

11.6

20.6

13.5

19.3

13.9

19.5

:

:

46.0

45.9

60.7

59.5

33.0

52.8

40.6

58.0

57.3

60.4

60.5

45.4

35.3

62.6

47.3

56.2

47.6

44.7

38.5

48.1

34.5

49.7

41.8

41.2

48.7

38.4

51.6

46.2

47.3

48.7

51.8

46.0

42.0

:

:

19.9

19.8

25.5

25.3

11.3

28.3

20.4

34.0

23.8

19.1

25.2

20.8

8.8

27.6

24.6

34.0

22.6

16.7

9.8

23.5

12.4

22.1

9.5

19.4

23.8

13.6

33.9

24.8

23.1

22.3

25.2

19.9

24.6

:

:

Exceptions to the reference year: LU, IS and CH 2005.
Eurostat estimations without LU and IS: EU-27, EU-25 and EEA-30.

74



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 75

HRST intensity in a specific sector of economic activity
can be defined as the share of employed people in that
sector  that  have  successfully  completed  tertiary
education in S&T — employed HRSTE. In turn, this can
be  used  as  a  proxy  for  knowledge  intensity  in  each
sector of economic activity.

Table 4.13 shows the HRST intensity in specific sectors
of  economic  activity  classified  according  to  NACE
Rev.1.1.

related 

for  example 

Knowledge-Intensive  Services  (KIS)  —  which  cover
activities 
to  post  and
telecommunications, computer and related activities as
well as research and development (see methodological
notes)  —  was  the  most  knowledge-intensive  sector  in
the EU in 2006, as almost half of all persons employed
in this sector had tertiary S&T education. Cyprus posted

the  highest  EU  rate  at  62.6%,  followed  by  Belgium
(60.7%),  Spain  (60.5%)  and  Greece  (60.4%).  In
contrast  the  corresponding  proportion  in  the  Czech
Republic amounted to only 33%.

As  expected, 
‘High  and  Medium  high-tech
manufacturing’ was the second sector in terms of high
HRST intensity, with an average EU rate close to 26%.
In  Finland,  43.5%  of  the  persons  employed  in  this
sector  were  tertiary  educated.  Ireland  and  Spain
followed with somewhat lower proportions (40.9% and
41.9% respectively). In contrast, Italy scored the lowest
HRST  intensity  in  ‘High  and  Medium  high-tech
manufacturing’, with only 9.9%. In addition, this country
had the lowest proportion in all sectors. This is mainly
to be linked with the comparatively low level of tertiary
level education in this country.

Unemployment

This  section  provides  results  on  the  unemployment
rates  for  human  resources  in  S&T  with  tertiary
education  (HRSTU)  and  human  resources  without
tertiary education (NHRSTU).

Unemployment rates in 2006 for those with tertiary level
education  were  much  lower  than  the  unemployment
rates for those without tertiary education in all countries
shown  (Figure  4.14).  The  share  of  tertiary  educated
unemployed  reached  a  low  3%  on  average  in  the 
EU-27,  while  the  unemployment  rate  for  non-tertiary
educated amounted to 8%.

For the tertiary educated population, individual Member
States  show  smaller  deviations  from  the  EU  average.
The highest unemployment rate was found in Greece,
with  5%,  while  the  lowest  rate  was  recorded  in  the
Czech Republic (1%). The Czech Republic introduced

many  reforms  to  develop  the  market  economy  and  a
high  education  level  was  encouraged.  Workers  with
greater levels of education and training are thought to
be  more  adaptable  to  the  changes  in  jobs  that  occur
with economic transformation.

However,  finding  and  keeping  a  job  when  you  do  not
possess  tertiary  level  education  is  more  difficult.  The
EU average unemployment rate in 2004 was 8%, but as
high  as  14%  in  Poland  and  Slovakia.  The  lowest
unemployment  rate  for  non  tertiary  educated  persons
was found in Denmark and in Norway (3%). 

When  examining  whether  countries  with  high  growth
rates of HRST, such as Ireland, Luxembourg, Slovenia
and Portugal (see Table 4.10), have low unemployment
rates, it can be seen that this is not the case.

igure 4.14 

Unemployment rates for tertiary and non-tertiary educated population,

25-64 years old, EU-27 and selected countries — 2006 

%

51

41

21

9

01

8

8

8

8

8

8

8

7

6

5

5

6

6

6

4

3

3

3

3

3

3

3

3

3

3

4

4

3

3

3

3

2

2

4

5

4

4

3

2

2

2

2

2

2

2

2

1

1

1

1

7

5

1

41

8

7

7

7

01

5

0

LE

SE

RF

LP

EE

TP

ED

GB

EB

ES

72-UE

03AEE52-UE

IF

YC

KD

UL

TI

EI

IS

KU

HC

LN

VL

UH

TL

OR

ON

KS

TA

ZC

UTSRH-detacudeyraitretrofetartnemyolpmenU

UTSRHN-detacudeyraitret-nonrofetartnemyolpmenU

Exceptions to the reference year: LU, IS and CH 2005.
Eurostat estimations without LU and IS: EU-27, EU-25 and EEA-30.



75

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 76

HRST stocks at regional level

This section describes the stocks of human resources
in  S&T  (HRST)  at  regional  level.  Regional  dynamism
varies considerably in Europe.

Particular  attention  needs  to  be  paid  to  the  quality  of
regional  results.  The  size  of  the  samples,  which  are
intended  to  provide  a  representative  estimate  of  the
population of the region, can become too small and be
prone  to  sampling  errors. This  is  especially  true  when
data are disaggregated by sector of economic activity,
the main reason why data by sector of economic activity
are presented at the NUTS 1 regional level only in Table
4.16.

In any case, the guidelines provided by the European
Union  Labour  Force  Survey with  regard  to  the
minimum  levels  at  which  data  can  be  considered
reliable were strictly applied. In most cases, data were
well above the minimum sample size guidelines set for
using  the  European  Union  Labour  Force  Survey.
Data  are  flagged  as  unreliable  when  this  was  not  the
case.

Regional picture of HRST among the labour force in the
European Union

Map 4.15 illustrates the regional distribution of human
resources in S&T (HRST), as a percentage of the total
labour  force,  at  the  NUTS  2  level  in  2006.  European
regions are not equally endowed with stocks of human
resources in S&T. 

Three Belgian regions concentrated in 2006 more than
50%  of  the  total  employed  persons  in  HRST.  These
regions  were  Région  de  Bruxelles-Capitale  (52.9%),
Province  Vlaams-Brabant  (55.3%)  and  Province
Brabant Wallon (56.3%).

Differences between the regions can be seen and pools
of  concentration  can  be  quoted.  The  highest
concentrations of HRST as a share of the labour force
are found in capital regions, in regions in central Europe
and  in  the  Nordic  countries.  The  English  region  Inner
London had the highest proportion of HRST among the
labour  force.  The  proportion  was  57.2%  in  2006.  In
contrast, in the same country, the regions of East Riding
and North Lincolnshire and Lincolnshire had the lowest
proportion  of  employed  HRST  in  terms  of  the  labour
force with a share below 30%. 

In the Netherlands all regions had a proportion of HRST
above  30%  in  2006.  In  contrast,  Greece  had  regional
proportions of HRST in terms of labour force below 30%
in  the  majority  of  its  regions,  except  for  the  capital
region,  Attiki,  where  this  proportion  amounted  to  only
36.8% in 2006.

European regional policy

European  regional  policy  is  designed  to  bring  about  concrete  results,  furthering  economic  and  social  cohesion  to
reduce  the  gap  between  the  development  levels  of  the  various  regions. The  idea  is  to  create  potential  so  that  the
regions can fully contribute to achieving greater growth and competitiveness and, at the same time, to exchange ideas
and best practices. This is the main purpose of the new initiative Regions for economic change. The whole regional
policy is in line with the priorities set by the EU for growth and jobs defined by the Lisbon Strategy.

The  European  Fund  for  Regional  Development  (EFRD),  the  European  Social  Fund  (ESF)  and  the  Cohesion  Fund
contribute to three objectives:

The Convergence objective is to promote growth-enhancing conditions and factors leading to real convergence for the
least-developed Member States and regions.

The Regional Competitiveness and Employment objective aims to strengthen competitiveness and attractiveness, as
well as employment, through a two-fold approach. First, development programmes will help regions to anticipate and
promote  economic  change  through  innovation  and  the  promotion  of  the  knowledge  society,  entrepreneurship,  the
protection  of  the  environment,  and  the  improvement  of  their  accessibility.  Second,  more  and  better  jobs  will  be
supported by adapting the workforce and by investing in human resources. 

The  European  Territorial  Cooperation  objective  will  strengthen  cross-border  cooperation  through  joint  local  and
regional  initiatives,  transnational  cooperation  aimed  at  integrated  territorial  development,  and  interregional
cooperation and exchange of experience.

Source: Internet website on Regional Policy – Inforegio, European Commission, 
http://ec.europa.eu/regional_policy/index_en.htm

76



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 77

ap 4.15 

Human Resources in Science and Technology (HRST) as a percentage of  the labour force — 2006

Human resources
in Science and Technology (HRST)

as a percentage of the labour force

2006 - NUTS 2

< 20%
20 - 30%
30 - 40%
40 - 50%
> 50%
Data not available

Statistical Data: Eurostat Database: REGIO
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat - GISCO, 05/2007

0

600km

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0

100

0

10

Iceland

0 100



77

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 78

Regional differences by sector of economic activities

Table 4.16 shows the first 30 regions in Europe in terms
of  their  proportion  of  employed  HRST  having  tertiary
education  (HRSTE).  Results  for  total  manufacturing
and total services are detailed at the NUTS 1 regional
level in 2006.

the highest proportion of HRSTE in the total employed
population working in manufacturing can also be found
in the top 10 classification for services: London (UK), Île
de France (FR), Bruxelles-Capitale (BE), Noreste (ES),
Comunidad de Madrid (ES).

Generally, the share of HRSTE working in the services
sector is higher than in manufacturing. The EU average
of  HRSTE  among  total  employment  in  the  services
sector was 31.0%; the figure for manufacturing 17.1%. 

London  (UK)  had  the  highest  proportion  of  employed
HRSTE  in  the  manufacturing  industry  as  a  whole
(44.9%).  In  parallel,  in  total  services,  this  region  took
third place in terms of the share of HRSTE among total
employment (45.7%). Five of the top 10 regions having

In  contrast,  Hamburg  (DE),  which  ranked  fifth  in  total
manufacturing, showed a specialisation of the HRSTE
in this sector. This region does not appear in the top 30
regions  in  services.  The  same  remark  is  also  true  for
Lietuva  (Latvia),  ranked  fourth,  and  Kypros/Kibris,
ranked tenth in services, which do not appear in the top
30  regions  in  manufacturing.  For  these  two  countries
and also Eesti (Estonia), the fact is noteworthy as they
are countries compared to leading regions.

able 4.16 

The top 30 EU-27 regions ranked according to the proportion of  employed human resources

in terms of  education (HRSTE), in total manufacturing and in total services, 

in thousands and as a percentage of  total employment — 2006

Total manufacturing

Total services

1 000s

As % of total 
employment in 
manufacturing

Country
(Ranking)

Region — NUTS 1

1 000s

As % of total 
employment in 
services

6 709 s

17.1 s

EU-27

43 798 s

31.0 s

103

208

10

170

36

103

148

44

129

86

104

65

74

68

118

316

87

76

103

23

123

102

33

378

43

73

42

38

138

126

44.9

41.5

39.2

37.0

34.1

32.2

30.4

29.5

29.1

28.6

28.3

28.0

27.8

26.3

26.2

25.6

25.1

25.0

24.9

24.5

24.2

24.2

23.7

23.6

23.3

23.2

23.2

23.0

22.8

22.7

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

BE

ES

UK

LT

FR

BE

EE

ES

BE

CY

ES

FI

DE

IE

PL

DE

UK

DK

ES

NL

ES

DE

EL

Bruxelles-Capitale

Noreste

London

Lietuva

Île de France

Vlaams Gewest

Eesti

Comunidad de Madrid

Région Wallonne

Kypros/Kibris

Noroeste

Manner-Suomi

Berlin

Ireland

Centralny

Sachsen

Scotland

Danmark

Centro

West-Nederland

Este

Brandenburg

Voreia Ellada

24 HU

Kozep-Magyarorszag

25

26

27

28

29

30

EL

SE

PL

UK

ES

LU

Attiki

Sverige

Wschodni

South East

Sur

Luxembourg

167

606

1 372

371

1 646

770

168

952

391

105

453

675

481

534

720

480

704

775

500

1 096

1 394

294

296

332

452

1 181

419

1 121

845

54

52.6

48.7

45.7

43.2

42.9

42.4

42.2

42.2

41.6

40.6

40.2

39.8

39.7

39.6

39.3

39.1

38.8

37.7

37.7

36.5

36.2

35.8

35.8

35.7

35.4

35.3

34.9

34.9

34.5

34.4

Country
(Ranking)

Region — NUTS 1

EU-27

London

Île de France

Bruxelles-Capitale

Noreste

Hamburg

Comunidad de Madrid

South East

Berlin

Manner-Suomi

Noroeste

Sachsen

Méditerranée

Ireland

Scotland

North West

Este

East of England

South-West

Sud-Ouest

Mecklenburg-Vorpommern

Vlaams Gewest

Danmark

Eesti

Baden-Württemberg

Région Wallonne

Zuid-Nederland

Wales

Brandenburg

Centre-Est

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

UK

FR

BE

ES

DE

ES

UK

DE

FI

ES

DE

FR

IE

UK

UK

ES

UK

UK

FR

DE

BE

DK

EE

DE

BE

NL

UK

DE

FR

DE

30
Eurostat estimation without LU: EU-27.

Hessen

78



04 HRST:Part2_Chap4  20/12/2007  15:55  Page 79

4.4 Mobility

This  section  analyses  the  mobility  of  highly  qualified
individuals.  Data  on  job-to-job  mobility  can  be  defined
as  the  movement  of  employed  HRST  from  one  job  to
another, within a one-year period. These criteria do not
from
the 
inflows 
include 
unemployment or inactivity. 

labour  market 

into 

Employed HRST are those who have:

- successfully completed tertiary level education
in an S&T field of study and are employed in any
type of occupation

or

- are not formally qualified as stated above but
are employed in an S&T occupation.

Table 4.17 shows the number of employed HRST aged
25-64  years  that  have  changed  jobs  during  2005,
broken  down  by  age  groups  and  gender  in  absolute
numbers and as a percentage of total HRST population.

able 4.17 

Job-to-job mobility of  employed HRST, broken down by age group and by gender, in thousands and

as a percentage of  employed HRST population, EU-27 and selected countries — 2005

25 to 34 years old

35 to 44 years old

45 to 64 years old

Female

Male

1000s

As % of 
HRST total

1000s

As % of 
HRST total

1000s

As % of 
HRST total

1000s

As % of 
HRST total

1000s

As % of 
HRST total

Job mobile HRST

EU-27

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

:

63

: u

24

45

331

5 u

: u

22

293

413

196

5

7 u

17 u

2

25

: u

: u

46 u

124

36

40

15

15

41

23 u

421

3

35

63

:

7.5

10.6

: u

5.2

14.4

10.3

9.6 u

: u

5.6

11.9

14.2

10.0

11.6

7.8 u

9.2 u

7.0

6.6

: u

: u

:

28

: u

19

43

271

5 u

: u

16

112

166

108

2

: u

9 u

1

8

: u

: u

4.2

4.6

: u

4.1

11.9

5.2

8.9 u

: u

3.7

5.8

6.7

4.4

4.6

: u

5.9 u

4.9

2.7

: u

: u

11.7 u

31 u

6.4 u

8.0

9.5

6.3

14.1

7.1

15.0

4.9 u

14.3

15.4

13.3

14.1

:

33

8

19

6

8

28

17 u

269

2

22

43

:

2.9

3.0

3.8

6.2

3.8

8.6

3.4 u

8.8

11.9

7.5

7.8

:

:

14

: u

17

28

160

9 u

: u

4

42

96

52

1

7 u

: u

: u

11

: u

: u

14 u

37

: u

27

4

8

23

10 u

248

1

14

27

:

2.5

2.1

: u

2.5

6.0

2.5

9.3 u

: u

1.1

2.6

3.3

2.0

3.6

5.5 u

: u

: u

2.2

: u

: u

3.1 u

2.5

: u

4.0

3.0

2.9

4.9

1.2 u

6.4

5.7

3.4

3.8

:

:

49

: u

27

56

344

11

4.4

5.5

: u

3.4

9.7

5.1

8.1

: u

: u

20

230

309

171

4

9

15 u

2

23

: u

: u

42 u

95

25

42

12

16

48

23 u

444

3

33

54

:

:

3.7

8.5

7.9

5.2

7.1

4.7

4.7 u

4.8

3.4

: u

: u

7.3 u

4.1

5.2

4.3

6.9

4.4

8.6

2.5 u

9.6

10.7

6.9

7.7

:

:

:

56

: u

32

61

417

9 u

: u

21

217

366

184

4

9

4.6

5.8

: u

4.1

10.7

5.2

11.1 u

: u

3.3

6.6

8.2

5.0

6.6

7.2

17 u

7.7 u

2

21

2

: u

49 u

99

23

44

12

14

43

27 u

495

3

38

79

:

:

4.3

4.1

6.5

: u

6.6 u

5.5

5.0

5.2

8.4

4.6

8.7

3.0 u

9.4

9.9

7.7

7.8

:

:

:

TR

:
Exceptions to the reference year:  ES, AT and SE 2004; EE 2003; FR 2002.
Owing to too many missing or unreliable data, the EU aggregate in thousands has not been calculated.
The EU aggregate as % of HRST total has been calculated by only using the available countries.

:

:

:

:



79

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 80

The 25-34 year olds were most likely to move from one
job to another in 2006. In absolute numbers, the United
Kingdom and France had the highest number of mobile
HRST  with  more  than  830  000  persons  in  total.  In
relative terms, 45% of the HRST that changed jobs in
2006 were aged 25-34, whereas only 26% were found
in the 45-64 age group. 

Looking  now  at  the  gender  distribution,  there  is  not
much  difference  between  male  and  female  job-to-job
mobility. Even when looking at the EU average, female
HRST  were  slightly  less  mobile  than  their  male
counterparts  (4.4%  against  4.6%).  However,  in  the
Baltic  countries  male  HRST  seem  to  be  somewhat
more mobile than female HRST. 

Looking  at  the  proportion  of  mobile  HRST  among  the
total HRST population in the EU, Finland and Denmark
had the most mobile HRST population aged 25-34, with
a  share  of  15%  and  14.4%  respectively.  The  HRST
aged  45-64  in  Denmark  were  also  relatively  mobile
compared  to  the  other  countries  (6%)  but  the  highest
share of mobile HRST for this age group was found in
Estonia (9.3%).

Overall it can be seen that HRST in Denmark, Estonia,
Finland, the UK and Iceland are most mobile. However,
National Labour Market conditions and policies applied
in  each  EU  country  play  a  major  role  in  the  job-to-job
mobility 
(loose
legislation  for  employment  protection  plus  a  generous
social safety net for the unemployed plus high spending
on labour market policies) implemented in Denmark is
one example that encourages mobility.

flexicurity  concept 

results.  The 

What do Europeans think about mobility?

Europeans and Mobility

Europeans  have  rather  positive  views  on  the  benefits  of  long-distance  mobility  and  think  that  it  is  a  good  thing  for
individuals (46% are in favour, against 11% who think it is a bad thing), as well as for the labour market (49% are in
favour and 19% against) and for European integration (57% are for and 10% against). They are more doubtful about
the impact of long-distance mobility on family life, where 32% think the impact might be positive, but a similar share
(27%) thinks it would be negative. 

What are the views of EU citizens that have already moved long distances?

For the large majority of long-distance movers, the experience was positive. Almost half of them (46%) declare that
no aspect of their life deteriorated after the move, and a fair percentage of them have seen their job (25%) and money
(22%) situation improve together with their housing conditions (37%).

Recent data from the European Labour Force Survey illustrates that, for people already in employment, moving to
another region or country seems to increase the risk of becoming unemployed or inactive. This is partly because when
a couple moves, it is often difficult for both partners to find a job at the same time. Even so, as mentioned above, 25%
of  long-distance  movers  have  seen  their  job  situation  or  working  conditions  improve,  while  very  few  have  seen  it
deteriorate (less than 5%). In fact, moving to another country (or to another region) appears to improve the chances
of  finding  a  job  for  unemployed  and  the  inactive.  59%  of  those  who  were  unemployed  in  another  EU  country  the
previous year had found a job in the current year. This was in contrast to 35% that stayed in the same country (see
Table A). Europeans seem to be well aware of the opportunity of mobility as a solution to unemployment or the difficulty
in  finding  a  job.  Two  thirds  of  those  asked  would  be  prepared  to  leave  their  region  in  search  of  new  work.  This
percentage remains high in all countries, but varies from almost 50% in Hungary, Ireland and Malta to more than 70%
in France, the Netherlands, Poland and Sweden. More strikingly, between 25% (Austria, Ireland, Hungary) and 50%
(Poland, Luxembourg) would be prepared to move to another EU country to find a job.

Table A: Links between cross-country mobility and year-to-year job mobility in Europe

EU-15 

Country of residence 

year before 

Work Status year before 

Employed 

Unemployed 

Inactive 

Current work status 

Employed 

Same Country 

Unemployed 

Inactive 

Employed 

Other EU-15 Country 

Unemployed 

Inactive 

 94% 

35% 

5% 

 74% 

59% 

26% 

 3% 

 43% 

 2% 

 13% 

 25% 

 10% 

 3% 

 100% 

 22% 

 100% 

 93% 

 100% 

 12% 

 100% 

 16% 

 100% 

 64% 

 100% 

Source: Europeans and mobility: First results of an EU-wide survey, 
Eurobarometer survey on geographic and labour market mobility, EC, 2006

80

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04 HRST:Part2_Chap4  20/12/2007  15:55  Page 81

4.5 Nationality

The  international  mobility  of  human  resources  in
science  and  technology  (HRST)  is  illustrated  in  Table
4.18. It compares the national labour force with the non-
national labour force, where the latter is persons having
different  nationality  than  of  the  country  of  residence
(see methodological notes).

Of  the  persons  that  have  moved  to  Slovakia  to  work,
58.7%  were  HRST  when  only  29.8%  of  employed
Slovakians were HRST. In Greece the share of HRST
among  non-nationals  was  much  smaller  than  among
nationals, at only 13.7%. 

In  Luxembourg,  the  large  majority  of  the  non-national
HRST  were  citizens  from  other  EU  countries  (93.2%).
This  is  partly  explained  by  the  combination  of  being  a
small  country,  its  geographical  location  and  the
presence  of  some  major  EU  institutions  requiring  EU
qualified human resources. Conversely, in Greece and
Poland,  seven  out  of  ten  non-national  HRST  were
citizens of countries outside the EU.

able 4.18 

Human Resources in Science and Technology (HRST), 15-74 years old, by nationality, 

in thousands and as a percentage of  respective labour force and distribution of  non-nationals, 

EU-27 and selected countries — 2006

Nationals

Non-nationals

% of non-nationals

1000s

2 227

1 209

1 896

1 398

18 147

283

As % of 
respective
labour force

45.3

28.4

33.7

43.7

42.1

44.6

: u

: u

1 590

8 303

11 843

:

141

437

669

55

1 561

53

4 175

1 484

5 637

1 186

2 301

416

875

1 348

2 236

11 713

66

1 125

1 698

552

:

30.3

38.1

39.0

:

40.3

33.9

37.4

42.7

31.1

29.1

44.4

36.5

29.3

20.3

20.7

36.4

29.8

43.5

43.2

38.8

38.3

43.0

48.7

25.8

:

1000s

174

: u

20

58

1 207

52

: u

54

778

484

:

22

: u

: u

44

14

2 u

135

139

17 u

38

8 u

1 u

3 u

19

117

879

2

50

373

2

:

As % of 
respective
labour force

Having EU-27 
citizenship

Having citizenship 
outside EU-27

37.3

: u

37.0

52.3

26.0

31.2

: u

13.7

23.4

25.1

:

32.0

: u

: u

40.9

36.2

27.7 u

37.3

26.6

37.8 u

17.8

37.7 u

15.9 u

58.7 u

37.7

42.7

41.1

39.7

43.8

36.4

32.8

:

75.9

: u

45.0

41.4

46.4

: u

: u

29.6

44.3

42.8

:

59.1

: u

: u

93.2

71.4

: u

60.7

61.2

29.4 u

31.6

: u

: u

: u

52.6

52.1

35.7

:

64.0

69.2

:

:

24.1

: u

55.0

58.6

53.6

: u

: u

70.4

55.7

57.2

:

40.9

: u

: u

6.8

28.6

: u

39.3

38.8

70.6 u

68.4

: u

: u

: u

47.4

47.9

64.3

:

36.0

30.8

:

:

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

TR

Exceptions to the reference year:  ES, AT and SE 2004; EE 2003; FR 2002.
Owing to many missing or unreliable data, the EU aggregates were not calculated.



81

04 HRST:Part2_Chap4  20/12/2007  15:55  Page 82

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 83

PART3

Chapter 5 - Innovation

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 84

5.1 Introduction

Innovation is a continuous process; measuring such a
dynamic  process  is  no  straightforward  operation.  The
Community Innovation Survey (CIS) was created to add
more  details  to  the  traditional  innovation  indicators,
such  as  R&D  expenditure  and  patent  statistics.  The
general  aim  of  the  CIS  is  to  collect  innovation  data  in
order  to  provide  a  better  understanding  of  innovation
and how it relates to economic growth.

European  studies  on  innovation  apply  a  series  of
instruments to obtain data on innovation and to assess
national  innovation  performance.  The  two  main
instruments  are  the  CIS  and  the  European  Innovation
Scoreboard (EIS). The two are interlinked; the EIS uses
–  inter  alia  –  data  collected  by  Eurostat  within  the
framework of the CIS. 

This  chapter  starts  with  an  introduction  to  the
Community  Innovation  Survey  (methodology,  history
and other innovation surveys). 

It then presents in detail the results of the latest survey,
CIS 4. The results are shown first at European and then
at national level.

Another part of this chapter briefly compares CIS 4 with
CIS 3.

The final part of the chapter focuses on the European
Innovation Scoreboard.

5.2 Community Innovation Survey

CIS 4

The  Community  Innovation  Survey  (CIS)  is  a  survey
conducted  every  four  years  by  EU  Member  States  to
innovation.  The
monitor  Europe’s  progress  on 
innovation  policies  of  the  Member  States  and  the
European  Union  depend  on  the  existence  of  a  sound
statistical basis. 

innovation  on 

The  CIS  provides  this  basis.  It  creates  a  better
understanding of the innovation process and analyses
the  effects  of 
the  economy  (on
competitiveness, employment, economic growth, trade
patterns, etc.). Data gathering and analysis have been
the  various  Community  RTD
supported  under 
Framework  Programmes.  Since  2000  the  CIS  has
become  a  major  source  of  data  for  the  “European
Innovation Scoreboard”. To keep the Scoreboard up to
date,  the  Commission  has  asked  Member  States  to
carry out the CIS more frequently.

CIS  4  was  launched  in  2005  in  nearly  every  country
concerned  (the  EU  Member  States  and  candidate
Iceland)  using  a
countries,  plus  Norway  and 
harmonised  questionnaire  and  survey  method  which
define  the  structure  of  the  questions  to  be  asked  and
the  statistical  methods  to  be  used  by  the  countries
participating. 

The CIS 4 survey is based on Commission Regulation
No  1450/2004,  which  establishes  the  legal  basis  for
innovation statistics and makes it compulsory to deliver
data  on  a  number  of  basic  variables.  The
methodological basis of the CIS is provided by the Oslo
Manual, a joint publication by Eurostat and the OECD.
CIS 4 goes beyond the 1997 Oslo Manual (2nd edition)
to  include  innovative  activities  such  as  organisational
innovations  that  are  included  in  the  2005  revision.
Generally, however, it is still based on the 2nd edition of
the Oslo Manual.

The  survey  seeks  information  about  both  product  and
process  innovation  and  organisational  and  marketing
innovation.  CIS  4  collects  information  on  a  number  of
dimensions  of  innovation,  including  the  number  of
enterprises  that  innovate  by  introducing  new  or
improved  products  or  new  or  improved  processes
within the company, and the number of enterprises that
introduce  at  least  one  innovation.  The  CIS  draws  a
distinction  between  innovations  that  are  new  to  the
enterprise and those which are new to the market. 

Expenditure  on  innovation  includes  R&D,  capital
investment, training and marketing costs. Data are also
collected on protecting innovations by different kinds of
intellectual  property  rights,  such  as  patents  and
copyrights.  Most  of  the  questions  cover  new  or
significantly  improved  goods  or  services  or  the
introduction of new or significantly improved processes,
logistics  or  distribution  methods.  Organisational  and
marketing  innovation  is  covered  by  one  specific
question.

The  CIS  4  questionnaire  not  only  focuses  on  product
and process innovation, but also looks at the effects of
innovation  and  the  sources  of  information  about
innovation activities such as cooperation and examines
the factors hampering innovation. It is shorter than the
CIS 3 questionnaire and is perceived as less difficult by
the countries participating.

The period covered by the survey is 2002-2004, i.e. the
three  years  from  the  beginning  of  2002  to  the  end  of
2004. The reference year for CIS 4 is 2004. 

84

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05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 85

Data are collected by the statistical offices or competent
research institutes in the Member States. The results of
the  surveys  are  processed  at  national  level  using  a
common  methodology  and  then  further  processed  by
Eurostat  to  increase  cross-country  comparability.  To
keep  enterprise-level  information  strictly  secret,  the
micro-level  database  remains  confidential  and  is
accessible  to  Eurostat  staff  only.  The  Oslo  Manual  is
currently being revised to adapt it to new directions of
European  innovation  policy.  Among  other  things,  the
next CIS should contribute to a better understanding of

the  “non-technical”  aspects  of  innovation,  such  as
management 
techniques,  organisational  change,
design and marketing issues.

The next round of the Community Innovation Survey –
CIS 2006 – has already been launched or soon will be.
It covers data for 2004 to 2006.

CIS 2008 is also being prepared and will be launched in
2008/2009 to gather data for the reference years 2006
to 2008.

CIS – History

four  rounds  of 

Countries  have  conducted 
the
Community  Innovation  Surveys. All  four  are  based  on
the appropriate version of the Oslo Manual. With each
round, the scope of the surveys has expanded and the
number of countries participating increased. 

The first round – CIS 1, conducted in 1993 – covered
innovative activities from 1990 to the end of 1992. CIS
1 was limited to the manufacturing sector. 

The  second  round  –  CIS  2,  conducted  in  1997  –
covered activities from 1994 to the end of 1996. CIS 2
was  based  on  the  1997  revision  of  the  Oslo  Manual
(OECD,  1997)  and  was  expanded  to  include  selected
services. Different surveys were used for manufacturing
industry  and  services.  The  CIS  2  data  show  wide
variations  in  the  proportion  of  innovative  firms  across
EU countries, industries and sectors. Just over half of
the  manufacturing  enterprises  in  the  EU  innovated
between 1994 and 1996 (51%), compared with 40% of
the  services  enterprises.  The  proportion  of  innovators
ranged  from  26%  (Portugal)  to  73%  (Ireland)  in
manufacturing  and  from  13%  (Belgium)  to  58%
(Ireland) in services.

The third round – CIS 3, conducted in 2001 – covered
activities  from  1998  to  the  end  of  2000.  CIS  3  was
expanded  to  include  not  only  manufacturing  but  also
the entire services sector. 

CIS  2  and  CIS  3  differed  substantially  in  a  number  of
ways.  Because  countries  were  not  required 
to
implement  CIS  3,  there  is  no  single  questionnaire  or
collection  methodology.  The 
conceptual  and
methodological  differences  make  it  hard  to  compare
results  between  countries  within  CIS  3  or  within  any
individual country between CIS 3 and CIS 1 or CIS 2. 

The CIS 3 statistics for the EU alone show that 44% of
enterprises had innovative activities between 1998 and
2000.  More  businesses  innovated  in  manufacturing
(“industry”) than in services (47% v. 40%). 

Roughly  40%  of  enterprises  in  16  countries  had
innovating  activities,  ranging  from  a  low  of  28%  in
Greece  to  a  high  of  51%  in  Germany. As  with  CIS  2,
countries  display  wide  heterogeneity  in  all  CIS
measures  of  innovation.  Strategic  and  organisational
changes, measured for the first time in CIS 3, are more
frequent in businesses with innovative activities than in
businesses without. 

Definitional  and  methodological  issues  may  contribute
to  the  wide  variations  in  the  reported  numbers  of
innovative  firms,  over  and  above  actual  differences  in
innovative  behaviour.  Widespread  non-technological
innovations,  such  as  organisational  change,  are
probably linked to technological innovation, particularly
in  services,  but  were  not  measured  in  CIS  2.  The
definition  of  “technological”  itself  appeared  to  pose
problems.  The  word  may  have  different  meanings  in
different  languages,  and  countries  did  not  always  use
the  word  in  their  questionnaires.  Response  rates  for
CIS 3 ranged from 20% to 30% (in Belgium, Denmark
and  Germany)  to  more  than  80%  (in  France  and
Norway). Part of the difference in response rates may
be  due  to  differences  in  collection  methods.  For
example,  CIS  3  was  mandatory  in  five  countries
(Norway,  Spain,  France,  Italy  and  Sweden),  including
the  two  with  the  highest  response  rates  (France  and
Norway),  but  voluntary  elsewhere.  Non-response
analysis  for  CIS  3  reveals  differences  between
respondents and non-respondents for some countries,
but no bias in the aggregate data. 

In  order  to  obtain  more  recent  data  for  the  main
indicators on innovation in 2003, a condensed version
of  the  CIS,  called  “CIS  light”,  has  been  launched  in
several countries.



85

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 86

Innovation surveys in other countries

Australia

Canada

Australia  conducted  four  innovation  surveys  between
1993  and  2005.  The  2003  survey  covered  more
industries,  excluded  businesses  with  fewer  than  five
employees  and  was  mailed  to  a  stratified  random
sample of businesses. The 2003 survey was based on
the  1997  Oslo  Manual,  making  it  comparable  with  the
CIS  surveys.  Australia  expanded  its  2003  survey  by
adding questions on non-technological innovation. 

Australia  decided  not  to  conduct  further  separate
innovation  surveys.  Instead,  it  plans  to  introduce  an
Integrated  Business  Characteristics  Strategy  (IBCS),
which  will  collect  information  on  innovation  and
information  technology  use  in  conjunction  with  its
Business  Characteristics  Survey  (BCS).  This  strategy
will  reduce  the  total  number  of  businesses  surveyed
information  on  basic  characteristics,
because 
innovation and information technology will be requested
from  the  same  sample.  Core  questions  on  innovation
and information technology will be asked each year. In
alternate  years,  detailed  questions  will  be  asked  on
innovation  or  information  technology.  Because  the
revised strategy directly collects data on innovation and
technology  and  also  on 
the  basic  business
characteristics  for  the  same  businesses,  researchers
will have business-level micro data making it possible to
model  complex  relationships  among  these  variables
and  productivity  and  economic  growth.  In  time,
longitudinal analyses will become possible as the IBCS
contributes data to the longitudinal business database
which Australia is developing.

Source: ABS ITU Bulletin 14, August 2006

Brazil

The  Brazilian  Institute  of  Geography  and  Statistics
(IBGE)  published  the  results  of  the  Pintec  survey  on
technological innovation in enterprises conducted every
two  years.  What  conclusions  can  be  drawn  from  the
2005  round?  In  general,  the  proportion  of  innovative
enterprises  did  not  change  significantly  between  2003
and 2005 with 33.3% and 33.4% respectively.

The  decrease  from  31.1%  to  28.9%  in  the  innovation
rate  observed  for  the  smallest  enterprises  (with  fewer
than  50  employees)  was  counterbalanced  by  a  big
increase  in  the  rate  for  enterprises  with  more  than  50
employees,  from  44%  to  50.4%.  Another  interesting
figure, the percentage of enterprises that introduced a
product  innovation  to  the  national  market,  rose  from
2.7% to 3.2% but is still very low. R&D expenditure has
also  increased  to  average  from  2.5%  to  2.8%  of  net
turnover.

In  2005  numerous  measures  were  taken  in  favour  of
innovation  (for  example,  the  Property  Law  and  the
Innovation Law). We will have to wait for the results of
Pintec 2007 to know the first effects of these moves

Translated from: http://www.bulletins-
electroniques.com/actualites/50792.htm

Canada  has  conducted  a  series  of  surveys  of
innovation and technologies since the early 1990s. The
most recent Surveys of Innovation were carried out in
2003 and 2005. 

The 2003 Survey of Innovation covered information and
communication 
industries,  selected
professional, scientific and technical services, selected
natural  resource  support  services  and  selected
transport industries. 

technology 

The 2005 Survey of Innovation surveyed manufacturing
and  logging  industries  for  the  reference  period  2002-
2004. 

The surveys were based on the relevant version of the
Oslo Manual. It is mandatory for Canadian businesses
to  respond  to  the  surveys.  The  response  rate  for  the
2005 survey was 72%.

Source: Canada Statistics

China

There is no recent innovation survey of China but a full
picture  of  the  state  of  the  art  in  innovation  in  China  is
given  in  the  OECD  Review  of  China’s  National
Innovation System.

http://www.oecd.org/dataoecd/54/20/39177453.pdf

New Zealand

In  2005,  52%  of  New  Zealand  businesses  reported
innovation activity. The rate includes businesses which
implemented  innovations  (47%)  and  businesses  with
ongoing  innovations  or  which  abandoned  innovations
(5%).

The innovation rate increases with the size of business:
68%  of  businesses  with  100  or  more  employees,
compared with 46% of businesses with six to nine staff.

The  industries  with  the  highest  innovation  rates  are
finance and insurance (68%) and manufacturing (65%).

A  small  number  of  other  countries  have  conducted
innovation surveys based on the definition in the Oslo
Manual  (2005).  Only  high-level  comparisons  can  be
made  between  countries,  because  of  differences
between  survey  design  and  methodology,  population
and  reference  periods.  The  innovation  rate  in  New
Zealand  is  higher  than  in  France  (46%)  and  Norway
(40%).

Source: Statistics New Zealand, 
Innovation in New Zealand 2005

86

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05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 87

South Africa

The  Centre  for  Science,  Technology  and  Innovation
Indicators  (CeSTII)  conducted  the  first  official  South
African Innovation Survey.
The  broad  objectives  of  the  2005  South  African
Innovation Survey were to:

-  produce  a  set  of  internationally  comparable
data and indicators providing insights into the
patterns  of 
the  mining,
manufacturing  and  services  sectors  in  South
Africa;

innovation 

in 

-  collect  information  on  the  sources  and

resources for innovation in enterprises;

-  provide  an  indication  of  the  extent  of  public
funding  for  innovation  activities  that  is  taken
up by enterprises;

- draw national and international comparisons of

innovation intensity; and

-  obtain  an  understanding  of  the  importance  of
R&D  and  non-R&D  based  innovation  in
different sectors.

The current survey is closely based on the fourth round
of the European Community Innovation Survey (CIS 4)
and CeSTII worked closely with the OECD and Eurostat
on this point.

Nearly  52%  of  South  African  enterprises  had
technological  innovation  activities,  comprising  both
product (goods and services) and process innovations.
A further 11% reported only marketing or organisational
innovations. South African levels of innovation compare
favourably  with  other  countries  such  as  Sweden,  the
United Kingdom and Portugal. In a previous Innovation
Survey 
of
Pretoria/Eindhoven  University,  2003)  44%  of  the
enterprises  were  recorded  as  innovative  over  the
survey period 1998-2000, which compared well with EU
countries at the time.

(University 

South 

Africa 

in 

Source: Centre for Science, Technology 
and Innovation Indicators (CeSTII), 
The South African Innovation Survey 2005

5.3 EU-27 aggregates

There  is  a  strong  correlation  between  innovation  activity  and  size  of
enterprise

Figure  5.1  shows  that  the  proportion  of  innovative
enterprises  increases  significantly  with  the  size  of
enterprise. 

In the EU-27, 71% of the large enterprises, 53% of the
medium-sized  and  35%  of  the  small  enterprises  are
innovative.  Although 
for  small
enterprises  are  much  lower  than  for  large  firms,
nevertheless  more  than  one  in  three  is  actively
innovating.

the  percentages 

It  should  be  borne  in  mind  that  in  the  Community
Innovation Survey (CIS) small enterprises have at least
10  employees.  Self-employed  entrepreneurs  and  very
small enterprises are not covered by the CIS. This does

not  mean  that  these  enterprises  are  not  actively
innovating, but they may not innovate in the same way
and  for  this  reason  are  not  comparable  with  larger
enterprises. 

Research  and  development  (R&D)  are  essential  for
innovation in new goods and services. A certain size is
necessary if an enterprise is to be continuously active
in  R&D.  Many  small  and  medium-sized  enterprises
cannot pay one or more employees to work full time on
R&D. The problem with an R&D employee is that he or
she  does  not  necessarily  produce  something  that  can
be sold in the short run. Many technical products need
long years of research.



87

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 88

igure 5.1 

EU-27 innovative and non-innovative enterprises by size class, 

as a percentage of  all enterprises — 2004

08

07

06

05

04

03

02

01

0

sesirpretneevitavonnI

sesirpretneevitavonni-noN

seeyolpme94-01

seeyolpme942-05

seeyolpme052nahterom

igure 5.2 

EU-27 innovative and non-innovative enterprises by selected NACE, 

as a percentage of  all enterprises — 2004

08

07

06

05

04

03

02

01

0

eroC-ECANllA
ECAN

yrtsudnilatoT
gnidulcxe(
)noitcurtsnoc

gnirutcafunaM

K_ot_GeroC
secivreS

,tropsnarT
dnaegarots
noitacinummoc

laicnaniF
noitaidemretni

eroC:K
egarevoc

rehtO:eroC47
ssenisub
secivres

sesirpretneevitavonnI

sesirpretneevitavonni-noN

88



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 89

Close to 40% of all EU enterprises are innovative. This
figure varies, depending on the economic activity of the
enterprises.  The  rate  for  transport,  storage  and
communication is noticeably lower than the average for
the  entire  NACE,  but 
financial
intermediation  and  for  economic  activities  classified  in
NACE section K (core coverage) is much higher. 

the  score 

for 

The  K  core  coverage  includes  computer  and  related
activities  (NACE  72),  architectural  and  engineering
activities  (NACE  74.2)  and  technical  testing  and
analysis  (NACE  74.3).  The  proportion  of  innovative
enterprises active in other business services which are
part  of  section  K  (47%)  is  higher  than  the  overall
average  for  all  economic  activities.  Comparing  the
whole of NACE section K with other business services
reveals  that  53%  of  the  enterprises  in  the  K  core
coverage  are  innovative.  Unsurprisingly,  the  highest
shares  of  innovative  enterprises  can  be  found  among
computer and related activities.

The  Community 
Innovation  Survey  not  only
differentiates  between  innovative  and  non-innovative
enterprises but also provides a further breakdown of the
enterprises  with  innovation  activities.  Enterprises  with
only  organisational  and/or  marketing  innovations  are
not considered innovative (see flowchart below).

The percentages shown in Figure 5.3 do not add up to
100%  because  data  are  missing  for  some  countries.
Nevertheless,  the  figure  gives  several  pieces  of
information.  Novel  innovators  make  up  by  far  the
largest group of innovative enterprises. Only 3% of the
innovative  enterprises  are  established  innovators  with
only  ongoing  and/or  abandoned  innovation  activities.
Product and process innovation are often linked. Some
16% of the novel innovators are active in both types of
innovation. The percentage of enterprises with new or
significantly  new  products  is  higher  than  those
introducing innovative processes.

sesirpretnellA

ytivitcanoitavonnihtiwsesirpretnE

rotavonnissecorpro/dnatcudorP

sesirpretneevitavonni-noN

ro/dnagniognoylnohtiwsesirpretnE
seitivitcanoitavonnidenodnaba

ylnorotavonnitcudorP
ylnorotavonnissecorP
rotavonnissecorpdnatcudorP

igure 5.3 

EU-27 enterprises by type of  innovator, as a percentage of  all enterprises — 2004

0

01

02

03

04

05

06

07

sesirpretneevitavonnI

5.93

,srotavonnilevoN
ylnotcudorp

3.21

,srotavonnilevoN
ylnossecorp

8.8

,srotavonnilevoN
ssecorpdnatcudorp
srotavonni

7.51

,srotavonnidehsilbatsE
ro/dnagniogno
noitavonnidenodnaba
ylnoseitivitca

5.2

evitavonni-noN
sesirpretne

Data missing on non-innovative enterprises for LV and UK; on types of innovator also for SI.



8.06

89

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 90

igure 5.4 

EU-27 innovative and non-innovative enterprises by markets, 

as a percentage of  all enterprises — 2004

54

04

53

03

52

02

51

01

5

0

tekramlanoitaN

tekramlanoiger/lacoL

-UEro/dnaATFE,UErehtO
seirtnuocCC

yrtnuocrehtoynA

Data missing for AT, FI, IE, LV, PL, SI and UK; on local/regional market also for BE and LT.

retneevitavonnI

p

sesir

retneevitavonni-noN

p

sesir

The  enterprises  surveyed  were  asked  about  the
geographical markets on which they sell their goods or
services. Although the data for the EU-27 in Figure 5.4
do not cover all the Member States, the results reveal
some differences between the geographical markets of
innovative and non-innovative enterprises.

Not only the rankings of the markets differ, but also the
shares of each subgroup. Whereas the non-innovative
enterprises  recorded  higher  percentages  on  their
local/regional and national markets than the innovative
enterprises,  the  opposite  was  the  case  for  the
European and non-European markets.

First of all, the ranking of the markets is not the same
for both groups. For innovative enterprises the national
market  is  the  most  important,  followed  by  the
local/regional market. The European market ranks third
and the “any other country” market fourth.

For  the  non-innovative  enterprises  the  local/regional
market  is  the  most  important  and  the  national  market
comes second, followed by the European market. As for
innovative  enterprises,  the  “any  other  country”  market
ranked last.

The  analysis  of  the  markets  should  take  that  into
account  because  of  transport  costs.  Indeed,  in  many
cases  markets  geographically  closer  to  the  enterprise
have a cost advantage. 

For  countries  outside  Europe  not  only  the  cost  of
transport  but  also  taxes,  legal  and  administrative
barriers may act as a hindrance, further explaining the
lower shares of these markets. 

90



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 91

igure 5.5 

EU-27 innovative enterprises and non-innovative enterprises by number of  employees, 

as a percentage of  employment of  all enterprises, in 2002 and 2004

65.2

64.7

34.8

35.4

70

60

50

40

30

20

10

0

Employment in 2002

Employment in 2004

Innovative enterprises

Non-innovative enterprises

Data missing for AT, LV, SI and UK; for 2002 also for FI and IE; on non-innovative enterprises also for IE.

Figure  5.5  compares  employment  in  innovative  and
non-innovative enterprises in 2002 and in 2004. 

Although data are missing for several countries and this
reduces the likelihood of forming a very precise picture
of  the  situation  in  the  EU-27,  it  can  be  said  that,  as
mentioned  earlier,  innovative  enterprises  made  up
about 40% of all enterprises in 2004. Taking only large
enterprises into account, this percentage rose to more
than 70%. Combining these two pieces of information,
it  is  not  very  surprising  that  close  to  two  thirds  of  the
jobs are provided by innovative enterprises.

Comparison  of  the  2002  and  2004  data  reveals  only
very  small  differences.  The  share  of  employment  in
non-innovative enterprises increased very slightly to the
detriment of innovative enterprises.

Figure  5.5  shows  only  percentages,  but  in  absolute
figures  employment  in  the  EU-27  increased  by  an
annual  average  growth  rate  of  1%  between  2002  and
2004  (taking  into  account  only  the  data  from  the
countries for which figures are available).

The growth rates for individual countries may be quite
different. They  are  shown  in Table  5.23  in  the  section
presenting the national data.



91

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 92

igure 5.6 

EU-27 innovative enterprises, turnover of  new or significantly improved products only new to the firm

and turnover of  new or significantly improved products new to the market by size class, 

as a percentage of  total turnover — 2004

5.8

3.9

2.3

5.3

1.5

2.4

01

9

8

7

6

5

4

3

2

1

0

stcudorpdevorpmiyltnacifingisrowenforevonruT
tekramehtotwen

stcudorpdevorpmiyltnacifingisrowenforevonruT
mrifehtotwenylno

olpme94-01

y

see

olpme942-05

y

see

olpme052nahteroM

y

see

Figure  5.6  shows  the  EU-27  turnover  from  new  or
significantly  improved  products  which  are  new  to  the
market and from new or significantly improved products
only new to the firm, as a percentage of total turnover. 

The  indicator  on  the  left  is  defined  as  turnover  in
products that are also new to the market and may also
include  world  firsts.  One  drawback  of  this  indicator  is
that  enterprises  may  not  know  if  their  innovative
products are really new to the national or global market
or only new to their own market. The term “market” can
be defined very strictly or more broadly. 

As  the  products  covered  by  the  indicator  on  the  right
are not new to the market but only to the enterprise, the
sales of these products can be used as a proxy for use

of  products  (or  technologies)  already  introduced
elsewhere.  This  indicator  measures  the  degree  of
technology dissemination. 

Both  indicators  are  broken  down  by  the  size  class  of
innovative  enterprises.  For  both  indicators  the  largest
enterprises  have  by  far  the  highest  turnover.  The
percentages  of  turnover  from  new  or  significantly
improved  products  which  are  new  to  the  market  are
slightly lower than the figures for turnover from new or
significantly improved products that are new to the firm. 

The difference in the shares of small and medium-sized
enterprises is quite low for turnover from products new
to  the  market  but  more  marked  for  turnover  from
products new to the firm.

92



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 93

igure 5.7 

EU-27 innovative enterprises engaged in intramural R&D, extramural R&D, 

acquisition of  machinery, equipment and software, acquisition of  other external knowledge, 

training, market introduction of  innovation and other preparations, 

as a percentage of  innovative enterprises — 2004

08

07

06

05

04

03

02

01

0

fonoitisiuqcA
,yrenihcam
dnatnempiuqe
erawtflos

D&RlarumartnI

gniniarT

noitcudortnitekraMsnoitaraperprehtO

noitavonnifo

rehtofonoitisiuqcAD&RlarumartxE
egdelwonklanretxe

Missing Data missing for AT, FI, LV, SI and UK; on training, other preparations and market introduction of innovation also for IE.

The involvement of innovative enterprises in R&D can
take  very  different  forms.  About  two  thirds  of  all
innovative  enterprises  in  the  EU-27  are  engaged  in
acquisition of machinery, equipment and software. But
for  this  indicator,  not  all  acquisitions  of  machinery,
equipment  and  software  are  taken  into  account.  The
definition  of  the  indicator  includes  only  acquisition  of
advanced  machinery,  equipment  and  computer
hardware  or  software  to  produce  new  or  significantly
improved products and processes.

More than half of the innovative enterprises are active
in  intramural  R&D.  Intramural  R&D,  also  called  in-
house R&D, consists of creative work undertaken within
the  enterprise  to  increase  its  stock  of  knowledge  and
use thereof to devise new and improved products and
processes (including software development).

In  nearly  one  out  of  every  two  innovative  enterprises,
employees  are  involved  in  training  activities.  The
training  can  be  provided  for  personnel  internally  or
externally  but  should  be  aimed  specifically  at
developing  and/or  introducing  new  or  significantly
improved products and processes.

More than one third of the innovative enterprises in the
EU-27  are  engaged  in  other  preparations.  This  term
covers  procedures  and  technical  preparations  to
introduce  new  or  significantly  improved  products  and
processes that are not covered elsewhere. 

Introduction of new or significantly improved goods and
services  onto  the  market  —  in  other  words  innovation
— , including market research and launch advertising,
concerns  almost  one  out  of  every  three  innovative
enterprises in the EU-27.

Outsourcing and buying R&D or other knowledge from
other  companies  (including  other  enterprises  in  the
same  group)  or  from  public  or  private  research
organisations are the options chosen least frequently. 

Only  slightly  over  20%  of  the  innovative  enterprises
declared that they were engaged in extramural R&D or
in acquisition of other external knowledge. 

Acquisition  of  other  external  knowledge  includes
purchasing  or  licensing  patents  and  non-patented
inventions,  know-how  and  other  types  of  knowledge
from other enterprises or organisations.



93

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 94

Commission welcomes Member States’ agreement 
on the European Institute of Technology

The  Competitiveness  Council  today  agreed  on  a  general  approach  for  the  European  Institute  of  Technology  (EIT),
proposed  by  the  European  Commission  in  October  2006.  By  combining  the  worlds  of  academia,  research  and
business,  the  EIT  will  be  a  flagship  for  excellence  in  innovation,  research  and  higher  education.  Subject  to  the
European  Parliament’s  agreement  later  this  year,  the  EIT  should  be  able  to  begin  operations  in  2008.  It  will  be
organised  on  the  basis  of  “Knowledge  and  Innovation  Communities”  (partnerships  of  universities,  research
organisations,  companies  and  other  stakeholders  in  the  innovation  process)  and  coordinated  by  a  small  governing
structure. 

Welcoming  the  Council’s  agreement,  European  Commission  President  José  Manuel  Barroso  said: "This  is  a  very
important step forward, bringing the EIT closer to lift-off. By strengthening Europe's capacity to bridge the innovation
gap with its major competitors, the EIT will help drive a Europe of results. It will help us boost jobs and growth in a
lasting and environmentally sustainable way. The Commission is grateful for the German Presidency's strong support
for  the  EIT  proposal  and  looks  forward  to  working  with  the  future  Portuguese  Presidency  and  with  the  European
Parliament to reach a final agreement and to get the EIT operational as soon as possible."

The EIT aims to integrate and boost innovation, research and higher education by pooling the best resources available
at European level and beyond. 

For  the  first  time,  Europe  will  promote  the  development  of  “Knowledge  and  Innovation  Communities”  (KICs),
partnerships of universities, research organisations, companies and other stakeholders. These will perform innovation
activities, cutting-edge and innovation-driven research, and postgraduate education and training activities. Each KIC
will develop activities in an area of key interest for businesses and citizens. For the selection of initial KICs, priority
EU policies, such as renewable energy and climate change, will be taken into account. 

The involvement of business at all levels, both strategic and operational, will be a cornerstone of the project. Business
will be strongly represented on the Governing Board of the EIT. 

The funding of the EIT will come from a variety of sources; including a contribution directly from the Community budget
– an amount of €308.7 million. This will cover the costs of the EIT’s governing structure and the European dimension
of the project, notably the costs of coordination and mobility that are necessary to sustain the KICs. 

Once the European Parliament and the Council of Ministers reach a final agreement and adopt the Regulation, the
EIT Governing Board will be appointed and the EIT structure and secretariat established. The selection of the first
KICs will take place no later than two years after the appointment of the Governing Board. 

For more information please see: http://ec.europa.eu/education/policies/educ/eit/index_en.html 

94



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 95

igure 5.8 

EU-27 expenditure by innovative enterprises on intramural R&D, extramural R&D, 

acquisition of  machinery, equipment and software, acquisition of  other external knowledge, 

as a percentage of  total innovation expenditure — 2004

05

54

04

53

03

52

02

51

01

5

0

D&RlarumartnI

,yrenihcamfonoitisiuqcA
erawtflosdnatnempiuqe

D&RlarumartxE

lanretxerehtofonoitisiuqcA
egdelwonk

Data missing for AT, FI, LV, SI and UK; on extramural R&D also for SE.

The innovative enterprises were also asked to estimate
their  expenditure  on  four  types  of  innovation  activity.
Figure  5.8  shows  the  estimated  percentages  of  total
innovation expenditure for the EU-27. 

On  average,  innovative  enterprises  spent  more  than
45% of their total innovation expenditure on intramural
R&D  in  2004.  This  is  the  highest  share  for  any
individual  innovation  activity,  because  in-house  R&D
covers  not  only  the  salaries  and  related  costs  for  the
research  personnel  but  also  capital  expenditure  on
buildings and equipment specifically for R&D.

Around  30%  of  the  innovation  expenditure  of  the
innovative  enterprises  went  on  acquisition  of
machinery, equipment and software.

Extramural  R&D  made  up  10%  of  the  innovation
expenditure. This is a much lower amount than for in-
house  R&D.  Strategically,  it  may  be  better  for  an
enterprise to keep most of its R&D in-house. 

The  lowest  share  (5%)  of  innovation  expenditure  by
innovative  enterprises  was  used  for  purchasing  other
external knowledge.



95

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 96

igure 5.9 

EU-27 innovative enterprises, highly important effects of  innovation, 

as a percentage of  innovative enterprises — 2004

04

53

03

52

02

51

01

5

0

devorpmI
niytilauq
rosdoog
secivres

desaercnI
sdoogfoegnar
secivresdna

wenderetnE
rostekram
desaercni
erahstekram

devorpmI
foytilibixelf
ronoitcudorp
ecivres
noisivorp

desaercnI
foyticapac
ronoitcudorp
ecivres
noisivorp

noitalugerteM
stnemeriuqer

decudeR
stsocruobal
tuptuotinurep

decudeR
dnaslairetam
tinurepygrene
tuptuo

decudeR
latnemnorivne
rostcapmi
devorpmi
dnahtlaeh
ytefas

Missing Data missing on “Reduced labour costs per unit output” and “Met regulation requirements” for UK.

p

The  highly  important  effects  of  innovation  shown  in
Figure  5.9  reflect  the  reasons  why  enterprises  in  the
EU-27 innovate. 

The three effects considered highly important by most
innovative  enterprises  are  typical  sales  targets:  better
quality, more choice and higher turnover.

the 

38%  of 
innovative  enterprises  considered
“improved  quality  in  goods  and  services”  a  highly
important  effect.  “Increased  range  of  goods  and
services” and ‘“entered new markets or increase market
share” scored 34% and 29% respectively.

Close to 25% of the innovative enterprises classified as
highly important one or both effects that, more or less,
concern  the  internal  organisation  of  the  enterprise.
“Improved  flexibility  of  production/service  provision”
may  also  lead  to  better  and/or  higher  production  but
primarily  develops  the  performance  of  the  enterprise.
The  same  applies 
“increased  capacity  of
production/service  provision”.  If  more  is  produced,
more  can  be  sold.  This  aim  may  be  achieved  by
replacing old by new and highly productive machinery.

to 

The  four  remaining  effects  were  chosen  as  highly
important  by  under  20%  of  the  innovative  enterprises.
For the last effect the figure even fell below 10%. 

from  outside 

the
Regulations  generally  come 
enterprise.  National  or  European  authorities  force  the
business  sector  to  meet  regulatory  requirements.  If
enterprises  innovate  to  meet  regulatory  requirements,
they  do  so  less  by  choice  but  more  because  they  are
under an obligation to do so. 

The last three effects do not seem to be the main aims
of  the  innovative  enterprises,  but  they  are  considered
more or less positive collateral effects of innovation. 

Reducing  environmental  impact  or  improving  health
and safety does not concern the innovating enterprise
alone  but  has  a  positive  effect  on  society  as  a  whole.
The enterprise may use this effect as a sales argument. 

Reducing  material  and  energy  consumption  and
reducing  labour  costs  both  have  a  positive  impact  on
production costs. Lower costs open up the possibility to
cut  prices  and  sell  more.  Another  choice  may  be  to
leave  prices  the  same  and  make  bigger  profits  which
can  then  be  invested,  for  example  in  innovative
activities. 

But in most cases the idea of innovation is associated
with  new  products  or  services  rather  than  with
improving those that already exist.

96



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 97

igure 5.10 

EU-27 innovative enterprises, public funding broken down by sources, 

as a percentage of  all enterprises — 2004

9.8

01

9

8

7

6

5

4

3

2

1

0

1.5

8.4

9.1

9.0

ynadeviecertahtesirpretnE
gnidnufcilbup

deviecertahtesirpretnE
lartnecmorfgnidnuf
gnidulcni(tnemnrevog
tnemnrevoglartnec
)seirtsinimroseicnega

deviecertahtesirpretnE
rolacolmorfgnidnuf
seitirohtualanoiger

deviecertahtesirpretnE
naeporuEehtmorfgnidnuf
noinU

deviecertahtesirpretnE
ht6roht5ehtmorfgnidnuf
emmargorPkrowemarF

Data missing on enterprises that received any public funding for IE, LV, SE, SI and UK; on breakdown also for PL and on Framework Programmes also
for MT.

Close to 9% of the innovative enterprises (expressed as
a percentage of all enterprises in the EU-27 countries
for  which  data  are  available)  received  public  funding
during  the  reference  period.  This  percentage  is  an
average and may vary substantially across countries.

FPs  have  been  implemented  since  1984  and  cover  a
period  of  five  years,  with  the  last  year  of  one  FP
overlapping  with  the  first  year  of  the  next.  As  the
reference  period  for  CIS  4  was  2002  to  2004,  two
different programmes were concerned:

Figure 5.10 also shows the breakdown by sources. On
average, central government was the source of funding
for 5% of the innovative enterprises.

Nearly  the  same  percentage  of  innovative  enterprises
received funding from a local or regional administration.

Conversely,  under  2%  of  the  innovative  enterprises
received funding from a European authority. Nearly half
of the innovative enterprises that received funds from a
European source were in fact financed by the 5th or 6th
Framework Programme.

Framework  Programmes  (FPs)  have  been  the  main
channels  through  which  the  European  Union  provides
financial  support  for  research  and  development
activities  covering  almost  every  field  of  science.  FPs
are  proposed  by  the  European  Commission  and
adopted  by  the  Council  and  the  European  Parliament
following the co-decision procedure. 

•  the  Fifth  Framework  Programme  of  the
research,
and

European  Community 
technological 
demonstration activities (1998–2002);

development 

for 

•  and  the  Sixth  Framework  Programme  for
Research  and  Technological  Development
(FP6) (2002–2006).

The current FP – FP7 – has been in operation since 1
January  2007  and  will  expire  in  2013.  It  is  the  first  to
cover a period of seven years. It is designed to build on
the  achievements  of  its  predecessor  towards  creating
the European Research Area and to go further towards
developing a knowledge-based economy and society in
Europe.



97

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 98

igure 5.11 

EU-27 innovative enterprises by type of  cooperation, as a percentage of  all enterprises — 2004

21

01

8

6

4

2

0

fosepytllA
noitarepooc

rehtO
sesirpretne
ruoynihtiw
esirpretne
puorg

srotitepmoC
rehtoro
fosesirpretne
emaseht
rotces

rostneilC
sremotsuc

fosreilppuS
,tnempiuqe
,slairetam
stnenopmoc
erawtfosro

seitisrevinU
rehgihrehtoro
noitacude
snoitutitsni

tnemnrevoG
cilbupro
hcraeser
setutitsni

,stnatlusnoC
laicremmoc
ro,sbal
D&Retavirp
setutitsni

At  EU-27  level,  about  10%  of  innovative  enterprises
cooperated  with  other  enterprises  and  universities,
public research institutes and the like. The data shown
draw  no  distinction  between  enterprises  cooperating
with one or more partners.

As  public 
funding  programmes  aim  mostly  at
strengthening  cooperation  between  the  business
enterprise  sector  and 
two  sectors
(government and higher education), the results are very
interesting. 

the  other 

Who  are  the  cooperation  partners  of  innovative
enterprises? The most important appear to be suppliers
of  equipment,  materials,  components  or  software,

followed  by  clients  or  customers.  These  forms  of
cooperation are facilitated by commercial links already
existing between the partners involved. The same also
applies to the type of partner which ranked third (“other
enterprises within your enterprise group”) with close to
4%.

The  other  four  types  of  partners  scored  between  2%
and 3% of the innovative enterprises at EU level.

Public-sector institutions are among the least frequently
used  cooperation  partners.  The  links  between  the
business  enterprise  sector  and  both  the  government
and the higher education sectors seem quite weak.

98



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 99

igure 5.12 

EU-27 innovative enterprises by source of  information, as a percentage of  all enterprises — 2004

02

81

61

41

21

01

8

6

4

2

0

ehtnihtiW
roesirpretne
puorgesirpretne

rostneilC
sremotsuc

fosreilppuS
,tnempiuqe
,slairetam
stnenopmoc
erawtfosro

srotitepmoC
rehtoro
sesirpretne
ehtfo
rotcesemas

,secnerefnoC
,sriafedart
snoitibihxe

cifitneicS
slanruoj
/edartdna
lacinhcet
snoitacilbup

,stnatlusnoC
laicremmoc
rosbal
D&Retavirp
setutitsni

lanoisseforP
dna
yrtsudni
snoitaicossa

seitisrevinU
rehtoro
rehgih
noitacude
setutitsni

rotnemnrevoG
hcraesercilbup
setutitsni

Data missing for AT, LV, SE, SI and UK; on “Consultants, commercial labs or private R&D institutes” and “Professional and industry associations” also
for PL.

Not  only  cooperation  but  also  information  plays  a  key
role in knowledge transfer and innovation.

fact  that  these  two  sources  of  information  are  also
among the most important cooperation partners.

The  enterprises  surveyed  were  offered  a  choice
between ten different sources of information.

All  other  sources  of  information  fell  short  of  the  5%
mark.

Figure  5.12  shows  the  results  for  the  innovative
enterprises.

The  enterprise  itself  or  its  own  group  clearly  led  the
ranking of sources of information, with close to 18% of
the  innovative  enterprises.  This  result  may  be  a  bit
surprising  at  first  glance,  but  a  short  analysis  shows
that it is perfectly in line with the other results of CIS 4.
Innovative  enterprises  are  often  large  and  many  of
them are also part of a group. They frequently conduct
intramural  R&D  but  do  not  cooperate  so  much.
However,  as  mentioned  earlier,  information  is  vital  for
innovation so the enterprise itself or its own group is the
most obvious choice as a source of information. 

Clients  and  customers  ranked  second  with  10%  and
suppliers  of  equipment,  materials,  components  or
software  third  with  9%.  This  can  be  explained  by  the

The  proportion  of  innovative  enterprises  that  used
private  sources  of  information,  such  as  conferences,
consultants  or  professional  associations,  was  higher
than in the public sector.

At the other end of the scale, the least used sources of
information  are  the  higher  education  and  government
sectors.  It  would  be  interesting  to  investigate  further
why these sources are the least used. There might be
barriers  to  using  these  sources  of  information.
Moreover, 
the  EU  average  may  mask  national
differences. 

Basically, these results are another sign of the weak link
between the public and private sectors. 



99

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 100

igure 5.13 

EU-27 innovative enterprises by highly important hampering factor,

as a percentage of  innovative enterprises — 2004

25

20

15

10

5

0

Innovation costs 
too high

Lack of 
funds within 
your enterprise
or
enterprise group

Lack of 
finance from 
sources outside 
your enterprise

Markets
dominated
by established 
enterprises

Uncertain
demand for 
innovative goods 
or services

Lack of 
qualified
personnel

Difficulty in 
finding
cooperation
partners
for innovation

Lack of 
information
on markets

Lack of 
information
on technology

Data missing on “lack of finance from sources outside your enterprise” and “lack of funds within your enterprise or enterprise group” for UK; on “difficulty
in finding cooperation partners for innovation” for UK and PL; and on “markets dominated by established enterprises” for PL.

Numerous factors hamper enterprises from carrying out
innovation activities.

The  innovative  enterprises  were  asked  to  rank  nine
different factors hampering innovation.

Figure  5.13  ranks  the  results  by  the  proportions  of
innovative  enterprises  that  considered  the  factors
concerned a significant hindrance.

The first three factors are financial: costs are too high
and financing too low. The fact that innovation is mostly
very expensive cannot be denied. Enterprises that are
doing  in-house  R&D  have  to  invest  in  technical
equipment and pay salaries for highly skilled personnel.
These costs are high and very often the outcome is not
immediate  and  does  not  emerge  until  in  the  long  run.
Once an invention is made, it may also be important to
protect 
it.  Protection  methods  such  as  patent
applications in turn require funds. 

The  next  three  factors  concern  the  market  situation:
domination  by  established  enterprises,  uncertain
demand and unsuitable job market. On many markets
competition  is  very  tough  and  product  cycles  are
becoming shorter. Some markets are saturated and it is
not  easy  continuously  to  come  up  with  ideas  for  new
products and services.

At the end of the scale, there are two factors that have
already been analysed in response to other questions:
cooperation and information.  

The last three factors mentioned did not reach the 10%
mark  as  highly  significant  hindrances.  This  result  is
somewhat  surprising.  On  the  one  hand,  Figures  5.11
and 5.12 show that innovative enterprises are not very
active  on  cooperation  and  do  not  seem  to  use  the
sources of information as intensively as they might. But
on  the  other  Figure  5.13  leaves  the  impression  that
really  consider
innovative  enterprises  do  not 
information  and  cooperation  as  key  elements  of
innovation.

This  analysis  of  hampering  factors  must  take  into
account that the EU-27 values only indicate trends. The
situation  may  be  somewhat  different  at  national  level.
Moreover,  even  at  national  level,  the  barriers  to
innovation  may  not  be  equally  distributed  between
enterprises. Instead, there are typical sets of barriers to
innovation,  depending  on  the  age,  size,  industry  and
innovativeness  of  the  firm.  For  this  reason,  global
solutions may not be efficient and the current trend is to
identify  clusters  and  find  solutions  adapted  to  their
needs.

100



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 101

igure 5.14 

EU-27 innovative and non-innovative enterprises by protection method, 

as a percentage of  all enterprises — 2004

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

thgirypocdemialC

lairtsudninaderetsigeR
ngised

kramedartaderetsigeR

tnetaparofdeilppA

sesirpretneevitavonnI

sesirpretneevitavonni-noN

Data missing for AT, LV, SE, SI and UK; on non-innovative enterprises which claimed copyright or registered a trademark also for MT.

In  most  cases,  innovation  implies  intensive  use  of
human  and  financial  resources.  For  this  reason,  the
outcome  of  innovative  processes,  such  as  inventions,
needs to be protected.

There are different methods, depending on what has to
be protected. A patent application is used for a technical
invention,  whereas  a  trademark  protects  a  specific
name.

Further details on intellectual property rights are given
in Section 5.4, which presents the national data. CIS 4
focuses on four methods of protection:

• claimed copyrights;

• registered an industrial designs;

• registered a trademarks

• applied for a patent.

level  by 

Figure 5.14 compares the protection methods used at
innovative  and  non-innovative
EU-27 
for  non-
the  scores 
enterprises.  Unsurprisingly, 
innovative  enterprises  are  much  lower  for  all  four
methods than those recorded by innovative enterprises. 

Innovative  enterprises  mostly  have  recourse 
to
trademarks.  Industrial  design  and  patents  are  used  in
equal  proportions.  Copyrights  are  used  less  because
they mainly cover non-technical works.

At a much lower level, non-innovative enterprises make
broadly  similar  use  of  protection  methods.  They  use
trademarks  most  frequently,  followed  by  registering
industrial  designs.  By  contrast,  non-innovative
enterprises  rarely  apply 
for  patents  and  claim
copyrights even less.

“Non-innovative” just means that the enterprise has not
developed any new product or introduced any process
innovation.  However,  as  revealed  in  Figure  5.15,  non-
innovative  enterprises  can  be  active  in  organisational
and/or  marketing  innovations,  but  are  less  active  in
these  two  areas  than  innovative  enterprises.  For  both
groups 
in
organisational innovations is higher than the proportion
carrying out marketing innovations.

the  proportion  of  enterprises  active 

As  in  the  case  of  product  and  process  innovators,
marketing  and  organisational  innovation  are  related.
The proportion of enterprises introducing organisational
and/or marketing innovations is lower than the sum of
the  enterprises  active  in  one  or  the  other  type  of
innovation because some of them are engaged in both.

As the data in Figure 5.15 are shown as percentages of
all enterprises, they can be added up. This shows that
18% of all enterprises introduced marketing innovation,
35% organisational innovation and 41% organisational
and/or marketing innovations.



101

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 102

igure 5.15 

EU-27 innovative and non-innovative enterprises that introduced organisational and/or marketing

innovations, organisational innovation and marketing innovation, as a percentage of  all enterprises — 2004

03

52

02

51

01

5

0

noitavonnignitekraM

noitavonnilanoitasinagrO

gnitekramro/dnalanoitasinagrO
snoitavonni

sesirpretneevitavonnI

sesirpretneevitavonni-noN

Data missing for FI, LV, SE, SI and UK.

The  enterprises  surveyed  were  asked  to  assess  four
effects  of  organisational  innovation.  Figure  5.16  once
innovative  and  non-innovative
again  compares 
enterprises. The figures for non-innovative enterprises
are lower because they introduced fewer organisational
innovations.

As  with  product  and  process  innovation,  commercial
aspects are also considered the most important effects.
Improved  quality  scored  highest,  followed  by  the

reduction in the time to respond to customer or supplier
needs.  Cost  reduction  and  positive  impacts  on
employees  ranked  third  and  last  respectively  for  both
groups.

Readers should note that in Figure 5.16 data for eight
countries are missing and that, due to this, the EU-27
values are only indicative.

able 5.16 

EU-27 innovative and non-innovative enterprises by highly important effect 

of  organisational innovation, as a percentage of  all enterprises — 2004

Highly important effects

Innovative enterprises

Non-innovative enterprises

Improved employee satisfaction and/or 
reduced rates of employee turnover

Reduced costs per unit output

Improved quality of goods or services

Reduced time to respond to customer or 
supplier needs

Data missing for AT, FI, IE, LV, PL, SE, SI and UK.

4.1

4.7

10.2

9.1

2.0

2.2

4.0

3.8

102



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 103

5.4 Innovation data at the national level 

German enterprises were the most numerous in the European innovation
landscape

This presentation of the CIS 4 results by country closely
follows the structure of the underlying questionnaire.

Close to two thirds of all German enterprises (65 896)
are  active  in  innovation,  which  means  that  they
introduced at least one production innovation (goods or
services)  and/or  process 
the
reference period from 2002 to 2004. 

innovation  during 

Innovation  activities  include  acquisition  of  machinery,
equipment,  software  and  licences,  engineering  and
development work, training, marketing and R&D when
they  are  specially  undertaken  to  develop  and/or
implement a product or process innovation.

Germany is followed by seven other countries in which
at  least  half  of  all  enterprises  are  innovative:  Austria,
Luxembourg,  Ireland,  Iceland,  Denmark,  Belgium  and
Sweden.

At  the  other  end  of  the  scale,  Romania,  Latvia  and
Bulgaria reported percentages below 20%.

Looking  at  the  absolute  figures,  the  ranking  is  quite
different,  due  essentially  to  the  different  sizes  of  the
national economies. Germany is still in the lead, but is
followed  by  Italy,  the  United  Kingdom,  France,  Spain
and Poland.

General information about the enterprises

igure 5.17 

Innovative enterprises, total number and as a percentage of  all enterprises,

by country, EU-27 and selected countries — 2004

DE

AT

LU

IE

IS

DK

BE

SE

EE

CY

FI

UK

PT

CZ

NO

IT

EL

ES

NL

FR

LT

SI

PL

SK

HU

MT

RO

LV

BG

52.5

52.2

52.2

52.0

52.0

51.3

50.0

48.7

46.1

43.3

43.0

40.9

38.3

37.0

36.3

35.8

34.7

34.3

32.6

28.5

26.9

24.8

22.9

20.8

20.7

19.5

17.5

16.1

65.1

Total number

65 896

8 414

713

3 222

419

4 924

7 529

7 812

1 844

601

3 548

36 629

8 549

8 001

3 233

44 160

4 184

28 118

9 389

28 170

1 604

1 018

11 781

1 428

3 201

144

5 041

909

2 206

0

10

20

30

40

50

60

70



103

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 104

igure 5.18 

Innovative and non-innovative enterprises which are part of  a group,

as a percentage of  innovative and non-innovative enterprises, by country, EU-27 and Norway — 2004 

07

06

05

04

03

02

01

0

EB

ZCGB

EDKD

EE

LE

SE

RF

TI

YC

TL

UL

LNTMUH

LP

KSORTP

IF

ONES

seitivitcanoitavonnihtiwsesirpretnE

sesirpretneevitavonnI-noN

MData missing/confidential for IE, LV, AT, SI, and UK.

To form a better picture of the structure of enterprises in
the countries concerned, the CIS 4 questionnaire asked
the enterprises to indicate if they were part of a group
and  which  country  the  head  office  is  located  in.  The
second  part  of  this  question  asked  the  enterprises
about their geographical markets.

A  group  consists  of  two  or  more  legally  defined
enterprises under common ownership. Each enterprise
in  the  group  may  serve  different  markets,  as  with
national  or  regional  subsidiaries,  or  serve  different
product  markets.  The  head  office  is  also  part  of  the
group.

Figure  5.18  compares  the  proportion  of  innovative
enterprises which are part of a group with the proportion
of non-innovative enterprises.

The  percentages  of  innovative  enterprises  which  are
part of a group vary between 65% in Sweden and 14%

in  Bulgaria.  For  non-innovative  enterprises 
the
percentages  range  from  45%  in  Sweden  to  2%  in
Poland.  

Comparison  of  the  results  for  the  individual  countries
shows higher percentages of enterprise groups among
the  innovative  enterprises  in  every  country.  Whereas
the difference between the two groups is 7% in Greece
it  rises  to  26%  in  Poland.  There  seems  to  be  a
correlation:  being  part  of  a  group  seems  to  have  a
positive influence on innovation activities. A group can
invest more easily in R&D activities which may lead to
innovation.

Figure 5.18 provides additional information. Enterprise
structures vary across EU Member States: in countries
such as Sweden and Luxembourg far more enterprises
are part of groups than in other countries, especially the
most  recent  EU  Member  States,  i.e.  Bulgaria  and
Romania. 

104



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 105

Figure  5.19  concentrates  on  one  subgroup  of  the
enterprises shown in Figure 5.18. It takes a closer look
at groups with a head office in another country.

The  proportion  of  innovative  enterprises  is  still  higher
than that of non-innovative enterprises. This means that
not only being part of a group but also having a head
office 
to  encourage
innovation.

in  another  country  seems 

Comparison  of  the  two  figures  points  to  a  further
finding.  Luxembourg,  Sweden  and,  to  a  lesser  extent,
Belgium and Denmark have the highest proportions of
groups with a head office in another country out of all
EU  Member  States  for  both  innovative  and  non-
innovative enterprises. 

Large economies, such as Germany and France, have
rather  high  proportions  of  groups  but  low  percentages
of groups with a head office in another country.

igure 5.19 

Innovative and non-innovative enterprises which are part of  a group 

and have a head office in another country, as a percentage of  innovative 

and non-innovative enterprises, by country, EU-27 and Norway — 2004 

05

54

04

53

03

52

02

51

01

5

0

EEEDKDZCGBEB

LE

SE

RF

TI

YC

TL

LNTMUHUL

KSORTP

IF

ONES

seitivitcanoitavonnihtiwsesirpretnE

sesirpretneevitavonnI-noN

Data missing/confidential for IE, LV, AT, PL, SI and UK; data unreliable for: FR.



105

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 106

able 5.20 

Innovative and non-innovative enterprises which sold goods or services during the three years 

2002 to 2004,  as a percentage of  innovative and non-innovative enterprises,

by geographic market and by country, EU-27 and Norway — 2004

Innovative enterprises

Non-innovative enterprises

Local/regional
market

National market

Other EU, EFTA 
and/or EU-CC 
countries

Any other 
country

Local/regional
market

National
market

Other EU, EFTA 
and/or EU-CC 
countries

Any other 
country

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

:

59.9

13.3

99.9

41.2

63.7

:

95.9

95.8

74.9

48.3

96.2

:

:

60.4

74.3

-

62.5

:

85.5

29.0

92.9

75.6

40.9

89.4

69.4

79.2

:

87.3

80.7

83.8

72.0

78.4

:

93.6

83.3

90.6

72.2

87.3

:

88.2

62.5

77.2

32.5

37.2

67.5

45.5

70.4

:

48.7

46.5

56.5

51.2

28.1

:

55.5

67.5

59.2

19.4

65.2

:

58.1

36.3

34.5

23.4

8.6

47.5

28.1

31.4

:

34.6

26.0

41.3

31.2

23.0

:

35.4

41.1

30.7

8.3

33.5

:

34.7

9.0

86.0

44.9

43.4

80.1

52.2

62.5

:

82.1

66.0

56.6

57.2

78.8

:

89.1

81.9

80.5

88.0

71.7

:

:

76.8

44.3

:

70.3

25.4

96.6

59.6

61.3

:

92.8

94.1

84.5

55.6

92.7

:

:

58.7

65.6

-

64.1

:

:

85.8

46.9

56.8

17.0

28.6

44.5

28.1

59.6

:

40.4

29.4

28.8

32.2

19.2

:

38.2

58.3

37.3

9.4

46.5

:

:

40.8

22.9

18.3

8.6

2.6

22.4

11.7

26.1

:

18.2

14.0

16.3

17.5

16.1

:

18.0

22.9

13.6

2.5

18.5

:

:

18.9

2.8

: c

: c

: c

: c

: c

: c

: c

: c

89.0

:

75.8

:

25.2

61.8

:

64.8

:

73.7

62.3

:

60.9

:

43.5

20.3

:

33.3

:

30.5

39.8

:

36.4

:

54.2

90.3

:

88.1

:

43.9

43.9

:

33.1

:

20.0

7.5

:

13.7

:

12.5

For MT and CZ only one answer chosen is taken into account. 

Table  5.20  compares  the  geographical  markets  of
innovative  and  non-innovative  enterprises.  It  draws  a
distinction between four markets:

• local/regional market within the country
• national market
• other EU, EFTA and or EU candidate countries
• any other country

The enterprises surveyed were not restricted to a single
answer but were allowed to choose up to four replies.

In general, all the percentages are higher for innovative
than for non-innovative enterprises. Their geographical
markets  seem  to  be  larger  and  more  diversified  than
those of non-innovative enterprises. Innovation seems
to stimulate sales of goods and/or services.

Every  country  displays  higher  percentages 
for
innovative enterprises selling goods and/or services on
international  markets.  For  regional/local  and  national
markets  there  is  no  general  rule.  The  results  vary
across  countries.  In  several  countries,  such  as  the

Czech Republic, Germany, Italy, Luxembourg, Hungary,
the Netherlands, Romania and Norway, non-innovative
enterprises  are  more  heavily 
represented  on
regional/local  markets  than  innovative  enterprises.  In
some  other  countries  this  is  the  case  for  the  national
market,  namely  in  Denmark,  Greece,  Spain,  France,
Cyprus, Slovakia and Sweden.

In  every  country  the  percentages  for  the  “other  EU,
EFTA  and/or  candidate  countries”  and  “any  other
country”  markets  were  always  higher  for  innovative
than for non-innovative enterprises.

In  general,  intra-European  trade  is  more  important  for
all  enterprises  than  trade  with  non-EU  countries.  This
may  be  explained  by  the  fact  that  the  geographical
distances to other EU countries are often shorter than
to other countries. Customs and tax issues also play an
important role.

106



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 107

able 5.21 

Innovative enterprises as a percentage of  all enterprises, by size-class and by country,

EU-27 and selected countries — 2004

Total

Between 10 and 
49 employees

Between 50 and 
249 employees

250 employees 
or more

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

51.3

16.1

38.3

52.0

65.1

48.7

52.2

35.8

34.7

32.6

36.3

46.1

17.5

28.5

52.2

20.8

20.7

34.3

52.5

24.8

40.9

19.5

26.9

22.9

43.3

50.0

43.0

52.0

37.0

46.6

13.5

32.3

48.6

59.7

45.3

47.2

33.9

32.3

26.8

33.3

42.7

14.1

22.4

46.9

16.9

16.9

29.5

48.3

18.4

35.9

15.7

19.1

16.0

36.9

45.1

39.9

49.5

32.4

66.0

22.8

50.2

58.7

74.4

57.9

65.4

43.1

43.8

51.3

52.7

60.9

27.2

42.0

62.6

30.5

28.9

48.4

63.8

39.4

60.4

24.3

40.9

34.3

60.1

66.5

52.7

59.5

53.5

83.0

33.3

69.8

77.8

88.6

79.8

75.1

66.6

66.0

72.6

68.9

81.5

53.5

64.3

79.2

52.4

66.7

71.4

81.9

64.4

72.0

41.8

69.9

57.8

76.0

77.8

62.5

63.3

63.4

As  shown  in  Table  5.21,  there  is  a  strong  correlation
between  innovation  activities  and  enterprise  size. The
percentage of innovative enterprises is higher amongst
large  and  medium-sized  enterprises  than  amongst
small businesses. 

The lowest figures in Table 5.21 are always for Bulgaria
and  the  highest  for  Germany.  The  reason  is  that  the
figures are expressed as percentages of all enterprises.
Taking only the innovative ones into account produces
the same ranking as shown in Figure 5.17. 

Similarly to the analysis of whether or not the enterprise
was part of a group, innovation seems to be facilitated
by  certain  infrastructure.  Enterprises  need  to  be  a
certain  size  before  they  can  have  their  own  R&D
department. One problem with small enterprises is that
they  indeed  innovate  but  do  not  have  sufficient
resources (financial, human, etc.) to make profits from
their innovations.

The  percentages  of  innovative  enterprises  vary  from
14% to 60% for small businesses, from 23% to 74% for
medium-sized  enterprises  and  from  33%  to  89%  for
large enterprises.



107

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 108

able 5.22 

Innovative enterprises, as a percentage of  all enterprises, by NACE and by country,

EU-27 and selected countries — 2004

All NACE - Core 
NACE

Total industry 
(excluding
construction)

Manufacturing

Core G_to_K 
Services

Transport,
storage and 
communication

Financial
intermediation

K: Core coverage

74 Core: Other 
business services

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

51.3

16.1

38.3

52.0

65.1

48.7

52.2

35.8

34.7

32.6

36.3

46.1

17.5

28.5

52.2

20.8

20.7

34.3

52.5

24.8

40.9

19.5

26.9

22.9

43.3

50.0

43.0

52.0

37.0

58.1

18.0

41.1

57.7

72.8

46.9

60.9

35.1

36.5

36.1

37.5

53.2

17.4

31.2

48.9

21.1

26.3

41.6

57.5

26.6

39.1

21.6

34.3

26.9

49.3

54.3

44.4

52.6

43.4

58.2

18.2

41.7

57.8

74.0

48.2

61.4

34.9

36.9

36.4

37.6

53.2

17.4

31.2

49.3

21.2

27.0

41.5

57.5

26.2

38.8

21.8

35.0

27.3

50.5

54.9

44.6

52.0

44.0

45.3

12.7

33.9

46.0

57.5

50.7

43.8

36.8

32.1

29.0

33.5

37.9

17.6

25.7

53.2

20.4

16.1

29.2

47.9

22.0

44.3

16.1

16.0

17.0

36.8

45.9

41.8

51.4

31.6

33.0

7.2

22.5

47.7

46.0

32.6

40.9

37.8

24.2

18.5

23.1

26.0

12.0

16.4

35.7

13.9

8.8

17.6

32.7

15.5

44.7

16.8

14.3

17.7

26.8

23.0

28.4

45.9

18.2

47.8

29.5

56.6

43.5

80.5

74.7

:

50.2

49.7

38.0

40.4

77.1

42.3

52.7

66.8

47.2

31.7

29.6

61.0

42.6

53.9

23.8

21.1

44.1

42.5

46.3

40.9

53.9

25.5

63.5

38.3

44.8

56.8

78.6

53.5

73.0

79.5

55.1

46.8

41.8

40.4

24.8

45.5

64.0

35.1

42.9

47.8

66.7

26.8

60.6

24.4

27.2

32.5

49.7

63.3

59.3

61.5

55.5

56.6

25.7

32.7

42.8

68.5

41.8

:

57.6

47.0

32.6

38.6

28.2

12.6

32.3

64.1

24.3

: c

42.9

54.0

20.8

52.2

15.2

28.8

15.9

33.7

57.8

49.6

26.2

45.7

the  NACE 

The  analysis  based  on 
(Statistical
Classification  of  Economic  Activities  in  the  European
Community) makes it possible to identify the sectors of
the  economy  in  which  innovative  enterprises  are  best
represented.

Sector K (Core coverage) includes K 72 “Computer and
related activities”, K 74.2 “Architectural and engineering
activities and related technical consultancy” and K 74.3
“Technical  testing  and  analysis”.  In  16  of  the  29
countries  shown  in  Table  5.22  the  highest  shares  of
innovative enterprises are in this sector. Unsurprisingly,
all  sectors  linked  to  computer  activities  are  highly
significant for innovation.

More  surprising,  perhaps,  are  the  results  for  the
financial intermediation sector. It covers J 65 “Financial
intermediation, except insurance and pension funding”,
J  66  “Insurance  and  pension 
funding,  except
compulsory social security” and J 67 “Activities auxiliary

to  financial  intermediation.”  Ten  of  the  29  countries
recorded  their  highest  percentages  in  the  financial
intermediation sector. 

Denmark,  Slovenia  and  Finland  are  the  exceptions.
These three countries reported their highest shares of
innovative  enterprises 
(NACE 
section D).

in  manufacturing 

A  cross-country  comparison  does  not  make  much
sense for this table, because the results are biased by
the  overall  ratio  of  innovative  to  non-innovative
enterprises  in  each  country.  Germany,  which  has  the
highest  percentage  for  every  NACE  section,  also  has
the  highest  percentages  for  most  of  the  sub-sections.
The  only  exceptions  are  “transport,  storage  and
communication”  where  the  percentage  of  innovative
enterprises is slightly higher in Denmark and sector K
(Core  coverage),  where  Greece  scores  higher  than
Germany.

108



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 109

able 5.23 

Innovative enterprises, by number of  employees in 2002 and 2004 and AAGR of  employees 

and turnover, by country, EU-27 and selected countries — 2004

Total number of 
employees in 2002

Total number of 
employees in 2004

AAGR - Number of 
employees

AAGR - Turnover

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

945 087

266 446

1 095 717

531 614 u

9 034 437

117 873

:

326 813

2 090 921 u

4 096 989

3 243 156

39 626

:

201 854

70 481

554 438

21 501

941 368

273 738

1 090 574

519 797

8 931 721

127 841

282 268

309 496

2 339 477

4 250 893

3 294 942

40 971

:

206 810

69 350

548 481

21 559

1 310 122

1 254 252

: c

: c

1 996 553

596 712

1 244 836

: c

365 609

:

820 731

:

:

2 112 436

588 223

978 997

: c

347 039

577 548

817 004

:

:

225 597

265 952

-0.2

1.4

-0.2

-1.1

-0.6

4.1

:

-2.7

5.8

1.9

0.8

1.7

:

1.2

-0.8

-0.5

0.1

-2.2

:

2.9

-0.7

-11.3

:

-2.6

:

-0.2

:

:

8.6

10.7

21.6

3.7

3.5

1.6

14.4

:

5.7

8.9

7.3

3.0

-4.1

:

7.5

1.4

8.9

-1.4

4.7

6.9

4.8

4.9

11.0

:

13.4

:

8.0

:

:

19.9

Table 5.23 shows the trend in the number of employees
and  turnover  between  2002  and  2004  for  innovative
enterprises.

There  is  no  general  trend  for  all  EU  Member  States.
Eleven  countries  recorded  a  negative  annual  average
growth  rate  (AAGR)  of  between  11%  (Romania)  and
less than 1% (Belgium, the Czech Republic, Germany,
Luxembourg,  Hungary,  Portugal  and  Sweden)  in  the
number  of  employees  in  enterprises  with  innovative
activities. 

for 

The  highly  negative  growth  rate 
innovative
enterprises in Romania is surprising because Romania
has  a 
fast  growing  economy  and  a  declining
unemployment  rate.  The  trend  may  be  explained  by
migration of highly educated people to countries where
wages  and  salaries  are  higher  and  living  conditions
better. 

Ten  other  countries  recorded  a  positive AAGR  for  the
number  of  employees  in  innovative  enterprises.  They
vary between 6% in Spain and less than 1% in Italy and
Malta.

The  overall  trend  in  the  turnover  of  innovative
enterprises is positive in nearly every country. The only
exceptions are Cyprus (-4%) and Malta (-1%). 

The positive AAGRs range from 22% (Bulgaria) to 1%
(Luxembourg). The  four  best-performing  countries  are
Bulgaria  (22%),  Estonia  (14%),  Slovakia  (13%)  and
Romania (11%), all of which joined the EU recently and
have fast-growing economies. Belgium, one of the “old”
Member  States,  ranks  fifth  with  11%  and  is  the  last
country in the ranking exceeding 10%.



109

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 110

Product (goods or services) and process innovation

Before  starting  to  analyse  the  results  on  product  and
process  innovation,  it  is  necessary  to  explain  what  is
meant by these terms and what is excluded and to give
some examples.

Product  innovations cover  goods  and  services  with
characteristics or intended uses that differ significantly
from previous products produced by an enterprise. This
includes significant changes in technical specifications,
components  and  materials,  incorporated  software,
user-friendliness  or  other  functional  characteristics.
Unlike  process  innovations,  they  are  sold  directly  to
customers. 

The innovation (new feature or improvement) must be
new to the enterprise, but does not have to be new to
the sector or to the market.

Product innovations do not include:

• minor changes or improvements;

• routine upgrades;

• seasonal changes (such as for clothing lines);

• customisation for a single client that does not
include  significantly  different  attributes 
to
products made for other clients;

• design  changes  that  do  not  alter  the  function
or  technical  characteristics  of  the  goods  or
services;

• the  simple  resale  of  new  goods  and  services
purchased  from  other  enterprises,  but  do
include  goods  and  services  developed  and
produced  by  foreign  affiliates  for  the  enterprise
in question.
Innovative goods

• Introducing entirely new products;

• Replacing inputs with materials with enhanced
characteristics  (breathable  textiles,  light  but
strong 
environment-friendly
plastics, etc.);

composites, 

• Introducing  new  or  improved  components  in
existing  product 
(global  positioning
lines 
systems  (GPS)  in  vehicles,  cameras  in  mobile
telephones, fasteners in clothing, etc.);

• Household  appliances 
incorporate
software  that  improves  user-friendliness  or
convenience, 
that
automatically shut off when the bread is toasted.

toasters 

such 

that 

as 

Innovative services

• Improving  customer  access,  such  as  home
pick-up and drop-off services for rental cars;

• DVD  subscription  services,  where  for  a
monthly  fee  customers  can  order  a  predefined
number  of  DVDs  via  the  Internet  with  mail
delivery  to  their  home  and  return  via  a  pre-
addressed envelope;

• Internet  services,  such  as  banking,  bill-
payment  systems,  electronic  purchase  and
issuing of travel and theatre tickets;

• New forms of warranty, such as an extended
warranty  on  new  or  used  goods,  or  bundling
warranties  with  other  services,  such  as  with
credit cards, bank accounts or customer loyalty
cards;

• Installing  gas  heaters  in  outdoor  restaurants
and bar terraces.

Process  innovations occur  in  both  services  and
manufacturing and include new or improved production
methods  or  delivery  and  distribution  systems.  They
include  significant  changes  in  specific  techniques,
equipment  and/or  software  intended  to  improve  the
quality, efficiency or flexibility of production or supply or
to reduce environmental and safety hazards.

The innovation (new feature or improvement) must be
new to the enterprise, but does not have to be new to
the sector or to the market.

Process innovations do not include:

• minor changes or improvements;

• increases in production or service capacity by
adding  manufacturing  or  logistics  systems  that
are very similar to those already in use;

• innovations  that  have  a  significant  client
interface,  such  as  pick-up  services  (these  are
product innovations).

Improved  methods  of  manufacturing  or  producing
goods or services

• Installation of new or improved manufacturing
technology,  such  as  automation  equipment  or
real-time sensors that can adjust processes;

• New equipment required for new or improved
products;

• Computer-assisted product development;

• Digitisation of printing processes.

Improved distribution and operations 

• Introduction  of  bar-coding  or  passive  radio
frequency  identification  (RFID)  chips  to  track
materials through the supply chain;

• GPS 
equipment;

tracking 

systems 

for 

transport

• Automated  feed-back  to  suppliers  using
electronic data exchange;
Improved ancillary operations

• Introduction  of  software  to  identify  optimum
delivery routes;

• New  or  improved  software  or  routines  for
purchasing,  accounting  or  maintenance
systems.

110



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 111

igure 5.24 

Breakdown of  innovative enterprises, by type of  innovator,

as a percentage of  all enterprises, by country, EU-27 and Norway — 2004

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE

IE

EL

ES

FR

IT

CY

LT

LU HU MT NL

AT

PL

PT RO SK

FI

SE NO

Novel innovators, product only

Novel innovators, process only

Novel innovators, product and process innovators

Data missing/confidential for LV, SI and UK.

Figure  5.24  splits  innovative  enterprises  into  three
types of innovators: 

• product innovators,

• process innovators,

• product and process innovators.

As explained earlier, “products” can be either goods or
services. The questionnaire drew a distinction between
three types of process innovations: improved methods
of  manufacturing  or  producing  goods  or  services,
improved  distribution  and  operation  and  improved
ancillary  operations  (see  previous  page  for  definitions
and examples.)

A first glance at Figure 5.24 gives the impression that
there  is  no  common  pattern  for  innovative  attitudes
across countries, but this may be misleading. 

In  most  EU  Member  States  the  novel  innovators  are
both  product  and  process  innovators.  The  highest
proportions can be found in Ireland with 29%, in Austria
with  28%  and  in  Luxembourg  with  24%.  Only  three
countries are exceptions to this rule. In Spain, Italy and

Cyprus  the  percentages  of  novel  process  innovators
are higher than the percentages of product and process
innovators.

Conversely,  comparing  the  percentages  of  novel
product  innovators  with  novel  process  innovators
produces a rather mixed picture. In 10 countries there
are  more  product  innovators  and  in  15  more  process
innovators. In most cases the differences between the
two groups are less than ten percentage points. Cyprus
can be singled out as an exception. Whereas only 1%
of  the  innovative  enterprises  in  Cyprus  are  novel
product innovators, 26% are novel process innovators.

The relatively high percentages of enterprises that are
both product and process innovators show that in many
cases  both  types  of  innovation  are  linked.  There  are
obviously spill-over effects. 

The percentages in Figure 5.24 do not add up to 100%,
because they are ratios of all enterprises and only the
shares  of  the  innovative  enterprises  are  presented  in
this figure.  



111

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 112

igure 5.25 

Breakdown of  product innovators by who developed the product innovation,

as a percentage of  all enterprises, by country, EU-27 and Norway — 2004

53

03

52

02

51

01

5

0

EB

GB

ZC

KD

ED

EE

EI

LE

SE

RF

TI

YC

TL

UL

UH

TM

LN

TA

LP

TP

OR

KS

IF

ES

ON

puorgesirpretneroesirpretneehtybylniamdepoleved,tcudorP

snoitutitsnirosesirpretnerehtohtiwrehtegotesirpretneehtybdepoleved,tcudorP

snoitutitsnirosesirpretnerehtoybylniamdepoleved,tcudorP

Data missing/confidential for LV, SI and UK.

The innovative enterprises were asked who developed
the product innovations. Three options were given:

• mainly by the enterprise or enterprise group;

•  by 

the  enterprise 
enterprises or institutions;

together  with  other

• mainly by other enterprises or institutions.

Figure  5.25  shows  that  the  majority  of  innovative
products  were  developed  “intra-muros”  in  every
country. The shares range from 33% in Ireland to 9% in
Hungary. 

For product innovations developed together with other
enterprises  or  institutions  the  shares  are  significantly
lower.  The  10%  recorded  in  Germany  is  the  highest
share.  In  all  the  other  countries  the  shares  are  lower.
The lowest value was found in Spain with 2%.

The  third  option  –  innovative  products  developed
mainly by other enterprises or institutions – was ticked
far less. Only between 5% (Estonia) and less than 1%
(Greece and France) chose this answer. 

The  percentages  are  relative  to  all  enterprises  and
show  only  the  innovative  enterprises  that  are  also
product  innovators.  Only  they  were  asked  to  reply  to
this question. These enterprises constitute a subgroup
of  between  14%  and  43%  of  all  enterprises.  The
percentages vary across countries.

Figure  5.26  shows  the  results  of  the  answers  to  the
same  question  once  again,  but  this  time  only  the
distribution inside the subgroup is taken into account. 

The  majority  of  the  product  innovators  developed  the
product  within  their  own  enterprise  or  group.  The
figures ranged from more than 80% in Ireland to more
than  50%  in  Cyprus.  Whereas  in  Cyprus  34%  of  the
product  innovators  developed  their  product  together
with other enterprises or institutions, in most countries
the  percentage  was  lower,  mostly  between  10%  and
20%.  The  proportion  of  product  innovators  who
answered  that  their  product  was  mainly  developed  by
other enterprises or institutions varied between 5% and
10% in most cases. The highest percentage was found
in Hungary with 17%. 

112



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 113

igure 5.26 

Breakdown of  innovative enterprises who developed the product innovation,

as a percentage of  all enterprises, by country, EU-27 and Norway — 2004

%001

%09

%08

%07

%06

%05

%04

%03

%02

%01

%0

EI

LERF

EBORSE

LPUL

TLONKDGB

ZCES

TA

IF

KSEDTP

EE

YCUHLNTMTI

snoitutitsnirosesirpretnerehtoybylniamdepoleved,tcudorP

snoitutitsnirosesirpretnerehtohtiwrehtegotesirpretneehtybdepoleved,tcudorP

puorgesirpretneroesirpretneehtybylniamdepoleved,tcudorP

Data missing/confidential for LV, SI and UK.

igure 5.27 

Breakdown of  process innovators by who developed the process innovation,

as a percentage of  all enterprises, by country, EU-27 and Norway—- 2004

35

30

25

20

15

10

5

0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU MT

NL

AT

PL

PT

RO

SK

FI

SE

NO

Process, developed mainly by the enterprise or enterprise group

Process, developed by the enterprise together with other enterprises or institutions

Process, developed mainly by other enterprises or institutions

Missing/confidential Data missing/confidential for LV, SI and UK..



113

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 114

Like Figure 5.25, Figure 5.27 takes a closer look at the
subgroup  of  process  innovators  and  tries  to  find  out
who developed the process innovations. 

Ireland ranks first with 34% of all enterprises developing
a process innovation mainly within their own enterprise
or  group.  It  is  followed  by  Greece  and  Belgium  with
26% and 24% respectively.

On development of innovative processes together with
other  enterprises,  Cyprus  leads  with  15%  of  all
enterprises, followed by Austria with 13% and Germany
with 12%.

13%  of  the  innovative  enterprises  in  Cyprus  declared
that  their  innovative  processes  had  been  developed
mainly  by  other  enterprises  or  institutions.  In  every
other country this was the case for fewer than 10% of
the innovative enterprises.

As a general rule, the highest scores were reported for
innovative  enterprises  that  developed  a  process

that  developed  a  process 

innovation  in-house  or  inside  their  group,  followed  by
those 
in
collaboration with other enterprises or institutions. The
scores 
that  outsourced  process
innovation  are  rather  low.  The  only  exceptions  to  this
rule are Spain and Hungary, where outsourcing scored
higher than collaboration.

for  enterprises 

innovation 

Showing  the  results  of  the  CIS  as  percentages  of  all
enterprises  allows  better  comparability  of  data  from
countries  with  different  sized  economies. This  method
avoids  putting  the  largest  EU  economies,  such  as
Germany,  France  and  the  United  Kingdom,  top
because of their economic weight.

If  the  analysis  of  the  results  focuses  solely  on
countries  with  higher
innovative  enterprises, 
proportions of innovative enterprises are more likely to
come out on top. This must be taken into account when
analysing  the  results,  in  order  to  avoid  a  country’s
weight in a subgroup masking information.

able 5.28 

Turnover of  innovative enterprises related to new or significantly improved products 

which are new to the enterprise (but not new to the market), as a percentage of  total turnover 

of  innovative enterprises, by sector, EU-27 and selected countries — 2004

All NACE - 
Core NACE 

Total industry 
(excluding construction)

Manufacturing

Services - 
Core G_to_K

K: Core coverage 
(NACE 72, 74.2 and 
74.3)

74 Core: Other 
business services 
(NACE 74.2, 74.3)

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

11.2

11.8

12.7

8.5

11.2

11.6

6.5

12.5

15.7

13.2

6.8

14.0

11.0

14.6

15.1

8.8

12.9

13.0

14.3

7.6

14.9

12.2

16.1

18.8

8.8

15.2

14.6

9.8

20.1

10.5

5.7

7.7

8.6

2.7

12.0

18.7

8.1 u

10.5 u

11.3 u

5.2 u

8.7

5.1

3.7

8.6

12.5

5.0

14.1

6.9

7.2

8.8

8.3

20.9

12.2

10.6

6.7

6.9

10.3

11.5

8.9

8.7

2.4

4.7

11.1

12.0

5.6

18.5

8.5

8.7

10.7

8.5

19.3

12.9

11.4

6.9

5.7

13.1

6.0

6.5

9.3

3.0

6.0

12.2

13.0

6.4

: c

9.0

9.2

12.2

9.7

22.7

: c

8.1

7.1

5.3

14.3

3.1

13.7

8.7

6.1

2.9

4.4

12.7

3.8

5.6

5.2

5.6

5.3

8.1

25.0

9.8

8.6

6.2

8.5

9.2

18.2

14.2

7.2

4.6

13.9

7.1

13.5

15.0

: c

10.4

13.5

9.0 u

: c

9.5

4.0

7.5

11.4

2.7

11.1

6.7

: c

3.2

9.3

26.2

: c

4.1

12.0

14.0

16.3

10.5

15.8

8.1

3.9

10.1

3.2

14.1

15.8

: c

2.8

13.2

8.6 u

: c

8.9

3.0

3.3

6.9

0.5

0.0

6.5

0.0

3.6

7.6

13.0

: c

0.5

10.5

: c

11.6

0.0

18.0

114



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 115

Table  5.28  tries  to  shed  some  light  on  the  impact  on
turnover  of  new  or  significantly  improved  products
which  are  new  to  the  enterprise  but  not  new  to  the
market.  Without  drawing  any  distinction  by  economic
sector, the impact is very diverse: the highest rate was
recorded  for  Romania  (21%),  followed  by  Spain  and
Malta with 16% and 14% respectively. At the other end
of the scale lay Latvia (4%), Hungary (5%) and Cyprus
(5%).

The results are somewhat different when broken down
by economic sector. 

For “total industry” Romania is in first place with 14%,
Malta (12%) second and Slovenia (10%) third.

For  “manufacturing”  Malta  (12%)  is  followed  by
Romania (12%) and the Czech Republic (9%). 

For  “services”  Luxembourg  leads  with  10%,  Spain
ranks second with 9% and Bulgaria third with 8%. 

Sector K (Core coverage) includes K 72 “Computer and
related activities”, K 74.2 “Architectural and engineering
activities and related technical consultancy” and K 74.3
“Technical testing and analysis”. For these sectors the
scores are very low.

igure 5.29 

Turnover of  innovative enterprises related to new or significantly improved products 

which are new to the market, as a percentage of  total turnover of  innovative enterprises, 

in manufacturing and in services, by country, EU-27 and selected countries —- 2004

53

03

52

02

51

01

5

0

EB

ZCGB

EDKD

EE

EI

LE

SE

RF

TI

YC

VL

TL

UL

TMUH

LN

TA

LP

ORTP

IS

KS

IF

ES

KU

SI

ON

gnirutcafunaM

secivreS

the 

turnover  of 

innovative
Figure  5.29  shows 
enterprises  related  to  new  or  significantly  improved
products which are new to the enterprise (as Table 5.28
also does), but this time for products that are also new
to the market. 

It compares manufacturing with the services sector. For
most  countries  the  results  do  not  exceed  10%,  and  in
many cases they are substantially lower. 

Bulgaria recorded a very high result for turnover in new
or significantly improved products new to the enterprise
and new to the market with 31% for the services sector.
Readers must take into account that the figures shown
are ratios. A high result does not necessarily mean that
the  absolute  turnover  in  these  products  is  high.  The
absolute  value  can  be  quite  low  if  the  turnover  of  the
innovative enterprises is low. 

Bulgaria and Slovakia are the only countries where both
sectors  pass  the  20%  mark,  but  as  Bulgaria  has  the
lowest proportion of innovative enterprises out of all the

EU  countries  (16%)  the  turnover  of  those  enterprises
can also be assumed to be low. 

In Slovakia, the proportion of innovative enterprises is
slightly higher, on 23%, but nevertheless lower than in
many other EU countries. To a lesser extent, the same
reasoning  may  be  applied  to  Slovakia,  which  has  a
comparatively  low  proportion  of  innovative  enterprises
but high results for relative turnover.

No  real  trend  can  be  distilled  from  comparison  of  the
results  for  manufacturing  with  those  of  the  services
sector. In 17 countries the results for manufacturing are
higher and in ten this is the case for services. For some
countries, such as Greece, Italy, Hungary and Norway,
the  results  for  the  two  sectors  are  very  close.  By
contrast,  in  Finland,  Iceland,  Sweden,  Poland  and
Bulgaria  the  differences  between  manufacturing  and
the services sector are significant, with 14%, 11% and
9% respectively for the last three of these countries.



115

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 116

Innovation activity and expenditure

Intramural expenditure on R&D is greater than extramural…

The  enterprises  surveyed  were  asked  if  they  had
undertaken  creative  work  within  their  enterprise  to
increase  their  stock  of  knowledge  and  had  used  it  to
devise  new  and  improved  products  and  processes
(including  software  development).  In  this  case  their
innovation activity was intramural (in-house). 

If  these  activities  were  performed  by  other  companies
(including  other  enterprises  in  the  same  group)  or  by
public or private research organisations and purchased
by the enterprise, the innovation activity is considered
extramural.

Figure  5.30  compares  the  shares  of  intramural  and
extramural expenditure on R&D as percentages of total
innovation expenditure.

In  almost  every  country  in  the  figure,  the  intramural
expenditure is significantly higher than the extramural.
The only exception is Cyprus, where the opposite is the
case.

In  five  European  countries  the  share  of  intramural
expenditure on R&D exceeded 50% of total innovation
expenditure, namely in Denmark (62%), France (68%),
the  Netherlands  (60%),  Sweden  (63%)  and  Norway
(64%). 

igure 5.30 

Intramural extramural expenditure on R&D by innovative enterprises, 

as a percentage of  total innovation expenditure, by country, EU-27 and Norway — 2004

08

07

06

05

04

03

02

01

0

EB

GB

ZC

KD

ED

EE

EI

LE

SE

RF

TI

YC

TL

UL

TMUH

LN

LP

TP

OR

KS

ES

ON

D&RlarumartninierutidnepxE

D&RlarumartxenierutidnepxE

Data missing/confidential for LV, AT, SI, FI and UK.

Whereas in France and Norway more than 80% of the
total innovation expenditure was covered by intra- and
extramural expenditure on R&D, in other countries both
categories together did not even add up to 10%, as, for
example, in the case of Slovakia. 

To complete the analysis of innovation expenditure, two
other categories have to be taken into account. One is
expenditure  on  acquisition  of  advanced  machinery,

equipment  and  computer  hardware  or  software  to
produce  new  or  significantly  improved  products  and
processes.

The other is purchases or licensing of patents and non-
patented  inventions,  know-how  and  other  types  of
knowledge from other enterprises or organisations. All
four categories are shown in Table 5.31.

116



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 117

… but acquisition of machinery, equipment and software seems essential for
many countries

In  2004  acquisition  of  machinery,  equipment  and
software played a major role for many of the enterprises
surveyed.  Most  of  the  new  Member  States  (from  the
2004  and  2007  enlargements)  but  also  Greece  and
Portugal spent more than 60% of their total innovation
expenditure on this category. 

The highest shares were recorded In Bulgaria, Cyprus,
Poland  and  Slovakia,  where  more  than  80%  of  the
innovation  expenditure  was  on  acquisition  of
machinery, equipment and software.

The  Czech  Republic  is  the  only  new  Member  State  in
Table  5.3.1  where  expenditure  on  acquisition  of

machinery,  equipment  and  software  fell  short  of  the
50%  mark.  Nevertheless,  it  was  still  the  category  with
the highest percentage.

These  results  are  not  very  surprising  because  of  the
need  for  the  new  Member  States  to  modernise  their
general  equipment.  This  modernisation  may  take
several years, but the shares of innovation expenditure
spent on this category can be expected to decrease in
the future as more funds will be spent on R&D. 

It must be added that capital expenditure on buildings
and  equipment  specifically  for  R&D  are  included  in
intramural expenditure on R&D.

able 5.31 

Breakdown of  innovation expenditure into four categories, 

as a percentage of  total innovation expenditure, by country, EU-27 and Norway — 2004

Expenditure in 
intramural R&D in 2004

Expenditure in 
extramural R&D in 2004

Expenditure for acquisition of 
machinery, equipment and software 
in 2004

Expenditure for acquisition of other 
external knowledge in 2004

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

34.0

8.0

21.5

61.7

43.9

19.9

24.1

27.1

37.0

68.4

32.1

3.4

:

18.4

41.1

17.3

26.4

59.8

:

7.6

15.5

12.4

: c

7.1

:

62.8

:

63.6

12.7

3.0

13.4

13.6

8.0

4.3

3.3

6.1

19.1

16.7

6.9

7.0

:

2.6

5.1

7.4

2.0

15.5

:

4.3

6.6

2.9

: c

2.2

:

: c

:

20.0

:

34.3

83.3

46.1

18.1

26.7

73.2

59.9

65.6

32.0

12.5

52.8

86.0

:

76.7

39.5

72.4

68.5

22.3

:

82.3

71.4

62.9

85.4

:

19.2

:

12.2

c

18.9

5.7

19.1

6.5

3.0

2.6

12.7

1.2

4.5

2.4

8.2

3.5

:

2.4

14.3

2.9

3.1

2.3

:

5.8

6.5

21.8

: c

5.3

:

3.0

:

4.1

Germany: “Total innovation expenditure” is the sum of all expenditure in 2004 on “innovation activities” which means intramural R&D, extramural R&D,
acquisition  of  machinery,  equipment  and  software,  acquisition  of  other  external  knowledge,  training,  market  introduction  of  innovations  and  other
preparations).
Spain: “Total innovation expenditure” includes expenditure on training, market introduction of innovations and other preparations. 



117

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 118

igure 5.32 

Breakdown of  innovation expenditure into four categories, 

as a percentage of  total innovation expenditure, by country, EU-27 and Norway — 2004

100%

80%

60%

40%

20%

0%

FR

NO

SE

DK

NL

DE

LU

ES

BE

IT

EL

MT

IE

CZ

EE

LT

HU

PT

RO

BG

PL

SK

CY

Expenditure in intramural R&D in 2004

Expenditure in extramural R&D in 2004

Expenditure for acquisition of machinery, 
equipment and software in 2004

Expenditure for acquisition of other external knowledge in 2004

Data missing/confidential for LV, AT, SI, FI and UK.

Expenditure on acquisition of other external knowledge
played only a minor role in most countries. Romania, on
22%, is the only country with a share over 20%; many
countries  do  not  even  reach  10%  in  this  category.  By
contrast,  Greece  recorded  the  lowest  share  with  only
1%.

Looking at Figure 5.32, which displays the figures from
Table  5.31  as  a  bar  chart,  a  clear  correlation  can  be
seen  between  expenditure  on  intramural  R&D  and
expenditure  on  acquisition  of  machinery,  equipment
and software. 

The  countries  with  a  high  share  of  intramural  R&D
expenditure  have  a  low  share  of  expenditure  on
acquisition of machinery, equipment and software and
vice versa. The other two categories are at a relatively
low level in every country.

Looking  at  the  different  categories  of  innovation
expenditure reveals some facts on the use of funds by
the innovative enterprises. Another interesting aspect is
to look at the number of innovative enterprises engaged
in intramural and extramural R&D. 

In  most  countries  40%  or  more  of  all  enterprises
engaged  in  innovation  activities  undertook  intramural
R&D during the period from 2002 to 2004 (see Figure
5.33). 

Ireland and France recorded the highest proportions of
innovative  enterprises  engaged  in  in-house  R&D,  with
86%  and  70%  respectively.  The  Netherlands  ranked
third  with  67%.  At  the  other  end  of  the  scale  came
Bulgaria, Poland and Romania, with 9%, 26% and 28%
respectively.

Innovative  enterprises  were  generally  less  likely  to  be
engaged  in  extramural  R&D,  with  shares  of  around
20%. 

the  exception  of  Bulgaria, 

innovative
With 
enterprises  in  every  country  give  priority  to  intramural
R&D. 

the 

The  proportions  of  innovative  enterprises  engaged  in
extramural  R&D  vary  between  9%  in  Malta,  Bulgaria
and Poland and 40% in Norway. The Netherlands was
the  Member  State  with  the  highest  percentage  of
innovative enterprises engaged in extramural R&D, on
35%.

118



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 119

igure 5.33 

Innovative enterprises engaged in intramural and extramural R&D, 

as a percentage of  innovative enterprises, by country, EU-27 and Norway—- 2004

90

80

70

60

50

40

30

20

10

0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU

MT

NL

PL

PT

RO

SK

SE

NO

Enterprises, engaged in intramural R&D

Enterprises, engaged in extramural R&D

Data missing/confidential for LV, AT, SI, FI and UK.
.

Looking  in  more  detail  at  intramural  R&D  (see  Figure
5.34), it is possible to split the data between continuous
and occasional involvement in this activity.

The  Netherlands  led  with  a  very  high  48%  of  all  its
in
innovative  enterprises  continuously  engaged 
intramural  R&D.  France  ranked  second  with  37%  and
Belgium third with 36%. 

Sweden, Norway, Italy and Luxembourg also recorded
over  30%  of  innovative  enterprises  continuously
engaged in intramural R&D.

Turning to innovative enterprises occasionally engaged
in  intramural  R&D,  France  ranked  first  (33%)  and
Norway  second,  also  with  33%,  followed  by  Sweden
with 31%.

than 

the  proportion  of 

that  occasionally  engaged. 

In  most  countries 
innovative
enterprises continuously engaged in intramural R&D is
higher 
the
Netherlands  the  difference  between  the  proportion  of
innovative enterprises engaged continuously and those
engaged only occasionally in intramural R&D stood at
28 percentage points.

In 

However,  for  Slovakia,  Portugal,  Hungary,  Cyprus,
Poland and Bulgaria the opposite is the case. In Poland
the  proportion  of  innovative  enterprises  occasionally
engaged  in  in-house  R&D  is  about  13  percentage
points higher than the proportion engaged continuously.

igure 5.34 

Breakdown of  occasional and 

continuous intramural R&D, 

as a percentage of  innovative enterprises, 

by country, EU-27 and Norway — 2004

NL

FR

BE

SE

NO

IT

LU

EL

DE

EE

CZ

SK

MT

ES

PT

DK

LT

HU

RO

CY

PL

BG

0

10

20

30

40

50

Occasionally R&D

Continuously R&D

Data missing/confidential for IE, LV, AT, SI, FI and UK.



119

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 120

able 5.35 

Enterprises engaged in innovation activities, as a percentage of  innovative enterprises,

by country, EU-27 and Norway — 2004

Enterprises, engaged in 
intramural R&D

Enterprises, engaged in 
extramural R&D

Enterprises, engaged in acquisition of 
machinery, equipment and software

Enterprises, engaged in acquisition of 
other external knowledge

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

AT

NL

PL

PT

RO

SI

SK

FI

SE

UK

NO

53.3

8.6

48.7

40.1

53.8

43.2

85.5

50.6

34.9

70.2

59.1

24.5

:

29.6

45.0

42.4

42.4

:

67.4

26.2

43.8

27.7

: c

54.8

:

66.1

:

65.9

26.4

12.6

24.3

23.2

20.9

23.0

22.2

32.0

20.3

24.9

21.1

15.5

:

16.8

25.0

16.1

9.0

:

35.0

9.2

29.0

9.1

: c

26.1

:

28.4

:

40.3

73.4

65.9

75.6

63.2

72.9

82.6

71.4

91.6

66.6

60.0

90.6

97.7

:

86.5

75.7

75.5

49.3

:

63.8

90.7

86.0

78.9

: c

77.3

:

65.5

:

30.4

19.6

24.5

24.3

35.6

23.5

35.9

23.7

14.7

12.6

23.9

20.2

33.4

:

27.2

24.3

17.3

13.2

:

24.8

7.8

24.8

12.8

: c

23.7

:

41.1

:

21.9

Table  5.35  displays  the  percentages  of  innovative
enterprises  broken  down  by  four  categories  of
innovative activities. 

In most countries the innovative enterprises are mainly
engaged  in  acquisition  of  machinery,  equipment  and
software.  The  figures  range  from  60%  (France)  up  to
98% (Cyprus). Only Malta (49%) and Norway (30%) fell
short of the 60% mark. 

As  an  enterprise  can  be  engaged  in  more  than  one
innovation  activity  at  the  same  time,  some  countries
that have relatively high percentages for engagement in
acquisition of machinery, equipment and software have
even higher percentages for engagement in intramural
R&D. These are Ireland with 86%, France with 70%, the
Netherlands with 67% and, to a lesser extent, Sweden
with  66%.  With  the  exception  of  Ireland,  these  were
also among the countries that showed high innovation
expenditure on intramural R&D (see Table 5.31). 

Comparison  between  innovation  expenditure  and
innovation activities points to the following outcomes:

Although  in  many  countries  around  two  thirds  of  all
innovative  enterprises  are  involved  in  acquisition  of

machinery,  equipment  and  software,  this  does  not
necessarily lead to high expenditure in this category.

By contrast, heavy involvement in intramural R&D often
goes  hand  in  hand  with  high  expenditure  in  this
category. This can be explained by the fact that a large
part  of  expenditure  on  intramural  R&D  consists  of
salaries of researchers and highly skilled employees.

The  highest  percentages  of  innovative  enterprises
engaged  in  extramural  R&D  can  be  found  in  Greece
(32%), the Netherlands (35%) and Norway (40%). 

For  innovative  enterprises  engaged  in  acquisition  of
external knowledge, scores over 30% were recorded in
Denmark (36%), Estonia (36%) and Cyprus (33%).

Heavy  involvement  in  one  of  the  last  categories  of
innovation  activities  mentioned  does  not  necessarily
result  in  a  higher  share  of  expenditure  in  the  same
category. Taking Denmark as an example, 36% of the
innovative  enterprises  in  Denmark  declared  that  they
were engaged in acquisition of external knowledge, but
only  7%  of  their  innovation  expenditure  was  spent  on
this category (see Table 5.31).

120



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 121

How do innovative manufacturing establishments acquire knowledge and technology:

Findings from the 2005 Survey of Innovation

The 2005 Survey of Innovation asked innovative manufacturing establishments questions related to how they acquired
knowledge  and  technology  for  innovation  and  from  whom.  This  article  analyses  the  two  thirds  of  manufacturing
establishments that were innovative – that is they introduced a new or significantly improved product or process during
the  three  reference  years,  2002  to  2004  –  and  sheds  light  on  their  purchases  of  knowledge  and  technology,  the
importance of information sources and their collaborative partners. In order to develop new and significantly improved
products  and  processes,  firms  acquire  knowledge  and  technology  from  various  external  sources  and  by  various
methods. In the most general terms, firms have three different options when acquiring knowledge and technology from
outside  the  firm. They  can  purchase  the  knowledge  and  technology,  they  can  acquire  information  relevant  to  their
innovation  activities  or  they  can  enter  into  collaborative  arrangements  to  jointly  develop  innovative  products  and
processes with partners. . 

[…]  From  the  results  presented  above,  it  can  be  concluded  that  suppliers  are  very  important  for  the  acquisition  of
knowledge and technology by innovative manufacturing establishments in terms of sources of information, purchases
of knowledge and technology, and collaborative partners. In general, market actors, including clients, were used more
frequently for acquiring knowledge and technology than public institutional sources. This being said, public institutions
were found to be of some importance as sources of information by between one third and one half of innovating firms.
They were also collaborating partners in innovation for between 10% and one third of establishments that entered into
such  arrangements.  Further  work  needs  to  be  done  to  better  understand  the  conditions  under  which  innovative
manufacturing  establishments  acquire  their  knowledge  and  technologies  from  actors  other  than  their  suppliers  and
clients  with  whom  they  have  on-going  and  market  relations.  The  results  of  this  study  show  that  the  acquisition  of
knowledge and technology from suppliers and clients is very widespread, with only a relatively small percentage of
innovators  not  being  involved  with  their  suppliers  and  clients.  Further  analysis  could  examine  whether  size,
geographical  location,  type  of  industry,  innovation  intensity  or  absorptive  capacity  play  a  significant  role  in  firms’
acquisition  of  knowledge  and  technology  from  market  actors  who  are  not  clients  or  suppliers  and  from  public
institutions. 

Source: Innovation Analysis Bulletin, Vol. 9, no. 1 (May 2007), Statistics Canada



121

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 122

able 5.36 

Innovative enterprises which received public funding for innovation activities,

as a percentage of  innovative enterprises, by source of  funds and by country, EU-27 and Norway — 2004

Enterprise that received 
any public funding

Enterprise that received 
funding from local or 
regional authorities

Enterprise that received funding 
from central government 
(including central government 
agencies or ministries)

Enterprise that received 
funding from the European 
Union

Enterprise that received 
funding from the 5th or 6th 
Framework Programme

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

22.8

4.9

15.9

15.0

14.1

9.7

:

29.0

25.9

20.4

38.6

35.5

:

12.7

24.8

27.3

16.7

37.5

33.9

12.4

11.1

10.8

: c

12.1

35.1

:

:

43.5

15.9

0.5

2.3

2.1

7.7

0.6

:

5.5

18.7

8.0

25.7

0.3

:

2.1

3.0

2.6

2.1

6.6

20.6

:

1.1

2.3

: c

3.4

6.6

:

:

1.7

9.2

1.4

10.9

8.7

7.6

8.2

:

19.9

10.3

15.1

14.9

33.8

:

7.5

22.4

25.5

14.6

32.5

24.7

:

6.8

3.2

: c

5.1

31.2

:

:

42.8

3.6

3.9

4.5

6.5

4.0

1.8

:

19.7

3.7

5.1

3.3

3.1

:

5.4

1.8

4.3

2.8

5.6

9.3

:

5.2

7.3

: c

5.3

8.4

:

:

1.9

2.2

1.2

3.2

3.4

3.2

0.5

:

7.8

1.4

1.8

1.2

1.0

:

0.6

1.2

1.9

: c

2.2

2.6

:

2.9

1.1

: c

0.6

4.3

:

:

1.7

In  2004  between  5%  (Bulgaria)  and  44%  (Norway)  of
the  enterprises  engaged  in  innovation  activities
declared that they had received public funds. In the new
Member States (from the 2004 and 2007 enlargements)
never  more  than  20%  the  proportion  of  all  innovative
enterprises which had received public funds was never
more  than  20%,  with  the  exceptions  of  Cyprus  and
Hungary which reported 36% and 27% respectively in
2004.  In  several  of  the  “old”  EU-15  Member  States
more  than  30%  of  the  innovative  enterprises  replied
that  they  had  received  public  funds,  namely  Italy  with
39%, the Netherlands with 38%, Austria with 34% and
Finland with 35%. 

Enterprises have the possibility to apply for public funds
from  different  national  and  European  authorities.  In
many  countries  the  majority  of  innovative  enterprises
received  funding  from  their  central  government.
However,  there  are  exceptions.  In  Belgium,  Estonia,
Spain  and  Italy  more  innovative  enterprises  received
funding from regional or local authorities.

In  some  countries  the  European  authorities  played  a
bigger  role  in  public  funding  of  innovative  enterprises
than  the  central  government.  This  was  the  case  in
Bulgaria  and  in  Slovakia.  In  many  countries  the
proportion  of  innovative  enterprises  that  received
funding from the European Union was higher than the
proportion turning to local or regional authorities.

The  role  of  public  funding  is  often  a  controversial
subject in economic literature. On the one hand, there
is  a  consensus  about  the  stimulating  effect  of  public
funding  on  innovative  activities.  On  the  other,  there  is
always apprehension about the possibility of crowding
out private financing. 

Indeed,  public  funding  is  necessary  for  R&D  and
innovation  but  must  be  targeted  and  follow  set
objectives 
funding  cannot  achieve.
Untargeted subsidies should be avoided.

that  private 

122

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05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 123

Innovation in Bulgaria: some improvement but much more to be done
(Extract from press release)

The  latest  report  on  the  innovation  performance  of  the  Bulgarian  economy  paints  a  mixed  picture.  Bulgarian
enterprises are displaying few signs of innovation, links between research and innovation remain weak, and human
and financial resources are lacking. On a more positive note, Bulgaria’s gross innovation product has increased, and
the entrepreneurship and business environment continues to improve. 

The report, by the Applied Research and Communications Fund of Sofia, analyses the state of the national innovation
system and makes recommendations for enhancing innovation performance. The report is known as Innovation.bg
2007. 

Among the key conclusions in this year’s report are: 

- the market component of the Bulgarian innovation system is at an early stage of development, and innovation
is not widespread in Bulgarian enterprises; 

-  innovation and research products are being developed independently of one another; 

- the  national  innovation  system  is  being  developed  and  influenced  predominantly  by  the  integration  and
financing of European innovation networks; 

- the major barriers to innovation in Bulgaria are the lack of financing and qualified personnel; 

- performance  has  started  to  improve,  and  this  turnaround  is  the  perfect  time  for  renewed  efforts  to  boost
performance further. 

Three  recommendations  are  targeted  primarily  at  the  Bulgarian  Government.  More  political,  administrative  and
financial resources should be channelled into formulating and implementing the national innovation policy, the paper
states.  More  effort  should  also  be  made  to  improve  coordination  between  strategy  documents,  policies  and
administrative and financial instruments. 

Having  noted  the  crucial  role  that  EU  funds  play  in  driving  Bulgarian  innovation,  the  report  calls  on  the  Bulgarian
authorities to direct these funds towards more complex, longer term projects at national and regional level, rather than
use them for the shorter term direct financing of enterprises. These longer term projects should also be implemented
in  coordination  with  other  EU  programmes,  such  as  the  Seventh  Framework  Programme  (FP7)  for  research  and
technological development and the Competitiveness and Innovation Framework Programme (CIP). […]

Source: Cordis FP7 newsroom, 12-09-2007



123

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 124

Sources  of   information  and  cooperation  for  innovation
activities

Knowledge  transfer  is  made  up  of  a  combination  of  information  and
cooperation

Information plays a key role in innovation, so it is vital to
identify  the  most  important  sources  of  information  for
innovative enterprises. 

Sources  of  information  can  be  split  into  four  main
groups:  internal  sources,  market  sources,  institutional

sources and other sources. Each of these main groups
consists of one or more sub-groups (see below).

After identifying the sources of information and the use
made of them by innovative enterprises, different forms
of collaboration will be analysed.

Sources of  information, main groups and sub-groups

secruoslanretnI

secruostekraM

roesirpretneruoynihtiW
puorgesirpretne

,tnempiuqefosreilppuS
ro,stnenopmoc,slairetam
erawtfos

sremotsucrostneilC

rehtorosrotitepmoC
rotcesruoynisesirpretne

,sballaicremmoc,stnatlusnoC
setutitsniD&Retavirpro

secruoslanoitutitsnI

secruosrehtO

rehgihrehtoroseitisrevinU
snoitutitsninoitacude

hcraesercilbuprotnemnrevoG
setutitsni

,sriafedart,secnerefnoC
snoitibihxe

dnaslanruojcifitneicS
snoitacilbuplacinhcet/edart

yrtsudnidnalanoisseforP
snoitaicossa

Besides and closely linked to information, another key
topic  has  emerged  in  the  ongoing  discussion  on
innovation: knowledge transfer.

There  is  no  doubt  that  knowledge  creation,  the  main
business of higher education, is essential. However, if
this knowledge is to be useful it has to be applied to the
walks of life where it can make a difference. Knowledge
needs to be transferred.

The  main  way  in  which  knowledge  is  transferred  from
higher education to the wider world is via the expertise
and experience built up by graduates. However, small
companies which could benefit from the knowledge of a
highly skilled graduate but have a small workforce are
reluctant to take on graduates. 

Knowledge  is  transferred  whenever  the  findings  or
works  of  academics  are  disseminated  more  widely.
There are many ways in which this can be done. One
key  way  that  knowledge  can  be  spread  is  via  the
training that higher education offers to industry. 

Creating  stronger  links  between  universities  and
businesses is a major aim of Europe’s innovation policy.
One  step  in  this  direction  is  commercialisation  of
research.  This  is  the  process  of  getting  ideas  which
have  a  commercial  application  out  of  the  laboratories
and into the marketplace. Commercialisation does not
exclusively  concern  technology.  Creative  arts  and
social  sciences  also  have  the  potential  to  generate
profitable commercial activity. 

Brussels, 4 April 2007 – COM(2007) 161 final 
GREEN PAPER
The European Research Area: New Perspectives (presented by the Commission) 
{SEC(2007) 412}

Knowledge  transfer  must  improve  in  order  to  accelerate  the  exploitation  of  research  and  the  development  of  new
products  and  services.  To  that  end,  European  universities  and  other  public  research  institutions  should  be  given
incentives to develop skills and resources to collaborate effectively with business and other stakeholders, both within
and across borders. A major hindrance is the inconsistent, and often inadequate, rules and approaches for managing
intellectual  property  rights  (IPR)  resulting  from  public  funding.  The  Commission  has  identified  good  practice  and
models of knowledge-sharing between the public research base and industry which will serve to inspire further action
at both EU and national levels.

124



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 125

able 5.37 

The three most used sources of  information, as a percentage of  innovative enterprises, 

by country, EU-27 and Norway — 2004

Within the enterprise 
or enterprise group

Suppliers of equipment, 
materials, components or 
software

Clients or customers

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

LV

CY

LT

LU

HU

MT

NL

PL

PT

RO

SI

SK

FI

SE

UK

NO

54.7

33.1

39.4

56.2

53.3

34.1

64.3

46.2

45.1

54.5

36.3

:

85.9

32.2

64.9

41.7

48.6

45.0

48.0

8.3

38.0

: c

37.1

56.9

:

:

52.1

30.0

26.7

23.2

27.6

21.6

22.6

36.4

42.6

30.2

20.3

21.8

:

50.6

15.8

36.8

23.4

21.5

20.9

19.7

11.9

37.6

: c

23.7

15.8

:

:

20.0

38.9

33.1

32.1

32.4

35.0

25.6

49.9

25.5

19.6

25.6

13.8

:

22.1

19.1

36.6

28.2

27.8

27.0

32.5

17.9

30.9

: c

30.1

38.1

:

:

35.0

Whereas  in  most  of  the  countries  surveyed  between
40% and 50% of innovative enterprises use information
available  inside  their  enterprise  or  group,  there  are
exceptions  to  this  (see Table  5.37).  In  Cyprus  86%  of
innovative  enterprises  make  use  of  internal  sources
while,  at  the  other  end  of  the  scale,  in  Lithuania  only
32% of innovative enterprises do so. The second figure
is significantly lower but nevertheless still close to one
in three innovative enterprises.

Use  of  market  sources  varies  significantly,  depending
on the source considered. Table 5.37 shows the results
for  only  two  market  sources:  suppliers  and  clients  or
customers.  The  others  are  less  used  by  innovative
enterprises.

Nearly  one  out  of  every  two  innovative  enterprises  in
Ireland declares that its clients or customers are highly
valuable sources of information, whereas in Italy this is
the case for just 14% of innovative enterprises. 

In  Cyprus  more  than  50%  of  innovative  enterprises
obtain  information  from  their  suppliers  of  equipment,
materials, components or software, whereas in Finland
only 16% use this source. 

The CIS 4 questionnaire differentiates two institutional
sources  of  information:  universities  or  other  higher
education institutes and government or public research
institutes. As  these  sources  of  information  are  quoted
less  frequently  than  internal  or  market  sources  in
almost every country, these data are not shown in the
table above. The same is the case for the three other
sources  of  information:  conferences,  trade  fairs  and
exhibitions;  scientific  journals  and  trade/technical
publications;  and  professional  and 
industrial
associations.



125

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 126

The  absence  of  institutional  sources  among  the  top
three  sources  of  information  shows  that  the  link
between  science  and  industry  is  markedly  weak  in
Europe and needs to be strengthened. One aim along
with  others  that  national  governments  and  European
institutions  are  trying  to  achieve  by  funding  research
programmes at universities and public research entities
is  to  create  a  kind  of  domino  effect.  Active  and
successful public research should stimulate research in

the  business  enterprise  sector.  Without  any  doubt,
commercial research commissioned from universities is
a key way of linking university expertise and industry. It
helps researchers to export their ideas and inventions
from the laboratory to the global market. However, there
should also be some interaction between the public and
private  sectors.  Commercial  gains  from  research
should help to finance public research.

Brussels, 4 April 2007 — 
COMMUNICATION FROM THE COMMISSION TO THE COUNCIL, THE EUROPEAN PARLIAMENT,
THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

Improving knowledge transfer between research institutions and industry across Europe:
embracing open innovation – Implementing the Lisbon agenda – {SEC(2007) 449} 

CONCLUSION: Interactions between the public research base and industry have been gradually increasing over the
past decade. These can vary from contractual research to collaborative research or even to structured partnerships.
Most of these interactions involve the transfer of knowledge between the stakeholders concerned and enhance the
socio-economic impact of publicly funded research, e.g. by creating new useful products, new jobs and sometimes
new  companies.  The  analysis  and  policy  orientations  set  out  in  this  Communication  constitute  a  starting  point  for
discussions on a common European framework for knowledge transfer in order to create a level playing field and a
more coherent European landscape for knowledge transfer. Furthermore, the voluntary guidelines presented in the
accompanying  Commission  Staff  Working  Document  are  intended  to  help  research  institutions  identify  shared
interests  with  industry  and  facilitate  mutually  beneficial  knowledge  transfer  arrangements.  These  guidelines  will
become a living document, complemented by additional work to be undertaken by a group of high-level industry and
academic actors. This group will be launched in 2007 and will provide advice on other actions which it could take to
promote knowledge transfer in Europe. In addition, cooperation between Member States and the Community level will
also continue in the context of the Lisbon strategy for growth and jobs. Major policy initiatives in this area taken by
Member  States  should  be  reflected  in  the  National  Reform  Programmes,  and  the  exchange  of  good  practice  will
continue to be promoted by the Commission.

126



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 127

igure 5.38 

Innovative enterprises broken down into those that cooperate and those that do not,

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

LT

SI

FI

SE

DK

PL

FR

NL

LV

CZ

SK

CY

HU

BE

EE

NO

IE

MT

UK

LU

IS

GR

BG

PT

ES

RO

AT

DE

IT

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

No co-operation

Any type of co-operation

One  very  efficient  form  of  knowledge  transfer  is
cooperation.  "Innovation  cooperation"  means  active
participation  with  other  enterprises  or  non-commercial
institutions on innovation activities. There is no need for
both  partners  to  benefit  commercially,  but  pure
contracting-out  of  work  with  no  active  cooperation  is
excluded.
Innovative  enterprises  cooperate  with
different  types  of  partners  at  rates  varying  between
56% in Lithuania and 13% in Italy.

Figure  5.38  shows  that  there  is  no  general  trend,  but
that  the  northern  and  eastern  countries  seem  to
cooperate  more  readily.  Lithuania,  with  the  highest
score,  is  followed  by  Slovenia,  Finland,  Sweden  and
Denmark.  The  other  end  of  the  scale  is  made  up  of
Bulgaria,  Portugal,  Spain  and  Romania,  followed  by
Austria, Germany and, at the very end, Italy.



127

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 128

At first glance it might be surprising to find Italy — one
of  the  larger  EU  Member  States  —  at  the  end  of  the
scale.  How  can  this  low  level  of  cooperation  by
innovative enterprises in Italy be explained? There are
several  reasons:  structural,  economic,  cultural  and
historical. 

In Italy, there are still many small, often family-owned,
enterprises. The size of Italian enterprises may explain
why  the  proportion  of  innovative  enterprises  in  that
country  is  relatively  small,  on  36%.  Only  large
enterprises or groups have the funds to invest in R&D,
but there are not many of them in this country. 

For a long time public financing of R&D was much lower
in Italy than in other European countries. Research and
innovation  are  long-term  processes,  but  for  Italian
enterprises the pressure of competition is very high and
they need to invest in short-term solutions. Cooperation
takes time and does not necessarily produce results in
the short run. 

Because  of  the  education  system  and  the  low
expenditure on R&D, the number of researchers is also
relatively  low  in  Italy  compared  with  other  European

countries (see also Chapters 2 and 4 of this Statistical
book.)  Basic  research  and  experimental  development
are  necessary  for  applied  research.  Research  needs
structures  and  investment  over  long  periods.  The
results,  however,  are  often  available  only  in  the  long
run. In many cases only products from applied research
can be commercialised rapidly. 

As mentioned earlier, Germany ranks last but one. The
reasons for this low level of cooperation by innovative
German  enterprises  are  very  different  to  those  for
Italian enterprises. Germany has the highest proportion
of  innovative  enterprises  of  all  EU  Member  States.
Owing  to  the  size  of  the  country’s  economy  and  to  its
different  type  of  entrepreneurship,  Germany  has
numerous  large  enterprises.  These  invest  substantial
funds in R&D and are less interested in cooperation. 

Among  the  top  20  groups  in  terms  of  total  R&D
investment  seven  are  German  and  two  Italian.  These
German groups invested nearly eight times the amount
invested by the two Italian groups in 2005 (see Chapter
8, Table 8.1).

igure 5.39 

Enterprises engaged in any type of  innovation cooperation, by region of  the partner,

as a percentage of  innovative enterprises, by country, EU-27 and Norway — 2004

60

50

40

30

20

10

0

LT

FI

SE

DK

FR

PL

LV

NL

HU

CZ

SK

BE

NO EE

CY

LU

EL

BG PT

ES MT

DE

AT

RO

IT

Data missing/confidential for IE, SI and UK.

National

 Within other Europe

Within United States and other countries

128



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 129

The enterprises surveyed were asked to indicate where
their cooperation partners were from. Figure 5.39 draws
a distinction between three different regions:

ranks  third  for  the  proportion  of  innovative  enterprises
cooperating at national level, whereas Slovakia is third
for European cooperation.

• national,  which  means  the  home  country  of

the enterprise;

• Other  Europe,  which  includes  all  EU,  EFTA

and candidate countries;

• United  States  and  other  countries  which  are

not included in the other two regions.

Italy can always be found at the other end of the scale
for  cooperation  with  all  regions.  At  national  level
Romania  ranks  last,  whereas  Spain  comes  last  for
cooperation at European and international level.

The  CIS  4  questionnaire  draws  a  distinction  between
seven different partners for cooperation:

• other enterprises in your enterprise group;

The  breakdown  by  region  reveals  more  about
cooperation by innovative enterprises. 

large  majority  of 

Unsurprisingly,  a 
innovative
enterprises  find  cooperation  partners  in  their  home
country.  There  are  two  exceptions  to  this  finding:
Luxembourg  and  Malta.  As  they  are  both  very  small
countries,  their  firms  cooperate  more  with  enterprises
from  other  European  countries  than  with  businesses
from  their  own  country.  Maltese  enterprises  even
cooperate  more  with  other  non-European  countries
than with other Maltese enterprises. Compared with the
other  European  countries,  Malta  has  the  highest
proportion  of  innovative  enterprises  cooperating  at
international  level.  Latvia  ranks  second  behind  Malta,
followed by Finland. 

For  national  and  European  cooperation  Lithuania  and
Finland  always  take  first  and  second  place.  Sweden

• competitors or other enterprises in the same

sector;

• clients or customers;

• suppliers 

of 

equipment,  materials,

components or software;

• universities  or  other  higher  education

institutions;

• government or public research institutes;

• consultants, commercial labs or private R&D

institutes.

Figures  5.40  to  5.46  show  the  percentages  of
innovative enterprises by country for each category of
partner.

igure 5.40 

Cooperation partner: Other enterprises in the same group, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries —- 2004

25

20

15

10

5

0

BE BG CZ DK DE EE

IE

EL ES FR IT CY LV

LT

LU HU MT NL AT PL PT RO SI

SK

FI

SE UK

IS NO

The proportion of innovative enterprises that cooperate
with  other  enterprises  in  the  same  group  varies
between 23% in Finland and 3% in Italy. In 18 of the 29
countries  shown  in  Figure  5.40  at  least  10%  of  the
actively  innovating  enterprises  use  this  form  of
cooperation. 

In Finland more than one out of every three enterprises
(34%) cooperates with competitors or other enterprises
in the same sector. Behind Finland, the highest scores
for this kind of cooperation were recorded in the three
Baltic  countries,  plus  Slovenia  and  Slovakia.  The
smallest score for this category of partner was found in
Spain with 3%.



129

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 130

igure 5.41 

Cooperation partner: Competitors or other enterprises in the same sector, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

igure 5.42 

Cooperation partner: Clients or customers, as a percentage of  innovative enterprises,

by country, EU-27 and selected countries — 2004

40

35

30

25

20

15

10

5

0

45

40

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

In 15 out of 29 European countries more than 20% of
the innovative enterprises cooperate with their clients or
customers (see Figure 5.42). Whereas in Spain only 4%
of  the  enterprises  with  innovative  activities  cooperate
with their clients or customers, in Finland 41% do so.

The most successful form of cooperation seems to be
with suppliers of equipment, materials, components or

software.  This  time  Lithuanian  innovators  are  in  the
lead with 45%, followed by Finland (41%) and Slovenia
(38%).  In  13  EU  Member  States  at  least  one  out  of
every four innovative enterprises is cooperating with its
suppliers. By contrast, the lowest figure for cooperation
with suppliers (7%) was recorded in Germany.

130



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 131

igure 5.43 

Cooperation partner: Suppliers of  equipment, materials, components or software,

by country, EU-27 and selected countries — 2004

50

45

40

35

30

25

20

15

10

5

0

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

igure 5.44 

Cooperation partner: Universities or other higher education institutions, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

institutions  are 

Compared  with  suppliers,  universities  or  other  higher
less  demand  as
education 
cooperation partners. Finland’s innovative enterprises –
which  are  very  active  in  all  types  of  cooperation  and
recorded  the  highest  scores  for  most  categories  of
cooperation  partner  –  were  also  in  the  lead  for

in 

cooperation  with  higher  education  on  33%.  Besides
Finland, only Slovenia and Sweden pass the 15% mark.
This  form  of  cooperation  is  almost  non-existent  in
Cyprus,  where  it  is  practised  by  just  2%  of  the
innovative enterprises. 



131

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 132

Worse  still  are  the  results  for  cooperation  with
government  or  public  research  institutes.  26%  of
Finnish  innovative  enterprises  use  this  type  of
cooperation,  as  do  more  than  10%  of  the  innovative
enterprises  and  in  three  other  Member  States,  but  in
most  other  countries  the  percentages  are  very  low.  In
Italy the share of this category of partner does not even
reach 2%.

Figures  5.44  and  5.45  demonstrate 
the  weak
cooperation between the public and private sectors on
innovation. 

Nevertheless this kind of cooperation is very important
for knowledge transfer, a key component of innovation.
It seems necessary to strengthen cooperation between
the  business  enterprise  sector  and  both 
the
government and the higher education sectors.

igure 5.45 

Cooperation partner: Government or public research institutes, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

igure 5.46 

Co-operation partner: Consultants, commercial labs, or private R&D institutes, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

132



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 133

Inside  the  private  sector  cooperation  seems  to  be
easier  because  more  enterprises  choose  consultants,
commercial labs or private R&D institutes as partners.
In Finland nearly one third of the innovative enterprises
do  so  and  in  Lithuania  one  out  of  every  four.  In  11
Member  States  the  figures  reach  15%  for  this  type  of
cooperation. By contrast, only 3% of German innovative
enterprises are involved in this form of cooperation.

The  question  about  the  most  valuable  method  of
cooperation  (Table  5.47)  is  not  a  real  poll.  In  many
suppliers  of  equipment,  materials,
countries 
components or software received the highest number of
votes, but not in all. In Germany and Ireland innovative

enterprises preferred to cooperate with their clients and
customers.  Maltese  innovative  enterprises  were  more
inclined  to  cooperate  with  other  enterprises  in  their
group.

To  summarise  the  results  on  cooperation  and  the
different methods, it should be said that the innovative
enterprises  do  cooperate  but  could  do  to  a  much
greater degree. As the situation with cooperation varies
along with the methods and across countries, it needs
to  be  studied  in  detail.  It  is  important  to  identify  the
barriers  to  cooperation  in  order  to  introduce  the
necessary  reforms  and  other  action  to  encourage
cooperation.

able 5.47 

Most valuable method of  cooperation by partner, as a percentage of  innovative enterprises,

by country, EU-27 — 2004

Other enterprises 
within your 
enterprise group

Competitors or 
other enterprises 
of the same 
sector

 Clients or 
customers

Suppliers of 
equipment,
materials,
components or 
software

 Universities or 
other higher 
education
institutions

 Government or 
public research 
institutes

Consultants,
commercial labs, 
or private R&D 
institutes

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

9.7

2.4

6.6

2.6

1.1

8.2

6.6

1.8

2.6

9.6

:

2.5

3.5

9.6

8.8

5.8

15.3

8.9

:

8.3

3.0

1.9

1.7

0.7

1.6

1.0

1.0

4.1

0.2

5.1

1.4

3.6

:

8.5

2.8

2.4

1.5

2.9

2.1

1.3

:

2.1

0.9

0.5

8.3

6.7

12.1

5.5

3.1

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10.3

4.4

1.6

6.9

:

1.7

12.4

9.9

5.5

7.3

11.1

8.5

:

6.8

4.3

2.9

10.3

9.3

12.8

6.0

1.5

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7.7

5.6

6.7

12.1

:

14.9

15.8

25.4

10.8

13.8

12.5

14.7

:

16.8

4.7

6.2

2.3

0.8

2.0

1.5

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3.6

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:

0.7

1.3

1.6

1.2

3.8

: c

1.4

:

1.8

2.6

1.3

: c

: c

: c

: c

: c

6.0

:

6.2

:

2.0

:

1.2

:

11.3

:

11.6

:

15.3

:

17.2

:

0.6

:

2.5

:

0.5

0.2

0.7

: c

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0.3

0.6

0.3

2.3

1.9

:

0.3

1.4

1.5

1.0

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2.1

1.9

:

4.1

1.1

: c

: c

0.5

:

0.5

:

2.5

2.0

2.6

1.6

0.5

1.1

2.3

2.8

1.6

3.2

:

8.4

1.7

5.7

1.9

2.5

3.5

2.7

:

2.3

2.7

0.7

: c

2.1

:

3.5

:



133

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 134

Effects of  innovation during 2002-2004

Innovative enterprises give priority to improving the quality of goods and
services

The  CIS  4  questionnaire  drew  a  distinction  between
three groups of effects of innovation, each with at least
two  different 
innovative  enterprises
surveyed were asked to indicate the appropriate degree
of  importance  for  the  nine  effects  listed.  This  analysis
takes  into  account  only  the  effects  ranked  highly
important.

items.  The 

• Product oriented effects

o Increase range of goods or services

o Enter  new  markets  or  increase  market

share

o Improve quality of goods or services 

• Process-oriented effects

o Improve  flexibility  of  production  or  service

provision

o Increase  capacity  of  production  or  service

provision

o Reduce labour costs per unit output

o Reduce  materials  and  energy  per  unit

output

• Other effects

o Reduce 

environmental 
improved health and safety

impacts 

or

o Meet regulatory requirements

TThe picture at national level is multi-faceted (see Table
5.48). More specifically, for innovative enterprises in 17
of the 27 EU Member States, improve quality of goods
and services recorded the highest vote. Greece led with
close to 60% of innovative enterprises, while Latvia was
at the other end of the scale with only 7%. 

Increase range of goods and services took first place as
a highly important effect of innovation in six countries –
the  Czech  Republic  (41%),  Germany  (38%),  Estonia
(35%),  Ireland  (41%),  Finland  (25%)  and  Sweden
(31%).

Portugal  and  Latvia  had  the  highest  percentages  of
innovative  enterprises  that  identified  reduce  material
and  energy  consumption  per  unit  output as  highly
important  –  26%  and  19%  respectively.  Romanian
innovative enterprises, on the other hand, did not feel in
the least concerned, recording 0% for this indicator. 

For  French  innovative  enterprises  the  most  important
effects  of  innovation  were  enter  new  markets  or
increase  market  share.  In  comparison  with  the  other
countries, France led in terms of its share of innovative
enterprises,  with  53%  considering  increase  range  of
goods  and  services a  highly  important  factor,  59%  for
enter new markets or increase market share’ and 35%
for reduce labour costs per unit output.

Nearly  65%  of  the  innovative  enterprises  in  Cyprus
chose  improve  flexibility  of  production  or  service
provision as the most important effect of innovation. 

At the same time, close to 30% of enterprises in Cyprus
also identified reduce environmental impact or improve
health  and  safety as  highly  important;  this  was  by  far
the highest percentage out of all the countries. 

Meet regulatory requirements was ranked highest of all
the  effects  of  innovation  by  innovative  enterprises  in
Cyprus, with 47%.

134



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 135

able 5.48 

Effects identified by enterprises as highly important for their innovation activities, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2004

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

135

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 136

Factors hampering innovation activities

able 5.49 

Highly important hampering effects, as a percentage of  innovative enterprises,

by country, EU-27 and selected countries — 2004

e
t
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136



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 137

For  policymakers  it  is  important  to  understand  the
barriers to innovation. Political intervention can only be
successful if it is targeted. But, as will be seen later, not
all  barriers  are  situated  outside  the  enterprise.  There
are some obstacles that the enterprise has to overcome
itself.

All the enterprises were asked to classify eleven factors
hampering  innovation  in  order  of  importance.  These
factors fell into the four groups listed below::

• Cost factors

o Lack  of  funds  within  your  enterprise  or

group.

o Lack of finance from sources outside your

enterprise.

o Innovation costs too high.

• Knowledge factors

o Lack of qualified personnel.

o Lack of information on technology.

o Lack of information on markets.

o Difficulty in finding cooperation partners for

innovation.

• Market factors

o Market 

dominated 

by 

established

enterprises.

o Uncertain demand for innovative goods or

services.

• Reasons not to innovate

o No need due to prior innovations.

o No  need  because  of  no  demand  for

innovations.

The  last  group  of  hampering  factors  concerns  non-
innovative  more  than  innovative  enterprises.  For  this
reason, the results are not shown in Table 5.49, but only
in Figure 5.53.

igure 5.50 

Cost factors rated as highly important factors hampering innovation, 

as a percentage of  innovative enterprises, by country, EU-27 and Norway — 2004

45

40

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE

IE

EL ES FR

IT CY LV

LT

LU HU MT NL AT PL PT RO SI

SK

FI

SE UK

IS NO

Lack of funds within your enterprise or enterprise group

Lack of finance from sources outside your enterprise

Innovation costs too high

Cost  factors  are  usually  considered  highly  important
factors hampering innovation. This analysis will take a
closer  look  at  the  three  different  factors  and  also
differentiate between countries.

In  14  of  the  27  EU  countries  more  than  20%  of  the
innovative  enterprises  considered 
that
innovation  costs  are  too  high  as  a  highly  important
factor  hampering  innovation.  In  Spain  close  to  40%
shared this opinion, whereas in Portugal the figure did
not reach the 10% mark.

fact 

the 

The  other  two  cost  factors  concern  the  sources  of
funds.  If  costs  are  hampering  innovation,  the  problem
could  be  lack  of  financial  resources.  This  lack  can  be
located either inside the enterprise or outside.

Lack of financial resources seems to be a real concern
for innovative enterprises in Greece, where the scores
for  both  sources  of  funds  were  higher  than  30%.
Whereas  lack  of  funds  within  the  enterprise  or  group
was considered a highly important hampering factor by
8%  of  the  innovative  enterprises  in  Romania,  lack  of
finance from outside the enterprise was chosen by only
5% of the innovative enterprises in Luxembourg.

Even if the spreads across countries of the proportions
of  innovative  enterprises  that  choose  one  of  these
factors or the other as highly important are similar, lack
of  funds  within  the  enterprise  or  group  seems  more
important  than  lack  of  outside  sources.  Fifteen  EU
countries reported percentages higher than 20% for the
first cost factor but only seven countries for the second.



137

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 138

igure 5.51 

Knowledge factors rated as highly important factors hampering innovation, 

as a percentage of  innovative enterprises, by country, EU-27 and Norway — 2004

54

04

53

03

52

02

51

01

5

0

EEEDKDZCGBEB

EI

LE

SE

RF

TI

YC

VL

TL

LNTMUHUL

TA

LP

ISORTP

KS

IF

KUES

SI

ON

lennosrepdeifilauqfokcaL

stekramnonoitamrofnifokcaL

ygolonhcetnonoitamrofnifokcaL

noitavonnirofsrentrapnoitarepoocgnidnifniytluciffiD

Knowledge  and 
linked.
Knowledge can be transferred by various means, such
as human resources, information and cooperation.

innovation  are  strongly 

At first glance, knowledge factors seem to be less often
considered  highly 
factors  hampering
important 
innovation. A  closer  look  at  country  level  and  at  each
individual factor is necessary for a deeper analysis. 

rated 

Out  of  the  four  knowledge  factors  ”lack  of  qualified
personnel“ scored highest in most countries (see Figure
5.51).  In  Portugal  nearly  one  out  of  every  four
innovative  enterprises 
”lack  of  qualified
personnel“  a  highly  important  hampering  factor.  The
innovative  enterprises  in  Estonia,  Greece  and  Latvia
also  reached  the  20%  mark  for  this  hampering  factor.
By  contrast,  in  Germany  lack  of  qualified  personnel
does  not  seem  to  be  a  real  hurdle.  Fewer  than  5%  of
the  German  innovative  enterprises  chose  this  as  a
highly important hampering factor.

For two other knowledge factors – ”lack of information
on  technology“  and  ”lack  of  information  on  markets“  –
Latvia  and  Portugal  came  out  highest.  They  both
recorded  over  30%  for  both  these  factors,  whereas
most of the other countries did not even attain 10%.

The  last  knowledge  factor  is  the  ”difficulty  in  finding
cooperation  partners  for  innovation“.  Cooperation  is
one  of  the  key  components  in  the  Community
Innovation  Survey.  The  degree  and  different  types  of
cooperation were shown in Figures 5.38 to 5.46 and in
Table  5.47.  The  highest  scores  for  this  factor  can  be
found  in  Latvia  (29%),  Greece  (24%)  and  Portugal
(21%).  Indeed  these  countries  might  be  able  to
cooperate  more.  The  proportion  of  their  innovative
enterprises  actively  engaged  in  cooperation  were:
Latvia (39%), Greece (24%) and Portugal (19%).

In  most  of  the  other  countries,  a  low  proportion  of
finding
innovative  enterprises  rated  difficulty 
cooperation partners a significant barrier to innovation.
In some countries there seem to be no major difficulties
with  arranging  cooperation  because  many  innovative
enterprises there are already actively cooperating. 

in 

In  others  the  situation  is  different.  In  Germany,  for
example, few innovative enterprises declared that they
were  actively  involved  in  cooperation  but  also  few
considered  that  this  lack  of  cooperation  hampered
innovation.  It  seems  that  the  act  of  cooperating  is  not
perceived in the same way in every country.

138



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 139

igure 5.52 

Market factors rated as highly important factors hampering innovation, 

as a percentage of  innovative enterprises, by country, EU-27 and Norway — 2004

30

25

20

15

10

5

0

BE BG CZ DK DE EE

IE

EL ES FR IT CY LV LT

LU HU MT NL AT PL PT RO SI

SK

FI

SE UK IS NO

Markets dominated by established enterprises

Uncertain demand for innovative goods or services

As  shown  in  Figure  5.52,  market  factors  can  also  be
considered  highly  important  barriers  to  innovation.  At
national  level  more  or  less  15%  of  the  innovative
enterprises felt affected by these two factors. 

The  results  vary  significantly  across  countries.
Whereas  in  the  Netherlands  fewer  than  5%  of  the
innovative  enterprises  rated  “markets  dominated  by
established  enterprises”  highly  important,  in  Slovenia
close to 26% did.

The spread was comparable for “uncertain demand for
innovative  goods  or  services”. Around  5%  of  German
innovative  enterprises 
factor  highly
important, but in Greece 24%.

found 

this 

In general, market domination seems to play a slightly
more  fundamental  role  than  uncertain  demand  as  a
market factor hampering innovation.



139

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 140

igure 5.53 

Reasons not to innovate rated as highly important factors hampering innovation, 

as a percentage of  non-innovative enterprises, by country, EU-27 and Norway — 2004

35

30

25

20

15

10

5

0

BE

BG CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU MT

NL

AT

PL

PT

RO

SI

SK

FI

SE NO

No need to innovate due to prior innovations

No need to innovate because no demand for innovations

Missing data: LV, UK.

By  contrast  to  the  three  previous  figures,  which
displayed  the  results  for  the  factors  rated  as  highly
important  for  hampering  innovation  by  innovative
enterprises,  Figure  5.53  shows  the  results  for  two
reasons  that  seem  to  be  important  barriers  to
innovation by non-innovative enterprises.

These  reasons  for  non-innovation  may  be  either  prior
innovations or no demand for innovations. The spread
of  the  results  is  relatively  wide  across  countries.
Whereas  in  Malta  only  2%  of  the  non-innovative

enterprises  considered  prior  innovations  a  highly
important reason not to innovate, in Portugal 26% did.
The  spread  is  almost  broader  for  the  second  reason.
While in Romania almost no non-innovative enterprises
chose this reason, almost one out of every three non-
innovative enterprises in Greece considered the lack of
demand a highly important reason. 

In 19 out of the 26 European countries in the figure lack
of  demand  for  innovation  scored  higher  than  prior
innovations. 

Intellectual property rights

How enterprises protect their innovations

All the enterprises surveyed were asked for information
about  their  innovation  activities  that  led  to  intellectual
property  rights  (IPR)  during  the  three  years  2002  to
2004.  The  CIS  4  questionnaire  split  the  forms  of
protection into:

• patent applications;

• registration of an industrial design;

• registration of a trademark; and

• copyright claims.

The  proportions  of  innovative  enterprises  that  applied
for a patent varied between 22% for France and 1% for
Cyprus.  Ten  of  the  23  European  countries  in  Figure
5.54 reached the 10% mark.

Patents protect the technical and functional aspects of
products and processes. An invention is patentable if it
meets  the  criteria  of  industrial  applicability,  novelty,
inventiveness and patentable subject matter. Patenting
is  a  relatively  expensive  procedure  that  requires  a
certain amount of administrative records for filing. 

140



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 141

Brussels, 4 April 2007 – COM(2007) 161 final
GREEN PAPER

The European Research Area: New Perspectives (presented by the Commission)
{SEC(2007) 412}

Patenting remains excessively complicated and costly in Europe, and fragmented litigation fails to provide sufficient
legal  certainty.  Given  the  deadlock  in  negotiations  on  the  Community  patent,  other  options  are  being  examined,
including  improving  the  current  European  patent  system.  The  objective  should  be  to  offer  cost-effective  European
patenting,  mutually  recognised  with  the  other  major  patenting  systems  worldwide  and  backed  by  a  coherent  pan-
European litigation system. In addition, a number of R&D-specific issues, such as the grace period, joint ownership
regimes and the research exception, should also be addressed in order to ensure consistent treatment across the EU. 

igure 5.54 

Innovative enterprises that applied for a patent, as a percentage of  innovative enterprises,

by country, EU-27 and Norway — 2004

25

20

15

10

5

0

BE

BG CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU MT

NL

PL

PT

RO SK

FI

NO

Data missing/confidential for LV, AT, SI, SE and UK.

Registered trademarks protect signs or combinations of
signs that identify the goods and services of individual
traders.

Trademarks are generally distinctive symbols, pictures
or words that sellers affix to distinguish and identify the
origin of their products. The owner of a trademark has
exclusive  rights  to  use  it  on  the  product  which  it  was
intended to identify and, often, on related products. 

During the three years 2002 to 2004 about one third of
all  innovative  enterprises  in  France  registered  a

trademark. They were the most active users of this form
of protection. In 10 European countries more than 15%
of  the  innovative  enterprises  registered  a  trademark
during the reference period. 

Although  more  use  is  made  of  this  method  than  of
applying  for  a  patent,  in  Estonia  only  2%  of  the
innovative enterprises used it.



141

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 142

igure 5.55 

Innovative enterprises that registered a trademark, as a percentage of  innovative enterprises,

by country, EU-27 and Norway — 2004

40

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE

IE

EL

ES FR

IT

CY

LT

LU HU MT NL

PL

PT RO SK

FI

NO

Data missing/confidential for LV, AT, SI, SE and UK.

Office for Harmonisation in the Internal Market (OHIM)
(Trade Marks and Designs) 

The OHIM is the official authority carrying out the procedures for the Community trade marks since 1996
and for the Community registered design from 2003. These intellectual property rights are valid in all the
countries of the EU.

Trade marks and designs belong to the world of private company law. The OHIM is both an agency of the European
Community and an industrial property office with its technical function: the registration of industrial property rights.

As a service agency, the Office has to place its clients, that is to say the undertakings that file their trade marks and
their designs with the OHIM, at the centre of the overall mechanism of the Office and it has to provide them with the
best service at the best price.

The Community trade mark and the Community registered design are the gateway to a single market. Their unitary
nature means that formalities and management can be kept simple: a single application, a single administrative centre
and a single file to be managed.

A  uniform  law  applies  to  trade  marks  and  designs,  thereby  providing  strong  and  unique  protection  throughout  the
European  Union.  The  simplification  results  in  considerably  reduced  costs  as  compared  with  the  overall  costs  of
national registration in all countries of the European Union.

The size of the OHIM today, the speed at which it has grown and the way it became self-financing from its second
year of operation are proofs of the success of the system at the service of the single market.

Source: http://europa.eu/agencies/community_agencies/ohim/index_en.htm 

142



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 143

Registered 
appearance or eye appeal of useful articles.

industrial  designs  protect 

the  visual

the  designer  has 

Industrial designs are linked with all the human aspects
of  machine-made  products  and  their  relationship  with
people and the environment. For the product’s human
into  account
factors 
engineering,  safety,  form,  colour,  maintenance  and
cost.  Professional  industrial  designers  deal  with  both
consumer  and  industrial  products.  In  order  to  achieve
these  ends,  designers  must  be  involved  in  four  major
design  and  research  activities:  human  behaviour,  the

take 

to 

human-machine  interface,  the  environment  and  the
product  itself.  Industrial  design  can  involve  numerous
areas,  such  as  furniture,  houseware,  appliances,
transport,  tools,  farm  equipment,  medical/electronic
instruments,  the  human  interface  and  recreational
support equipment. 

Whereas  in  Greece  nearly  one  out  of  every  four
innovative  enterprises  registered  an  industrial  design
over  the  reference  period,  in  Cyprus  only  1%  of  the
innovative enterprises used this form of protection.

igure 5.56 

Innovative enterprises that registered an industrial design, 

as a percentage of  innovative enterprises, by country, EU-27 and Norway — 2004

30

25

20

15

10

5

0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU MT

NL

PL

PT

RO

SK

FI

NO

Data missing/confidential for LV, AT, SI, SE and UK.

Industrial property: Commission adopts necessary measures 
for linking EU design registration system with WIPO international system 

The  European  Commission  adopted  two  Regulations  which  are  necessary  to  give  effect  to  the  accession  of  the
European Community to the Geneva Act of the Hague Agreement concerning the international registration of industrial
designs.  The  adoption  follows  the  Council’s  approval  of  the  EC  accession  to  the  international  design  registration
system of the World Intellectual Property Organisation (WIPO) on 18 December 2006. The EC accession will allow
EU  companies,  with  a  single  application,  to  obtain  protection  of  a  design  not  only  throughout  the  EU  with  the
Community Design, but also in the countries which are members of the Geneva Act. 

Source: OHIM news release, 25/07/2007



143

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 144

igure 5.57 

Innovative enterprises that claimed copyright, as a percentage of  innovative enterprises, 

by country, EU-27 and Norway — 2004

14

12

10

8

6

4

2

0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LT

LU

HU

NL

PL

PT

RO

SK

FI

NO

Data missing/confidential for LV, AT, SI, SE and UK.

Copyright protects artistic creations, such as literature,
art,  music,  sound  recordings,  films,  broadcasts  and
computer programs.

The copyright defines general conditions for producing,
monopolising,  distributing  and  using  particular  cultural
information.  The  copyright  gives  the  holder  the
exclusive  right  to  protect  his  or  her  interests  in
artistically creative work. 

Since  the  creation  of  the  Internet  and  all  the  new
technological  possibilities  which  it  opened  up  to  copy

different kinds of creative work, copyright has assumed
growing importance in the public debate. 

For  the  enterprises  surveyed,  claiming  copyright  is  a
less  important  method  of  protection.  Nevertheless,
more  than  12  of  the  innovative  enterprises  in
Luxembourg  claimed  at  least  one  copyright  between
2002 and 2004. By contrast, only 1% of the innovative
enterprises  in  Cyprus  claimed  at  least  one  copyright
over the same period.

144



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 145

Organisational and marketing innovations

Organisational innovations

Marketing innovations

Organisational innovations involve a significant change
in  business  practices,  workplace  organisation  or
external  relations,  intended  to  improve  the  firm’s
innovative capacity or performance, such as the quality
or  efficiency  of  workflows.  Organisational  innovations
usually  involve  changes  to  more  than  one  part  of  the
firm’s supply chain and are less technology-dependent
than process innovations.

Organisational innovations do not include:

• changes  of  management  strategy,  unless
accompanied  by  significant  organisational
changes; or

• introduction of new technology that is used by
only  one  division  of  a  firm  (for  example,  in
production).  These  are  usually  process
innovations.

Knowledge management systems

• Establishment  of  formal  or  informal  teams  to
improve  access  to  and  sharing  of  knowledge
from different departments, such as marketing,
research, production, etc.;

• Introduction  of  quality  control  standards  for

suppliers and subcontractors;

• Supply  management  systems  to  optimise
allocation of resources, from sourcing inputs to
final delivery of products.

Changes to work organisations

• Reduction 

in 

the  number  of 

layers  of

management;

• Change  in  responsibilities,  such  as  giving
substantially  more  control  and  responsibility
over work processes to production, distribution
or sales staff;

• Creation  of  a  new  division,  for  example  by
splitting  management  of  marketing  and
production into two divisions.

Changes in external relations

• First use of outsourced research or production
if  it  requires  a  change  in  how  workflows  are
organised within the firm.

Marketing innovations cover significant changes in how
an enterprise markets its goods and services, including
changes to design and packaging. 

Marketing innovations do not include:

• routine or seasonal changes, such as clothing

fashions;

• advertising,  unless  based  on  use  of  new

media for the first time.

Innovative design & packaging

• Novel  designs  of  existing  products,  such  as
flash card memory sticks designed to be worn
as jewellery;

• New designs for consumer products, such as
small

designed 

very 

for 

appliances 
apartments;

• Adapting  packaging  to  specific  markets  (e.g.
different covers and typeface for children's and
adult versions of the same book).

New sales methods

• Bundling  existing  goods  or  services  in  new

ways to appeal to market segments;

• Developing trademarks for new product lines;

• Targeting marketing on sub-populations using
personalised information. This information can
be  collected 
individuals  who  visit
websites  for  information  or  join  frequent  user
or buyer reward schemes;

from 

• Product  seeding  via  opinion 

leaders,
celebrities  or  particular  groups  that  are
fashion- or product trend-setters;

• First use of product placement on television, in

books, films, etc.;

• Media  programming  for  a  specific  institution,
such  as  closed  circuit  television  for  hospitals
that  contain  educational  programming  to
stimulate sales of specific products;

• In-store sales accessible only to holders of the

store’s credit card or reward card.



145

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 146

able 5.58 

Innovative and non-innovative enterprises that introduced organisational and/or 

marketing innovations, as a percentage of  innovative and non-innovative enterprises, 

by country, EU-27 and Norway — 2004

Innovative enterprises

Non-innovative enterprises

Enterprise introduced 
organisational and/or 
marketing innovations

Enterprise introduced 
organisational innovation

Enterprise introduced 
marketing innovation

Enterprise introduced 
organisational and/or 
marketing innovations

Enterprise introduced 
organisational innovation

Enterprise introduced 
marketing innovation

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

68.3

52.4

69.3

80.9

72.1

72.1

69.6

71.9

60.2

70.9

58.7

74.9

:

69.9

81.6

60.8

69.4

57.0

76.0

69.7

72.6

70.6

:

61.8

:

:

:

66.0

58.2

41.0

62.8

75.8

64.9

61.6

64.2

65.0

56.6

61.4

52.3

59.4

:

58.9

75.6

49.5

57.6

48.0

69.2

57.4

66.1

28.9

:

54.8

:

:

:

42.7

38.9

38.2

38.8

30.6

33.4

42.2

36.7

38.7

24.6

37.0

30.9

51.7

:

36.8

45.4

36.8

44.4

27.8

42.2

51.2

37.6

5.0

:

30.0

:

:

:

48.5

23.7

7.8

22.9

44.4

40.4

25.0

:

31.1

14.2

28.8

26.2

40.4

:

14.1

43.7

17.4

16.5

18.4

:

13.2

27.5

19.6

:

12.6

:

:

:

17.6

19.5

6.0

20.4

38.5

35.8

20.4

:

25.8

13.1

25.2

21.7

29.7

:

11.5

41.0

12.6

12.2

16.6

:

9.0

24.2

12.9

:

11.6

:

:

:

13.2

8.9

3.7

8.7

10.5

9.8

8.0

:

13.2

3.9

9.3

10.8

25.0

:

5.6

13.7

9.2

9.4

5.2

:

8.1

9.4

1.3

:

3.0

:

:

:

8.9

As  observed  earlier,  process  and  product  innovations
are  linked.  Many  product  innovators  are  also  process
innovators  and  vice  versa.  The  same  applies  to
organisational and marketing innovations; they are also
linked.  Innovating  enterprises  often  introduce  more
than  one  innovation  and  these  may  concern  different
domains of the enterprise.

In general, the proportion of innovative enterprises that
introduced organisational innovations is higher than the
proportion  that  introduced  marketing  innovations.
Consequently,  the  proportion  of  innovative  enterprises
that 
introduced  organisational  and/or  marketing
innovations is the highest but is lower than the sum of
the  enterprises 
introduced  organisational
innovations  plus  the  enterprises  that  introduced
marketing 
these
enterprises introduced both.

innovations  because  some  of 

that 

that 

they  had 

Cross-country  comparison  reveals  large  differences
between  countries.  More  than  half  the  innovative
enterprises  in  Cyprus  (52%)  introduced  marketing
innovations, but in Romania only 5%. More than three
out of every four innovative enterprises in Luxembourg
replied 
introduced  organisational
innovations.  By  contrast,  less  than  one  third  of  the
innovative enterprises in Romania declared the same.
Luxembourg led with 82% of its innovative enterprises
having  introduced  marketing  and/or  organisational
innovations.  Bulgaria  is  at  the  other  end  of  the  scale
with  52%.  The  figure  for  Bulgaria  is  lower  but  is
nevertheless  still  more  than  half  of  all  innovative
enterprises.

146



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 147

Non-innovative enterprises – which means enterprises
that  introduced  neither  a  product  nor  a  process
innovation during the reference period (2002 to 2004) –
did 
introduce  organisational  and/or  marketing
innovations.  The  figures  are  lower  than  for  innovative
enterprises but nevertheless significant.

having 

Denmark’s non-innovative enterprises led, with 44% of
them 
and/or
organisational  innovations.  Denmark  was  followed  by
Luxembourg, Germany and Cyprus, all also with scores
higher than 40%.

introduced  marketing 

non-innovative 

Whereas  in  Luxembourg  more  than  four  out  of  every
ten 
introduced
organisational  innovations,  in  Cyprus  one  in  four
introduced marketing innovations.

enterprises 

• improved the quality of goods or services;

• reduced costs per unit output;

• improved  employee  satisfaction  and/or

reduced rates of employee turnover.

Between 21% (Luxembourg) and 1% (Portugal) of the
innovative  enterprises  considered  “reduced  time  to
respond  to  customer  or  supplier  needs”  a  highly
important effect of organisational innovation. 

In general, this effect is not so important because only
eight out of the 20 countries in Figure 5.59 reached the
10% mark. Most innovative enterprises seem to regard
time  savings  more  as  a  positive  collateral  effect  of
innovation  which  do  not  play  a  large  part  in  the
enterprise’s overall innovation strategy.

The  enterprises  surveyed  were  asked  to  evaluate  the
importance  of  four  different  effects  of  organisational
innovation:

• reduced  time  to  respond  to  customer  or

supplier needs;

igure 5.59 

Reduced time to respond to customer or supplier needs: innovative enterprises that rate this as a

highly important effect of  organisational innovation, as a percentage of  all enterprises, 

by country, EU-27 and Norway — 2004

25

20

15

10

5

0

BE

BG

CZ

DK

DE

EE

EL

ES

FR

IT

CY

LT

LU

HU

MT

NL

PT

RO

SK

NO

Data missing/confidential for IE, LV, AT, SI, FI, SE and UK.
. 



147

05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 148

igure 5.60 

Improved quality of  goods or services: innovative enterprises that rate this as a highly important

effect of  organisational innovation, as a percentage of  all enterprises, 

by country, EU-27 and Norway — 2004

25

20

15

10

5

0

BE

BG

CZ

DK

DE

EE

EL

ES

FR

IT

CY

LT

LU

HU

MT

NL

PT

RO

SK

NO

Data missing/confidential for IE, LV, AT, SI, FI, SE and UK.
. 

innovative  enterprises 

“Improved quality of goods and services” seems to be
rated  as  a  highly  important  effect  of  organisational
innovation  by  more 
than
“reduced time to respond to customers and suppliers”.
The  percentages  of  innovative  enterprises  selecting
this answer vary between 23% for Luxembourg and 1%
for  Portugal.  In  the  majority  of  the  countries  in  Figure
5.60  at  least  10%  of  the  innovative  enterprises  rated
this effect highly important. 

This result is in line with the figures in Table 5.48 where
innovative enterprises were asked to evaluate the same
effect,  but  in  that  case  as  a  result  of  product  and/or
process innovation. 

The quality of their goods and services seems to be of
primary  importance  to  many  innovative  enterprises.
This  is  not  surprising  because  the  core  activity  of  any
enterprise is to produce goods or to provide services. 

The key objective of innovation is to improve the quality
of goods and services. But improving the output of the
enterprise  is  not  the  ultimate  aim:  it  is  also  a  way  to
make  more  profits,  to  get  to  more  clients  and  to
safeguard the future of the enterprise.

148



05 Innovation:Part3_Chap5  17/12/2007  17:00  Page 149

igure 5.61 

Reduced costs per unit output: innovative enterprises that rate this as a highly important effect of

organisational innovation, as a percentage of  all enterprises, 

by country, EU-27 and Norway — 2004

12

10

8

6

4

2

0

BE

BG

CZ

DK

DE

EE

EL

ES

FR

IT

CY

LT

LU

HU

MT

NL

PT

RO

SK

NO

Data missing/confidential for IE, LV, AT, SI, FI, SE and UK. 

“Reduced  costs  per  unit  output”  seems  to  be  a  less
important  effect  of  organisational  innovation  for
innovative  enterprises  than  the  two  effects  discussed
earlier. Danish and German innovative enterprises felt
the  most  concerned  by  this  effect,  because  10%  of
them  chose  it.  The  lowest  score  can  be  found  in
Hungary with 1%.

The percentages shown are calculated in relation to all
enterprises,  but  only  the  results  for  the  innovative
enterprises are taken into account in the figure. As not
all  innovative  enterprises  introduced  organisational
innovations, the figures are not very high.  

igure 5.62 

Improved employee satisfaction and/or reduced rates of  employee turnover: innovative enterprises

that rate this as a highly important effect of  organisational innovation, 

as a percentage of  all enterprises, by country, EU-27 and Norway — 2004

10

8

6

4

2

0

BE

BG

CZ

DK

DE

EE

EL

ES

FR

IT

CY

LT

LU

HU MT

NL

PT

RO

SK

NO

Data missing/confidential for IE, LV, AT, SI, FI, SE and UK.

The results for “improved employee satisfaction and/or
reduced rates of employee turnover” are very close to
those on “reduced costs per unit output”. Once again,
the  highest  proportion  of  innovative  enterprises  is  in
Luxembourg  with  9%  and  the  lowest  in  Hungary  with
1%.

These  results  are  not  very  surprising  because  these
effects  do  not  really  concern  the  enterprises’  main
objective  of  maximising  the  profit  from  selling  goods
and  services.  These  effects  are  often  considered
collateral effects rather than priorities.



149

05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 150

5.5 Comparison between CIS 3 and CIS 4

This  section  compares 
fourth  Community
Innovation Survey (CIS 4) with the third (CIS 3), taking
a  closer  look  at  some  of  the  main  results  of  the  two
surveys. 

the 

The  CIS  data  produced  are  based  on  harmonised
survey  questionnaires  which  were  not  fully  identical
between  CIS  3  and  CIS  4.  To  a  certain  extent,  this
hampers the comparability of the results between CIS 3
and CIS 4. 

igure 5.63 

Enterprises with innovation activity in CIS 3 (2000) and in CIS 4 (2004), 

as a percentage of  all enterprises, by country, EU-27 and selected countries — 2000 and 2004

DE

AT

LU

IE

IS

DK

BE

SE

EE

CY

FI

UK

PT

CZ

NO

IT

EL

ES

NL

FR

LT

SI

PL

SK

HU

MT

RO

LV

BG

0

10

20

30

40

50

60

70

CIS 3 (2000)

CIS 4 (2004)

The  European  innovation  landscape  shows  marked
contrasts,  as  can  be  seen  from  the  proportions  of
innovative enterprises in 2004, which ranged from 16%
in Bulgaria to 65% in Germany. 

The  proportion  of  innovative  enterprises  increased  in
most  EU  Member  States  from  2000  to  2004.  By

contrast, in Ireland and the Netherlands it fell by about
10  percentage  points.  In  2004  in  seven  EU  Member
States  (Germany,  Austria,  Luxembourg, 
Ireland,
Denmark,  Belgium  and  Sweden)  at  least  half  of  all
enterprises were engaged in innovative activities.

150



05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 151

able 5.64 

Novel innovators in CIS 3 (2000) and in CIS 4 (2004) by type of  innovator, 

as a percentage of  all enterprises, by country, EU-27 and selected countries — 2000 and 2004

CIS 3 - 2000

CIS 4 - 2004

Product and 
process innovators

Product
innovators only

Process
innovators only

Product and 
process innovators

Product
innovators only

Process
innovators only

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

21.2

4.1

11.5

20.8

22.5

15.3

:

9.7

11.9

14.2

16.3

16.6

9.4

11.6

18.0

8.3

4.3

21.0

17.1

:

14.7

12.7

12.8

4.5

18.1

13.0

9.3

28.5

19.4

19.1

5.7

11.9

15.8

19.7

11.4

:

9.4

9.8

14.4

8.3

7.3

4.4

9.4

17.1

8.5

5.2

16.7

17.6

:

13.2

2.5

5.6

10.7

17.0

19.5

12.2

17.2

10.3

9.7

1.1

5.2

5.1

11.4

7.7

:

8.2

10.0

7.0

10.0

21.9

4.7

6.7

9.6

4.2

3.7

4.6

8.4

:

16.4

1.8

1.8

2.0

5.4

7.2

7.6

5.5

2.8

23.0

7.2

19.8

19.2

23.3

22.5

29.3

21.8

13.0

13.1

12.2

18.8

:

10.9

24.1

8.1

8.4

14.3

27.6

10.4

17.4

13.0

:c

10.1

18.8

21.3

:

:

12.0

7.2

7.3

13.6

20.1

14.6

8.6

3.3

5.7

6.3

6.2

1.2

:

6.4

14.4

6.0

5.3

10.2

10.2

4.7

5.5

1.8

:c

4.5

10.9

15.7

:

:

12.8

12.6

13.2

1.2

10.1

13.6

12.9

10.3

13.4

10.1

14.1

12.2

17.0

25.9

:

9.5

11.8

4.8

2.0

8.4

12.8

9.0

16.3

4.6

:c

7.2

9.1

10.6

:

:

6.3

Comparing  the  results  of  CIS  3  and  CIS  4  for  novel
innovators reveals a slightly upward overall trend in the
proportions of product and process innovators. 

Whereas  the  numbers  of  process  innovators  mostly
increased  from  2002  to  2004,  for  many  countries  the
opposite is the case for product innovators. 

However,  separate  comparisons  for  product  and
process innovators suggest that the trend is different. 



151

05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 152

able 5.65 

Enterprises which introduced new or improved products for the market, 

as a percentage of  innovative enterprises by size-class, CIS 3 (2000) and CIS 4 (2004), by country, 

EU-27 and selected countries — 2000 and 2004

CIS 3 - 2000

CIS 4 - 2004

Total

10 to 49 
employees

50 to 249 
employees

More than 250 
employees

Total

10 to 49 
employees

50 to 249 
employees

More than 250 
employees

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

36.1

53.6

38.2

50.9

30.5

38.6

31.7

40.1

34.0

34.7

54.7

13.5

44.8

46.0

39.9

35.4

53.7

41.8

28.3

:

43.4

80.4

60.7

41.5

62.7

37.0

27.5

21.1

38.5

32.3

53.3

35.2

45.2

26.8

39.0

:

40.3

33.1

28.3

53.1

11.0

43.8

45.5

:c

38.5

56.3

39.8

19.8

:

39.2

81.4

67.4

36.5

62.3

39.5

26.7

19.8

39.6

42.2

52.5

41.2

62.7

33.5

35.7

:

38.7

34.8

37.7

60.5

20.8

46.5

46.8

28.5

23.5

56.1

43.4

35.4

:

48.6

79.0

56.4

46.3

62.7

26.9

27.8

22.8

33.4

52.3

59.5

46.3

66.7

45.2

45.0

:

44.3

45.2

49.0

64.7

24.1

45.6

47.0

:c

39.0

35.0

51.8

62.5

:

70.0

80.1

57.1

49.1

64.9

43.9

33.3

32.0

41.6

40.7

56.4

41.5

47.7

26.9

41.9

44.5

44.4

20.9

38.6

31.1

14.6

34.5

34.5

51.6

36.3

25.0

48.3

48.4

46.4

30.1

27.9

46.6

41.6

49.6

52.4

47.8

77.6

36.5

38.5

57.6

39.0

46.2

22.7

43.7

38.0

43.3

18.0

34.1

28.7

11.6

33.8

30.9

51.4

36.5

25.0

47.5

47.3

44.8

27.3

25.1

40.8

39.7

47.4

52.8

47.3

82.4

37.6

44.0

52.9

44.4

49.3

31.7

35.4

57.2

47.6

28.2

43.3

37.8

21.7

36.4

38.4

48.8

33.9

25.0

48.3

47.1

47.6

35.8

29.2

50.1

42.6

52.2

49.9

48.2

59.6

32.5

53.1

58.6

48.3

58.0

42.1

44.7

62.8

54.2

43.2

57.9

52.2

40.9

34.1

43.8

64.2

40.7

25.0

56.8

64.7

50.4

44.6

36.2

58.1

45.1

58.0

56.5

51.9

89.5

38.6

In  2004  almost  36%  of  the  enterprises  engaged  in
innovation  in  the  EU-27  brought  new  or  significantly
improved  goods  or  services  onto  the  market.  But  the
EU  average  masks  national  differences.  The  figure
varied between 15% in Cyprus and 56% in Bulgaria. 

Comparing  the  results  from  CIS  3  and  CIS  4  reveals
that  in  15  EU  Member  States  the  relative  proportion
increased. Among  these,  the  United  Kingdom, Austria
and  Sweden  recorded  the  highest  growth  in  the
numbers  of  innovative  enterprises  which  brought  new
or improved products onto the market, ranging from 15
to 20 percentage points.

At EU-27 level, there is a positive correlation between
the size of an enterprise and its propensity to innovate:
49%  of  all  large  enterprises  with  250  or  more
employees  and  40%  of  all  enterprises  with  50  to  249
employees had brought new or improved products onto
the  market,  whereas  for  enterprises  with  10  to  49
employees  the  figure  was  only  33%.  Small  and
medium-sized enterprises need to join forces with other
enterprises much more. Small enterprises never show
higher  rates.  This  correlation  holds  true  in  many
Member States, but in some of them small businesses
brought more innovative products onto the market than
medium-sized  enterprises.  This  was  the  case  in
Bulgaria,  Estonia,  Luxembourg,  Hungary,  Austria  and
Sweden.

152

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05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 153

igure 5.66 

Enterprises with innovation activity in CIS 3 (2000) and in CIS 4 (2004), 

Innovation costs too high as a highly important factor hampering innovation activities, 

as a percentage of  innovative enterprises, by country, EU-27 and selected countries — 2002 and 2004

45

40

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

CIS 3 (2000)

CIS 4 (2004)

CIS 4 reveals two factors that hamper innovation in the
EU most. The first is that innovation costs are too high
and the second lack of finance from sources outside the
enterprise.

points  or  more  was  observed  in  Lithuania  (21%),
France (19%), Poland (13%) and Denmark (12%). But
in  Germany  (-13%)  and  Portugal  (-17%)  far  fewer
enterprises felt hampered by this factor.

In  2000  “innovation  costs  too  high”  was  perceived  as
the  highest  barrier  to  innovative  enterprises  in  Spain
(33%), Germany (32%) and Greece (30%). Four years
later  this  factor  had  become  even  more  important  in
Spain (40%) and Greece (39%), which were followed by
Poland (32%). In general, comparing the results of CIS
3 and CIS 4, this factor seems to be gaining importance
for  innovative  enterprises.  Growth  of  10  percentage

As  for  the  second  factor,  in  2000  the  highest
proportions of innovative enterprises hampered by “lack
of  finance  from  sources  outside  the  enterprise”  were
found  in  Bulgaria  (32%),  Greece  (33%)  and  Slovakia
(32%).  Looking  at  the  results  from  CIS  4,  innovative
enterprises  in  Bulgaria,  Germany,  Portugal  and
Slovakia  were  less  concerned  by  lack  of  finance  for
innovation. 

igure 5.67 

Enterprises with innovation activity in CIS 3 (2000) and in CIS 4 (2004), 

Lack of  finance from sources outside the enterprise as a highly important factor hampering 

innovation activities, as a percentage of  innovative enterprises, by country, 

EU-27 and selected countries — 2002 and 2004

35

30

25

20

15

10

5

0

BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK IS NO

CIS 3 (2000)

CIS 4 (2004)



153

05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 154

5.6 European Innovation Scoreboard (EIS) 2006 

The  European  Innovation  Scoreboard  (EIS)  is  a
statistical  instrument  developed  by  the  European
innovation  efforts
Commission 
undertaken  by  the  EU  Member  States  and  to  make
them comparable. 

to  evaluate 

the 

Most of the indicators included in the EIS are based on
raw  data  from  Eurostat.  Seven  of  the  25  indicators
analysed  in  the  EIS  2006  are  based  on  data  from  the
Community Innovation Survey (CIS).

The  core  of  the  EIS  2006,  which  covers  32  European
countries  plus  the  United  States  and  Japan,  is  an

analysis  of  the  Summary  Innovation  Index  (SII).  This
index is based mainly on Eurostat data. To calculate the
index,  25  indicators  covering  different  aspects  of
innovation are used. 

Fifteen  of  them  are  innovation  input  indicators  (e.g.
innovation drivers, knowledge creation, innovation and
ten  are  based  on
the  other 
entrepreneurship); 
innovation  outputs  (e.g.  applications  and  intellectual
property).  The  SII  tries  to  reflect  the  complexity  of
innovation and to measure it in a realistic way. 

igure 5.68 

Summary innovation Index (SII) in 2006 and average growth rate of  SII,

EU-27 and selected countries 

x
e
d
n

I

n
o
i
t
a
v
o
n
n

I

y
r
a
m
m
u
S
6
0
0
2

08.0

07.0

06.0

05.0

04.0

03.0

02.0

01.0

00.0

srewolloF

ES

IF

SU

EB

KU

RF

EI

KD

HC

PJ

ED

SI

LN

TA

ON

TI

EE

TM

SE

UH

KS

RH

gniliarT

RT

sredaeLnoitavonnI

UL

YC

OR

TP

LE

GB

IS

TL

ZC

LP

VL

pu-gnihctaC

0.4-

0.3-

0.2-

0.1-

0.0

0.1

0.2

0.3

0.4

0.5

0.6

IISfoetarhtworgegarevA

Doted lines show EU25 mean performance.

Source: European Innovation Scoreboard 2006

154



 
 
 
05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 155

The SII 2006, combined with the average growth rate of
the  SII  over  five  years,  allows  both  current  innovation
performance  and  trends  to  be  evaluated  for  each
country. Most of the countries fall into four main groups
with similar characteristics in terms of their actual and
estimated innovation capacity.  

These are: 

leaders

innovation 

• The 
–  Sweden,
Switzerland,  Finland,  Denmark,  Japan  and
Germany.  These  countries  display  the  highest
results in the SII 2006; however, only Denmark
recorded  a  positive  average  growth  rate  in  the
SII.

• The  innovation  followers –  the  United  States,
the  United  Kingdom,  Iceland,  France,  the
Netherlands,  Belgium,  Austria  and  Ireland.
These  countries  are  also  more  innovation-
efficient than the EU-25 average but the trend is
declining.

• The  catching-up  countries –  Slovenia,  the
Czech  Republic,  Lithuania,  Portugal,  Poland,
Latvia, Greece and Bulgaria. On the one hand,
these countries show SII results below the EU-
25  average;  on  the  other,  they  record  positive
average SII growth rates.

• The  trailing  countries –  Estonia,  Spain,  Italy,
Malta, Hungary, Croatia and Slovakia. Their SII
results  are  below  the  EU-25  average  and  their
growth rates are decreasing, with the exception
of Estonia.

Cyprus and Romania have relatively low SII results but
seem to be catching up rapidly.

The  innovation  performance  and  trends  observed  for
Luxembourg, Norway and Turkey are very different, so
they do not fit into any of these groups.

Taking into account current innovation performance and
the trends for all European countries, there seems to be
a  process  of  convergence.  Many  countries  with  SII
results  higher  than  the  EU-25  average  have  declining
average SII growth rates, whereas more than half of the
countries  with  an  SII  below  the  EU-25  average  have
increasing average SII growth rates.

The  EIS  2006  also  shows  that  the  innovation  gap
between the EU-25 and the United States is continuing
to decrease, narrowing from 0.14 index points in 2002
to 0.08 in 2006. 

The innovation gap with Japan is wider and tending to
decline less. In 2006 it was about 0.16 index points, not
much lower than the 0.17 index points in 2002.

The EU-25 has made considerable progress on some
indicators,  such  as  broadband  penetration  rate,  new
patent  applications  to  the  European  Patent  Office  and
new Community trademarks and designs.

At the same time, other indicators have not improved at
all – for example, venture capital investment, exports of
high-tech  products  and  the  population  with  tertiary
education.

igure 5.69 

Innovation gap between EU-25 and United States and EU-25 and Japan 

EU25-US

EU25-JP

-0.06

-0.08

-0.10

-0.12

-0.14

-0.16

-0.18

2002

2003

2004

2005

2006

Source: European Innovation Scoreboard 2006



155

05 Innovation:Part3_Chap5  17/12/2007  17:01  Page 156

able 5.70 

EIS 2006 indicators by sub-group 

INPUT – Innovation drivers

S&E graduates per 1000 population aged 20-29

Population with tertiary education per 100 population aged 25-64

Broadband penetration rate (number of broadband lines per 100 population)

Participation in life-long learning per 100 population aged 25-64

Youth education attainment level (% of population aged 20-24 having completed at least upper secondary education)

INPUT – Knowledge creation

Public R&D expenditures (% of GDP)

Business R&D expenditures (% of GDP)

Share of medium-high-tech and high-tech R&D (% of manufacturing R&D expenditures)

Share of enterprises receiving public funding for innovation

INPUT – Innovation & entrepreneurship

SMEs innovating in-house (% of all SMEs)

Innovative SMEs co-operating with others (% of all SMEs)

Innovation expenditures (% of total turnover)

Early-stage venture capital (% of GDP)

ICT expenditures (% of GDP)

SMEs using organisational innovation (% of all SMEs)

OUTPUT – Application

Employment in high-tech services (% of total workforce)

Exports of high technology products as a share of total exports

Sales of new-to-market products (% of total turnover)

Sales of new-to-firm products (% of total turnover)

Employment in medium-high and high-tech manufacturing (% of total workforce)

OUTPUT – Intellectual property

EPO patents per million population

USPTO patents per million population

Triadic patent families per million population

New community trademarks per million population

New community designs per million population

Eurostat

Eurostat, OECD

Eurostat

Eurostat

Eurostat

Eurostat, OECD

Eurostat, OECD

Eurostat, OECD

Eurostat (CIS 4)

Eurostat (CIS 3)

Eurostat (CIS 4)

Eurostat (CIS 4)

Eurostat

Eurostat

Eurostat (CIS 4)

Eurostat

Eurostat

Eurostat (CIS 4)

Eurostat (CIS 4)

Eurostat

Eurostat

Eurostat, OECD

Eurostat, OECD

OHIM

OHIM

1.1

1.2

1.3

1.4

1.5

2.1

2.2

2.3

2.4

3.1

3.2

3.3

3.4

3.5

3.6

4.1

4.2

4.3

4.4

4.5

5.1

5.2

5.3

5.4

5.5

Source: European Innovation Scoreboard 2006

156

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06 Patent:Part3_Chap6  20/12/2007  15:58  Page 157

PART3

Chapter 6 - Patents

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 158

6.1 Introduction

Converting  technological  knowledge  into  economic
growth  and  welfare  is  one  of  the  keys  to  boosting  the
competitiveness of any country in the modern economy.
This  is  a  complex  process,  and  evaluating  how
countries  perform  in  developing  and  commercialising
technology is no easy task. 

Patents  statistics  have  made  rapid  progress  in  recent
times. They are increasingly used by decision makers
in  innovation  policy  or  in  patent  offices  in  order  to
monitor  developments  in  their  fields.  The  Worldwide
Patent  Statistics  Database 
recently
developed by the European Patent Office (EPO) offers
a unique tool for analysts and producers of patents data
and indicators.

(PATSTAT) 

PATSTAT

PATSTAT  was  developed  by  patent  information  experts  at  the  EPO’s  Vienna  sub-office,  and  includes  patent  data  from  73  offices
world-wide  and  post-grant  data  from  about  40  offices.  It  was  developed  specifically  with  the  needs  of  policy  makers,  academics,
analysts and Intellectual Property (IP) institutions in mind. Researchers working in this field have previously had to assemble data
sets from various and disparate sources and were obliged to perform extensive “cleaning” of the data, which was costly and time-
consuming. The PATSTAT dataset addresses these issues, efficiently harmonising data, resolving issues over family members and
addressing such problems as applications from one applicant appearing under several different names. The database also contains
related information on citations, procedural information and legal status, which are all of interest to statisticians.

Updates  to  the  PATSTAT  database  will  be  released  twice  a  year  (in  March  and  September),  and  will  be  available  to  any  user
committing to non-commercial use.

Source: Giovanna Oddo, IPR Helpdesk Bulletin, No 30, Nov-Dec. 2006

An invention has to meet several conditions if it is to be
patentable. It must be new, involve an inventive step, be
capable of industrial application and not be “excluded”.
“Excluded” 
inventions  are  discoveries,  scientific
theories or mathematical methods, aesthetic creations
such as literary, dramatic or artistic works, schemes or
methods for performing a mental act, playing a game or
doing  business,  presentations  of  information  or
computer programs.

A patent is an intellectual property right for inventions of
a technical nature. A patent is valid for one country if it
is granted by a national patent office and generally for
20 years. A patent application to the EPO can be valid
in several countries and at most in all of the Contracting
States of the European Patent Convention. As of March
2007, the Convention is in force in 32 countries (all EU
Member  States 
Iceland,
Liechtenstein,  Monaco  and  Turkey).  In  addition  to  the
Contracting States, five other countries have concluded
a so-called extension agreement with the EPO. These
states  can  also  be  designated  in  a  European  patent
application.

plus  Switzerland, 

Although patents do not cover every kind of innovation,
they  do  include  a  large  proportion  of  them.  There  are
good  reasons  why  patents  have  become  one  of  the
most widely used sources of data in the construction of
indicators of inventive output, for example because they
provide  detailed  information  in  relatively  long  time-
series or because they are closely linked to invention.

Nevertheless,  patent  indicators  also  have  several
shortcomings  and  should  therefore  be  combined  with
other Science & Technology (S&T) output indicators in
order  to  obtain  a  full  picture  of  innovation  activities  in
individual countries and regions. Two major drawbacks
are that not all inventions are patented and that not all

patents  have  the  same  value.  It  is  widely  recognised
that  the  value  distribution  of  patents  is  skewed:  a  few
patents have a high value, whereas a greater number
have lower values. However, as there are no generally
recognised,  easily  applicable  methods  for  measuring
the  value  of  patents,  this  chapter  does  no  more  than
count  the  number  of  patents  meeting  various  criteria.
Another  drawback  is  that  only  some  of  the  patents
granted are applied commercially and/or lead to major
technological improvements.

This chapter analyses the structure and development of
patenting in the EU-27, Iceland, Liechtenstein, Norway,
the candidate countries, Japan and the United States.
Priority  is  given  to  data  on  patent  applications  to  the
EPO.  Nearly  all  indicators  for  patents  granted  by  the
United  States  Patent  and  Trademark  Office  (USPTO)
are  also  available  from  Eurostat.  It  this  edition  few
USPTO data are shown owing to space constraints.

The  chapter  starts  with  a  glance  at  the  “triadic  patent
families” and then focuses on performance at national
level, using EPO and some USPTO data. The analysis
covers the period from 1993 to 2003 for the EPO data,
whereas  the  USPTO  and  triadic  patent  family  time-
series  cover  the  period  from  1991  to  2000.  Patent
statistics are very sensitive to the type of data collected
and to the methods used in counting the patents. Data
from  the  period  following  the  reference  years  are  not
comparable  because  they  are  incomplete.  The  EPO
data  refer  to  patent  applications  by  priority  year
whereas  the  USPTO  data  are  for  patents  granted  by
priority year. The “priority year” is the year in which the
first  application  was  submitted.  In  general,  inventors
first apply for a patent from their national patent office.
They  then  also  have  12  months  to  apply  to  another
patent office, such as the EPO or the USPTO.

158

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06 Patent:Part3_Chap6  20/12/2007  15:58  Page 159

Although  not  all  applications  are  granted,  each
application  nevertheless  represents  the  inventor’s
technical  efforts.  Patent  applications  can  therefore  be
considered  as  an  appropriate  indicator  of  inventive
activities. It takes, on average, just over four years for a
patent to be granted by the EPO. In an effort to provide
data  promptly,  Eurostat  has  therefore  chosen  patent
applications  in  preference  to  patents  granted.  In  the
United States, until recently, only information on patents
granted  was  published  and  therefore  no  data  on
applications are presented in this chapter. The USPTO
takes  between  two  and  five  years  to  grant  patents.
Triadic patent families are counted on the basis of the
earliest priority year, i.e. the year in which a patent was
first  applied  for  from  any  patent  office.  They  refer  to
applications  to  the  EPO  and  to  the  Japanese  Patent
Office (JPO), and to patents granted by the USPTO. 

When  interpreting  the  data  at  international  level,
readers  should  bear  in  mind  that,  thanks  to  “home
advantage”,  European  countries  dominate 
the
European  patent  system,  whereas  the  United  States
dominates  the  US  patent  system.  At  the  same  time,
figures  may  also  be  influenced  by  the  countries’

industrial  structures  as  different  industries  have  a
different propensity to patent. Some of these problems
are less visible in the triadic patent family indicators as
they  only  take  into  account  patents  that  have  been
applied  for  from  the  EPO  and  the  JPO,  and  those
granted  by  the  USPTO.  Besides  improving  the
international  comparability  of  patent  indicators,  triadic
patent  family  data  also  balance  the  differences  in  the
value  of  the  patents  associated  with  the  other
indicators. This is because patenting in all three offices
is very costly, owing not only to administrative fees but
also  to  translation  costs.  Under  these  circumstances,
patentees  will  proceed  with  such  applications  only  if
they deem it worthwhile, i.e. if the expectation of having
the  patent  granted  and  the  expected  return  from
protection  through  sales  or  licences  in  the  designated
countries  are  high  enough.  Because  of  differences  in
data processing methods, direct comparisons between
the EPO, the USPTO and triadic patent family data are
not advisable. 

For  further  explanations  on  the  methodology  used,
please  refer  to  the  methodological  notes  or  to  the
section on patent statistics on the Eurostat webpage.

Enhancing the patent system in Europe 

Conclusion of the Communication from the Commission to the European Parliament and the Council 

The Commission strongly believes that an improved patent system is vital if Europe is to fulfil its potential for innovation. For this
reason, the Commission has set out its proposals for the way forward for a reform of the patent system in Europe and is proposing
supporting measures in this Communication. The purpose of this Communication is to revitalise the debate on the patent system in
Europe,  in  a  way  which  encourages  Member  States  to  work  towards  consensus  and  real  progress  on  this  issue.  Making  the
Community patent a reality and at the same time improving the existing fragmented patent litigation system would make the patent
system  significantly  more  accessible  and  bring  cost  savings  for  all  who  have  a  stake  in  the  patent  system.  In  parallel  supporting
measures  to  maintain  and,  where  necessary,  improve  the  quality  and  efficiency  of  the  current  system,  together  with  targeted
measures  to  improve  SME  access,  should  ensure  that  Europe’s  patent  system  will  play  its  role  in  boosting  innovation  and
competitiveness in Europe. The EU must also engage actively with its international partners to increase awareness of IP issues and
proper and balanced enforcement of them. By providing the basis for Member States to agree concrete actions, the Commission
aims  to  provide  a  solid  basis  for  progress  on  patent  reform  in  other  areas,  especially  as  regards  the  Community  patent  and  the
litigation system.

The  Commission  will  work  with  the  Council  and  Parliament  to  build  consensus  on  the  way  forward.  When  broad  consensus  is
achieved, the Commission will take the necessary steps for implementing the agreed strategy and make relevant proposals.

Further information at: http://ec.europa.eu/internal_market/indprop/patent/index_en.htm

Source: European Commission, Brussels, 3.4.2007, COM(2007) 165 final



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06 Patent:Part3_Chap6  20/12/2007  15:58  Page 160

6.2 Triadic patent families

High concentration of triadic patent families

A patent is a member of the triadic patent family if and
only if it has been applied for and filed at the European
Patent Office (EPO) and at the Japanese Patent Office
(JPO), and if it has been granted by the US Patent and
Trademark Office (USPTO). Data on patent families are
generally  less  biased  as  the  “home  advantage”
disappears  to  a  certain  extent.  These  data  also
emphasise  the  value  of  such  triadic  patents,  which  is
supposedly  higher  than  the  value  of  other  patent
applications  or  patents  granted.  Looking  at  the
geographical distribution of triadic families (see Figure
6.1),  the  shares  of  the  EU  and  Japan  in  2000  were
respectively 27% and 32% of all triadic patent families
counted.  The  biggest  share  was  held  by  the  United
States with 34% and the smallest (only 7%) by the rest
of  the  world.  Triadic  patent  family  applications  and

grants  are  therefore  concentrated  in  the  three  main
economies. 

The  picture  is  quite  different  when  triadic  patenting
activity  is  set  in  relation  to  the  population  (see  Figure
6.2).

Looking at triadic patent families per million inhabitants,
Japan  led  by  a  wide  margin  during  the  1991-2000
observation period. The United States ranked second,
followed  by  the  EU-27.  Whereas  the  trend  was  stable
for  the  United  States  and  the  EU-25,  the  indicator  for
Japan fell in the early 1990s before recovering. In 2000,
the  EU-27  registered  19.5  triadic  patent  families  per
million  inhabitants,  having  fallen  below  the  20  mark
after 1999. With 88.2 triadic patent families per million
inhabitants, Japan achieved more than twice the figure
recorded in the United States (41.8). 

igure 6.1 

Distribution of  triadic patent families, as a percentage of  total, 

EU-27, Japan, the United States and other — 2000

rehtO
%7

72-UE
%72

SU
%43

PJ
%23

160



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 161

igure 6.2 

Triadic patent families per million inhabitants, EU-27, Japan and the United States — 1991 to 2000

stnatibahninoillimrepseilimaftnetapcidairT

001

08

06

04

02

0

1991

2991

3991

4991

5991

6991

7991

8991

9991

0002

72-UE

PJ

SU

6.3 Total patent applications to the EPO and
patents granted by the USPTO

Germany was the best performing European country in terms of patent
applications in 2003

The  intensity  of  patenting  activity  is  very  different  in
each  country.  As  explained  in  the  introduction,
patenting procedures differ in Europe and in the United
States.  The  USPTO  statistics  are  based  on  patents
granted  and  the  EPO  statistics  on  applications  for
patents. Given the different underlying methodologies,
data relating to these two patent offices should not be
compared. 

With 62 250 patent applications to the EPO in 2003, the
EU-27 was the most active world economy in patenting.
Amongst  the  EU  Member  States,  Germany  was  the
undeniable  leader,  with  25  728  patent  applications,
followed  by  France  (9  202)  and  the  United  Kingdom 
(7 217). Germany also led in relative terms. With 11.9%,
the  country  recorded  the  highest  ratio  of  patent
applications  as  a  percentage  of  GDP  out  of  all  EU

Member  States.  In  terms  of  this  ratio,  Finland  ranked
second and Sweden third with respectively 10.9% and
9.5%  of  GDP.  None  of  the  new  Member  States  (2004
and 2007) reached the average EU-27 ratio of 6.2% of
GDP. 

The  best-performing  non-EU  countries  in  patent
applications  to  the  EPO  were  Israel  (15.6%  of  GDP),
Switzerland (10.9%) and South Korea (10.0%)

The  USPTO  is  the  national  patent  office  for American
inventors. This explains the lower numbers recorded for
the  EU  Member  States.  Besides  the  United  States 
(77  585  patents  granted)  some  other  countries  were
very  active  in  patenting  as  the  numbers  of  patents
granted by the USPTO show: Japan (35 013), Taiwan
(5 177) and Canada (3 216).



161

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 162

able 6.3

Patent applications to the EPO: total number and as a percentage of  GDP, EU-27 and selected 

countries - 2003 and Patents granted by the USPTO: total number and as a percentage of  GDP, 

EU-27 and selected countries — 2000

Patent applications to the EPO

Patents granted by the USPTO

2003

2000

Total

As a % of GDP

Total

As a % of GDP

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

LI

NO

EEA30

CH

HR

TR

AU

CA

CN

IL

IN

JP

KR

RU

TW

US

62 250

62 191

1 496

34

163

1 270

25 728

21

306

123

1 274

9 202

5 002

12

14

20

90

192

4

3 956

1 581

160

78

26

101

44

1 591

2 547

7 217

44

25

533
62 852

3 113

81

133

1 958

2 736

1 898

1 587

1 003

27 987

5 400

641

572

48 786

6.2

6.2

5.4

1.9

2.0

6.7

11.9

2.5

2.2

0.8

1.6

5.8

3.7

1.0

1.4

1.2

3.5

2.6

0.8

8.3

7.0

0.8

0.6

0.5

4.0

1.5

10.9

9.5

4.5

4.6

:

2.7
6.1

10.9

3.1

0.6

4.2

3.6

1.3

15.6

:

7.5

10.0

1.7

2.1

5.0

23 723

23 716

550

4

28

382

10 509

1

145

14

288

3 235

1 694

1

6

6

36

54

2

1 307

556

20

14

3

24

7

614

1 172

3 050

20

10

203
23 956

1 253

14

12

706

3 216

398

884

301

35 013

3 837

226

5 177

77 585

2.6

2.6

2.2

0.3

0.5

2.2

5.1

0.2

1.4

0.1

0.5

2.2

1.4

0.1

0.7

0.5

1.6

1.0

0.5

3.1

2.6

0.1

0.1

0.1

1.1

0.3

4.6

4.5

2.0

2.1

:

1.1
2.6

4.7

0.7

0.1

1.7

4.1

0.3

6.7

:

6.9

6.9

0.8

15.7

7.3

162



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 163

Looking  at  the  1993,  1998  and  2003  data,  almost  all
European  countries  significantly  increased  national
patenting per million inhabitants. The only exception is
Sweden,  where  the  number  of  patent  applications  per
million inhabitants rose strongly from 165 in 1993 to 296
in 1998, but then slipped back slightly to 285 in 2003.
Compared  with  1998,  Sweden  lost  first  place  at  EU
level in 2003. Amongst the EU-27 countries, Germany
ranked first in 2003 with 312 patent applications to the

EPO  per  million  inhabitants,  followed  by  Finland  with
306  and  Sweden  with  285.  The  number  was  even
higher in Switzerland with 426 patent applications to the
EPO  per  million  inhabitants  (see  Figure  6.4).  Most  of
the new EU Member States remain at a rather low level
of national patenting measured in terms of EPO patent
applications  per  million  inhabitants.  Slovenia  was  an
exception  to  the  rule  with  50  patent  applications  per
million inhabitants in 2003.

igure 6.4 

Patent applications to the EPO per million inhabitants, EU-27 and selected countries 

(with at least 10 patent applications per million inhabitants in 2003) — 1993, 1998 and 2003 

HC

ED

IF

ES

LN

LI

KD

PJ

UL

TA

SU

SI

RF

EB

52-UE

72-UE

03AEE

KU

ON

RK

UA

TI

AC

EI

IS

SE

WT

UH

RH

YC

ZC

EE

LE

05

13

52

91

81

61

61

61

11

0

624

213

603

582

442

732

632

912

002

591

861

451

941

441

631

821

821

121

711

311

89

78

68

77

001

002

003

3991

8991

3002

004

noillimrep
stnatibahni



163

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 164

able 6.5

Breakdown of  patent applications to the EPO by IPC section, total number and

as a percentage of  total, EU-27 and selected countries — 2003

Human
necessities

Performing
operations;
transporting

Chemistry;
metallurgy

Textiles; paper

Fixed
constructions

Mechanical
engineering; lighting; 
heating; weapons; 
blasting

Physics

Electricity

IPC section 

15.6

15.6

16.9

20.7

18.3

26.8

12.2

20.6

29.0

21.4

23.8

17.0

20.2

19.2

43.8

5.0

1.7

28.6

:

13.0

15.8

18.1

13.0

21.5

21.6

22.2

7.9

17.0

20.1

25.2

21.5

21.4
15.6

21.2

35.3

20.6

24.0

17.1

19.0

31.5

24.7

9.3

13.0

19.3

20.1

22.9

20.9

20.9

17.8

11.8

18.2

13.5

23.7

:

14.6

16.7

25.0

19.0

27.7

28.5

16.1

5.0

35.9

14.3

28.6

13.9

22.4

18.9

25.6

4.4

14.9

11.6

15.2

20.4

15.1

4.5

15.3

16.5
20.8

20.8

12.8

8.2

17.5

13.7

10.5

8.8

5.8

14.9

8.1

17.3

22.1

12.4

13.4

13.4

27.2

11.8

23.4

18.0

13.0

30.0

8.1

8.4

13.7

13.2

10.3

8.5

40.1

18.3

14.6

20.7

:

12.4

12.8

14.6

17.4

7.8

19.0

18.4

9.4

9.3

15.9

20.8

16.3

9.8
13.3

13.9

19.4

8.8

14.3

16.3

13.3

11.4

48.6

16.9

9.3

18.3

7.6

16.1

1.9

1.9

2.8

:

8.2

0.6

2.1

:

:

0.8

1.8

1.2

3.4

:

:

:

1.4

0.5

14.3

1.1

3.2

1.2

3.7

:

:

1.5

5.7

1.9

0.9

:

:

0.2
1.9

3.0

:

11.3

0.6

0.7

1.0

0.3

0.7

1.1

2.8

0.5

2.1

0.8

4.5

4.5

5.3

8.9

8.9

6.5

4.0

:

2.6

6.9

7.3

3.9

5.5

8.5

:

:

6.5

3.7

:

4.3

7.7

7.8

5.1

19.6

8.9

9.1

2.5

4.3

4.8

2.3

8.1

11.0
4.5

4.3

11.1

3.8

8.1

4.1

3.2

1.9

0.6

0.7

2.0

3.7

4.0

2.0

10.7

10.7

6.4

8.9

7.5

8.5

13.6

4.7

2.4

12.6

8.5

10.2

12.4

17.1

:

5.0

21.4

7.0

:

4.2

11.3

13.2

13.0

9.1

8.4

12.6

3.7

10.1

7.0

5.6

26.5

10.2
10.7

7.1

7.4

25.6

7.4

7.2

5.4

3.1

1.7

7.8

7.7

9.8

6.9

4.8

17.0

17.0

12.3

20.8

8.3

11.2

16.3

27.5

23.1

18.0

10.6

16.8

9.6

4.3

:

65.6

10.5

10.4

57.1

32.1

12.0

13.0

14.1

14.7

9.6

11.9

18.5

14.7

21.3

34.0

8.3

16.5
17.0

18.5

8.2

11.3

18.9

18.2

15.6

24.7

11.7

24.0

22.7

16.1

18.9

22.8

16.1

16.1

11.4

17.0

7.2

14.9

15.1

17.1

20.2

15.2

9.3

18.7

10.7

13.8

:

1.2

8.1

14.9

:

19.0

14.8

13.1

8.1

22.8

14.7

12.6

37.1

22.4

14.9

7.5

4.1

14.5
16.1

11.1

5.7

10.4

9.2

22.8

31.9

18.5

6.2

25.3

34.4

15.0

18.3

18.1

Total

62 250

62 191

1 496

34

163

1 270

25 728

21

306

123

1 274

9 202

5 002

12

14

20

90

192

4

3 956

1 581

160

78

26

101

44

1 591

2 547

7 217

44

25

533
62 852

3 113

81

133

1 958

2 736

1 898

1 587

1 003

27 987

5 400

641

572

48 786

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

LI

NO

EEA30

CH

HR

TR

AU

CA

CN

IL

IN

JP

KR

RU

TW

US

164



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 165

in  accordance  with 

Patents  are  classified 
the
International  Patent  Classification  (IPC).  The  IPC  is
based on a multilateral treaty administered by the World
Intellectual  Property  Organisation  (WIPO),  i.e.  the
Strasbourg  Agreement  concerning  the  International
Patent  Classification.  In  the  IPC,  each  invention  is
assigned  to  an  IPC  class,  depending  on  its  function,
intrinsic  nature  or  field  of  application.  The  IPC  is
therefore a combined function/application classification
system  in  which  function  takes  precedence.  A  patent
may  cover  several  technical  aspects  and  therefore  be
assigned  to  several  IPC  classes.  If  a  patent  spans
several  technological  fields,  it  is  assigned  to  the  first
IPC  code  indicated  on  the  patent.  The  IPC  is  divided
into  sections,  classes,  sub-classes,  groups  and  sub-
groups.  The  eighth  edition  of  the  IPC,  which  entered
into  force  on  1  January  2006,  divides  technology  into
eight sections with approximately 70 000 sub-divisions.
In  this  publication,  only  the  eight  IPC  sections  are
shown. Further details on the various sections’ contents
are available in the methodological notes. 

Table  6.5  shows  patent  applications  by  IPC  section.
The following analysis only takes into account countries
with more than 100 patent applications to the EPO. In
many  countries  one  IPC  section  accounted  for  more
than  25%  of  all  national  applications.  Israel,  Ireland,

Hungary  and  Denmark  specialised  in  patenting  linked
to  IPC  section  A  -  Human  necessities.  Section  B  -
Performing  operations;  transporting  -  was  the  most
important IPC section for Italy, Spain, Germany, Austria
and  Taiwan.  Nearly  one  out  of  two  Indian  patent
applications dealt with an invention in IPC section C -
Chemistry; metallurgy. Belgium and the Czech Republic
lodged  more  than  20%  of  their  national  patent
applications in this IPC section. In contrast, patenting is
less  frequent  in  IPC  sections  D  -  Textiles;  paper,  E  -
Fixed  constructions,  and  F  -  Mechanical  engineering;
lighting; heating; weapons; blasting. 

At  EU-27  level,  Germany  always  had  the  highest
absolute  number  of  patent  applications  in  all  IPC
sections, followed by France and the United Kingdom.
In  four  IPC  sections,  Germany  surpassed  even  the
United States.

Section D - Textiles; paper - was an exception, with Italy
taking  a  higher  profile  and  having  the  second  highest
score of all European countries in this IPC section. For
the  Netherlands,  IPC  section  G  -  Physics  -  was  the
most  significant.  Finland  lodged  the  highest  national
share  of  all  patent  applications  to  the  EPO  in  IPC
section  H  -  Electricity.  Electricity  was  also  the  most
important  IPC  section  for  the  three  Asian  countries:
South Korea, China and Japan.

igure 6.6 

Member States’ patent applications to the EPO, as a percentage of  total EU-27 applications — 2003

rehtO
%3.11

dnalniF
%6.2

nedewS
%1.4

sdnalrehteN
%4.6

ylatI
%0.8

ynamreG
%3.14

modgniKdetinU
%6.11

ecnarF
%8.41

Patenting in the European Union is highly concentrated
in  just  a  few  Member  States.  In  2003  Germany  was
undeniably  the  Member  State  generating  the  largest
number  of  patent  applications  (see  also  Table  6.3).
More than 40% of all patent applications by the EU-27
came  from  a  German  inventor.  France  followed  in
second place, with about 15%, and the United Kingdom
ranked  third,  with  12%  (see  Figure  6.6).  These  three
countries  accounted  for  two  thirds  of  all  patent
applications  to  the  EPO  from  the  EU-27.  The  EU-27
aggregate is highly influenced by the German figures.

Patent  applications  to  the  EPO  can  also  be  broken
the  NACE
down  by  economic  activity,  using 
classification.  This  breakdown  is  based  on  the
concordance  tables  between  the  IPC  and  the  NACE

created  by  the  Fraunhofer  Institute  for  Systems  and
Innovation  Research  in  Karlsruhe  (Germany).  As  one
criterion for patents is usability for industrial application,
all  NACE  codes  to  which  patent  applications  are
allocated  are  exclusively  those  of  manufacturing
industries. 

In  2003,  the  two  main  manufacturing  activities
concerned  by  patenting  were  DL  -  Manufacture  of
electrical  and  optical  equipment,  followed  by  DG  -
Manufacture of chemicals, chemical products and man-
made fibres. Two other sections (DM - Manufacture of
transport  equipment,  and  DK  -  Manufacture  of
machinery and equipment n.e.c.) took nearly the same
shares  of  patent  output,  with  around  10%.  Patenting
activity in all the other branches of manufacturing was
less significant (see Table 6.7).



165

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 166

able 6.7

Breakdown of  patent applications to the EPO by economic activity (NACE), total number and

as a percentage of  total, EU-27 and selected countries — 2003

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4.2

4.2

4.4

9.5

5.2

9.4

9.1

3.1

2.2

9.3

6.3

2.2

8.2

5.1

2.7

3.4

3.2

8.2

6.0

3.3

7.1

4.5

7.2

5.1

3.2

2.4

5.1

8.1

7.2

8.3

1.1

4.3
4.2

0.3

3.4

1.3

9.2

3.2

4.2

5.2

1.6

7.1

7.1

5.2

8.1

5.2

5.0

5.0

5.0

4.0

5.0

5.0

5.0

3.0

5.0

4.0

7.0

5.0

6.0

4.0

5.0

1.0

5.0

6.0

3.0

4.0

6.0

5.0

8.0

4.0

4.0

5.0

4.0

4.0

4.0

4.0

5.0

4.0
5.0

6.0

7.0

4.0

6.0

4.0

4.0

5.0

3.0

4.0

4.0

5.0

7.0

4.0

2.0

2.0

1.0

1.0

3.0

1.0

2.0

1.0

1.0

1.0

2.0

2.0

3.0

3.0

4.0

0.0

2.0

1.0

0.0

1.0

3.0

1.0

2.0

2.0

3.0

2.0

1.0

2.0

2.0

5.0

2.0

2.0
2.0

1.0

1.0

2.0

2.0

1.0

2.0

1.0

0.0

1.0

2.0

2.0

4.0

1.0

1.0

1.0

2.0

1.0

2.0

1.0

1.0

0.0

1.0

1.0

4.0

1.0

2.0

1.0

1.0

0.0

1.0

1.0

0.0

1.0

2.0

1.0

1.0

0.0

3.0

1.0

1.0

1.0

1.0

0.0

1.0

2.0
1.0

2.0

1.0

1.0

2.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

2.1

2.1

5.1

2.1

3.1

3.1

2.1

7.0

4.1

1.1

5.1

1.1

5.1

0.1

1.1

5.0

5.1

4.1

4.1

2.1

4.1

7.1

5.1

9.0

3.2

2.1

5.1

3.1

2.1

2.1

3.1

0.1
2.1

6.1

8.1

8.0

6.1

1.1

0.1

4.1

9.0

0.1

7.0

2.1

4.1

2.1

5.1

5.1

2.2

9.0

4.2

2.1

5.1

9.3

2.1

0.1

2.1

4.1

6.1

7.0

0.2

2.1

3.1

8.1

6.0

5.1

4.1

1.2

1.1

5.1

1.1

6.1

1.1

0.1

7.1

8.0

1.1

6.2
5.1

4.1

9.0

9.0

4.1

3.1

1.1

8.0

1.2

4.1

9.0

6.2

0.1

5.1

0.22

0.22

1.33

2.42

5.03

9.23

3.02

6.42

3.22

3.32

4.52

6.22

4.12

0.51

3.33

5.81

8.41

6.53

1.11

8.91

0.91

7.22

2.82

2.32

1.23

8.62

0.51

6.81

5.62

4.03

5.71

7.12
0.22

5.52

5.93

1.12

1.52

0.52

2.22

9.72

9.95

4.02

2.51

5.62

4.61

7.72

2.2

2.2

7.2

2.3

0.2

9.1

2.2

9.0

8.1

5.2

9.2

3.2

0.3

4.3

3.1

7.0

2.6

9.1

4.1

6.1

5.2

2.2

1.2

4.1

1.2

3.2

3.1

7.1

0.2

6.1

4.2

7.1
2.2

1.2

1.2

8.1

2.2

7.1

2.1

0.1

1.1

5.1

2.1

6.1

8.1

4.1

7.1

7.1

5.2

9.1

3.2

9.1

8.1

2.1

4.1

2.2

1.2

6.1

9.1

0.3

0.3

9.0

0.3

0.1

1.3

7.1

5.2

5.2

9.1

4.2

4.1

0.3

4.1

6.1

5.1

9.0

3.4

4.1
7.1

9.1

4.1

8.1

2.2

5.1

2.1

1.1

0.1

7.1

4.1

9.1

6.1

5.1

1.5

1.5

8.4

0.6

8.6

5.4

5.5

5.4

1.3

6.5

7.5

7.4

3.6

9.4

7.5

6.1

9.8

0.4

4.5

6.3

1.7

8.5

7.6

0.6

2.5

8.5

9.3

1.5

2.4

4.2

0.9

9.4
1.5

2.5

6.4

2.5

7.5

9.3

5.3

0.3

0.2

0.4

2.3

1.5

7.6

3.3

5.21

5.21

2.01

1.9

1.61

0.01

5.31

5.6

1.8

4.21

5.31

8.01

1.61

0.21

8.31

1.5

3.61

9.8

6.01

2.01

4.41

6.31

4.01

6.51

2.01

3.01

9.01

0.21

4.01

3.21

9.51

9.51
5.21

0.31

6.7

0.02

1.21

6.9

7.8

6.6

9.3

3.9

1.01

6.11

8.01

1.8

9.43

9.43

4.72

6.53

6.12

0.13

9.23

6.64

4.74

1.53

3.62

5.63

4.72

8.52

1.11

8.85

9.22

3.03

1.34

6.74

0.23

5.92

2.72

0.33

2.92

9.82

9.25

3.04

9.63

5.63

1.82

9.13
9.43

8.33

7.42

0.03

9.23

8.04

2.74

4.74

7.81

0.64

8.45

8.23

7.93

5.24

6.31

6.31

8.8

4.9

1.01

5.8

7.61

5.6

2.7

4.9

9.21

8.31

5.31

4.62

7.7

8.7

2.12

5.9

4.12

3.7

0.31

8.01

8.51

8.01

7.01

2.21

1.8

6.31

7.9

9.4

5.71

3.11
6.31

3.9

5.11

7.11

4.9

6.9

9.7

0.6

3.3

0.11

8.7

8.01

9.21

5.7

8.1

8.1

4.1

1.2

8.2

2.1

5.1

9.2

2.2

5.2

4.3

8.1

0.3

4.5

8.21

4.0

8.0

0.2

6.0

3.1

8.3

9.2

4.1

8.2

3.2

0.3

1.1

0.2

1.2

9.1

8.0

2.3
8.1

0.2

8.0

9.2

0.3

9.1

5.2

3.1

3.0

1.1

1.2

5.2

7.4

6.1

Foreign ownership

Foreign  ownership  of  domestic  inventions  in  patent
applications  is  one  of  three  indicators  of  international
cooperation in patenting. The two others are domestic
ownership  of  foreign  inventions  in  patent  applications
and  patent  applications  with  foreign  co-inventors.
These  indicators  simply  count  each  patent  application
from  both  the  inventor  country  or  countries  and  the
applicant country or countries. 

The  total  number  of  patent  applications  from  each
country  therefore  consists  of  all  applications  in  which
the country is involved, whether as an applicant or as
an  inventor.  Therefore,  the  total  number  of  cases  of
international cooperation is not equal to the sum of the
number  of  cases  per  partner  country  since  several
partner  countries  can  be  involved  in  any  case  of
cooperation. Also, these patent indicators should not be
fractional
compared  with  previous  ones,  where 
counting  rather  than  simple  counting  was  applied.
Furthermore,  these  indicators  should  not  be  added

across  countries,  as  this  would  mean  counting  the
same  patent  more  than  once.  Data  on  foreign
ownership  measure  the  number  of  patents  invented
within (or applied for by) a given country that involve at
least one foreign applicant (or a foreign inventor). 

Figure  6.8  shows  foreign  ownership  of  domestic
inventions  in  patent  applications  to  the  EPO,  as  a
percentage of all applications to the EPO from countries
that  submitted  more  than  50  patent  applications  in
2003.  Luxembourg  had  the  highest  rate  by  far  with
57%, followed by Hungary with 49% and Belgium with
47%.  The  Russian  Federation  with  39%  and  Canada
with  35%  were  the  non-European  countries  with  the
highest  rates  of  foreign  ownership  of  domestic
inventions in patent applications to the EPO. The lowest
rate at EU level was recorded in Finland, with only 7%.
The  United  States,  South  Korea  and  Japan  were  also
situated at this end of the scale with respectively 11%,
4% and 4%.

166



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 167

igure 6.8 

Foreign ownership of  domestic inventions in patent applications to the EPO, 

selected countries (with at least 50 patent applications to the EPO in 2003) — 2003

as a percentage of  all national applications, 

forebmunlatoT
snoitacilppatnetap
OPEehtot

UL

UH

EB

UR

EI

TA

KU

ZC

AC

LE

LP

NC

TP

SE

NI

LI

RH

RF

HC

WT

IS

RT

LN

ON

KD

ES

UA

TI

ED

51-UE

52-UE

SU

IF

RK

PJ

0

11

7

4

4

75

94

74

93

93

73

63

63

53

43

43

23

13

92

92

72

62

52

42

42

42

42

22

12

12

02

91

81

61

51

51

52

05

57

%

09

291

6941

146

603

1851

7127

361

6372

321

061

8981

87

4721

3001

7851

18

2029

3113

275

101

331

6593

335

0721

7452

8591

2005

82752

26416

19126

68784

1951

0045

78972

The  Patent  Cooperation  Treaty  (PCT)  was  signed  in
Washington on 19 June 1970 and came into force on 1
June  1978.  The  PCT  allows  for  the  filing  of  an
international  application  to  have  the  same  effect  as  a
national  application  in  each  of  the  contracting  states
(March 2007: 137) designated in the application. 

In the cases in which the EPO is designated, the patent
is  known  as  a  Euro-PCT  patent.  The  PCT  system  is
superimposed on the national and European systems,
but patents are always granted nationally.

All PCT applications are centralised through the World
Intellectual  Property  Organisation  (WIPO).  In  March
2007, 184 States were members of the WIPO. 

For  a  patent  application  filed  via  the  Euro-PCT  route,
two  phases  are  identified:  the  international  phase  and
the  national  or  regional  (European)  phase.  During  the
international  phase,  a  search  is  carried  out  and,
eighteen months after the priority date (the date of the
first application at any patent office), the application is
published.  When  the  international  search  report  is
finalised,  the  applicant  has  to  choose  between  three
alternatives: transferring the application to a national or
regional  patent  office  among  those  designated  in  the
application  (in  which  case  it  will  enter  the  national  or
regional  phase);  electing  an  international  preliminary
examination;  or  withdrawing  the  application.  If  the
application enters the regional or national phase, formal
search  and  substantive  examination  are  undertaken,
ending  with  the  application  being  either  granted,
refused, or withdrawn by the applicant.



167

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 168

igure 6.9 

Breakdown of  PCT applications designating the EPO by main countries — 2003

CH
2%

KR
3%

Rest of the world
9%

EU-27
33%

FR
4%

DE
12%

UK
4%

NL
4%

SE
2%

Other EU Member 
States
7%

JP
16%

US
37%

Extracts from the summary of the PATSTAT database review workshop
Organised by the OECD Patent Statistics Taskforce Geneva – 21 May 2007

On 21 May 2007, the OECD (Organisation for Economic Cooperation and Development) Patent Statistics Taskforce
arranged  for  a  workshop  to  review  the  status  of  the  EPO  Worldwide  Patent  Statistical  Database  (PATSTAT).  The
production and distribution of this database is a contribution by the European Patent Office (EPO) to the Taskforce.

The workshop was hosted by the World Intellectual Property Organisation (WIPO) Patent Cooperation Treaty (PCT)
section  at  their  headquarters  in  Geneva,  Switzerland.  The  workshop  was  attended  by  40  participants,  from  22
organisations,  drawn  from  national  patent  offices,  government  statistical  and  research  organisations  and  academic
research institutes in mainly Europe and America.

The database is provided with a suggested standard data model, such that all users can then exchange their methods
and research algorithms between themselves. Clearly with the increase in computing power, most researchers are
now  in  a  position  to  set  up  their  own  databases  in  their  organisations.  Previously  this  advantage  was  restricted  to
organisations with large computing departments and budgets. Several small organisations have successfully set up
PATSTAT and completed analysis work with it. A clear message from the attendees was the motivation of PATSTAT
users to increase their reliance on PATSTAT and to encourage the database provider to add more data. 

The feedback concerned five main issues:

(a) Data quality
(b) Format and delivery of data
(c) Extensions to the data model
(d) Derived data
(e) Meta-data and documentation.

The  European  Patent  Office  explained  that  a  wide  range  of  data  products  is  available  from  the  Patent  Information
section at the EPO. Further details and the product price list are available from patentdata@epo.org

168



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 169

6.4 High-tech patent applications

The  IPC  makes  it  possible  to  aggregate  patents
allocated  to  certain  IPC  classes  into  technological
fields. One of these fields is “high technology”.

Most  of  the  high-tech  patent  applications  to  the  EPO
came  from  Germany  (3  635)  in  2003,  followed  by
France  (1  980)  and  the  United  Kingdom  (1  526).  In
terms  of  high-tech  patent  applications  per  million
inhabitants,  Finland  led  by  a  wide  margin,  with  126
applications.  Sweden  ranked  second  with  63  and  the
Netherlands  third  with  56.  Countries  with  fewer  than
100  high-tech  patent  applications  are  not  taken  into
consideration in the analysis set out below. 17.4% of all
patent  applications  by  the  EU-27  concerned  high
technology. The leaders were Finland (41.1%) and the
Netherlands (23.0%). 

The  annual  average  growth  rates  were  always  higher
for high-tech patent applications than they were for total
patent  applications.  This  is  true  for  both  observation
periods (1993 to 1998 and 1998 to 2003) and also for
most  of  the  EU  countries.  Some  countries  performed
better than others, however, and surpassed the EU-27
average.  In  Finland  (29.9%)  and  in  Sweden  (35.6%),
the growth rates of high-tech patent applications were
particularly  high  in  the  first  period.  In  the  second
observation period, Spain (11.9%) and Austria (20.0%)
caught  up.  In  contrast,  Sweden  was  the  only  Member
State with a negative growth rate (-1.2%) in the second
observation period. 

Looking  at  the  annual  average  growth  rates  of  total
patent  applications  to  the  EPO,  Spain  (14.0%),  the
Netherlands  (12.9%),  Finland  (13.5%)  and  Sweden
(12.8%)  recorded  significantly  higher  rates  than  the 
EU-27 average (10.2%) between 1993 and 1998. In the
second  period  (1998  to  2003)  only  Spain  (9.0%)  and
Austria  (8.1%)  performed  well  above  the  EU-27
average (4.0%), which also slipped back considerably.

Figure 6.11 shows the high-tech patent applications to
the EPO per million inhabitants in 1993, 1998 and 2003,
and  confirms  the  upward  trends  mentioned  previously
for all countries except Sweden in 2003.

In 1993 the three best performers at EU level in terms
of  high-tech  patent  applications  per  million  inhabitants
were  Finland  (30),  the  Netherlands  (19)  and  Sweden
(15). Five years later Finland (110) was still in the lead,
but  Sweden  (67)  ranked  second  and  the  Netherlands
(48) third.

In  2003  the  ranking  of  the  three  best-performing  EU
countries  was  still  the  same:  Finland  (126),  Sweden
(63)  and  the  Netherlands  (56),  but  Sweden  had  lost
some ground.

When taking into account the non-EU countries, Israel
ranked  second  in  2003  with  73  high-tech  patent
applications  per  million  inhabitants.  Japan  and  the
United  States  had  54  and  48  high-tech  patent
applications  per  million 
inhabitants  respectively,
outperforming the EU-27 average of 22.



169

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 170

able 6.10

High-tech patent applications to the EPO and annual average growth rates, 

EU-27 and selected countries — 1993 to 2003

High-tech patent applications in 2003

Annual average growth rates in %

Total

Per million 
inhabitants

As % of 
all patents

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

LI

NO

10 840

10 834

242

3

10

246

3 635

8

63

21

165

1 980

481

4

1

2

6

34

:

908

235

23

15

3

9

5

654

562

1 526

15

2

90

EEA30
EEA30

10 947
10 947

331

4

13

396

793

703

490

164

6 834

1 924

108

119

13 845

CH

HR

TR

AU

CA

CN

IL

IN

JP

KR

RU

TW

US

170

22

24

23

0

1

46

44

6

16

2

4

32

8

5

0

1

14

3

:

56

29

1

1

0

4

1

126

63

26

53

59

20

22
22

45

1

0

20

25

1

73

:

54

40

1

5

48

17.4

17.4

16.2

9.6

6.2

19.4

14.1

37.6

20.5

16.8

12.9

21.5

9.6

30.9

7.3

9.6

7.0

17.8

:

23.0

14.8

14.2

18.7

10.1

8.6

11.4

41.1

22.0

21.1

34.2

8.1

16.8

17.4
17.4

10.6

4.4

9.9

20.2

29.0

37.0

30.9

16.4

24.4

35.6

16.8

20.8

28.4

High-tech patents

All patents

1993-98

20.6

20.6

14.9

1.7

61.6

22.5

24.5

24.4

43.0

25.3

25.5

14.9

10.4

:

:

:

:

19.2

:

21.1

16.9

32.2

-6.0

:

14.1

:

29.9

35.6

15.7

27.9

-12.9

32.2

20.6
20.6

19.3

14.9

88.8

22.4

26.6

54.0

31.1

38.0

9.7

28.5

24.8

24.2

16.7

1998-2003
5.3

5.2

1.8

15.2

-1.8

7.1

5.6

31.7

5.4

32.0

11.9

7.8

8.1

:

4.6

:

4.8

14.6

:

4.1

20.0

27.5

44.3

14.2

15.6

11.4

3.0

-1.2

2.7

11.4

14.9

14.6

5.3
5.3

4.7

12.3

17.0

13.3

9.0

57.1

9.2

47.2

10.1

43.8

7.4

24.2

6.0

1993-98

10.2

10.2

8.6

7.0

26.1

10.4

11.4

20.9

20.3

25.6

14.0

7.6

8.8

46.9

59.0

7.3

14.1

4.9

38.0

12.9

8.1

11.9

5.6

24.0

11.7

33.7

13.5

12.8

7.7

42.8

18.4

15.0

10.3
10.3

8.0

14.3

66.1

8.2

14.2

32.7

24.0

39.8

8.2

27.4

12.8

9.4

10.2

1998-2003
4.0

4.0

2.6

7.4

10.0

6.1

3.5

25.0

6.2

9.1

9.0

4.4

6.2

11.4

6.1

69.9

2.3

9.8

-6.9

6.1

8.1

21.5

19.6

-0.2

15.1

13.3

1.4

-0.6

2.5

4.2

-10.4

0.9

4.0
4.0

3.4

21.2

20.3

9.6

7.2

40.4

7.8

45.8

10.2

34.5

3.7

23.6

4.9



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 171

igure 6.11 

High-tech patent applications to the EPO per million inhabitants, 

selected countries  (with at least 10 high-tech patent applications per million inhabitants in 2003) — 

IF

LI

ES

LN

PJ

SI

SU

KD

HC

ED

RK

RF

TA

KU

AC

52-UE

EB

72-UE

03AEE

UA

ON

EI

UL

0

23

92

62

52

42

32

22

22

02

02

61

41

1993, 1998 and 2003

621

37

36

65

45

35

84

64

54

44

04

05

001

051

3991

8991

3002

stnatibahninoillimrep

The  aggregate  “high-tech  patent  applications”  can  be
broken down into six groups:

- AVI Aviation;
- CAB Computer and automated business

equipment;

- CTE Communication technology;
- LSR Lasers;
- MGE Micro-organism and genetic engineering;
- SMC Semi-conductors.

Taking  into  account  only  countries  with  100  or  more
high-tech patent applications to the EPO, in every EU
Member  State,  35%  or  more  of  the  high-tech  patent
applications were concentrated in the “Communication
technology” group. Finland was the most specialised in
this area as 70.9% of its high-tech patent applications
were linked to this group. Only 36.5% of Belgian high-
tech  patent  applications  were  made  in  the  area  of
“Communication  technology”,  but  Belgium  was  very

active  in  two  other  groups  -  “Micro-organism  and
genetic  engineering”  (25.5%)  and  “Computer  and
automated business equipment” (24.4%). With a share
of  26.1%  in  the  latter,  Spain  also  stood  above  the 
EU-27  average  in  the  group  “Micro-organism  and
genetic  engineering”  (12.4%).  In  “Aviation”,  France
scored  3.9%,  whereas  the  EU-27  average  was  only
2.4%.  Austria  was  more  dynamic  than  the  other
countries  in  patenting  activities  related  to  “Lasers”.
Belgium and Austria displayed shares that were above
the  EU-27  average  of  8.7%  in  the  “Semi-conductors”
group, with 12.3% and 12.4% respectively.

Nearly  one  in  two  of  the  high-tech  patent  applications
made by Australia, China and Taiwan can be classified
in  the  high-tech  group  “Computer  and  automated
business equipment”. More than 60% of Japanese and
Korean  high-tech  patent  applications  were  involved
with “Communication technology”.



171

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 172

able 6.12

Breakdown of  high-tech patent applications to the EPO by high-tech group, total number and as a

percentage of  total, EU-27 and selected countries — 2003

Total

Aviation

Computer and 
automated
business
equipment

Communication
technology

Lasers

Micro-organism
and genetic 
engineering

Semi-conductors

 High-tech group as a percentage of total

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

LI

NO

10 840

10 834

242

3

10

246

3 635

8

63

21

165

1 980

481

4

1

2

6

34

:

908

235

23

15

3

9

5

654

562

1 526

15

2

90

EEA30

10 947

331

4

13

396

793

703

490

164

6 834

1 924

108

119

13 845

CH

HR

TR

AU

CA

CN

IL

IN

JP

KR

RU

TW

US

172

2.4

2.4

1.2

0.0

19.9

1.6

3.0

0.0

0.0

2.4

1.8

3.9

2.2

27.6

100.0

52.1

0.0

2.9

:

0.5

0.9

8.8

0.0

0.0

0.0

0.0

0.6

0.7

1.8

0.0

0.0

1.1

2.4

2.0

0.0

0.0

0.3

1.3

0.7

1.2

0.0

0.3

0.1

9.2

0.0

1.1

29.9

29.9

24.4

46.2

10.0

16.3

30.1

37.8

42.9

30.3

29.2

29.3

31.1

0.0

0.0

0.0

68.4

19.1

:

36.5

24.5

32.2

20.6

48.4

7.7

30.0

23.2

20.7

36.0

27.5

0.0

27.2

29.9

31.4

55.9

34.8

47.8

29.0

18.7

36.6

51.2

30.5

20.5

27.3

48.1

39.6

45.5

45.5

36.5

0.0

11.6

41.6

41.0

25.2

32.9

56.3

39.5

47.9

42.3

44.8

0.0

0.0

31.6

65.2

:

40.4

47.9

30.7

24.0

41.9

69.2

70.0

70.9

67.6

41.0

22.0

0.0

56.1

45.5

38.8

37.2

40.5

21.8

49.9

64.0

41.8

21.1

37.8

62.5

38.0

21.4

32.3

1.1

1.1

0.0

7.7

0.0

1.5

1.3

0.0

3.7

0.0

1.5

1.0

1.8

0.0

0.0

0.0

0.0

0.0

:

0.3

2.5

0.0

6.9

0.0

0.0

0.0

0.2

0.5

1.3

0.0

0.0

0.0

1.1

2.8

0.0

0.0

0.9

1.0

0.0

1.6

0.6

1.3

0.8

2.2

0.0

1.1

12.4

12.4

25.5

30.8

58.5

36.4

12.7

16.8

2.8

9.3

26.1

10.0

11.7

27.6

0.0

34.9

0.0

12.7

:

10.4

11.8

19.7

41.6

0.0

11.5

0.0

3.7

8.4

14.2

50.5

50.0

14.5

12.5

18.1

7.0

17.1

25.1

15.8

12.7

14.4

24.5

10.3

5.3

13.7

9.6

14.9

8.7

8.7

12.3

15.4

0.0

2.6

11.9

20.2

17.7

1.6

1.9

7.8

11.0

0.0

0.0

13.0

0.0

0.0

:

11.9

12.4

8.6

6.9

9.7

11.5

0.0

1.4

2.0

5.8

0.0

50.0

1.1

8.6

6.9

0.0

7.6

4.2

3.0

3.9

4.3

2.7

19.8

10.8

9.7

21.0

11.0



06 Patent:Part3_Chap6  20/12/2007  15:58  Page 173

technological 

The 
Information  and
Communication  Technology  (ICT)  can  be  divided  into
four sub-categories:

field  of 

- Consumer electronics;
- Computers, office machinery;
- Other ICT;
- Telecommunications.

In 2003 the three major economies led in terms of their
total number of ICT patent applications to the EPO.

In  the  EU-27,  patenting  in  the  ICT  group  “Consumer
electronics” played a minor role but the shares of patent

applications  in  the  three  other  groups  were  nearly
equal,  making  up  a  share  of  around  30%  each.  This
global  picture  hides  discrepancies  at  national  level,
however.  In  the  Netherlands,  the  second-largest  ICT
group for patenting was “Consumer electronics”.

in 

With respectively 58% and 53% of all their ICT patent
ICT  group
applications  being  made 
“Telecommunications”, 
Sweden
specialised in this group. China specialised in the same
group  whereas  close  to  half  of  all  the  ICT  patent
applications  made  by  India,  Australia  and  Taiwan
concerned “Computers, office machinery”.

Finland 

and 

the 

igure 6.13 

Breakdown of  ICT patent applications to the EPO by sub-category, as a percentage of  total,

EU-27 and selected countries (with at least 10 ICT patent applications in 2003) — 2003

US

EEA30

EU-27

EU-25

JP

DE

KR

FR

UK

NL

CA

FI

SE

CN

IT

IL

CH

AU

AT

BE

DK

ES

IN

TW

RU

NO

IE

HU

EL

PL

IS

SI

TR

CZ

LU

LT

PT

BG

All ICT patent 
applications

16 823

16 167

16 010

15 994

10 507

5 859

2 704

2 616

2 232

1 618

909

787

783

764

735

595

589

453

323

283

258

197

162

147

139

137

114

37

33

30

17

17

16

14

13

12

12

10

0%

20%

40%

60%

80%

100%

Consumer electronics

Computers, office machinery

Other ICT

Telecommunications



173

06 Patent:Part3_Chap6  20/12/2007  15:58  Page 174

Another  interesting  field  is  biotechnology.  Taking  the
absolute figures of biotechnology patent applications to
the EPO in 2003, the United States led, followed by the
EU and Japan.

The ratio per million inhabitants reveals a very different
ranking.  Denmark  led  by  a  wide  margin,  followed  by
Israel and Switzerland. 

A  closer  look  at  the  results  for  1993,  1998  and  2003
reveals a heterogeneous picture. Whereas all countries
display  increasing  ratios  when  1993  and  1998  figures
are  compared,  the  comparison  of  1998  with  2003
figures  brings  no  common  trend  to  light.  In  some
countries  the  ratio  increased,  while  in  others,  it
stagnated.

igure 6.14 

Biotechnology patent applications to the EPO, total number and per million inhabitants, 

EU-27 and selected countries (with at least 10 biotechnology patent applications in 2003) — 

1993, 1998 and 2003

All biotechnology 
patent applications

US

EEA30

EU-27

EU-25

JP

DE

UK

FR

CA

NL

DK

AU

CH

IL

BE

SE

AT

FI

NO

IE

11

11

11

10

11

5

5

6

6

7

7

8

8

7

7

31

18

19

3

5

5

0

10

15

20

25

30

35

1993

1998

2003

3 331

2 606

2 576

2 573

1 035

901

416

370

224

174

169

165

133

127

107

99

56

38

21

12

174



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PART3

Chapter 7 - High-tech industries and 
knowledge based services

07 High-tech:Part3_Chap7

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7.1 Introduction

technologies 

In  the  industrialised  world,  creating,  exploiting  and
commercialising  new 
is  absolutely
essential  if  a  country  is  to  stay  competitive  vis-à-vis
other  countries.  High-technology  sectors  are  key
drivers  for  economic  growth,  productivity  and  welfare,
and  are  generally  a  source  of  high  value  added  and
well-paid employment.

This  chapter  explores  Europe’s  performance  in  high-
technology 
industries  and  knowledge-intensive
services  by  looking  at  the  different  facets  in  using
statistics  on  enterprises  (value  added,  production
value,  etc.),  on  venture  capital  investments,  on  high-
tech trade, on employment and on R&D personnel and
expenditure. 

Technology-intensive  enterprises  are  often  referred  to
as  high-technology  -  or  high-tech  -  companies.  They
are vital to the competitive position of nations because:

- They are associated with innovation and hence
tend  to  gain  a  larger  market  share,  create  new
product and service markets, and use resources
more  efficiently.  Environmental  aspects  play  a
more and more important role in this context.

- They are linked to high value-added production
and success in foreign markets, which helps to
support  higher  returns  to  the  workers  they
employ.

- The industrial R&D they perform has spill-over
effects  which  benefit  other  commercial  sectors
by  generating  new  products  and  processes,
often  leading  to  productivity  gains,  business
expansion and the creation of high-wage jobs.

Firstly,  Section  7.2  takes  a  closer  look  at  structural
statistics on enterprises by analysing the performance
of  high-tech  industries  and  high-tech  knowledge-
intensive services sectors in 2003.

The  next  section  presents  a  financial  aspect  with
statistics  on  Venture  Capital  Investment  (VCI)  both  at
the early stage and at the expansion and replacement
stage. 

Section 7.4 describes the pattern of international high-
tech trade, which makes up a considerable proportion
of total trade in many advanced economies.

The  employment  situation  in  high-tech  manufacturing
and  high-tech  knowledge-intensive  services  sectors,
both  at  national  and  at  regional  level,  is  analysed  in
Section 7.5. In this context regional data are analysed
at NUTS 2 level.

The  final  section  sheds  light  on  R&D  expenditure  and
personnel in the high-tech manufacturing sectors.

High technology: Multi-approaches and multi-sources

Two main approaches are used to identify technology-intensive industries and products. These are:

The  sectoral  approach classifies  manufacturing  industries  and  services  according  to  their  technological  intensity
(R&D expenditure/value added). This approach is based on NACE rev. 1.1 at 3-digit level (due to data availability, in
some cases, the classification can only be made on NACE rev. 1.1 at 2-digit level).

The  product  approach was  devised  to  complement  the  sectoral  approach.  It  opens  the  way  to  far  more  detailed
analysis of trade and competitiveness. High-tech products are defined according to their high value of R&D intensity
(R&D expenditure/total sales). This approach is based on SITC Rev 3.

For  detailed  definitions  of  high-tech  products,  high-tech  manufacturing  and  high-tech  knowledge-intensive  service
sectors (see Methodological Notes).

Data 

Source 

Approach 

Enterprises in high-tech industries and  
knowledge-intensive services 

Structural Business Statistics (SBS) 

Sectoral approach 
(NACE at 3-digit level) 

Trade in high-tech products 

COMEXT/COMTRADE 

Product approach 

Employment in high-tech industries and  
in knowledge-intensive services 

Labour Force Survey (LFS) 

R&D in high technology 

Research and development (R&D) 

Sectoral approach 
(NACE at 2-digit level) 

Sectoral approach 
(NACE at 2-digit level) 

Source: Eurostat - NewCronos, 
metadata on high-tech industries and knowledge-intensive services, 2007

176



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7.2 Enterprises in high-tech industries and
knowledge-intensive services

example,  manufacturers 
medicinal 
and 

High-tech sectors are defined according to their notable
value  of  R&D  intensity.  High-tech  manufacturing
comprises, 
of
for 
pharmaceuticals 
products,
communication  equipment  and  computers  whereas
high-tech  knowledge-intensive  services  (KIS)  cover
activities  relating  to  post  and  telecommunications,
computer and related activities, as well as research and
development.  Table  7.1  uses  different  economic
statistics  to  monitor  the  performance  of  these  sectors
(see also Methodological Notes).

In  2003,  the  EU-27  had  approximately  138  000  high-
tech manufacturers and 545 000 high-tech KIS. 

High-tech  manufacturers  were  most  numerous  in  Italy
(with  over  33  000),  followed  by  Germany  (20  000),
France  (17  000)  and  Poland  (15  000).  These  four
countries together were responsible for more than 60%
of European high-tech manufacturers.

However,  as  regards  turnover  in  the  high-tech
manufacturing  sector,  the  ranking  was  quite  different.
France  led  with  a  total  turnover  of  EUR  147  billion,
followed by Germany (EUR 143 billion) and the United
Kingdom (EUR 92 billion). One of the main reasons for
this is that, even though there were more enterprises in
Italy,  the  whole  high-tech  manufacturing  sector  was

smaller  than  in  the  other  main  European  countries  (in
terms of number of persons employed, turnover, etc.).

The ranking was the same for the total production value
generated by this sector. For France, this is mainly due
to  “aircraft  and  spacecraft”  and  to  a  lesser  extent  to
enterprises  that  are  active  in  the  “pharmaceuticals,
medicinal chemicals and botanical” sectors.

In  terms  of  the  value  added  generated  by  high-tech
manufacturers,  Germany  was  well  ahead,  at  almost
EUR 47 billion.

The United Kingdom registered the most enterprises in
the high-tech KIS sector – 134 000 – making up almost
one  quarter  of  the  EU-27  total.  This  was  followed  by
Italy,  with  almost  as  many  enterprises  in  KIS  as
Germany and France put together.

However,  when  it  came  to  turnover,  production  value
and  value  added,  it  is  striking  that  the  figures  for  the
United Kingdom were practically twice those of Italy. As
for high-tech manufacturers, this is due to the fact that
the total number of persons employed was much lower
in Italy.

The high-tech KIS sectors in Germany and France were
also ahead of Italy both in terms of turnover, production
value  and  value  added  and  in  terms  of  the  number  of
persons employed.

able 7.1 

Economic statistics on high-tech sectors, EU-27 — 2003

gnirutcafunamhcet-hgiH

secivresevisnetni-egdelwonkhcet-hgiH

forebmuN
sesirpretne

revonruT
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nideddaeulaV
noillimRUE

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7881

7421

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7196

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9419

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7971

7681

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7858

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3206

9656

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:

846

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8711

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178

195

822

762

3321

4971

04731

76111

848

9843

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523

8047

:

59652

49175

10844

924

654

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032

21902

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:

0624

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708

619

2415

01711

01239

KU

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EU-27 excludes missing countries.
Exceptions to the reference year: 2002: LU, MT, PL and SE; High-tech manufacturing in LT; High-tech KIS in CY. 

539331

079281

85923

15408

1492

447571

2001: High-tech manufacturing in CY.



177

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Industry and research institutions – Working together towards a knowledge economy

The need to share knowledge between research institutions and industry has become increasingly evident in recent
years. Historically, research institutions were perceived as a source of new ideas and industry offered a natural route
to maximising the use of these ideas. However, the past decade has seen a significant change in the roles of both
parties.

Many companies are developing open innovation approaches to R&D, combining in-house and external resources,
and aiming to maximise economic value from their intellectual property, even when it is not directly linked to their core
business. In particular, they have begun to treat public research as a strategic resource.

In  parallel,  it  has  become  clear  that  research  institutions  need  to  play  a  more  active  role  in  their  relationship  with
industry in order to maximise the use of their research results. This new role requires specialist staff to identify and
manage knowledge resources with business potential, i.e. how best to take a new idea to market, ensure appropriate
resources (funding, support services, etc.) to make it happen, and to obtain adequate buy-in by all stakeholders.

Source: European Commission, 2007

The  average  European  enterprise  in  all  high-tech
sectors  (high-tech  manufacturing  and  high-tech  KIS)
generated  a  production  value  of  EUR  1.9  million.
However, looking at the individual Member States, the
production  value  per  enterprise  gives  a  very  varied
picture of the situation.

Ireland was well in the lead with an average production
value  per  enterprise  of  EUR  7.9  million.  However,  the
high figures for the production value in Ireland show to
what extent data are influenced by foreign ownership of
enterprises,  outsourcing  of  activities  and  accounting
practices of multinational companies.

Seven other Member States are listed with production
values per enterprise above the EU-27 average of EUR
1.9  million:  these  are  Finland,  France,  Germany,
Portugal, Denmark, Belgium and Cyprus. 

Apart from Cyprus (EUR 1.9 million) and Slovakia (EUR
1.5  million),  the  production  value  per  enterprise  in  the
high-tech sectors was below EUR 1 million for all new
Member States (2004 and 2007 enlargements).

igure 7.2

Production value per enterprise in EUR million, total high-tech sectors (1), EU-27 — 2003

esirpretnerepnoillimRUE

9.7

5.4

6.3

5.3

1.3

6.2

4.2

8

6

4

2

0

9.1

9.1

8.1

8.1

7.1

6.1

5.1

5.1

3.1

1.1

9.0

8.0

8.0

6.0

6.0

5.0

5.0

4.0

4.0

3.0

EI

IF

RF

ED

TP

KD

EB

YC

72-UE

UL

KU

LN

ES

SE

KS

TA

TI

IS

TL

EE

VL

UH

LP

TM

GB

ZC

OR

(1) Total high-tech sectors include high-tech manufacturing and high-tech KIS sectors. Exceptions are:

High-tech KIS only: EE, LV, LU, MT and NL;
High-tech manufacturing only: PL.

Eurostat estimate: EU-27.
Exceptions to the reference year:

2002: LU, MT, PL and SE; High-tech manufacturing in LT; High-tech KIS in CY.
2001: High-tech manufacturing in CY.

178

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7.3 Venture capital investments

Venture  capital  investment  (VCI)  is  defined  as  private
equity raised for investment in companies. 

Venture  capital  investments  are  generally  used  to
finance  start-ups  and  fast-growing  enterprises.  These
investments  are  often  risky,  but  where  they  succeed
they  can  yield  a  substantial  return.  For  smaller  and
medium-sized  enterprises,  having  access  to  venture
capital investment is regarded as crucial for their growth
and employment.

The  venture  capital  investment  data  are  broken  down
into two investment stages: early stage and expansion
and replacement stage (see Methodological Notes). 

Venture capital investment at an early stage is made at
the seed and start-up stages of a business, (i.e. before
or  when  a  business  is  launched),  whereas  venture

capital  investment  at  the  expansion  and  replacement
stage  supports  enterprises  at  a  later  stage  of  their
business development. Expansion capital helps to fund
the growth and expansion of a company, which may or
may  not  break  even  or  trade  profitably,  whereas
replacement  capital  means  the  purchase  of  existing
shares  in  a  company  from  another  private  equity
investment organisation or from other shareholder(s).

For the EU-15, venture capital investment at the early
stage amounted to 0.022% of GDP in 2005, which was
approximately  five  times  less  than  the  value  at  the
expansion  and  replacement  stage  (0.12%  of  GDP).
However,  the  European  average  conceals  major
differences  between  Member  States.  Denmark,
Sweden  and  the  United  Kingdom  were  the  three
leading countries for both stages.

igure 7.3 

Venture capital at early stage as a percentage of  GDP, 

EU-15 and selected countries — 2005

PDGfo%

250.0

150.0

740.0

440.0

040.0

60.0

50.0

40.0

30.0

20.0

10.0

00.0

530.0

820.0

820.0

620.0

320.0

220.0

120.0

410.0

310.0

210.0

500.0

400.0

200.0

200.0

100.0

000.0

000.0000.0

ES

KD

KU

IF

TP

SU

ON

RF

HC

EI

EB51-UE

ED

SE

TA

OR

UH

TI

LN

KS

LP

ZC

LE

Eurostat estimate: EU-15.

igure 7.4 

Venture capital at expansion and replacement stage as a percentage of  GDP, 

EU-15 and selected countries — 2005

PDGfo%

53.0

23.0

04.0

53.0

03.0

52.0

02.0

51.0

01.0

50.0

00.0

52.0

61.0

51.0

21.0

11.0

01.0

80.0

80.0

70.0

50.0

50.0

40.0

40.0

40.0

40.0

40.0

20.0

10.0

10.0

00.0

00.0

KD

KU

ES

LN

ON51-UESU

TP

HC

SE

RF

IF

UH

TI

LP

ED

EI

TA

EB

OR

ZC

KS

LE

Eurostat estimate: EU-15.



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Intellectual Property – The Basis for Venture Capital Investment

While technology has been seen as one of the engines for the dramatic economic growth and productivity the United
States  has  experienced  over  the  last  few  decades,  an  underlying  factor  has  been  the  strength  of  its  intellectual
property  during  that  period.  Intellectual  property  provided  the  basis  for  investors  to  place  their  resources  at  risk.
Intellectual property is an integral part of value creation in a technology-based enterprise and as such is a critical factor
in  obtaining  venture  capital  for  SMEs.  Appropriate  use  of  the  intellectual  property  system  is  a  powerful  tool  for
competition, stability and mitigation of risks on capital investments. 

Modelling the interaction

Without  the  strength  of  intellectual  property  and  its  protection,  little  if  any  investment  would  be  made  into  new  or
growing enterprises. The figure below is a model of the interaction of intellectual property and venture capital. This
model  shows  that  even  at  this  simplified  level  a  degree  of  complex  interactions  exist.  However,  without  any  of  the
elements shown in this model, serious constraints would be placed on this major economic driver.

Figure: Model of Interaction of Intellectual Property and Venture Capital

We should consider this model from four critical points:

1. intellectual property stimulates more intellectual property,
2. intellectual property which is indigenous leads to respect for all intellectual property,
3. intellectual property stimulates and stabilises markets, and
4.  intellectual  property  generates  capital  and  is  impacted  by  capital  and,  in  turn,  impacts  the  availability  of
venture capital.

As  the  Figure  shows,  there  is  a  feedback  mechanism  to  the  generation  of  additional  intellectual  property.  This
becomes evident if we look at the growth of patent applications. Economies that develop intellectual property seem to
stimulate the development of additional intellectual property.

Source: World Intellectual Property Organization (WIPO), 2006

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7.4 Trade in high-tech products

High-tech  trade  is  a  way  of  estimating  a  country’s
capacity to carry out R&D, to develop new knowledge,
and to transform it into high-tech goods to be sold.

Two  approaches  exist  for  the  calculation  of  high-tech
trade: the sectoral approach and the product approach.
As  not  all  goods  produced  by  high-tech  industries  are
really  high-tech  products,  the  product  approach  is
preferred  and  presented  below.  The  term  ‘high-tech
products’  includes  such  miscellaneous  products  as
pharmaceuticals,  aerospace  products  and  scientific
instruments, for example (see Methodological Notes).

In  2005,  the  EU-27  was  the  leading  importer  and
exporter of high-tech products in the world, with goods
worth EUR 230 billion and EUR 198 billion respectively.
However, compared with China, Japan and the United
States, the EU-27 was also the one showing the largest
high-tech trade deficit.

In  absolute  terms,  Germany  was  the  largest  importer
(EUR  105  billion)  and  exporter  (EUR  115  billion)  of
high-tech  products  in  the  EU-27  in  2005.  As  a
proportion of total trade, Malta and Luxembourg had the
largest shares of high-tech products.

The EU-27’s high-tech exports increased between 2000
and 2005 whereas high-tech imports decreased during
the same period. Consequently, the EU-27’s high-tech
deficit decreased. 

The  countries  with  large  increases  in  high-tech  trade
between  2000  and  2005  were  mainly  new  Member
States (2004 and 2007 enlargements). Looking beyond
the  EU  borders,  this  was  also  true  for  Iceland  and
China.

able 7.5 

High-tech trade in 2005, in EUR million, as a share of  total exports, share of  extra-EU-27 trade and

AAGR 2000-2005 of  high-tech imports and exports, EU-27 and selected countries

Imports

Balance

Exports

EU-27

EUR million
229 505

as a % of  total 
imports
19.5

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

MK

TR

CN

JP

US

20 376

1 096

8 550

8 844

105 101

1 212

14 860

4 189

23 895

66 783

32 430

687

502

1 013

5 078

10 249

855

61 163

13 184

8 454

5 329

3 009

1 162

3 224

7 870

12 242

64 518

415

5 131

15 963

1 388

167

8 913

166 367

69 393

215 849

8.0

8.8

13.9

14.6

16.8

14.8

27.0

9.6

10.3

16.5

10.5

13.5

7.2

8.1

28.9

19.2

29.6

20.9

12.9

10.4

10.8

9.2

7.1

11.6

16.6

13.7

15.6

10.4

11.5

16.4

9.3

6.4

9.5

31.4

16.7

15.5

% of extra
EU-27 imports

100

33.9

53.3

22.2

33.0

54.7

33.0

54.1

33.8

23.4

46.3

36.2

21.7

18.1

23.0

76.6

47.7

38.7

72.8

32.9

20.8

19.4

48.5

18.5

26.9

35.3

32.5

48.1

:

:

:

:

:

:

:

:

:

AAGR
2000-2005
-1.3

EUR million
-31 669

EUR million
197 837

as a % of  total 
exports
18.8

0.6

17.0

12.1

3.3

0.8

9.6

-6.2

0.9

3.3

-4.8

-0.6

14.5

11.0

21.6

17.5

8.1

-12.7

1.2

1.6

4.6

2.8

10.3

3.6

23.3

1.9

-3.5

-6.9

3.1

-2.7

-1.4

8.6

5.5

1.0

26.0

-3.4

-5.1

-1 433

-828

-1 226

1 322

10 304

-574

11 175

-3 364

-15 148

4 259

-11 608

-315

-369

-709

662

-309

75

4 970

-307

-6 155

-3 240

-2 317

-502

-1 583

3 832

2 023

3 888

-252

-2 687

5 482

-825

-154

-8 117

7 289

31 724

-25 772

18 943

268

7 324

10 166

115 405

638

26 036

826

8 747

71 042

20 822

372

133

304

5 739

9 941

930

66 133

12 876

2 299

2 089

691

660

1 641

11 701

14 264

68 406

163

2 444

21 445

563

13

796

173 656

101 117

190 077

7.1

2.9

11.7

14.9

14.8

10.3

29.5

6.0

5.7

19.1

6.9

31.6

3.2

3.2

38.0

19.7

50.8

20.3

12.8

3.2

6.8

3.1

4.3

6.4

22.1

13.6

22.1

6.6

2.9

21.2

8.0

0.8

1.4

28.4

21.1

26.2

% of extra
EU-27 exports

100

24.8

35.9

21.5

38.4

41.8

11.3

41.7

23.9

34.8

58.2

45.5

15.7

43.8

45.5

3.9

29.2

61.8

21.0

40.2

29.1

65.0

23.5

56.4

16.0

62.7

59.8

47.7

:

:

:

:

:

:

:

:

:

AAGR
2000-2005
1.7

1.4

25.6

24.5

4.9

3.7

-5.9

-5.2

-2.7

1.9

-4.7

-1.3

94.8

24.0

25.3

25.2

7.1

-11.5

2.8

4.6

18.7

7.3

5.8

9.2

34.8

0.0

-4.2

-5.2

35.9

3.4

4.0

6.6

4.4

-7.9

30.8

-6.3

-5.6

EU-27 does not include intra-EU trade and therefore does not correspond to the sum of Member States.
Exceptions to the reference period 2000-2005: 2002-2005: HR and MK.



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High-tech trade indicators 2006: EU-27 vs. USA, China and Japan

WORLD MARKET SHARE – Definition 1 (product approach)

The world market share is defined as the ratio between the export (or import) of high-tech products from the country
under study and the total export (or import) of high-tech products all over the world, which is calculated as the sum of
high-tech exports (or imports) of all countries (excluding intra-EU-27 exports).

hcet-hgihniedartlatotnoseirtnuocfo)stropxe(erahstekramdlroW

hcet-hgihniedartlatotnoseirtnuocfo)stropmi(erahstekramdlroW

52

02

51

01

5

0

52

02

51

01

5

0

9991

0002

1002

2002

72-UE

SU

3002

NC

4002

5002

PJ

9991

0002

1002

2002

72-UE

SU

3002

NC

4002

5002

PJ

EU-27 does not include intra-EU trade.

EU-27 does not include intra-EU trade.

Since 2004, the EU-27 took the leadership. EU-27 was
closely followed by the US. While the US and Japan are
on  the  decrease,  the  EU-27  remains  quite  stable
between 1999 and 2005. On the other hand, China is
growing  rapidly,  catching  up  with  Japan  in  2003  and
overtaking it in 2004 and 2005.

The  the  EU-27  and  US  led  the  world  but  they  are
decreasing  with  time.  Japan  shows  a  slight  decrease,
but China a sharp increase thus doubling Japan.

Source: Based on European Commission, Joint Research Centre, 2007

China: Foreign trade in high-tech products sets record high

BEIJING, July 11 2006 - China posted a record high of 235.36 billion US dollars
in imports and exports of high-tech products over the first six months, up 30.6
percent on the same period last year, reports the Ministry of Commerce. 

The figure accounts for 29.6 percent of the country's total foreign trade volume
of 795.74 billion US dollars. 

This is the first time that both imports and exports of high-tech products have
exceeded  the  benchmark  of  100  billion  US  dollars. According  to  the  report,
imports of these products stood at 111.89 billion US dollars while exports came
in at 123.47 billion US dollars. 

About 55.2 percent of the country's imports and exports, or 439.39 billion US
dollars, came from machinery and electronic products, representing year-on-
year growth of 28.7 percent.

Source: Ministry of Commerce of the People’s Republic of China, Chinese
Government’s Official web portal, July 2006

182

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7.5 Employment in high-tech industries and in
knowledge-intensive services

Performance at national level in Europe

Although  data  on  high-tech  trade  are  a  way  of
estimating  a  country's  capacity  to  transform  new
knowledge  into  high-tech  goods  (an  output  indicator),
data  on  employment  in  high-tech  sectors  are  much
more  of  an  input  indicator,  or,  in  other  words,  of  the
resources  available  and  needed  to  create  and
transform this new knowledge.

Almost  a  third  of  EU-27  total  employment  in  2006
(32.6%) was employed in knowledge-intensive services
(KIS) and only 6.6% in high-tech and medium high-tech
manufacturing.

Another  third  of  European  workers  (33.6%)  were
employed  in  less  knowledge-intensive  services  and  a
little  more  than  10%  in  other  manufacturing  (low-tech
and medium low-tech manufacturing).

The remaining jobs (15.4%) were in other sectors of the
economy,  such  as  ‘agriculture,  hunting  and  forestry’;
‘mining  and  quarrying’;  ‘electricity,  gas  and  water
supply’ and ‘construction’.

The knowledge-intensive services sector is particularly
well  developed,  mainly  in  northern  Europe.  Indeed,
employment in this sector provided more than 40% of
total  employment  in  Denmark,  Luxembourg,  the
Netherlands, Finland, Sweden and the United Kingdom
- also in Iceland, Norway and Switzerland.

At  more  than  80%  of  total  employment  (knowledge-
intensive  and  less  knowledge-intensive),  the  whole
services  sector  was  especially  well  developed  in
Luxembourg.

Employment  in  high-tech  and  medium  high-tech
manufacturing exceeded 10% of total employment only
in Germany (10.8%) and the Czech Republic (10.3%).
By  contrast,  in  some  countries,  such  as  Cyprus,
Luxembourg and Latvia, employment in high-tech and
medium  high-tech  manufacturing  did  not  reach  2%  of
the  total.  In  Luxembourg,  the  main  reason  is  that  the
country is a service economy. In Cyprus and Latvia, this
is  principally  due  to  other  sectors  of  the  economy
accounting for a large percentage of employment.

32.6

38.6

21.7

25.0

33.6

34.3

36.1

31.1

18.4

15.6

43.8

29.8

igure 7.6 

EU-27

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO

CH

HR

26.8

24.9

27.0

34.3

34.9

36.4

30.1

28.3

24.4

25.0

42.0

42.3

28.5

31.2

30.4

24.6

22.7

14.5

23.9

26.3

24.9

41.1

47.5

43.0

43.1

46.2

40.0

20%

22.1

0%

Distribution of  employment by sector as a percentage of  total, 

EU-27 and selected countries — 2006

15.4

10.0

11.7

10.4

18.9

6.6

6.6

4.8

17.9

11.4

10.3

9.2

5.8

9.9

11.3

10.8

17.3

17.8

3.5

19.8

21.4

18.6

7.6

5.6

10.4

2.2

11.2

4.5

13.3

23.9

25.3

15.2

13.4

16.4

9.5

13.4

15.2

10.4

13.5

13.1

11.2

33.7

32.1

34.7

41.1

38.6

35.8

44.7

36.7

32.1

38.9

34.5

38.4

35.2

12.0

10.0

30.7

14.3

9.5

3.2

35.9

15.1

25.7

23.4

39.8

17.5

16.9

11.8

15.5

15.7

16.0

18.9

16.6

28.3

12.6

11.2

28.1

33.5

29.9

27.5

31.0

9.4

10.5

8.7

7.4

15.6

14.1

12.6

11.7

7.0

2.1

4.3

7.6

14.5

7.3

4.9

25.7

60%

80%

100%

29.0

35.1

28.9

31.8

32.8

40%

6.3

7.4

1.0

1.6

2.4

7.3

1.4

8.4

6.1

6.8

5.1

3.2

5.7

8.5

9.7

6.8

6.3

5.5

Knowledge-intensive services

Less knowledge-intensive services

Other (neither manufacturing, nor services)

Other manufacturing

High- and medium high-tech manufacturing

Exceptions to the reference year: 2005: LU, IS and CH.



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The manufacturing sector

In  2006,  the  total  manufacturing  sector  added  up  to
almost 40 million jobs in the EU-27, which is equivalent
to 18.3% of EU employment. With more than 8 million,
Germany  had  the  largest  European  manufacturing
sector  in  terms  of  employment.  Eight  other  Member
States  had  more 
in
manufacturing.

than  one  million  workers 

Of  these  40  million  workers,  almost  12  million  were
employed in medium high-tech manufacturing and only
2.3 million in high-tech manufacturing.

In  the  EU-27,  30.7%  of  all  persons  employed  in
manufacturing  sectors  were  female.  In  all  individual 
EU-27  Member  States,  female  employment  in  total
manufacturing was below 50%. Nonetheless, the ratio
was often higher in the new Member States (2004 and
2007 enlargements).

In  medium  high-tech  manufacturing,  the  share  of
female  employment  (23.5%)  was  lower  than  in  high-
tech  manufacturing  (34.1%),  where  it  even  exceeded
parity in Bulgaria, Hungary and Slovakia.

The  higher  percentages  of  women  employed  in  high-
tech manufacturing could partly be explained by the fact
that jobs in this sector consist much more of precision
work  than  work  that  needs  physical  strength  and  is
generally carried out by men. 

European employment in total manufacturing increased
slightly between 2001 and 2006. This was also true of
the  medium  high-tech  manufacturing  sector.  However,
the  number  of  jobs  in  high-tech  manufacturing
decreased  during  the  same  period,  at  an  annual
average rate of 1.6%.

At  Member  State  level,  however,  employment  in  this
sector  increased  in  ten  Member  States,  this  increase
being the most marked in Slovakia and Poland. Where
the  growth  or  decline  of  employment  in  high-tech
manufacturing  is  notable  in  some  countries,  this  is
generally  due  to  the  fact  that,  in  absolute  terms,  the
sector is small in these specific countries, which makes
it sensitive to analysis.

able 7.7 

Employment in manufacturing in 2006, by selected sectors, in thousands, percentage of  women and

AAGR 2001-2006, EU-27 and selected countries

Total manufacturing

High-tech manufacturing

Medium high-tech manufacturing

EU-27

EU-25

Total
39 144 s

36 348 s

% of women

30.7 s

29.3 s

AAGR
2001-2006

0.6 s

0.5 s

Total
2 309 s

2 265 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO
EEA30

HR

CH

719

744

1 363

420

8 193

138

267

563

3 103

4 040

4 808

37

161

264

17

861

26

1 052

727

2 985

977

2 052

266

605

444

664

3 664

22

266
39 410 s

300

591

23.8

49.7

37.3

30.2

28.4

49.2

30.3

27.0

24.5

28.4

28.6

31.4

44.7

48.2

18.9

38.1

25.9

22.0

26.0

33.0

42.6

48.3

36.7

37.4

28.7

25.0

25.7

31.0

-0.6

2.4

0.6

-3.2

-1.0

0.0

-2.2

0.2

0.6

-1.6

-0.2

-0.2

0.5

-0.7

-4.1

-2.0

-4.8

-0.8

-0.3

3.8

-2.2

0.7

-0.9

2.2

-1.4

-2.6

-4.5

-0.9

24.5
30.6 s

35.7

27.5

-1.6
0.6 s

-0.3

-1.5

27

17

76

22

650

8 u

53

10

86

298

275

1 u

:

9 u

1 u

97

5

57

51

90

23

28

10 u

40

51

40

288

:

10
2 319 s

8

89

% of women

AAGR
2001-2006

34.1 s

33.9 s

22.4

52.6 u

49.6

45.4

30.4

:

40.9

:

29.5

35.8

29.8

:

:

:

:

51.6

49.4 u

21.6

31.2

42.9

43.4

35.0 u

44.7 u

59.7

29.1

30.0

29.8

:

:
34.1 s

29.6

31.8

-1.6 s

-1.6 s

-6.1

2.1

0.6

-4.6

-1.7

1.3 u

-2.8

2.1

-1.5

-2.3

3.4

:

:

0.4 u

13.6 u

-1.1

-2.3

-9.0

-5.2

14.4

-2.4

-4.3

4.6 u

14.6

-0.5

-11.9

-7.8

:

-9.4
-1.6 s

4.7

-0.8

Total
11 848 s

11 206 s

% of women

23.5 s

22.8 s

AAGR
2001-2006

0.7 s

0.7 s

252

134

422

141

3 336

17

61

89

807

1 264

1 434

3

16

27 u

2

234

4

210

214

651

141

508

72

183

116

239

1 272

3

90

11 938 s

67

199

22.6

31.3

34.0

29.3

20.5

:

33.7

21.0

19.4

24.5

22.3

36.5 u

31.6 u

:

:

30.1

:

17.1

19.7

26.5

28.5

35.4

33.9

33.4

19.8

23.1

20.7

:

14.0
23.4 s

20.6

22.0

1.9

-0.3

3.4

-2.9

-0.3

-5.3

-0.9

2.8

0.6

-1.5

1.2

0.5

1.1

-6.6 u

-11.4

-0.1

-10.9

-3.7

4.3

4.1

-1.9

0.5

0.1

8.3

-1.6

-1.7

-4.3

5.4

2.7
0.7 s

5.3

-0.7

Exceptions to the reference year: 2005: LU, IS and CH; high-tech manufacturing in EE.
Exceptions to the reference period: 2000-2005: LU, IS and CH; high-tech manufacturing in EE;

2002-2006: MT; 2003-2006: HR; 2004-2006: PL.

184

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Figure 7.8 shows the proportion of employment in high-
tech manufacturing and the annual average growth rate
(AAGR) of this proportion between 2001 and 2006.

slightly  higher  share  of  employment  in  high-tech
manufacturing  than  the  EU-27  average  and  also  a
somewhat higher AAGR.

For  the  EU-27,  high-tech  manufacturing  contributed
1.13%  of  total  employment  in  2006.  This  share
decreased  between  2001  and  2006,  at  an  AAGR  of 
-3.9%.

Four  main  groups  of  countries  can  be  distinguished
when  combining  the  share  in  employment  with  its
AAGR. 

The  first  group  can  be  seen  as  the  leading  group  in
terms of employment in high-tech manufacturing. In this
group,  the  proportion  of  employment  in  high-tech
manufacturing  was  notably  higher  than  the  EU-27
is  composed  of  Finland,
average.  This  group 
Switzerland,  Hungary,  Ireland  and  Malta.  With  the
exception of Ireland, the AAGR in this group was higher
than that of the EU-27.

The  second  group  –  which  includes  Italy,  France,
Estonia, the Czech Republic and Germany – showed a

In the third group of countries, the share of employment
in high-tech manufacturing was below the EU average,
but  with  an  AAGR  above  that  of  the  EU.  These
countries were lagging behind, but the gap is obviously
narrowing.  This  group  includes  Greece,  Portugal  and
five  of  the  new  Member  States  (2004  and  2007
enlargements).

The  fourth  group  comprises  countries  where  the
proportion  of  employment  in  high-tech  manufacturing
was below average, compounded by a below average
AAGR. All the countries in this group apart from Norway
were  EU-15  Member  States,  such  as  Spain,  the
Netherlands and the United Kingdom, for example.

Luxembourg,  Poland  and  Slovakia  displayed  a
remarkable  growth  rate  in  employment  in  high-tech
manufacturing. For Luxembourg, and to a lesser extent
for  Slovakia,  this  is  mainly  due  to  the  fact  that,  in
absolute terms, this sector is small.

igure 7.8 

Employment in high-tech manufacturing as a percentage of  total employment in 2006 and AAGR

2001-2006, EU-27 and selected countries

51

01

5

0

5-

01-

51-

0.0

RGAA
)%(6002-1002

UL

LP

KS

RH

TL

GB

LE

OR

TP

IS

TI

72-UE

RF

EE

ZC

ED

HC

IF

UH

KD

SE

EB

ON

LN

KU

ES

TA

TM

EI

5.0

0.1

5.1

0.2

5.2

0.3

5.3

0.4

gnirutcafunamhcet-hgihnideyolpmesnosrepfo%

AAGR is calculated on employment expressed as a percentage of total employment.
Exceptions to the reference year:
2005: EE, LU and CH.
Exceptions to the reference period:

2000-2005: EE, LU and CH;
2002-2006: MT;
2003-2006: HR;
2004-2006: PL.

Unreliable data: EE, LT and SI.



185

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The services sector

With  two  thirds  of  EU  employment  in  2006,  the  total
services  sector  was  responsible  for  more  than 
140  millions  jobs,  almost  half  of  them  in  knowledge-
intensive  services  (KIS).  Germany  ranked  first  with 
25  million  persons  employed  in  services,  followed  by
the United Kingdom. The same ranking prevails in KIS.
Only one tenth of the jobs in KIS were in fact in high-
tech KIS (7 million). Germany and the United Kingdom
were  the  only  Member  States  where  employment  in
high-tech KIS added up to more than one million.

female. 

In  the  EU-27,  53.7%  of  persons  employed  in  services
were 
in
manufacturing  (see  Table  7.7),  female  employment  in
services exceeded parity in all Member States except in
Greece, Italy, Luxembourg and Malta.

In  contrast  with  employment 

By contrast, the lowest ratio of female employment was
observed  in  high-tech  KIS  (33.1%),  where  parity  was
exceeded only in Latvia and Lithuania.

Employment in total services between 2001 and 2006
increased not only at EU level, but also in all individual
Member States. 

For employment in the KIS sector, trends were similar
to those observed in total services. The only exception
was Lithuania, where employment in this specific sector
decreased.

Employment  in  high-tech  KIS  also  increased  in  the 
EU-27,  but  at  lower  rate  than  total  services.  Nine  EU
Member  States  plus  Norway  experienced  a  drop  in
employment in high-tech KIS.

In  KIS,  the  share  of  female  employment  (60.3%)  was
even higher than in total services. The only country that
did not achieve parity was Malta.

able 7.9 

Employment in services in 2006, by selected sectors, in thousands,percentage of  women and AAGR

2001-2006, EU-27 and selected countries

Total services

Knowledge-intensive services (KIS)

High-tech KIS

EU-27

EU-25

Total
141 305 s

135 853 s

% of women

53.7 s

53.7 s

AAGR
2001-2006

3.1 s

3.1 s

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

IS

NO
EEA30

HR

CH

3 074

1 817

2 710

2 054

25 201

399

1 351

2 936

12 927

17 696

15 294

260

655

858

157

2 476

106

6 010

2 597

7 756

2 992

3 635

535

1 302

1 707

3 346

21 609

115

1 786
143 091 s

851

2 822

52.9

53.2

53.8

55.3

54.8

60.0

55.3

45.3

52.6

55.4

47.8

52.0

61.9

60.4

48.1

55.4

38.3

52.6

55.2

55.8

54.7

51.4

56.3

56.1

59.2

56.1

54.6

56.8

0.8

2.8

1.1

1.3

1.3

3.3

4.4

4.1

5.6

1.4

2.6

4.5

2.7

0.6

2.5

1.7

1.8

0.9

1.6

3.3

1.9

2.5

2.8

1.7

1.2

1.0

0.9

1.2

56.2
53.7 s

54.1

53.1

1.2
3.1 s

1.5

1.5

Total
69 528 s

67 472 s

1 629

683

1 207

1 223

12 711

174

703

1 108

5 325

8 994

6 989

101

262

376

81

1 120

47

3 483

1 192

3 563

1 174

1 374

254

572

1 012

2 100

12 154

70

1 084
70 612 s

342

1 590

% of women

60.3 s

60.2 s

AAGR
2001-2006

3.5 s

3.5 s

59.5

65.3

63.9

63.3

59.9

67.9

61.3

53.2

56.4

62.0

55.8

59.8

70.4

69.2

52.7

64.7

48.3

59.1

59.3

65.9

63.3

64.1

62.6

65.8

65.8

62.7

59.7

64.6

1.2

1.4

1.2

1.0

2.3

1.5

5.1

4.4

6.1

1.6

4.0

5.6

1.9

-1.1

4.8

2.2

2.8

1.6

2.0

3.5

3.5

3.0

3.9

1.3

1.5

1.0

1.4

2.7

62.7
60.4 s

62.4

55.1

1.8
3.5 s

2.5

2.6

Total
6 949 s

6 732 s

167

82

145

118

1 290

18

78

87

527

915

689

7

25

32

6

133

4

337

113

343

96

135

28

58

113

224

1 187

8

92
7 041 s

34

151

% of women

33.1 s

32.6 s

AAGR
2001-2006

1.5 s

1.5 s

30.7

48.3

41.6

35.8

33.0

:

28.0

31.4

34.0

37.0

35.0

32.1

55.5

55.7 u

28.8

40.4

:

25.2

27.3

38.6

34.1

47.2

29.9 u

43.6

36.2

31.6

24.2

37.0

34.9
33.1 s

46.2

32.1

0.2

2.0

-0.9

-2.6

2.0

-1.8

2.0

5.5

4.4

-1.0

1.1

5.2

3.5

1.6

6.0

1.3

-4.4

0.1

0.2

8.4

5.7

-2.7

2.3

-1.9

1.3

-0.1

-2.4

3.2

-1.7
1.4 s

1.0

1.0

Exceptions to the reference year: 2005: LU, IS, CH.
Exceptions to the reference period: 2000-2005: LU, IS, CH; 2002-2006: MT; 2003-2006: HR; 2004-2006: PL.

186

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Figure  7.10  outlines  the  proportion  of  employment  in
high-tech knowledge-intensive services and the annual
average growth rate (AAGR) of this proportion between
2001 and 2006.

At  EU-27  level,  high-tech  KIS  accounted  for  3.3%  of
total employment in 2006. Between 2001 and 2006, this
share decreased at an AAGR of -0.8%. 

Four  main  groups  of  countries  can  be  distinguished
when  taking  a  combined  look  at  the  share  of  total
employment  and  AAGR  over 
the  2001-2006
observation period. 

In the first group both the proportion of employment in
high-tech KIS and the AAGR of this share were above
the  EU-27  average.  It  comprised  northern  European
countries,  which  were  the  leaders  within  this  group.
Other countries such as Switzerland and Hungary were
also  part  of  this  group.  Luxembourg  had  a  share  of
employment  in  high-tech  KIS  similar  to  the  European
average, but its growth rate was especially high.

In the second group, the share of employment in high-
tech KIS was below the EU average, but the AAGR lay
above  that  of  the  EU. These  countries  are  behind  but
the gap is gradually closing. This group includes mainly
Mediterranean  countries  and  New  Member  States
(2004  and  2007  enlargements).  The  same  was  also
true  in  Portugal  and  Poland,  with  a  particularly  high
value in growth. 

The  third  group  comprises  countries  where  the
proportion  of  employment  in  high-tech  KIS  was  lower
than  the  average,  compounded  by  a  below  (EU)
average AAGR. This group includes, namely, the Czech
Republic, Austria, Estonia, Malta and Slovakia.

includes  Norway, 

The  fourth  group,  which  is  made  up  of  only  EU-15
the
Member  States  and 
‘counterpart‘  of  the  second  group.  In  other  words,  the
share of employment in high-tech KIS was higher than
the  European  average  but  the AAGR  was  lower.  This
group,  like  the  second  group,  tends  to  move  towards
the European average.

is 

igure 7.10 

Employment in high-tech KIS as a percentage of  total employment in 2006 and AAGR 2001-2006, 

EU-27 and selected countries

RGAA
)%(6002-1002

6

4

2

0

2-

4-

6-

0

TP

LP

LE

TL

YC

VL

OR

RH

IS

SE

UL

ED

UH

HC

IF

GB

TI

72-UE

EB

LN

TA

ZC

KS

EE

TM

RF

EI

ON

KU

KD

SI

ES

1

2

3

4

5

6

SIKhcet-hgihnideyolpmesnosrepfo%

AAGR is calculated on employment expressed as a percentage of total employment.
Exceptions to the reference year:  2005: LU, IS, CH.
Exceptions to the reference period:
2000-2005: LU, IS, CH;
2002-2006: MT;
2003-2006: HR;
2004-2006: PL.



187

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Performance at national level in Europe

The manufacturing sector

Figure  7.11  shows  the  top  20  regions  in  terms  of
employment  in  high-tech  and  medium  high-tech
manufacturing  in  2006,  both  in  absolute  (thousands)
and  in  relative  terms  (as  a  percentage  of  total
employment). 

In  2006,  the  leading  region  in  terms  of  absolute
employment was Lombardia (IT) with 448 000 persons
employed 
in  high-tech  and  medium  high-tech
manufacturing.  This  Italian  region  was  followed  by
Stuttgart (DE) and Cataluña (ES). 

Among the 20 leading regions in terms of absolute jobs
held,  more  than  half  were  German  and  six  Italian.
Denmark  (the  entire  country  is  classified  at  NUTS  2
level) was the 14th leading ‘region’ in absolute terms. 

Looking at Denmark in relative terms, the proportion in
high-tech  and  medium  high-tech  employment
amounted to only 5.8% of total employment, a situation
that was similar in the regions of Île de France (FR) and
Lazio (IT), which ranked 5th and 20th in absolute terms,
but  had  respective  shares  of  only  5.6%  and  5.3%  of
total  employment.  However,  Île  de  France  was  the
leading  region  in  high-tech  manufacturing  in  absolute
terms, with 79 000 persons employed.

Of the top 20 leading regions in absolute terms, twelve
experienced  a  decrease  in  employment  in  high-tech
and  medium  high-tech  manufacturing  between  2002
and  2006.  By  contrast,  in  the  leading  Italian  regions,
especially Lazio, employment increased over the same
period, with the exception of Piemonte (IT).

Looking  at  relative  employment  in  high-tech  and
medium  high-tech  manufacturing,  German  regions
dominated even more clearly. Indeed, the eight leading
European regions in 2006 were all located in Germany.
Of the German regions, Stuttgart (DE) ranked first with
19.9%  of  total  employment  in  high-tech  and  medium
high-tech manufacturing. 

Employment  in  high-tech  and  medium  high-tech
manufacturing  increased  in  relative  terms  between
2002  and  2006  in  twelve  of  the  top  20  regions.
Moreover, six of the eight regions which experienced a
decline in employment in this sector were German. 

Map  7.12  sets  out  the  share  of  employment  taken  by
high-tech and medium high-tech manufacturing in 2006
across  the  EU-27  and  EFTA  regions  at  NUTS  2  level
(see  Methodological  Notes).  It  is  again  obvious  that
German  regions  and  regions  from  central  Eastern
Europe were clear leaders in this sector.

igure 7.11 

Top 20 leading EU-27 and EFTA regions in terms of  employment  in high-tech and medium high-tech

manufacturing in 2006, in thousands, as a percentage of  total employment and AAGR 2002-2006

smretetulosbanisnoigergnidaeL

latotfo%
tnemyolpme

RGAA
6002-2002

smretevitalernisnoigergnidaeL

latoT
s0001ni

RGAA
6002-2002

)TI(aidrabmoL

)ED(tragttutS

)SE(añulataC

)ED(nreyabrebO

)RF(ecnarFedelÎ

)TI(oteneV

)ED(ehurslraK

)TI(angamoR-ailimE

)ED(frodlessüD

)TI(etnomeiP

)ED(nlöK

)RF(seplA-enôhR

)ED(tdatsmraD

)KD(kramneD

)ED(grebsnrA

)ED(grubierF

)ED(negnibüT

)ED(zlafP-nessehniehR

)ED(giewhcsnuarB

)TI(oizaL

5.01

9.91

4.8

7.21

6.5

6.01

2.71

3.11

4.9

8.11

0.01

4.7

8.9

8.5

3.01

4.51

9.61

1.41

7.61

3.5

0.1

5.1-

0.0

3.1-

3.2-

2.3

9.1

7.3

0.1

1.2-

4.1-

7.2-

4.7-

6.1-

5.0-

4.1

1.2-

7.1-

8.0-

2.01

)ED(tragttutS

)ED(ehurslraK

)ED(negnibüT

)ED(giewhcsnuarB

)ED(nreyabredeiN

)ED(zlafprebO

)ED(grubierF

)ED(neknarfretnU

)UH(lútnánuD-taguyN

)ED(zlafP-nessehniehR

)ZC(dohcývoreveS

)UH(lútnánuD-pézöK

)RF(ecaslA

)KS(oksnevolSéndapáZ

)ED(nebawhcS

)ZC(avaroMíndertS

)ED(nreyabrebO

)RF(étmoC-ehcnarF

)ZC(yhceCíndertS

)ZC(dapázohiJ

773

022

541

711

29

87

951

79

26

921

001

46

801

211

901

37

072

46

86

96

2.1-

5.1

7.1-

6.0-

5.0

1.3

1.1

2.0

5.2

3.1-

2.4

2.3-

5.1

1.6

3.2-

5.4

1.1-

1.6-

3.2

7.2

0

001

002

003

004

005

0

5

01

51

02

hcet-hgiH

hcet-hgihmuideM

hcet-hgiH

hcet-hgihmuideM

Calculated on employment expressed in thousands.

188



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ap 7.12 

Regional employment in high and medium high-tech manufacturing

as a percentage of  total employment — 2006

Employment in high- and medium
high-tech manufacturing,

as a percentage of total employment

2006 - NUTS 2

< 5.0%
5.0 - 7.5%
7.5 - 12.5%
> 12.5%
Data not available

Statistical Data: Eurostat Database: REGIO
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat - GISCO, 05/2007

0

600km

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0 100

0

10

Iceland

0 100

Exceptions to the reference year:

2005: LU, PL, IS and CH.

Unreliable data: See Methodological Notes.



189

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The services sector

Figure  7.13  shows  the  leading  regions  in  terms  of
employment  in  knowledge-intensive  services  in  2006,
both in absolute and in relative terms.

Five  of  the  20  leading  regions  in  absolute  terms  were
German. Île de France (FR) was the leading region with
2.1 million jobs in KIS. With 321 000 persons employed,
Île de France (FR) was also the leading region in high-
tech KIS (a sub-set of KIS).

Lombardia  (IT)  came  second  with  1.4  million  persons
employed  in  total  KIS.  However,  in  relative  terms,  it
accounted for 31.6% of total employment in the region.
This  was  less  than  the  EU-27  average  of  32.6%. The
same was also true for Cataluña (ES), Andalucia (ES)
and Mazowieckie (PL).

Denmark ranked third in absolute terms, with 1.2 million
people employed in KIS. This corresponded to 43.8% of
total employment in the country.

Most leading regions were urban regions, nine of them
capitals.

In relative terms (as a percentage of total employment),
five of the 20 leading regions were located in Sweden
and four in the United Kingdom.

Nine  leading  regions  in  relative  terms  were  in  fact
capital  regions.  In  the  number  one  region,  Stockholm
(SE), 57% of total employment consisted of knowledge-
intensive services.

Among  the  leading  regions,  both  in  absolute  and  in
relative  terms,  employment  in  KIS  often  increased
between 2002 and 2006. However, it decreased during
the same period in two dominant regions, Île de France
(FR) and Inner London (UK).

Map  7.14  provides  an  overview  of  the  employment
share  taken  by  knowledge-intensive  services  in  2006
across the regions of the EU-27 and EFTA regions, at
NUTS 2 level. 

In  addition  to  the  capital  regions,  regions  with  a  high
proportion  of  employment  in  knowledge-intensive
services were mainly located in Northern Europe. 

By contrast with employment in high-tech and medium
high-tech  manufacturing,  it  should  be  noted  that
employment  in  KIS  is  underdeveloped  in  Eastern
Europe.  This  was  also  true  in  southern  European
countries such as Spain, Portugal, Italy and Greece.

igure 7.13 

Top 20 leading EU-27 and EFTA regions in terms of  employment in knowledge-intensive services in

2006, in thousands, as a percentage of  total employment and AAGR 2002-2006

smretetulosbanisnoigergnidaeL

latotfo%
tnemyolpme

RGAA
6002-2002

smretevitalernisnoigergnidaeL

latoT
s0001ni

RGAA
6002-2002

)RF(ecnarFedelÎ

)TI(aidrabmoL

)KD(kramneD

)KU(nodnoLretuO

)SE(dirdaMeddadinumoC

)SE(añulataC

)RF(seplA-enôhR

)ED(frodlessüD

)ED(nreyabrebO

)TI(oizaL

)LN(dnalloH-diuZ

)SE(aiculadnA

)ED(tdatsmraD

)ED(nlöK

)KU(nodnoLrennI

)ED(nilreB

)KU(xessuS.W&.E,yerruS

)LN(dnalloH-drooN

)RF(ruzA'detôC-seplA-.vorP

)LP(eikceiwozaM

1.64

6.13

8.34

9.15

9.73

4.72

9.33

2.53

8.73

7.63

3.44

8.42

5.24

6.04

6.65

5.74

9.94

1.74

9.73

1.03

3.2-

8.4

4.0

5.0

4.6

0.7

6.0

1.4

3.2

9.3

4.2

7.6

1.3

3.4

7.1-

6.1

6.1

9.1

2.0

:

)ES(mlohkcotS

)KU(nodnoLrennI

)ON(suhsrekAgoolsO

)KU(nodnoLretuO

)EB(elatipaC-sellexurB.géR

)IF(dnalÅ

)KU(xessuS.W&.E,yerruS

)LN(thcertU

)ES(dnalrroNervÖ

)KU(.sdrofxOdnaskcuB,.skreB

)ON(egroN-droN

)ED(nilreB

)HC(hcirüZ

)ES(dnalrroNatsrelleM

)LN(dnalloH-drooN

)ES(egirevsdyS

)RF(ecnarFedelÎ

)ES(egirevsnalleMartsÖ

)EB(tnabarBsmaalV.vorP

)ON(galednørT

655

537

003

8311

481

7

146

403

711

155

701

496

633

28

046

582

4412

133

312

29

1.1

7.1-

9.1

5.0

3.2

3.1-

6.1

1.3

6.0

0.2

1.1

6.1

1.3

6.0-

9.1

8.0

3.2-

0.1

1.4

1.1

0

005

0001

0051

0002

0052

0

01

02

03

04

05

06

SIKhcet-hgiH

SIKrehtO

SIKhcet-hgiH

SIKrehtO

Calculated on employment expressed in thousand.
Exception to the reference year:

2005: Mazowieckie (PL).

Åland (FI): Details for high-tech KIS are not available.
Exception to the reference year:

2005: Zürich (CH).

Exception to the reference period:

2002-2005: Zürich (CH).

190

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ap 7.14 

Regional employment in knowledge-intensive services (KIS) 

as a % of  total employment — 2006

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0 100

0

10

Iceland

0 100

Employment in knowledge intensive
services (KIS),

as a % of total employment

2006 - NUTS 2

< 20%
20 - 30%
30 - 40%
> 40%
Data not available

Statistical Data: Eurostat Database: REGIO
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat - GISCO, 05/2007

0

600km

Exceptions to the reference year:

2005: LU, PL, IS and CH.

Unreliable data: See methodological notes.



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7.6 R&D in high-technology

Figure  7.15  shows  the  business  enterprise  R&D
expenditure  in  the  manufacturing  sector  broken  down
by technological intensity.

Member  States,  with  92%  spent  on  high-tech  or
medium  high-tech  manufacturing.  Hungary  and  the
United Kingdom followed close behind on 91%.

The highest share of business R&D expenditure taken
by high-tech manufacturing was recorded in Greece, at
46%, followed by the Netherlands, at 41%. However, in
absolute terms, spending in the Netherlands amounted
to  EUR  3  750  million,  whereas  it  was  only  EUR  188
million in Greece. 

While high-tech manufacturers in Greece accounted for
a  large  share  of  business  R&D  expenditure  in  the
manufacturing  sector,  this  is  exclusively  due  to  the
particular  sector  of  activity  of  “Manufacturing  of  radio,
television  and  communication  equipment  and
apparatus”.

The  proportion  of  business  R&D  expenditure  in  high-
tech manufacturing was also significant in Ireland and
in Austria: both at 35%.

In Russia, 93% of business R&D expenditure was spent
in  high-tech  or  medium  high-tech  manufacturing.  This
was  followed  by  Germany,  which  led  among  the

At the other end of the scale, a considerable share of
R&D  was  performed  in  low-tech  or  medium  low-tech
manufacturing in Romania (32%), Norway (32%), Malta
(29%) and Spain (26%).

In  absolute  terms,  Germany  was  way  ahead  in
business R&D expenditure in the manufacturing sector,
at almost EUR 35 billion. This can be mainly explained
by the fact that the manufacturing sector is much more
developed  in  Germany  than  in  France  and  the  United
Kingdom, as was reported for enterprises (Section 7.1)
and employment (Section 7.5). 

Germany  was  followed  by  France  and  the  United
Kingdom,  at  EUR  19  billion  and  EUR  15  billion
respectively.

Business R&D expenditure in the manufacturing sector
exceeded EUR 1 billion in four other Member States (for
which  data  are  available):  the  Netherlands,  Austria,
Belgium and Spain.

igure 7.15 

Business enterprises R&D expenditure in manufacturing by technological intensity, 

selected countries — 2003

latoT
noillimRUEni

LE

LN

EI

TA

TM

ON

RF

IS

ED

EB

UH

KU

UR

SE

LP

ZC

64

14

53

53

92

52

42

02

91

81

71

31

01

01

01

9

OR

7

GB

2

0

02

53

64

25

74

34

34

36

07

37

16

47

87

28

46

07

67

16

48

04

91

41

31

81

92

23

31

11

8

9

9

7

02

62

02

23

51

41

881

0573

766

3722

2

098

97681

602

18543

7392

781

42251

786

5732

491

393

37

8

06

08

001

hcet-hgiH

hcet-hgihmuideM

hcet-wolmuidemdna-woL

Due  to  the  unavailability  of  business  R&D  expenditure  by  NACE  (level  2),  it  was  not  possible  to  calculate  data  by  technological  intensity  for  all
Member States.
Exceptions to the reference year: 2004: SI and BE; 2002: HU, MT, AT and BG.

192

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In  2004,  R&D  personnel  in  manufacturing  enterprises
across  the  EU-27  totalled  more  than  800  000  when
expressed in terms of Full-Time Equivalent (FTE).

R&D personnel were mainly working in three countries:
Germany,  France  and  the  United  Kingdom,  with 
267 000, 135 000 and 119 000 respectively, expressed
in FTE.

As already stated for employment (see Section 7.5) and
for  R&D  expenditure  in  the  manufacturing  sector,
Germany had as many R&D personnel as France and
the  United  Kingdom  put  together.  In  other  words,
Germany  alone  accounted  for  almost  a  third  of
Europe's R&D personnel in the manufacturing sector.

In  high-tech  manufacturing,  Germany  again  had  most
R&D  staff  (57  000),  followed  by  France,  the  United
Kingdom and Italy, with 36 000, 18 000 and 12 000 FTE
respectively.

the  same
In  medium  high-tech  manufacturing, 
countries,  namely  Germany,  the  United  Kingdom  and
France  ranked  top  in  absolute  terms,  with  184  000, 
85 000 and 78 000 persons FTE respectively.

Reflecting  in  part  the  economic  structure  of  the
respective  country,  this  can  be  explained  by  the  fact
that  more  than  40%  of  Irish  R&D  personnel  in  the
manufacturing sector were in reality employed in high-
tech  manufacturing.  Other  countries,  such  as Austria,
Slovenia  and  to  a  lesser  extent  France  and  Portugal,
also  had  a  high  share  of  R&D  in  the  high-tech
manufacturing  sector.  By  comparison,  in  Poland,
Romania  and  the  United  Kingdom,  more  than  70%  of
R&D personnel were actually employed in the medium
high-tech manufacturing sector.

employed 

In  2004,  more  than  half  (52.1%)  of  all  EU-27  R&D
personnel 
in  manufacturing  were
researchers.  This  share  varied  considerably  from  one
country to another, with a figure of over 60% in Estonia,
Ireland,  Lithuania,  Hungary,  Malta,  Poland,  Romania,
Finland  and  the  United  Kingdom,  but  below  40%  in
Greece,  Italy  and  Slovenia. This  was  also  the  case  in
Switzerland and Croatia.

With  the  exception  of  Malta  and  Slovenia,  the
proportion  of  researchers  among  R&D  personnel  was
higher  in  high-tech  manufacturing  than  in  total
manufacturing.  At  84.3%,  Hungary  had  the  highest
proportion  of  researchers  in  high-tech  manufacturing.
Turkey followed closely on 84.1%.

able 7.16 

Business enterprises R&D personnel in full-time equivalent (FTE) and percentage of  researchers in

manufacturing sector by technological intensity, EU-27 and selected countries — 2004

R&D
personnel
816 870

806 526

EU-27

EU-25

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

NO

CH
HR
TR

22 886

940

8 282

17 173

267 404

298

5 130

5 543

37 059

135 378

50 174

89

314

459

1 511

4 665

46

33 186

19 137

8 929

2 673

9 404

3 217

1 025

24 665

38 748

118 535

7 071

25 747
603
4 588

Total

High-tech

Medium high-tech

Medium low-tech

Low-tech

% of 
researchers
52.1

R&D
personnel
:

% of 
researchers
:

R&D
personnel
:

% of 
researchers
:

R&D
personnel
:

% of 
researchers
:

R&D
personnel
:

% of 
researchers
:

Manufacturing

52.0

46.7

52.9

44.1

54.8

53.3

64.8

62.2

30.5

41.5

47.5

34.0

59.7

56.1

65.1

50.0

61.3

65.2

42.3

56.1

62.1

52.9

60.0

39.8

45.3

75.1

55.7

62.1

68.2

36.4
36.8
59.2

:

4 306

:

1 148

:

57 820

86

2 090

:

4 265

36 280

12 380

:

:

:

:

744

12

:

6 408

912

709

486

1 012

:

:

:

18 044

1 762

:
44
845

:

51.1

:

52.5

:

65.8

70.9

77.5

:

58.8

66.8

41.1

:

:

:

:

84.3

58.3

:

70.5

71.8

79.9

74.7

39.6

:

:

:

76.5

80.3

:
75.0
84.1

:

12 751

623

5 561

:

184 138

:

2 043

2 273

22 094

77 524

30 561

:

:

:

:

3 368

26

16 645

8 996

6 580

:

6 589

1 700

:

:

:

85 427

3 285

:
411
2 422

:

46.7

50.1

45.8

:

50.7

:

61.4

44.6

40.3

41.7

34.9

:

:

:

:

59.3

73.1

48.0

47.6

61.5

:

60.0

38.4

:

:

:

60.5

70.9

:
36.0
57.9

:

3 699

:

1 065

:

17 540

:

397

407

5 026

12 651

4 040

:

:

:

:

229

2

2 248

:

794

:

1 643

327

:

:

:

6 363

721

:
33
:

:

41.9

:

36.2

:

44.2

:

31.5

30.9

38.3

35.0

19.6

:

:

:

:

56.3

0.0

43.1

:

56.0

:

63.5

46.2

:

:

:

57.9

62.6

:
:
:

:

2 131

67

508

:

7 907

:

600

:

5 674

8 924

3 194

:

:

:

:

324

6

:

:

643

:

686

178

:

:

:

8 701

1 303

:
115
:

:

46.5

:

23.4

:

42.9

:

31.7

:

35.7

36.3

16.2

:

:

:

:

:

66.7

:

:

62.5

:

41.7

42.1

:

:

:

50.7

48.3

:
:
:

Due to the unavailability of business R&D personnel by NACE (level 2), it was not possible to calculate data by technological intensity for all Member
States.
Exceptions to the reference year:

2003: BG, DE, EE, EL, IT, CY, LT, LU, PT, SE, UK, NO;  2002: FR, MT, AT, TR.

EU aggregates are estimated as the sum of available countries.



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PART3

Chapter 8 - The 2006 EU Industrial
R&D Investment Scoreboard

08 Scoreboard:Part3_Chap8

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8.1 Introduction

The  2006  EU  Industrial  R&D  Investment  Scoreboard
has been prepared from companies’ annual reports and
accounts  and  presents  data  on  the  top  1000  EU
companies  (1)  and  the  top  1000  non-EU  companies,
ranked  by 
in  Research  and
investment 
Development  (R&D).  The  main  indicators  are  R&D
investment,  net  sales,  operating  profit,  capital
expenditure,  number  of  employees  and  market
capitalisation. The  data  in  the  2006  Scoreboard  cover
the previous four financial years (2002, 2003, 2004 and
2005). 

their 

The  term  ‘EU  company’  refers  to  an  enterprise  group
whose  ultimate  parent  has  its  registered  office  in  a
Member State of the EU. Likewise, a ‘non-EU company’
is  one  where  the  ultimate  parent  company  is  located
outside the EU. The enterprise groups are broken down
Industry
by 
Classification  Benchmark  (ICB),  jointly  owned  by  Dow
Jones & Company, Inc. and FTSE International Limited. 

industrial  sectors,  based  on 

the 

The ICB is a detailed and comprehensive structure for
sector and industry analysis, facilitating the comparison
of  companies  across  four  levels  of  classification  (10
industries,  18  super-sectors,  39  sectors  and  104  sub-
sectors)  and  national  boundaries.  The  67  countries
covered  by  the  ICB  also  include  the  27  EU  Member
States.  Enterprise  groups  are  assigned  to  the  sub-
sector  whose  definition  most  closely  describes  the
nature  of  their  business.  The  nature  of  an  enterprise
group’s business is determined by its source of revenue
or where it makes the majority of its revenue.

Data  in  the  2006  EU  Industrial  R&D  Investment
Scoreboard are not collected or monitored by Eurostat,
but  by  the  Commission’s  Industrial  Research  and
Innovation (IRI) initiative, run jointly by the Directorate-
General  for  Research  (DG-RTD)  and  the  Joint
Research  Centre  (JRC)  (2).  Unlike  the  R&D  data
collected  officially  by  Eurostat  on  all  institutional
sectors,  the  data  in  the  2006  EU  Industrial  R&D
Investment  Scoreboard  cover  only  the  business
enterprise sector (BES). 

For the sectoral breakdown of R&D statistics, however,
Eurostat uses the Statistical Classification of Economic
Activities in the European Community (NACE Rev.1.1.).
This very detailed four-digit classification is subdivided
into  17  sections,  31  sub-sections,  62  divisions,  224

groups  and  514  classes.  The  data  tables  of  the  2006
EU  Industrial  R&D  Investment  Scoreboard  show  not
only  the  ICB  codes  but  also  the  corresponding  NACE
codes. The industrial sectors mentioned in this chapter
are, however, based on the ICB. 

The enterprise group data cover all R&D investment, no
matter where the investment is made. The ultimate goal
is  to  provide  recent  information  on  industrial  R&D
investment by European and non-European companies
so  that  new  policy  measures  can  be  tailored  more
closely to attainment of the Barcelona target — i.e. that
by  2010  overall  EU  R&D  investment  should  approach
3% of GDP, at least two thirds of which should be from
private sources. 

After  the  data  collected  from  the  annual  company
reports had been validated, the information was fed into
a database. This database allows updates or searches
for  general  information  at  EU  level,  regardless  of  the
search  method  or  criteria  used  (by  country,  R&D
variable or indicator, or information source). 

The R&D investment included in the Scoreboard is the
cash investment funded by the companies themselves.
It  excludes  R&D  undertaken  under  contract  for
customers,  such  as  governments  or  other  enterprise
groups.  It  also  excludes  the  share  of  R&D  investment
undertaken  by  any  associated  company  or  joint
venture.  Where  part  or  all  of  R&D  costs  have  been
capitalised,  the  additions  to  the  relevant  intangible
assets  are  included  to  calculate  the  cash  investment
and any amortisation avoided. 

By  contrast,  Eurostat  R&D  statistics  are  based  on
Commission  Regulation  (EC)  No  753/2004  of  22 April
2004  implementing  Decision  No  1608/2003/EC  of  the
European  Parliament  and  of 
the  Council.  The
requirements for the R&D statistics are also consistent
with those of the OECD and are based on the Frascati
Manual.

The information in the Scoreboard differs therefore from
other information such as the Business Enterprise R&D
(BERD)  data  generated  by  the  OECD,  Eurostat  and
National  Statistics  Offices.  The  BERD  data  focus  on
R&D  activity  within  the  countries,  independent  of  the
source  of  funding  and,  at  national  level,  exclude  R&D
carried  out  by  enterprise  groups  in  other  countries.  In
brief, the distinction can be seen as ‘funding vs activity’.

(1) The term “EU company” refers to a company whose ultimate parent has its registered office in a Member State of the EU. Likewise, the term “non-
EU  company”  applies  where  the  ultimate  parent  company  is  located  outside  the  EU  (see  also  the  Annex  on  glossary  and  definitions).  The  term
“enterprise group” is used in parallel.

(2) See: http://iri.jrc.es/. 

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8.2 Overview of  industrial R&D investment

Enterprise group dynamics

In  2005  the  number  of  EU  companies  among  the  50
largest  R&D  investors  remained  the  same  as  the
previous year. The Top 50 included 18 companies each
from the EU and the US, 10 from Japan (two fewer than
in 2004), and 2 each from Switzerland and Korea. The
two  Korean  enterprise  groups,  Samsung  Electronics
and  Hyundai  Motor,  were  also  among  the  fastest
growing R&D investors on the Scoreboard.

As  in  the  year  before,  DaimlerChrysler  led  the Top  20
European companies in terms of total R&D investment,
but  was  no  longer  the  world’s  number  one.  At
international  level,  DaimlerChrysler  fell  back  to  fourth

place:  it  decreased  its  R&D  investment  only  very
slightly  to  EUR  5  646  million  in  2005.  The  first  three
places  are  taken  by  the  American  enterprise  groups
Ford  Motor,  Pfizer  and  General  Motors.  While  the
national  distribution  in  the  European  Top  20  was  the
same as in the previous year, the non-European Top 20
includes  one  more  American  enterprise  group,
replacing a Japanese firm.

able 8.1 

Top 20 enterprise groups in terms of  total R&D investment (EUR million) — 2005 

UE

)ED(relsyrhCrelmiaD
)ED(snemeiS
)KU(enilKhtimSoxalG
)ED(negawskloV
)RF(sitnevA-ifonaS
)IF(aikoN
)ED(WMB
)ED(hcsoBtreboR
)KU(aceneZartsA
)ES(nosscirE
)LN(SDAE
)LN(scinortcelEspilihP
)RF(tluaneR
)RF(ASP-toegueP
)KU(smetsySEAB
)ED(reyaB
)RF(letaclA
)TI(acinaccemniF
)ED(miehlegnIregnirheoB
)TI(taiF

1
2
3
4
5
6
7
8
9
01
11
21
31
41
51
61
71
81
91
02

0.9465
0.5515
1.4654
0.5704
0.4404
0.8793
0.5113
0.1392
5.4682
0.0372
0.7632
0.7332
0.4622
0.1512
9.8012
0.6881
0.2971
0.6471
0.0631
0.8131

UE-noN

)SU(rotoMdroF
)SU(rezifP
)SU(rotoMlareneG
)SU(tfosorciM
)PJ(rotoMatoyoT
)SU(nosnhoJ&nosnhoJ
)RK(scinortcelEgnusmaS
)SU(MBI
)SU(letnI
)HC(sitravoN
)PJ(cirtcelEatihsustaM
)PJ(ynoS
)HC(ehcoR
)PJ(rotoMadnoH
)SU(kcreM
)SU(alorotoM
)SU(drakcaP-ttelweH
)PJ(ihcatiH
)SU(cirtcelElareneG
)PJ(rotoMnassiN

9.1876
9.8036
9.9765
5.1855
9.3245
9.0535
6.2164
2.9554
6.1634
2.8014
6.6504
7.9183
7.9663
7.9533
1.2623
7.9113
6.8592
5.9092
5.3092
8.9582

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”



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Slight decline in R&D intensity due to relative high net sales
growth

increased  strongly 

Net  sales  continued  to  grow  in  all  regions  at  a  faster
pace  than  both  R&D  investment  and  operating
earnings,  which 
the  EU
enterprise groups. Due to the higher growth of net sales
compared with R&D investment, average R&D intensity
(R&D as % of sales) declined slightly worldwide. R&D
investment  per  employee  by  the  EU  1 000  enterprise
groups  is  nearly  half  of  that  of  the  non-EU  1 000
enterprise groups.

for 

An  average  growth  rate  of  5.3%  for  EU  companies  in
2005 contrasted with the previous year’s growth rate of
0.7%  and  a  contraction  of  2%  in  2003.  The  growth  in
R&D investment by enterprise groups in the rest of the
world  in  2005  was  7.7%,  one  percent  higher  than  the
previous year. 

Over  the  most  recent  three  years  covered  (2003  to
2005), annual R&D growth was 1.7% for the 1 000 EU
companies and 6.7% for the 1 000 non-EU companies.
Together,  the  1  000  companies  from  the  EU  and  the 
1  000  companies  from  outside  the  EU  invested  EUR
371  billion  in  R&D,  representing  around  an  estimated
80%  of  worldwide  business  R&D  expenditure.  The 
1 000  EU  companies  accounted  for  almost  one  third
(EUR  112.9  billion),  compared  with  the  EUR  257.7
billion spent by the 1 000 non-EU companies. 

able 8.2 

Overall performance by the companies on the Scoreboard — 2005 

rotcaF
)noillibRUE(tnemtsevniD&R
)noillibRUE(ynapmocreptnemtsevniD&R
aeysuoiverpmorftnemtsevniD&RniegnahC
)5002-2002(sraey3tsalgnirudD&RniegnahclaunnA

r

)noillibRUE(selasteN
aeysuoiverprevoselastenniegnahC
r
)RUE(eeyolpme/tnemtsevniD&R
aeysuoiverprevoseeyolpmeforebmunniegnahC
)ytisnetniD&R(oitarselasten/tnemtsevniD&R
tilibatiforP
raeysuoiverprevosgninraegnitareponiegnahC

y

r

0001UE-noN
7.752
62.0
%7.7
%7.6

0.6656
%5.9
70621
%0.3
%9.3
%0.11
%8.11

0001UE
9.211
11.0
%3.5
%7.1

0.7054
%0.7
2956
%8.1
%5.2
%8.01
%2.12

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

R&D concentration likely to remain high

R&D  investment  is  concentrated  in  very  few  sectors
and  sub-sectors.  The  three  main  (sub-)sectors  of  the
EU accounted for 47% of total R&D investment by the
top 1 000 EU companies. The three are: automobiles &
parts  (sector),  pharmaceuticals  (sub-sector)  and
telecommunications  equipment  (sub-sector).  R&D
investment is slightly less concentrated for the non-EU
companies.  The 
(sub-)sectors  are
three  main 
pharmaceuticals  (sub-sector),  automobiles  &  parts
(sector)  and  computer  hardware  (sub-sector),  which
are responsible for 39% of total R&D investment by the
top 1 000 non-EU companies. 

When  Table  8.3  is  compared  with  Table  8.4,  many
differences emerge between the top 10 (sub-)sectors in
the EU and outside the EU. In general, the figures for
non-EU  enterprise  groups  are  higher.  Non-EU
enterprise  groups  invest  more  in  R&D  at  sector  level,

per enterprise and also per employee. In particular, the
data  for  market  capitalisation  are  lower  for  EU
enterprise groups, but this is also partly due to the non-
availability of such data.

One  example  of  the  differences  is  illustrated  by
comparing  the  leading  sectors  in  terms  of  R&D
investment  in  the  EU  ‘automobiles  &  parts’  with  the
same  sector  for  non-EU  companies.  Accounting  for
23% of total R&D investment, the ‘automobiles & parts’
sector plays a major role in R&D in the EU. In 2005 the
sector  invested  close  to  EUR  26  billion.  The  same
sector  outside  the  EU  invested  EUR  12  billion  more.
The gap is also visible at enterprise group level: R&D
investment  amounted  to  EUR  591  000  per  enterprise
group inside the EU and to EUR 733 000 per enterprise
group outside.

198

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able 8.3 

Top 10 (sub-)sectors in terms of  R&D investment, by top EU enterprise group — 2005 

k
n
a
R

rotces-buS

strap&selibomotuA1
slacituecamrahP2
snoitacinummoceleT3

tnempiuqe

ecnefed&ecapsoreA4
&stnenopmoclacirtcelE5

tnempiuqe
slacimehC6
enildexiF7

snoitacinummocelet

srotcudnocimeS8
erawtfoS9

01

yrenihcamlairtsudnI

esirpretnE
puorg

D&R
/tnemtsevnI
rotces-bus

/tnemtsevnID&R
puorgesirpretne

#

44
46

03

22

13

94

61

62
001
56

%

0.32
2.61

2.8

6.7

0.6

7.5

1.3

0.3
7.2
4.2

K€

5.095
3.682

9.803

1.193

9.612

9.131

7.022

8.131
7.03
7.14

5002

m€

0.48952
5.42381

9.5629

4.3068

1.4276

7.2646

2.1353

1.7243
5.6603
4.3172

tnemtsevnID&R

egnahC
40/50

RGAC
3y sr

seeyolpmE

/tnemtsevnID&R
eeyolpme

tekraM
noitasilatipaC

%

6.3
5.6

5.8

6.41

3.1

0.8-

0.91

0.9
4.9
3.6

%

6.2
9.9

1.4-

4.9

8.3-

2.3-

3.4

6.11
4.7
3.0

#

6710812
294525

885912

844774

089557

109026

854799

421011
816331
790675

K€

9.11
8.43

1.24

0.81

9.8

4.01

5.3

1.13
9.22
7.4

m€

447281
382893

213521

71888

375011

869061

445423

88653
98877
03359

Note: the number of enterprise groups may vary for each indicator (see methodological notes).

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

able 8.4 

Top 10 (sub-)sectors in terms of  R&D investment, by top non-EU enterprise group — 2005 

k
n
a
R

rotces-buS

1
2
3
4
5
6
7

8
9
01

slacituecamrahP
strap&selibomotuA
e
rawdraHretupmoC
srotcudnocimeS
tnempiuqelacinortcelE
erawtfoS
snoitacinummoceleT
tnempiuqe
sdoogerusieL
slacimehC
slairtsudnilareneG

esirpretnE
puorg

D&R
/tnemtsevnI
rotces-bus

/tnemtsevnID&R
puorgesirpretne

#

96
25
95
501
85
09

44

52
47
13

%

8.51
8.41
3.8
0.8
3.6
8.5

4.5

1.5
1.4
2.3

K€

5.885
8.237
5.063
6.591
2.082
0.761

8.513

1.225
5.141
4.762

5002

m€

5.80604
4.70183
4.07212
5.63502
9.15261
3.82051

4.69831

4.35031
7.76401
8.8828

tnemtsevnID&R

egnahC
40/50

RGAC
3y sr

seeyolpmE

/tnemtsevnID&R
eeyolpme

tekraM
noitasilatipaC

%

6.8
7.6
1.6
8.5
8.8
6.01

7.01

5.3
0.4
2.01

%

1.21
1.7
1.1
3.4
1.21
6.9

1.2-

7.6
4.2
8.8

#

355038
2629362
7198051
131445
350499
727843

928383

618677
317597
7193241

K€

2.74
4.31
6.31
5.63
9.01
6.24

1.53

4.61
5.21
1.5

m€

6871211
733664
437663
213564
069273
307054

397003

673261
540243
671735

Note: the number of enterprise groups may vary for each indicator (see methodological notes).

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

R&D investment in EU countries

In 2005 just three countries (Germany, France and the
UK)  accounted  together  for  around  three  quarters  of
both total R&D investment and sales and about 60% of
the total number of EU Scoreboard companies. British,
Dutch, Belgian, Danish and Swedish enterprise groups
increased  their  shares  of  total  R&D  investment,  while
German,  Italian  and  Finish  groups  saw  a  reduction
compared with the previous year. 

Ten enterprise groups from five new Member States —
Czech Republic (2), Hungary (3), Poland (2), Slovenia
(2)  and  Slovakia  (1)  —  are  included  on  the  2006
Scoreboard. 

33%  of  the  Scoreboard  companies  were  British,  but
their R&D share amounted to only 19% and the share
of  sales  to  29%.  By  contrast,  only  17%  of  the
Scoreboard  companies  were  German  but  they  were
responsible  for  34%  of  R&D  and  26%  of  sales.  This
shows that the link between number of companies and
proportions  of  R&D  and  sales 
is  not  always
straightforward.



199

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able 8.5 

Shares of  R&D and sales in total for all EU enterprise groups and number of  enterprise groups, 

etatSrebmeMUE

ynamreG
KU
ecnarF

RF+KU+EDlatotbuS

00.27

sdnalrehteN
nedewS
dnalniF
ylatI
kramneD
muigleB
niapS
airtsuA
dnalerI
gruobmexuL
yragnuH
eceerG
ainevolS
dnaloP
lagutroP
cilbupeRhcezC
aikavolS
UELATOT

D&RfonoitroporP
)%(latotni

selaSfonoitroporP
)%(latotni

01.47

01.43
00.91
09.81

05.7
01.6
06.4
01.4
09.1
06.1
00.1
04.0
53.0
03.0
01.0
50.0
40.0
20.0
10.0
10.0
10.0
00.001

03.62
08.82
00.91

09.3
00.4
09.2
06.5
03.1
03.2
09.2
01.1
04.0
01.1
20.0
40.0
10.0
61.0
51.0
21.0
10.0
00.001

by EU Member State — 2005 

esirpretneforebmuN
draoberoc
Snispuorg
5002
761
723
211

606

44
18
07
04
73
73
22
82
21
6
3
6
1
2
2
2
1
0001

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

In the United States, the level of R&D intensity was the
highest  in  2005,  with  more  than  two  thirds  of  the
Scoreboard companies having an R&D intensity of over
5%.  By  contrast,  in  the  EU  only  a  little  more  that  one

third  of  the  enterprise  groups  recorded  a  high  R&D
intensity,  although  more  than  half  the  EU  companies
had a medium R&D intensity of between 2% and 5%.

igure 8.6 

Share of  R&D investment by level of  R&D intensity, EU, Japan, 

the United States and Rest of  the World — 2005 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

35

52

7
6

EU

40

51

6
3

JP

68

29

1

2

US

43

44

5
8

RoW

Very low (<1%)

Low (1-2%)

Medium (2-5%)

High (> 5%)

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

200

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The  American  companies  in  the  Top  50  recorded  the
highest R&D intensity of more than 5.5% in 2005. EU
and  Japanese  enterprise  groups  were  nearly  at  the
same  level  with  more  than  4.5%.  R&D  intensity  fell
steadily  for  the  Japanese  enterprise  groups  but  much
faster for those in the EU. 

For  the  top  51  to  500  EU  companies,  R&D  intensity
varied between 1.7% and 0.6%. However, the picture is
very  different  for  US  companies.  The  top  two  ranking
steps (top 50 and 51-101) show almost the same high
R&D  intensity.  R&D  intensity  decreases  for  the
following steps, but surprisingly returns to a high level
of 5% for the last category — 451 to 500. 

igure 8.7 

Share of  R&D investment by level of  R&D intensity, breakdown of  500 US, 500 European and 200

Japanese enterprise groups by ranking step — 2005 

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Top50

51-100

101-150

151-200

201-250

251-300

301-350

351-400

401-450

451-500

EU

US

JP

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”

The  breakdown  by  sector  is  different  when  EU
enterprise  groups  are  compared  with  non-EU
companies.  In  Figure  8.8  only  sectors  with  shares
higher than 4% are shown. Smaller sectors are grouped
in  the  category  ‘others’.  Whereas  the  sector  size  is
comparable  for  pharmaceuticals  &  biotechnology,
electronics  &  electrical  equipment  and  chemicals,  the

other  sectors  exhibit  fairly  significant  differences.  For
EU companies, the automobiles & parts and aerospace
&  defence  sectors  play  a  more  important  role  in  R&D
investment  than  for  non-EU  companies.  However,  the
opposite is true for technology hardware & equipment,
software & computer services and leisure goods, where
the shares of non-EU companies are higher.

igure 8.8 

Breakdown of  Scoreboard enterprise groups, by sector — 2005

spuorgesirpretneUE

spuorgesirpretneUE-non

srehtO
%72

strap&otuA
%32

srehtO
%91

&erawdrah.hceT
tnempiuqe
%22

slacimehC
%6

tcele&scinortcelE
tnempiuqe
%7

ecnefed&ecapsoreA
%8

hcetoib&amrahP
%71

&erawdrah.hceT
tnempiuqe
%21

slacimehC
%4

sdoogerusieL
%5

tcele&scinortcelE
tnempiuqe
%8

hcetoib&amrahP
%91

retupmoc&erawtfoS
secivres
%8

strap&otuA
%51

Source: Eurostat, based on “The 2006 EU Industrial R&D Investment Scoreboard”



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8.3 Other key findings

Considerable number of  smaller and medium-sized EU
companies in high R&D-intensive sectors

A concern raised in previous editions of the Scoreboard
was  the  relative  scarcity  of  smaller  and  medium-sized
EU  companies  in  highly  R&D-intensive  sectors.
However, the extension of the 2006 EU Scoreboard to
1 000 companies has revealed a significant number of
such  firms  in  these  sectors.  Many  of  these  enterprise
groups  belong  to  R&D-intensive  sectors,  especially
software & computer services. 

The EU enterprise groups have their registered offices
in 20 Member States. New entrants are mostly from the
UK (+117), Germany (+32), France (+31), Finland (+27)
and Sweden (+21). In the non-EU listing, new entrants
are  mostly  from  the  US  (+189),  Japan  (+39),  Taiwan
(+24),  Canada  (+13)  and  Switzerland  (+9).  A  few
Member States have more than their proportional share
of  enterprise  groups  in  R&D-intensive  sectors  (United
Kingdom, Denmark, Ireland, Hungary or Sweden).

Pharmaceuticals, biotechnology and service sectors play an
important role in R&D investment growth

The  highest  R&D  growth  rates  by  sector  in  2005  and
the previous four years are found in pharmaceuticals &
biotechnology  and  in  a  number  of  services  sectors
(software & computer services, travel & leisure, media,
health  care  equipment  &  services,  and  support
services), which the present Scoreboard covers in more

detail  compared  with  previous  editions.  One  of  the
services  sectors,  ‘market’  services,  has  shown  a
positive trend since 2000, which is also reflected in the
rapid  growth  of  the  number  of  companies  in  these
sectors on the Scoreboard.

The role of  R&D for business performance

The  role  of  R&D  investment  as  an  input  factor  for  a
company  and  its  impact  on  performance  parameters
such  as  profits,  net  sales  and  market  share  are
analysed. Some descriptive statistics are presented to
illustrate  this  issue  for  sectors  with  a  high  reliance  on
R&D. The analysis shows how the Scoreboard may be
a useful tool to compare the relative performance and
behaviour  of  enterprise  groups.  The  relationship
between R&D investment, sales and market shares is
illustrated  by  descriptive  statistics  for  automobiles  &
parts,  pharmaceuticals  and  the  car  manufacturing
sector. The link between R&D investment and company
size  and  profitability  is  examined.  Analyses  of  longer
time-series for a sub-sample of Scoreboard companies
confirm the findings. 

A difficult question for a company is to establish what is
the  optimum  level  of  R&D  to  maximise  return  on
investment.  At  sector  level,  it  seems  that  there  is  a
standard  set  by  the  major  R&D  players  in  the  sector.
Large companies increasing their R&D intensity beyond
this level may run the risk that this additional effort will
be  inefficient.  In  contrast,  a  higher-than-average  R&D
intensity in smaller firms may mean that they rely more
on  R&D  to  grow  and  increase  market  share.  The
analysis  indicates  that,  at  least  in  some  sectors,
enterprise groups with an R&D intensity lower than the
sector-wide standard or decreasing over a long period
may lose market share.

Scoreboard webpage

The  electronic  version  of  the  2006  EU  Industrial  R&D
Investment Scoreboard is available on the Scoreboard
webpage at:

http://iri.jrc.es/research/Scoreboard_2006_data.htm

Most  data  are  also  available  in  Eurostat’s  reference
database NewCronos.

202

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 203

Annexes

Methodological Notes

PartM_Methodo:Methodo  20/12/2007  16:05  Page 204

This part presents, in some detail, the methodology used for the data set out in this publication. After some general
information, specific details are given for the following domains: 

• Government budget appropriations or outlays on R&D — GBAORD, 
• R&D expenditure and personnel, 
• Human Resources in Science and Technology — HRST, 
• Innovation, 
• Patents, 
• High-tech industries and knowledge based services and 
• The 2006 EU industrial R&D investment scoreboard.

1. General information

1.1 Currency

Series in current euro have been calculated by using the annual average euro-national currency exchange rate. 

The Purchasing Power Standard (PPS) is a fictive 'currency' unit created to remove differences in purchasing power.
Data expressed in PPS are derived from figures expressed in national currency by applying the PPS-national currency
exchange rate.

Data measured in 1995 constant PPS are first corrected for inflation using the GDP deflator (a Paasche index with
1995 = 100 as base year) of the country in question before applying the PPS-national currency exchange rate.

1.2 GDP

Gross domestic product (GDP) at market prices is the final result of the production activity of resident producer units
(ESA 95, 8.89). It can be defined in three ways:

- Output approach:
GDP is the sum of gross value added of the various institutional sectors or the various industries plus taxes
and less subsidies on products (which are not allocated to sectors and industries). It is also the balancing
item in the total economy production account.

- Expenditure approach 
GDP  is  the  sum  of  final  uses  of  goods  and  services  by  resident  institutional  units  (final  consumption
expenditure and gross capital formation), plus exports and minus imports of goods and services.

- Income approach 
GDP is the sum of uses in the total economy generation of income account: compensation of employees,
taxes  on  production  and  imports  less  subsidies,  gross  operating  surplus  and  mixed  income  of  the  total
economy.

1.3 Population

The population on 1st January is the number of inhabitants of a given area on 1 January of the year in question (or,
in some cases, on 31 December of the previous year). The population figures are based on data from the most recent
census  adjusted  by  the  components  of  population  change  produced  since  the  last  census,  or  based  on  population
registers.

For HRST indicators, population totals are calculated from the LFS data, thus using the same source for numerators
and denominators. Population totals derived from LFS may differ from the population totals from demographic statistics
used in other chapters mainly because of a different reference date and the non-inclusion of some institutionalised
persons.

1.4 Employment

Employed persons are persons aged 15 and over who during the reference week performed work, even for just one
hour  per  week,  for  pay,  profit  or  family  gain  or  were  not  at  work  but  had  a  job  or  business  from  which  they  were
temporarily absent because of, e.g. illness, holidays, industrial dispute and education or training.

204

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 205

1.5 Labour force

The labour force iis the active population; this is the sum of employed and unemployed persons as defined by the EU
Labour Force Survey. Persons in employment are those who during the reference week did any work for pay or profit,
or  were  not  working  but  had  jobs  from  which  they  were  temporarily  absent,  including  family  workers.  Unemployed
persons comprise persons aged 15 to 74 who were:

- without work during the reference week, i.e. neither had a job nor were at work (for one hour or more) in
paid employment or self-employment;

- currently available for work, i.e. were available for paid employment or self-employment before the end of
the two weeks following the reference week;

- actively seeking work, i.e. had taken specific steps in the four-week period ending with the reference week
to seek paid employment or self-employment or who found a job to start later, i.e. within a period of at most
three months.

1.6 Annual average growth rate

Annual average growth rates (AAGR) in this publication are calculated according to the following formula:

AAGRT, T-n = [(XT/XT-n)1/n -1] x 100

Where X = value,
T = final year, 
n = period in years for which the annual growth rate is calculated.

1.7 Institutional classification by sectors

The business enterprise sector - BES

With regard to R&D, the business enterprise sector includes: all firms, organisations and institutions whose primary
activity is the market production of goods or services (other than higher education) for sale to the general public at an
economically significant price and the private non-profit institutions mainly serving them - Frascati Manual, § 163.

The government sector - GOV

In  the  field  of  R&D,  the  government  sector  includes:  all  departments,  offices  and  other  bodies  which  furnish  but
normally do not sell to the community those common services, other than higher education, which cannot otherwise
be  conveniently  and  economically  provided,  and  administer  the  state  and  the  economic  and  social  policy  of  the
community (public enterprises are included in the business enterprise sector) as well as PNPs controlled and mainly
financed by government - Frascati Manual, § 184.

The higher education sector - HES

This  sector  comprises:  all  universities,  colleges  of  technology  and  other  institutes  of  post-secondary  education,
whatever their source of finance or legal status. It also includes all research institutes, experimental stations and clinics
operating under the direct control of or administered by or associated with higher education establishments - Frascati
Manual, § 206.

The private non-profit sector - PNP

This sector covers: non-market, private non-profit institutions serving households (i.e. the general public) and private
individuals or households - Frascati Manual, § 194.



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PartM_Methodo:Methodo  20/12/2007  16:05  Page 206

1.8 Nomenclature - NACE Rev 1.1

NACE (1) is the statistical classification of economic activities; it is designed to categorise data relating to "statistical
units", in this case a unit of activity, for example an individual plant or group of plants constituting an economic entity
such as an enterprise.

Section/sub-section

Description

NACE Rev 1.1 codes

CA

CB

DA

DB

DC

DD

DE

DF

DG

DH

DI

DJ

DK

DL

DM

DN

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

Agriculture, hunting, forestry 

Fishing

Mining and quarrying

Mining and quarrying of energy producing materials

Mining and quarrying, except of energy producing materials

Manufacturing

Manufacture of food products, beverages and tobacco

Manufacture of textiles and textile products

Manufacture of leather and leather products

Manufacture of wood and wood products

01 to 02

5

10 to 14

10 to 12

13 to 14

15 to 37

15 to 16

17 to 18

19

20

Manufacture of pulp, paper and paper products; publishing and printing

21 to 22

Manufacture of coke, refined petroleum products and nuclear fuel

Manufacture of chemicals, chemical products and man-made fibres

Manufacture of rubber and plastic products

Manufacture of other non-metallic mineral products

Manufacture of basic metals and fabricated metal products

Manufacture of machinery and equipment n.e.c.

Manufacture of electrical and optical equipment

Manufacture of transport equipment

Manufacturing n.e.c.

Electricity, gas and water supply

Construction

Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and 
household goods

Hotels and restaurants

Transport, storage and communication

Financial intermediation

Real estate, renting and business activities

Public administration and defence; compulsory social security

Education

Health and social work

Other community, social and personal service activities

Activities of households

Extra-territorial organizations and bodies

23

24

25

26

27 to 28

29

30 to 33

34 to 35

36 to 37

40 to 41

45

50 to 52

55

60 to 64

65 to 67

70 to 74

75

80

85

90 to 93

95 to 97

99

(1) NACE is derived from the French "Nomenclature statistique des Activités économiques dans la Communauté Européenne" (Statistical classification
of economic activities in the European Community)

206

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 207

Aggregations of manufacturing based on NACE Rev. 1.1

Eurostat uses the following aggregation of the manufacturing industry according to technological intensity and based
on  NACE  Rev.  1.1  at  3-digit  level  for  compiling  aggregates  related  to  high-technology,  medium  high-technology,
medium low-technology and low-technology.

seirtsudnignirutcafunaM

sedoc1.1veRECAN

ygolonhcet-hgiH

4.42

;stcudorplacinatobdnaslacimehclanicidem,slacituecamrahpfoerutcafunaM

03

23

33

;sretupmocdnayrenihcameciffofoerutcafunaM

;sutarappadnatnempiuqenoitacinummocdnanoisivelet,oidarfoerutcafunaM

;skcolcdnasehctaw,stnemurtsnilacitpodnanoisicerp,lacidemfoerutcafunaM

3.53

tfarcecapsdnatfarcriafoerutcafunaM

ygolonhcet-hgih-muideM

42

92

13

43

53

,tcudorplacimehcdnaslacimehcfoerutcafunaM

4.42gnidulcxe

;stcudorplacinatobdnaslacimehclanicidem,slacituecamrahpfoerutcafunaM

;.c.e.ntnempiuqednayrenihcamfoerutcafunaM

;.c.e.nsutarappadnayrenihcamlacirtcelefoerutcafunaM

;sreliart-imesdnasreliart,selcihevrotomfoerutcafunaM

,tnempiuqetropsnartrehtofoerutcafunaM

1.53gnidulcxe

spihsfogniriaperdnagnidliuB

staobdna

3.53gnidulcxedna

.tfarcecapsdnatfarcriafoerutcafunaM

32

;leufraelcundnastcudorpmuelortepdenifer,ekocfoerutcafunaM

ygolonhcet-wol-muideM

82ot52

;stcudorplarenimcillatem-nonrehto;stcudorplatemdetacirbafdnaslatemcisab;stcudorpcitsalpdnarebburfoerutcafunaM

1.53

.staobdnaspihsfogniriaperdnagnidliuB

ygolonhcet-woL

22ot51
;gnitnirpdnagnihsilbup,stcudorprepapdnarepap,plup;stcudorpdoowdnadoow;stcudorprehtaeldnarehtael

;stcudorpelitxetdnaselitxet;occabotdnasegareveb,stcudorpdooffoerutcafunaM

73ot63

.c.e.ngnirutcafunaM

Please note that in a few cases (R&D, Employment in high-tech and HRST), due to restrictions of the data sources
used, the aggregations are only made on a NACE 2-digit level. This means that High-technology includes the NACE
codes 30, 32 and 33, Medium-high-technology 24, 29, 31, 34 and 35, Medium-low-technology 23 and 25 to 28 and
Low-technology 15 to 22 and 36 to 37.

Aggregations of services based on NACE Rev. 1.1

Following  a  similar  approach  as  for  manufacturing,  Eurostat  defines  the  following  sector  as  knowledge-intensive
services (KIS) or as less knowledge-intensive services (LKIS):

secivresdesabegdelwonK

sedoc1.1veRECAN

16

26

46

;tropsnartretaW

;tropsnartriA

;snoitacinummoceletdnatsoP

)SIK(secivresevisnetniegdelwonK

76ot56

47ot07

;noitaidemretnilaicnaniF

seitivitcassenisubdnagnitner,etatselaeR

;

SIKhcethgiH

noitaidemretnilaicnanif.lcxe(SIKtekraM
)secivreshcet-hgihdna

secivreSevisnetnI-egdelwonKsseL
)SIKL(

08

58

29

46

27

37

16

26

07

17

47

;noitacudE

;krowlaicosdnahtlaeH

seitivitcagnitropsdnalarutluc,lanoitaerceR

;snoitacinummoceletdnatsoP

;seitivitcadetalerdnaretupmoC

.tnempoleveddnahcraeseR

;tropsnartretaW

;tropsnartriA

;seitivitcaetatselaeR

;sdoogdlohesuohdnalanosrepfodnarotarepotuohtiwtnempiuqednayrenihcamfognitneR

.seitivitcassenisubrehtO

25ot05

;edartrotoM

55

06

36

57

09

19

39

59

99

;stnaruatserdnasletoH

;senilepipaivtropsnart;tropsnartdnaL

;seicnegalevartfoseitivitca;seitivitcatropsnartyrailixuadnagnitroppuS

;ytiruceslaicosyroslupmoc;ecnefeddnanoitartsinimdacilbuP

;seitivitcaralimisdnanoitatinas,lasopsidesuferdnaegaweS

;.c.e.nnoitazinagropihsrebmemfoseitivitcA

;seitivitcaecivresrehtO

;ffatscitsemodfosreyolpmesasdlohesuohfoseitivitcA

seidobdnasnoitazinagrolairotirret-artxE

SIKsselsecivrestekraM

55

06

36

;stnaruatserdnasletoH

;senilepipaivtropsnart;tropsnartdnaL

.seicnegalevartfoseitivitca;seitivitcatropsnartyrailixuadnagnitroppuS

25ot05

;sdoogdlohesuohdnalanosrepdnaselcycrotom,selcihevrotomforiaper;edartliaterdnaelaselohW



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PartM_Methodo:Methodo  20/12/2007  16:05  Page 208

1.9 Nomenclature of territorial units for statistics - NUTS

The regional data presented in this publication are broken down according to the Nomenclature of Territorial Units for
Statistics - NUTS - classification, 2003 version. The NUTS was established by the Statistical Office of the European
Communities  (Eurostat),  in  co-operation  with  the  Commission’s  other  departments,  to  provide  a  single,  uniform
breakdown of territorial units for the production of regional statistics for the European Union.

The NUTS is a five-level hierarchical classification comprising three regional and two local levels. In this way, NUTS
subdivides  each  Member  State  into  a  whole  number  of  NUTS  1  regions,  each  of  which  is  in  turn  subdivided  into  a
whole number of NUTS 2 regions, and so on. In the present publication most data are presented at NUTS 2 level on
the basis of the NUTS 2003 version. The exceptions have been indicated in the tables or figures.

For eight countries (Denmark, Estonia, Cyprus, Latvia, Lithuania, Luxembourg, Malta and Slovenia) the national level
coincides  with  the  NUTS  2  level,  which  explains  their  potential  presence  amongst  the  regional  rankings  in  this
publication.

Iceland and Norway are not included in the NUTS classification but do have similar statistical regions. Iceland is also
classified at the statistical region level 2.

Some data are presented at NUTS 1 level. For twelve countries (Czech Republic, Denmark, Estonia, Ireland, Cyprus,
Latvia, Lithuania, Luxembourg, Malta, Slovenia, Slovakia and Sweden) the national level coincides with the NUTS 1
level, which explains their potential presence amongst the regional rankings in this publication.

For Bulgaria, Romania and Croatia, the NUTS level 2 has been revised and no one-to-one correspondence is possible
between the previous and the new NUTS level 2. This could explain the lack of data at NUTS level 2 for these countries
in some figures of the present panorama.

2. Methodological notes by domain

2.1 Government Budget Appropriations or Outlays on R&D - GBAORD

Definition

Government budget appropriations or outlays on R&D (GBAORD) are all appropriations allocated to R&D in central
government or federal budgets and therefore refer to budget provisions, not to actual expenditure. Provincial or state
government should be included where the contribution is significant. Unless otherwise stated, data include both current
and capital expenditure and cover not only government-financed R&D performed in government establishments, but
also government-financed R&D in the business enterprise, private non-profit and higher education sectors, as well as
abroad (Frascati Manual, § 496). Data on actual R&D expenditure, which are not available in their final form until some
time after the end of the budget year concerned, may well differ from the original budget provisions. This and further
methodological information can be found in the Frascati Manual, OECD, 2002.

GBAORD  data  are  assembled  by  national  authorities  using  data  for  public  budgets.  These  measure  government
support to R&D activities, or, in other words, how much priority Governments place on the public funding of R&D.

Eurostat collects aggregated data which are checked and processed, and compared with other data sources such as
OECD. Then, all the necessary aggregates are calculated (or estimated).

Sources

The basic data are forwarded to Eurostat by the national administrations of Member States and other countries. Data
for Japan and the United States come from the OECD – Main Science and Technology Indicators (MSTI).

Statistical data compilation

Until 2003, data on GBAORD were collected under a gentlemen’s agreement. From the reference year 2004 on, data
collection is based on the Commission Regulation on statistics on science and technology, No 753/2004 (OJ L 118,
page 23 of 23 April 2004).

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 209

Breakdown by socio-economic objective

Government R&D appropriations or outlays on R&D are broken down by socio-economic objectives on the basis of
NABS — Nomenclature for the analysis and comparison of scientific programmes and budgets, Eurostat 1994. The
1993 version of NABS applies from the 1993 final and the 1994 provisional budgets onwards. 

The NABS socio-economic objectives are:

- 01: Exploration and exploitation of the earth
- 02: Infrastructure and general planning of land-use
- 03: Control and care of the environment
- 04: Protection and improvement of human health
- 05: Production, distribution and rational utilization of energy
- 06: Agricultural production and technology
- 07: Industrial production and technology
- 08: Social structures and relationships
- 09: Exploration and exploitation of space
- 10: Research financed from GUF
- 11: Non-oriented research
- 12: Other civil research
- 13: Defence
- Total civil GBAORD (sum of socio-economic objectives 01 to 12)
- Total GBAORD (sum of socio-economic objectives 01 to 13)

Not all countries collect the data directly by NABS. Some follow other compatible classifications (OECD, Nordforsk),
which  are  then  converted  to  the  data  compiled  according  to  the  NABS  classification  (see Table  8.2  of  the  Frascati
Manual).

Exceptions

No data exist for Bulgaria, and therefore EU aggregates exclude Bulgaria.
No GBAORD data exist for Luxembourg before 2000, and therefore EU aggregates exclude Luxembourg before that
year. 
No GBAORD data exist for Cyprus before 2004, and therefore EU aggregates exclude Cyprus before that year. 
No GBAORD data exist for Hungary before 2005, and therefore EU aggregates exclude Hungary before that year.

Time series

The analysis in the present Panorama covers the period 1995 to 2005, with 2005 being provisional.

2.2 R&D expenditure and personnel

Concepts and definitions

The basic concepts, guidelines for collecting data and the classifications used in compiling statistics on research and
experimental  development  are  given  in  the  Frascati  Manual  —  OECD,  2002.  R&D  expenditure  and  personnel  are
particularly detailed in chapters 5 and 6 respectively. Regional data are collected according to the standards defined
by the Regional Manual — Eurostat 1996. 

Research and experimental development (R&D) activities comprise creative work undertaken on a systematic basis in
order to increase the stock of knowledge, including knowledge of man, culture and society and the use of this stock of
knowledge  to  devise  new  applications.  There  are  two  basic  statistical  variables  in  this  domain,  namely  R&D
expenditure and personnel.

Sources

The basic data are forwarded to Eurostat by the national administrations of Member States and other countries. Data
for China, Japan and the United States come from the OECD – Main Science and Technology Indicators (MSTI).



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PartM_Methodo:Methodo  20/12/2007  16:05  Page 210

Statistical data compilation

Until  2003,  data  on  R&D  were  collected  under  a  gentlemen’s  agreement.  From  the  reference  year  2003  on,  data
collection is based on the Commission Regulation on statistics on science and technology, No 753/2004 (OJ L 118,
page 23 of 23 April 2004).

R&D expenditure

Intramural expenditures are all expenditures for R&D performed within a statistical unit or sector of the economy during
a specific period, whatever the source of funds (Frascati Manual, § 358).

R&D intensity
R&D intensity is R&D expenditure expressed as a percentage of GDP.
For the computation of R&D intensity at the national level (EEA countries), GDP from national accounts is used as
reference data. At the regional level, GDP data are taken from the regional accounts. Both data series were extracted
from NewCronos.

R&D personnel

Data on R&D personnel measure the resources going directly to R&D activities. The total R&D personnel is defined
as follows:

All persons employed directly on R&D should be counted, as well as those providing direct services such as
R&D  managers,  administrators  and  clerical  staff.  Those  providing  indirect  services,  such  as  canteen  and 
security staff, should be excluded (Frascati Manual, § 294-296).

Full-time equivalent - FTE

Full-time  equivalent  corresponds  to  one  year's  work  by  one  person. Thus,  someone  who  normally  devotes  40%  of
his/her time to R&D and the rest to other activities (e.g. teaching, university administration or counselling) should be
counted as only 0.4 FTE.

Personnel in head count - HC

Head count corresponds to the number of individuals who are employed mainly or partly on R&D. For purposes of
comparison  between  different  regions  and  periods,  this  indicator  is  often  used  in  conjunction  with  employment  or
population variables.

Classifications

Institutional classification

Internal expenditure and R&D personnel are broken down by institutional sector, i.e. the sector in which the R&D is
performed. There are four main sectors::

- The business enterprise sector - BES; 
- The government sector - GOV; 
- The higher education sector - HES; 
- The private non-profit sector - PNP.

For definition of institutional sectors, please refer to general information.

Source of funds

R&D expenditure is subdivided into five sources of funds: Business Enterprise, Government, Higher Education, PNP
and Abroad — Frascati Manual, § 389 ff. Since the amounts from the Higher Education and PNP sectors are small,
they have been combined as "other national sources".

Field of sciences

Data on R&D expenditure and personnel may be broken down by six fields of science. The classification of field of
science  is  based  on  the  nomenclature  suggested  by  UNESCO:  Recommendation  concerning  the  International
Standardisation  of  Statistics  on  Science  and  Technology.  These  fields  are:  natural  sciences,  engineering  and
technology, medical sciences, agricultural sciences, social sciences and humanities.

Sector of activity

Data on R&D expenditure and personnel in the BES may be broken down by sector of economic activity on the basis
of the NACE Rev 1.1(see general methodologies). 

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 211

Size class of enterprise

Data on R&D personnel in the BES may be broken down by size class of enterprises. The size classes of enterprises
are:

- 0 persons employed,
- 1 to 9 persons employed,
- 10 to 49 persons employed,
- 50 to 249 persons employed,
- 250 to 499 persons employed, 
- 500 and more persons employed.

Type of cost

R&D expenditures include both current and capital expenditures.

- Current costs are composed of labour costs and other current costs. The current costs comprise annual
wages  and  salaries  and  all  associated  costs  or  fringe  benefits,  such  as  bonus  payments,  holiday  pay,
contributions to pension funds and other social security payments, payroll taxes, etc. The other current costs
comprise  non-capital  purchases  of  materials,  supplies  and  equipment  to  support  R&D  performed  by  the
statistical unit in a given year.

-Capital expenditures are the annual gross expenditures on fixed assets used in the R&D programmes of
statistical  units.  They  should  be  reported  in  full  for  the  period  when  they  took  place  and  should  not  be
registered as an element of depreciation.

Occupation

- Researchers: They are professionals engaged in the conception or creation of new knowledge, products,
processes, methods and systems, and in the management of the projects concerned (Frascati Manual, §
301).

-  Technicians  and  equivalent  staff:  they  are  persons  whose  main  tasks  require  technical  knowledge  and
experience  in  one  or  more  fields  of  engineering,  physical  and  life  sciences  or  social  sciences  and
humanities (Frascati Manual, § 306).

-  Other  supporting  staff:  This  includes  skilled  and  unskilled  craftsmen,  secretarial  and  clerical  staff
participating in R&D projects or directly associated with such projects (Frascati Manual, § 309).

Qualification

ISCED provides the basis for classifying R&D personnel by formal qualification. Six classes are recommended for the
purposes of R&D statistics but only four are usually collected::

- ISCED level 6: holders of university degrees at Doctorate level

- ISCED level 5A: holders of basic university degrees below the Doctorate level: 

- ISCED level 5B: holders of other tertiary level diplomas: 

-  Others:  this  includes  holders  of  other  post-secondary  non-tertiary  diplomas  (ISCED  level  4),  holders  of
diplomas of secondary education (ISCED level 3) and all those with secondary diplomas at less than ISCED
level 3 or with incomplete secondary qualifications or education not falling under any of the other classes.

Geographical coverage

These data are available for EU-27 Member States, candidate countries, Iceland, Norway, Switzerland, China, Japan,
Russia and the United States at the national level and for European countries at the regional level NUTS level 2 (see
general methodologies).

Aggregates

For both R&D expenditure and personnel, EU totals are calculated as the sum of the national data by sector. Where
data are missing, estimates are first made for the country in question, reference period, institutional sector or relevant
R&D variable, as appropriate. This method is not applied identically to the calculation of R&D personnel in head count
(HC).  The  estimates  for  R&D  personnel  in  full  time  equivalents  (FTE)  serve  as  a  basis  for  the  HC  calculation. An
FTE/HC ratio based on available FTE and HC personnel data at the national level is estimated for the EU aggregates,
by institutional sector and by year. This ratio is then applied to the FTE data to calculate the EU totals in HC.

- EU and EEA aggregates are estimated values,
- EEA: Liechtenstein is not included.



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PartM_Methodo:Methodo  20/12/2007  16:05  Page 212

Time series

Data are presented for the period 2000-2005. However, data series in NewCronos are available from 1981 onwards
with differences in terms of availability according to variables and institutional sectors. Not all years are complete, and
therefore the latest year available for each country is presented in the analysis.

Additional information on the methodology used may be found at Eurostat's reference database – NewCronos.

2.3 Human resources in science and technology

Statistics on Human Resources in Science and Technology — HRST — can improve our understanding of both the
demand for, and supply of highly qualified personnel. The data presented in this publication focus on two main aspects:
stocks and flows. The former serves to show the needs and the current situation of the labour force, and the latter
indicates  to  what  degree  this  demand  is  likely  to  be  met  in  the  future  by  looking  at  the  current  participation  and
graduation output of educational systems. 

The general recommendations for the collection of HRST data are laid down in the Canberra Manual (1), where HRST
is defined as a person fulfilling one of the following conditions: 

- successfully completed education at the third level in an S&T field of study (ISCED ’97 version levels 5a,
5b or 6)  or; 
-  not  formally  qualified  as  above  but  employed  in  an  S&T  occupation  where  the  above  qualifications  are
normally required (ISCO ’88 COM codes 2 or 3). 

The  conditions  of  the  above  educational  or  occupational  requirements  are  considered  according  to  internationally
harmonised standards: 

-  the  International  Standard  Classification  of  Education -  ISCED  -  giving  the  level  of  formal  education
achievement; 
- the International Standard Classification of Occupation - ISCO - detailing the type of occupation. 

Stocks

Stocks provide information on the number of HRST at a particular point in time. In this publication, stock data relate to
the employment status as well as the occupational and educational profiles of individuals in quarter 2 of any given year. 

HRST stock data and their derived indicators are extracted and built up using data from the EU Labour Force Survey.
The EU Labour Force Survey is based on a sample of the population. All results conform to Eurostat guidelines on
sample-size limitations and are therefore not published if the degree of sampling error is likely to be high and flagged
as unreliable if the degree of reliability is too small. 

The basic categories of HRST are as follows:  

Category

People that have/are

HRST: Human Resources in Science and 
Technology

• successfully completed education at the third level in a S&T 
field of study (ISCED '97 version levels 5a, 5b or 6); or

(cid:127) not formally qualified as above but are employed in a S&T 
occupation where the above qualifications are normally 
required (ISCO '88 COM codes 2 or 3).

(1) Manual on the Measurement of Human Resources devoted to S&T - Canberra Manual, OECD, Paris, 1994.

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PartM_Methodo:Methodo  20/12/2007  16:05  Page 213

Sub-categories of HRST

People belonging to HRST that have/are

HRSTO: Human Resources in Science 
and Technology — Occupation

HRSTE: Human Resources in Science 
and Technology — Education

HRSTC: Human Resources in Science
 and Technology — Core

S&E: Scientists and Engineers

HRSTU: Human Resources in Science 
and Technology — Unemployed

NHRSTU: Unemployed non-HRST

(cid:127) employed in a S&T occupation (ISCO '88 COM codes 2 or 
3).

(cid:127) successfully completed education at the third level in a S&T 
field of study (ISCED '97 version levels 5a, 5b or 6).

(cid:127) successfully completed education at the third level in a S&T 
field of study (ISCED '97 version levels 5a, 5b or 6) and

(cid:127) are employed in a S&T occupation (ISCO '88 COM codes 2 
or 3).

(cid:127) employed in “Physical, mathematical and engineering” 
occupations or “life science and health” occupations (ISCO 
'88 COM codes 21 and 22).
(cid:127) successfully completed education at the third level in a S&T 
field of study (ISCED '97 version levels 5a, 5b or 6) and are 
unemployed.
(cid:127) no education at the third level in a S&T field of study and are
unemployed.

Note  that  according  the  Canberra  Manual,  §  71,  the  seven  broad  fields  of  study  in  S&T  are:  natural  sciences,
engineering and technology, medical sciences, agricultural sciences, social sciences, humanities and other fields.

Inflows

HRST inflows are the number of people who do not fulfil any of the conditions for inclusion in HRST at the beginning
of a time period but gain at least one of them during the period. 

The number of graduates from a country’s higher education system represents the main inflow into the national stock
of HRST.

inflow  data  are  extracted 

HRST  education 
the
UNESCO/OECD/Eurostat questionnaire on education, which is based on the International Standard Classification of
Education  —  ISCED.  The  user  should  note  that  European  education  systems  differ  between  countries  and  that
duplications of degrees might exist for some countries.

the  Eurostat  Education  database  building  on 

from 

The International Standard Classification of Education - ISCED 97 

noitacudeyraitretfosleveL

A5levelDECSI

B5levelDECSI

6levelDECSI

eradnadesabyllaciteroehtylegraleratahtsemmargorp(cid:127)
yrtnegniniagrofsnoitacifilauqtneiciffusedivorpotdednetni
htiwsnoisseforpdnasemmargorphcraeserdecnavdaotni
.stnemeriuqerllikshgih

/lacitcarperomyllarenegeratahtsemmargorp(cid:127)
A5DECSInahtcificepsyllanoitapucco/lacinhcet
.semmargorp

otdaeltahtsemmargorpyraitretrofdevresersilevelsiht(cid:127)
ehT.noitacifilauqhcraeserdecnavdanafodrawaeht
lanigirodnaydutsdecnavdaotdetovederasemmargorp
.hcraeser



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PartM_Methodo:Methodo  20/12/2007  16:05  Page 214

This publication includes the following totals and sub-totals (for ISCED 1997 version):

eltiT

latoT

emantrohS

noitpircseD

tcejbus79‘DECSI
sedoc

latoT

ydutsfosdleiffomuS

gnireenignEdnaecneicS

E&S

dnascitamehtaM,secneicslacisyhP,secneicsefiL
gnireenignednagnireenignE,gnitupmoC,scitsitats
erutcetihcrA,gnissecorpdnagnirutcafunaM,sedart
.gnidliubdna

,25,84,64,44,24
.85,45

The International Standard Classification of Occupations - ISCO (S&T occupations)

eltiT

OCSI
sedoctcejbus

noitpircseD

slanoisseforP

2OCSI

lanoisseforpfolevelhgihaeriuqersksatniamesohwsnoitapucco(cid:127)
,secneicsefildnalacisyhpfosdleifehtniecneirepxednaegdelwonk
.seitinamuhdnasecneicslaicosro

etaicossAdnasnaicinhceT
slanoisseforp

3OCSI

dnaegdelwonklacinhceteriuqersksatniamesohwsnoitapucco(cid:127)
ro,secneicsefildnalacisyhpfosdleiferomroenoniecneirepxe
.seitinamuhdnasecneicslaicos

The user should note that the definition of S&T occupations deviates to a certain extent from the recommendations
laid down in the Canberra Manual. In addition to ISCO major groups 2 and 3, the Canberra Manual proposes also
considering  the  following  as  HRST:  production  and  operations  managers,  other  specialist  managers,  managers  of
small enterprises (ISCO 122, 123 and 131) who may work in the field of S&T. However, they are not included in the
term HRST as used here (but they are included in HRSTE if they have successfully completed third level education). 

The limitation applied here is justified, as a pilot survey conducted in 1995 tested the validity of the original definitions
for HRST and the results indicated that, for the EU, the inclusion of these particular managerial occupations distorted
the results significantly, due to variations between countries in the treatment and classification of managers.

Doctorate students

The  term  "doctorate"  defines  in  general  tertiary  education  programmes  which  lead  to  the  award  of  an  advanced
research degree (ISCED level 6), e.g. a doctorate in economics. 

For the definition of this level, the following criteria are relevant:

• Main criterion: It typically requires the submission of a thesis or dissertation of publishable quality which is
the product of original research and represents a significant contribution to knowledge.
• Subsidiary criterion: It prepares graduates for faculty posts in institutions offering ISCED 5A programmes,
as well as research posts in government, industry, etc.

The programmes are therefore devoted to advanced study and original research and are not based on coursework
only. They usually require 3-5 years of research and coursework, generally after a Master’s degree. Indicators of the
number of doctorate students therefore provide an idea of the degree to which countries will have researchers at the
highest level of education.  

Foreign students 

A  foreign  student  is  defined  as  someone  not  having  the  citizenship  of  the  country  in  which  he/she  is  educated.
Overestimation of non-national students may occur in some countries where permanently resident second generation
migrants with foreign nationalities constitute an important group of students.

Mobility

Data on job-to-job mobility can be defined as the movement of employed HRST from one job to another, during the
past 12-month period. They do not include inflows into the labour market from unemployment or inactivity. 
Employed HRST are those who have:

• successfully completed tertiary level education in an S&T field of study and are employed in any type of
occupation or
• are not formally qualified as above but are employed in an S&T occupation.

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Breakdown by sector of activity 

HRST  data  by  sector  of  activity  are  collected  according  to  the  statistical  classification  of  economic  activities  in  the
European  Community  —  NACE  Rev.  1.1.  For  further  information  on  the  sector  groups,  please  refer  to  the  General
Information part.

Breakdown by nationality 

HRST data by nationality are based according the citizenship of the person. It is defined as the particular legal bond
between an individual and his/her state acquired by birth or naturalisation whether by declaration, option, marriage or
other means in accordance with national legislation. The following aggregates are distinguished in this publication:

- Nationals: Persons having citizenship of the country of residence.
- Non-nationals: Persons having a citizenship different to the country of residence.

Time series 

Data are available in many countries from 1994 onwards, but differences exist and certain years are missing. Users
should note that the existence of data in this NewCronos domain also depends on their reliability. The guidelines on
the  sample  size  reliability  of  the  data  established  by  the  EU  LFS  are  applied  to  the  HRST  database.  Therefore,
breakdowns for which quality levels are considered insufficient are either flagged as not available or unreliable.

The readers should note that, in mid-2007, HRST results would be updated in Eurostat’s reference database by using
a slightly different methodology compared to the data shown in this Panorama. This new methodology will take into
account the changes in the EU LFS data collection process. In addition, the population of reference will be based the
age group 15-74 years old and not the entire population as is the case in this publication.

Sources

Additional information on the methodology used may be found at Eurostat's reference database 
(http://epp.eurostat.ec.europa.eu/portal/page?_pageid=0,1136250,0_45572555&_dad=portal&_schema=PORTAL)
under Science and Technology / Human Resources in Science & Technology.

2.4 Innovation 

Community Innovation Survey

At  European  level,  the  Community  Innovation  Survey  (CIS) data  are  the  main  source  of  information  to  study
innovation drivers and company behaviour towards innovation.

The  Community  Innovation  Survey  (CIS) is  a  survey  on  innovation  activity  in  enterprises  covering  EU  Member
States, candidate countries, Iceland and Norway.

The  data  are  collected  on  a  two-yearly  basis  (from  2004  onwards).  The  third  survey  (CIS  3)  was  implemented  in
2000/2001  in  most  countries. The  latest  survey  (CIS  4)  was  carried  out  in  25  Member  States,  candidate  countries,
Iceland and Norway in 2005, based on the reference year 2004.

In  order  to  ensure  comparability  across  countries,  Eurostat,  in  close  cooperation  with  the  EU  Member  States,
developed standard core questionnaires for CIS 3 and CIS 4, accompanied by a set of definitions and methodological
recommendations.

CIS 3 and CIS 4 are based on the Oslo Manual (2nd edition, 1997), which gives methodological guidelines and defines
the concept of innovation, and on Commission Regulation No 1450/2004. As the questionnaires for the two surveys
are not fully identical, the results are sometimes not fully comparable.

STATISTICAL UNITS

The main statistical unit for both CIS 3 and CIS 4 was the enterprise. 

The target population for CIS 3 and CIS 4 was the total population of enterprises (with 10 or more employees) engaged
primarily  in  the  following  market  activities:  mining  and  quarrying  (NACE  10-14),  manufacturing  (NACE  15-37),
electricity, gas and water supply (NACE 40-41), wholesale trade (NACE 51), transport, storage and communication
(NACE 60-64), financial intermediation (NACE 65-67), computer and related activities (NACE 72), architectural and
engineering activities (NACE 74.2) and technical testing and analysis (NACE 74.3). 

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CALCULATION OF THE EU-27 AGGREGATE

The  present  publication  only  shows  EU-27  data  as  percentages.  These  percentages  sum  up  available  data  for  all 
EU-27 Member States in the numerator and in the denominator, but the number of countries included in the numerator
and in the denominator is always the same. 

The notes below the figures and tables indicate the countries for which data are missing. 

TYPE OF SURVEY

Most Member States and other countries carried out CIS 3 and CIS 4 by means of a stratified sample survey, while a
number used a census or a combination of the two.

The enterprise size classes referred to in this publication are: 

• small: 10-49 employees; 
• medium-sized: 50-249 employees;
• large: 250+ employees. 

The economic activities covered by this publication are based on the NACE Rev. 1.1 classification. The two sectors
used are:

• industry, which includes mining and quarrying (NACE C), manufacturing (NACE D) and electricity, gas and
water supply (NACE E); and

• services, which includes NACE I and J plus NACE divisions 51, 72, 74.2 and 74.3.

The CIS 3 and CIS 4 data are organised in the Eurostat reference database following broadly the same structure as
the questionnaire. 

REFERENCE PERIOD

CIS 3 covered the observation period 1998-2000 inclusive, i.e. the three-year period from the beginning of 1998 to the
end of 2000. The reference period for CIS 3 was the year 2000. 

Norway  used  the  period  1999  to  2001  instead  of  1998  to  2000.  Spain  used  an  earlier  version  of  the  CIS  3  core
questionnaire  than  that  used  by  the  other  countries.  The  Czech  Republic,  Hungary,  Latvia,  Lithuania  and  Slovakia
chose  1999-2001  as  the  observation  period,  while  Romania  opted  for  2000-2002.  Slovenia  used  a  two-year
observation period (2001-2002) and Bulgaria 2001-2003.

The  data  for  Poland  are  generally  based  on  the  observation  periods  1998-2000  for  industry  and  1997-1999  for
services.

CIS 4 covered the observation period 2002-2004 inclusive, i.e. the three-year period from the beginning of 2002 to the
end of 2004. The reference period for CIS 4 was the year 2004.

All the countries covered collected data for this observation period; only the Czech Republic took 2003-2005 as the
observation period.

DEFINITION

OSLO MANUAL 1997

Innovation: a new or significantly improved product (good or service) introduced to the market or a new or significantly
improved  process  introduced  within  an  enterprise.  Innovations  are  based  on  the  results  of  new  technological
developments, new combinations of existing technology or utilisation of other knowledge acquired by the enterprise. 

Enterprises engaged in innovation activity (propensity to innovate): enterprises that introduce new or significantly
improved  products  (goods  or  services)  to  the  market  or  enterprises  that  implement  new  or  significantly  improved
processes.  Innovations  are  based  on  the  results  of  new  technological  developments,  new  combinations  of  existing
technology  or  utilisation  of  other  knowledge  acquired  by  the  enterprise. The  term  covers  all  types  of  innovator,  i.e.
product innovators, process innovators and enterprises with only ongoing and/or abandoned innovation activities.

Product innovation is introduction to the market of a new good or service or of a good or service with significantly
improved capabilities, such as improved software, user-friendliness, components or sub-systems. 

Process innovation is implementation of a new or significantly improved production process, distribution method or
support activity for goods or services. Purely organisational innovations are excluded.

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Organisational  innovation is  implementation  of  new  or  significant  changes  in  a  firm's  structure  or  management
methods that are intended to improve the firm’s use of knowledge, the quality of its goods and services or the efficiency
of its workflows. 

Marketing innovation is implementation of new or significantly improved designs or sales methods to increase the
appeal of goods and services or to enter new markets.

Intramural (in-house) R&D: Creative work undertaken within the enterprise to increase the stock of knowledge and
use it to devise new and improved products and processes (including software development). 

Extramural R&D: Same activities as above, but performed by other companies (including other enterprises within the
same group) or by public or private research organisations and purchased by the enterprise.

Acquisition of machinery, equipment and software: Acquisition of advanced machinery, equipment and computer
hardware or software to produce new or significantly improved products and processes. 

Acquisition of other external knowledge: Purchase or licensing of patents and non-patented inventions, know-how
and other types of knowledge from other enterprises or organisations.

European Innovation Scoreboard 2006

The  2006  version  is  the  sixth  edition  of  the  European  Innovation  Scoreboard  (EIS).  The  EIS  is  the  instrument
developed  by  the  European  Commission,  under  the  Lisbon  Strategy,  to  evaluate  and  compare  the  innovation
performance of the EU Member States. 

The  EIS  2006  includes  innovation  indicators  and  trend  analyses  for  the  EU-25  Member  States,  plus  the  two  new
Member  States:  Bulgaria  and  Romania,  as  well  as  for  Croatia,  Turkey,  Iceland,  Norway,  Switzerland,  the  US  and
Japan. 

The Annex includes tables with definitions as well as comprehensive data sheets for every country. The EIS report and
its  annexes,  accompanying  thematic  papers  and  the  indicators’  database  are  available  on  this  website.
http://www.proinno-europe.eu/. 

Various  documents  on  the  European  Innovation  Scoreboard  2006  and  the  scoreboard  itself  can  be  found  at  this
address http://www.trendchart.org/scoreboards/scoreboard2006/scoreboard_papers.cfm.

The Methodology Report discusses the indicators that may be added in the next edition of the European Innovation
Scoreboard.

2.5 Patents

Patents reflect part of a country’s inventive activity. Patents also show the country’s capacity to exploit knowledge and
translate it into potential economic gains. In this context, indicators based on patent statistics are widely used to assess
the inventive performance of the country or regions.

The grounds for the assumption that a patent represents a codification of inventive activity rely on the novelty, utility
and inventiveness that an invention requires in order to be patented. On the basis of this assumption, Eurostat collects
patent statistics to build up indicators of R&D output.

In 2005, just one single raw database – mainly compiled on the basis of input from the European Patent Office (EPO),
the  US  Patent  and  Trademark  Office  (USPTO)  and  the  Japanese  Patent  Office  (JPO)  –  was  used  to  produce  an
extended set of tables and indicators on Eurostat's webpage. The same will also be done in the years to come. The
aggregated  patent  statistics  are  produced  using  a  raw  data  set  delivered  by  the  OECD.  This  raw  data  set  will  be
replaced by PATSTAT for the next data productions. 

Since 2005 Eurostat has produced patent statistics using the priority year of the application and not, as previously, the
year of filing. However, the data values are similar. These data are in general less extensive than the data released by
Eurostat  before  2005.  This  is  because  Eurostat  takes  into  consideration  all  PCT  applications  filed  to  the  EPO  (i.e.
applications made in accordance with the procedure under the Patent Cooperation Treaty), whereas the OECD data
sets do so only in part. The data produced provide a better reflection of the innovation and R&D performance of an
economy.

Since 2004 the interinstitutional Patent Statistics Task Force has developed the concept of a worldwide patent statistics
database (PATSTAT). PATSTAT has to be understood as a single patent statistics raw database, held by the European
Patent  Office  (EPO)  and  developed  in  cooperation  with  the  World  Intellectual  Property  Organisation  (WIPO),  the
OECD and Eurostat. PATSTAT should fulfil the user needs of the various international organisations which will use this
raw database for production. Designed to be sustainable over time, PATSTAT – which has been operational since 2006



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–  concentrates  on  raw  data,  leaving  the  'production'  of  indicators  mainly  to  PATSTAT  users,  such  as  the  OECD,
Eurostat and others.

At  the  end  of  2007  the  patent  data  will  be  updated  in  Eurostat’s  reference  database,  with  data  entirely  based  on
PATSTAT but following a slightly different methodology compared to the data shown in this Statistical Book. This new
methodology, which is also used by the OECD includes only EPO patent applications to the EPO (EPO direct) and
PCT patent applications designating the EPO as the receiving office which was involved in the regional phase. The
PCT patent applications which are in the international phase are no longer taken into account at this stage. This is
because they were already included in the calculations of the indicators in the previous years, and so the new data are
lower than the data shown before. For all further details, please see the Eurostat metadata on patent statistics posted
on the webpage.

Eurostat’s patents database contains data on patent applications to the European Patent Office (EPO) and patents
granted by the United States Patent and Trademark Office (USPTO). In addition, Chapter 6 of this publication looks at
data on triadic patent families. Owing to methodological differences in the manner of processing the data, no cross
comparisons are advisable between the EPO, USPTO and patent family data. Methodological issues specific to each
type of data are explained below.

Patent applications to the EPO by priority year

Data  in  Eurostat’s  EPO  database  refer  to  patent  applications  to  the  EPO  by  priority  year,  which  include  both
applications  filed  directly  under  the  European  Patent  Convention  (EPC)  and  applications  filed  under  the  Patent 
Co-operation Treaty (PCT) and designating the EPO (Euro-PCT) for protection. The regional (national) distribution of
patent applications is according to the inventor’s place of residence. If an application has more than one inventor, the
application is divided equally among all of them and subsequently among their regions, thus avoiding double counting.

EPO data are shown from 1993 to 2003; longer time series are available, but more recent data are not comparable,
as they are incomplete due to the patenting procedure.

For  further  information  on  definitions  and  explanatory  notes  concerning  EPO  patent  data  see  Eurostat’s  reference 
database  NewCronos:  http://epp.eurostat.ec.europa.eu/portal/page?_pageid=0,1136250,0_45572555&_dad=
portal&_schema=PORTAL under Science and Technology / Patent statistics / Patent applications to EPO by priority
year.

Patents granted by the USPTO by priority year

Data on patents granted by the USPTO refer to patents granted, and not to applications as is the case for data coming
from the EPO. Data in these two collections are therefore not comparable.

USPTO  data  are  available  from  1989  to  2000;  longer  time  series  are  available,  but  more  recent  data  are  not
comparable as they are incomplete due to the patenting procedure.

For  further  information  on  definitions  and  explanatory  notes  concerning  EPO  patent  data  see  Eurostat’s  reference 
database  NewCronos: 
http://epp.eurostat.ec.europa.eu/portal/page?_pageid=0,1136250,0_45572555&_dad
=portal&_schema=PORTAL under  Science  and  Technology  /  Patent  statistics  /  Patents  granted  by  the  USPTO  by
priority year.

Triadic patent families by priority year

A patent family is defined as a set of patents taken in various countries for protecting the same invention, i.e. related
patents are grouped together in a single record to derive a unique patent family. A patent is a member of a triadic patent
family if and only if it has been applied for and filed at the European Patent Office (EPO) and the Japanese Patent
Office (JPO) and if it has been granted by the US Patent and Trademark Office (USPTO). Patent families, as opposed
to patents, are intended to improve international comparability (the home advantage is removed; the patents are more
homogeneous in terms of their value).

Data on triadic patent families are presented by priority year, i.e. the year of the first international filing of a patent. This
compounds the disadvantage of traditional patent counts with respect to timeliness, and therefore latest available data
refer to 2000 only.

For further methodological notes please refer to: OECD triadic patent families, OECD, 2004.

are 

http://epp.eurostat
reference 
Metadata 
.ec.europa.eu/portal/page?_pageid=0,1136250,0_45572555&_dad=portal&_schema=PORTAL under  Science  and
Technology Patent statistics / Triadic patent families by earliest priority year.

database  NewCronos: 

Eurostat’s 

available 

in 

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Patent Cooperation Treaty

The Patent Cooperation Treaty (PCT) provides the possibility to seek patent rights in a large number of countries by
filing a single international application with a single patent office, and is increasingly being used for patent applications.
The PCT procedure consists of two main phases: (a) an “international phase”; and (b) a PCT “national/regional phase”.
In order to measure inventive activity, Eurostat has included both of these phases of PCT applications.

European Patent Convention

The European Patent Convention (EPC) is the convention on the granting of European patents. The first version of the
convention entered into force on 5 October 1973. The latest version, from April 2006, is the twelfth.

Costs - mainly translation costs - are one of the problems of patent applications to the EPO. The official languages of
the  EPO  are  governed  by  Article  14  Languages  of  the  European  Patent  Office  (see  http://www.european-patent-
office.org/legal/epc/e/ar14.html#A14 ) and translations by Article 65 of the EPC Translation of the specification of the
European patent (see http://www.european-patent-office.org/legal/epc/e/ar65.html#A65 ).

Foreign ownership

Data  on  foreign  ownership  measure  the  number  of  patents  invented  within  (or  applied  for  by)  a  given  country  that
involve at least one foreign applicant (or a foreign inventor).

To  make  this  definition  clearer  let  us  take  as  an  example  a  patent  with  three  inventors  (one  French  resident,  one
German resident and one American resident) and two applicants (one German resident and one American resident).
Combining  the  resident  countries  of  inventors  and  applicants  there  are  six  partnerships,  of  which  four  are  foreign,
because they involve two different resident countries, and two are national.

International Patent Classification

Patent data follow the International Patent Classification (IPC), which assigns an invention to one or more IPC-classes
according to its function or intrinsic nature or its field of application. If a patent is assigned to more than one IPC code,
only the first listed is taken into account. Only the first four digits of the IPC are used for breakdowns and aggregations.

SECTION A - HUMAN NECESSITIES

AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING

BAKING; EDIBLE DOUGHS
BUTCHERING; MEAT TREATMENT; PROCESSING POULTRY OR FISH
FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
TOBACCO; CIGARS; CIGARETTES; SMOKERS' REQUISITES

WEARING APPAREL
HEADWEAR
FOOTWEAR
HABERDASHERY; JEWELLERY
HAND OR TRAVELLING ARTICLES
BRUSHWARE
FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN

AGRICULTURE
A 01
FOODSTUFFS; TOBACCO
A 21
A 22
A 23
A 24
PERSONAL OR DOMESTIC ARTICLES
A 41
A 42
A 43
A 44
A 45
A 46
A 47
GENERAL
HEALTH; AMUSEMENT
A 61
A 62
A 63

MEDICAL OR VETERINARY SCIENCE; HYGIENE
LIFE-SAVING; FIRE-FIGHTING
SPORTS; GAMES; AMUSEMENTS

SECTION B - PERFORMING OPERATIONS; TRANSPORTING

SEPARATING; MIXING
PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL 
B 01
CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
B 02
B 03
SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC
OR  ELECTROSTATIC  SEPARATION  OF  SOLID  MATERIALS  FROM  SOLID  MATERIALS  OR  FLUIDS;  SEPARATION  BY  HIGH-
VOLTAGE ELECTRIC FIELDS 
B 04
B 05
IN GENERAL
B 06
B 07

CENTRIFUGAL APPARATUS OR MACHINES FOR CARRYING-OUT PHYSICAL OR CHEMICAL PROCESSES
SPRAYING OR ATOMISING IN GENERAL; APPLYING LIQUIDS OR OTHER FLUENT MATERIALS TO SURFACES,

GENERATING OR TRANSMITTING MECHANICAL VIBRATIONS IN GENERAL
SEPARATING SOLIDS FROM SOLIDS; SORTING

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CLEANING
DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL 

MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING
CASTING; POWDER METALLURGY
MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR 
GRINDING; POLISHING
HAND  TOOLS;  PORTABLE  POWER-DRIVEN  TOOLS;  HANDLES  FOR  HAND  IMPLEMENTS;  WORKSHOP

HAND CUTTING TOOLS; CUTTING; SEVERING
WORKING OR PRESERVING WOOD OR SIMILAR MATERIAL; NAILING OR STAPLING MACHINES IN GENERAL
WORKING CEMENT, CLAY, OR STONE
WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
PRESSES
MAKING PAPER ARTICLES; WORKING
LAYERED PRODUCTS

B 08
B 09
SHAPING
B 21
B22
B 23
B 24
B 25
EQUIPMENT; MANIPULATORS
B 26
B 27
B 28
B 29
B 30
B 31
B 32
PRINTING
B 41
B 42
B 43
B 44
TRANSPORTING
B 60
B 61
B 62
B 63
B 64
B 65
B 66
B 67
B 68
MICRO-STRUCTURAL TECHNOLOGY; NANO-TECHNOLOGY
B 81
B 82

MICRO-STRUCTURAL TECHNOLOGY
NANO-TECHNOLOGY

PRINTING; LINING MACHINES; TYPEWRITERS; STAMPS
BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
WRITING OR DRAWING IMPLEMENTS; BUREAU ACCESSORIES
DECORATIVE ARTS

VEHICLES IN GENERAL
RAILWAYS
LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
AIRCRAFT; AVIATION; COSMONAUTICS
CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
HOISTING; LIFTING; HAULING
OPENING OR CLOSING BOTTLES, JARS OR SIMILAR CONTAINERS; LIQUID HANDLING 
SADDLERY; UPHOLSTERY

SECTION C - CHEMISTRY; METALLURGY

DYES;  PAINTS;  POLISHES;  NATURAL  RESINS;  ADHESIVES;  MISCELLANEOUS  COMPOSITIONS;

PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS;

INORGANIC CHEMISTRY 
TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
GLASS; MINERAL OR SLAG WOOL
CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES 
FERTILISERS; MANUFACTURE THEREOF 
EXPLOSIVES; MATCHES
ORGANIC CHEMISTRY 
ORGANIC  MACROMOLECULAR  COMPOUNDS;  THEIR  PREPARATION  OR  CHEMICAL  WORKING-UP;

CHEMISTRY
C 01
C 02
C 03
C 04
C 05
C 06
C 07
C 08
COMPOSITIONS BASED THEREON 
C 09
MISCELLANEOUS APPLICATIONS OF MATERIALS
C 10
LUBRICANTS; PEAT
C 11
DETERGENTS; CANDLES
C 12
ENGINEERING
C 13
C 14
METALLURGY
METALLURGY OF IRON
C 21
METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS 
C 22
C 23
COATING  METALLIC  MATERIAL;  COATING  MATERIAL  WITH  METALLIC  MATERIAL  ;  CHEMICAL  SURFACE
TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING,
BY  ION  IMPLANTATION  OR  BY  CHEMICAL  VAPOUR  DEPOSITION,  IN  GENERAL  ;  INHIBITING  CORROSION  OF  METALLIC
MATERIAL OR INCRUSTATION IN GENERAL 
C 25
C 30

ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR 
CRYSTAL GROWTH

BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC

ANIMAL  OR  VEGETABLE  OILS,  FATS,  FATTY  SUBSTANCES  OR  WAXES;  FATTY  ACIDS  THEREFROM;

SUGAR INDUSTRY 
SKINS; HIDES; PELTS; LEATHER

SECTION D - TEXTILES; PAPER

TEXTILES OR FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
D 01
D 02
D 03
D 04

NATURAL OR ARTIFICIAL THREADS OR FIBRES; SPINNING 
YARNS; MECHANICAL FINISHING OF YARNS OR ROPES; WARPING OR BEAMING
WEAVING
BRAIDING; LACE-MAKING; KNITTING; TRIMMINGS; NON-WOVEN FABRICS

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D 05
D 06
FOR
D 07
PAPER
D 21

SEWING; EMBROIDERING; TUFTING
TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED

ROPES; CABLES OTHER THAN ELECTRIC

PAPER-MAKING; PRODUCTION OF CELLULOSE

SECTION E - FIXED CONSTRUCTIONS

BUILDING
E 01
E 02
E 03
E 04
E 05
E 06
EARTH OR ROCK DRILLING; MINING
E 21

CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL-SHIFTING
WATER SUPPLY; SEWERAGE
BUILDING
LOCKS; KEYS; WINDOW OR DOOR FITTINGS; SAFES
DOORS, WINDOWS, SHUTTERS, OR ROLLER BLINDS, IN GENERAL; LADDERS

EARTH OR ROCK DRILLING; MINING

SECTION F - MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING

STORING OR DISTRIBUTING GASES OR LIQUIDS 

FLUID-PRESSURE ACTUATORS; HYDRAULICS OR PNEUMATICS IN GENERAL
ENGINEERING  ELEMENTS  OR  UNITS;  GENERAL  MEASURES  FOR  PRODUCING  AND  MAINTAINING

MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
MACHINES  OR  ENGINES  FOR  LIQUIDS;  WIND,  SPRING,  WEIGHT,  OR  MISCELLANEOUS  MOTORS;

ENGINES OR PUMPS
F 01
F 02
F 03
PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
F 04
POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS 
ENGINEERING IN GENERAL
F 15
F 16
EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
F 17
LIGHTING; HEATING
F 21
F 22
F 23
F 24
F 25
SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION OR SOLIDIFICATION OF GASES
F 26
F 27
F 28
WEAPONS; BLASTING
F 41
F 42

LIGHTING
STEAM GENERATION 
COMBUSTION APPARATUS; COMBUSTION PROCESSES
HEATING; RANGES; VENTILATING 
REFRIGERATION  OR  COOLING;  COMBINED  HEATING  AND  REFRIGERATION  SYSTEMS;  HEAT  PUMP

DRYING
FURNACES; KILNS; OVENS; RETORTS 
HEAT EXCHANGE IN GENERAL 

WEAPONS
AMMUNITION; BLASTING

SECTION G - PHYSICS

MEASURING; TESTING
OPTICS
PHOTOGRAPHY;  CINEMATOGRAPHY;  ANALOGOUS  TECHNIQUES  USING  WAVES  OTHER  THAN  OPTICAL

INSTRUMENTS
G 01
G 02
G 03
WAVES; ELECTROGRAPHY; HOLOGRAPHY 
HOROLOGY
G 04
CONTROLLING; REGULATING
G 05
COMPUTING; CALCULATING; COUNTING 
G 06
CHECKING-DEVICES
G 07
SIGNALLING 
G 08
EDUCATING; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
G 09
MUSICAL INSTRUMENTS; ACOUSTICS
G 10
INFORMATION STORAGE
G 11
G 12
INSTRUMENT DETAILS
NUCLEONICS
G 21

NUCLEAR PHYSICS; NUCLEAR ENGINEERING

SECTION H - ELECTRICITY

H 01
H 02
H 03
H 04
H 05

BASIC ELECTRIC ELEMENTS
GENERATION, CONVERSION, OR DISTRIBUTION OF ELECTRIC POWER
BASIC ELECTRONIC CIRCUITRY
ELECTRIC COMMUNICATION TECHNIQUES
ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR

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IPC-NACE correspondence

The  breakdown  by  NACE  sector  codes  is  based  on  the  IPC-NACE  concordance  tables  created  by  the  Fraunhofer
Institute for Systems and Innovation Research in Karlsruhe (Germany). For further information on the methodology
used 
reference  database  NewCronos:  http://epp.eurostat.ec.europa.eu/portal/page?
_pageid=0,1136250,0_45572555&_dad=portal&_schema=PORTAL under Science and Technology / Patent statistics.

see  Eurostat’s 

The easiest way to explain the link between the two classifications is to give an example. Let us take two patents from
the IPC sector A – Human necessities. The first patent has the code IPC A24B (Manufacture or preparation of tobacco
for smoking, chewing; tobacco; snuff). With the help of the concordance tables this patent is converted to NACE code
DA  (Manufacture  of  food  products,  beverages  and  tobacco). The  second  patent  has  the  code A24C  (Machines  for
making cigars or cigarettes). The NACE code for the second patent is, after conversion, DK (Manufacture of machinery
and equipment n.e.c.).

NACE-ISIC correspondence

Table 6.7 in Chapter 6 of the publication shows patents by NACE sectors. The table below gives the correspondence
between these NACE sectors and the divisions of the International Standard Industrial Classification (ISIC). ISIC codes
are currently used at the world-wide level, whereas the NACE codes are used at the EU level.

NACE Rev. 1.1

ISIC Rev. 3.1

DA

Manufacture of food products, beverages and tobacco

DB

DC

DD

DE

DF

DG

DH

DI

DJ

DK

Manufacture of textiles and textile products

Manufacture of leather and leather products

Manufacture of wood and wood products

Manufacture of pulp, paper and paper products; publishing and printing

Manufacture of coke, refined petroleum products and nuclear fuel

Manufacture of chemicals, chemical products and man-made fibres

Manufacture of rubber and plastic products

Manufacture of other non-metallic mineral products

Manufacture of basic metals and fabricated metal products

Manufacture of machinery and equipment n.e.c.

DL

Manufacture of electrical and optical equipment

DM

Manufacture of transport equipment

DN

Manufacturing n.e.c.

Technological fields

D 15

D 16

D 17

D 18

D 19

D 20

D 21

D 22

D 23

D 24

D 25

D 26

D 27

D 28

D 29

D 30

D 31

D 32

D 34

D 35

D 36

D 37

Manufacture of food products and beverages

Manufacture of tobacco products

Manufacture of textiles

Manufacture of wearing apparel; dressing and dyeing of fur

Tanning and dressing of leather; manufacture of luggage, handbags, 
saddlery, harness and footwear 

Manufacture of wood and of products of wood and cork, except furniture; 
manufacture of articles of straw and plaiting materials 

Manufacture of paper and paper products

Publishing, printing and reproduction of recorded media

Manufacture of coke, refined petroleum products and nuclear fuel 

Manufacture of chemicals and chemical products 

Manufacture of rubber and plastics products

Manufacture of other non-metallic mineral products 

Manufacture of basic metals

Manufacture of fabricated metal products, except machinery and equipment

Manufacture of machinery and equipment n.e.c. 

Manufacture of office, accounting and computing machinery 

Manufacture of electrical machinery and apparatus n.e.c.

Manufacture of radio, television and communication equipment and 
apparatus

Manufacture of motor vehicles, trailers and semi-trailers 

Manufacture of other transport equipment 

Manufacture of furniture; manufacturing n.e.c. 

Recycling

1.  Biotechnology: The  OECD  definition  is  the  application  of  Science  & Technology  to  living  organisms  as  well  as
parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and
services. An  indicative  list  of  technologies  is  DNA,  Proteins  and  molecules  (the  functional  blocks),  cell  and  tissue
culture and engineering, process biotechnologies, sub-cellular organisms (gene therapy, viral vectors). 

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Patent applications/patents granted with the IPC codes (7th edition, 2000) listed below are aggregated to calculate the
indicator “biotechnology patent applications/patents granted”: 

A01H1/00, A01H4/00, A61K38/00, A61K39/00, A61K48/00, 
C02F3/34, C07G(11/00, 13/00, 15/00), C07K(4/00, 14/00, 16/00, 17/00, 19/00), C12M, C12N, C12P, C12Q,
C12S, 
G01N27/327, G01N33/(53*, 54*, 55*, 57*, 68, 74, 76, 78, 88, 92).

2. High tech: Based on the data on patent applications/patents granted by IPC codes (7th edition, 2000), Eurostat has
calculated data on patent applications/patents granted in high-technology fields. 

The aggregation “high-tech patents” is made up as follows in the IPC. For each of the six high-tech groups the patents
with the IPC codes in brackets are used.

1. Aviation - AVI [B64B, B64C, B64D, B64F, B64G]; 
2. Computer and automated business equipment - CAB [B41J, G06C, G06D, G06E, G06F, G06G, G06J, 
G06K, G06M, G06N, G06T, G11C]; 
3. Communication technology - CTE [H04B, H04H, H04J, H04K, H04L, H04M, H04N, H04Q, H04R, H04S]; 
4. Lasers - LSR [H01S]; 
5. Micro-organism and genetic engineering - MGE [C12M, C12N, C12P, C12Q]; 
6. Semi-conductors - SMC [H01L].

3. Information and Communication Technologies (ICT): The IPC codes (7th edition, 2000) listed behind each ICT
sub-category are added up for the aggregation of each ICT-sub-category: 

1. Telecommunications [G01S, G08C, G09C, H01P, H01Q, H01S3/(025, 043, 063, 067, 085, 0933, 0941,
103, 133, 18, 19, 25), H1S5, H03B, H03C, H03D, H03H, H03M, H04B, H04J, H04K, H04L, H04M, H04Q]; 
2. Consumer electronics [G11B, H03F, H03G, H03J, H04H, H04N, H04R, H04S]; 
3. Computers, office machinery [B07C, B41J, B41K, G02F, G03G, G05F, G06, G07, G09G, G10L, G11C,
H03K, H03L]; 
4. Other ICT [G01B, G01C, G01D, G01F, G01G, G01H, G01J, G01K, G01L, G01M, G01N, G01P, G01R,
G01V, G01W, G02B6, G05B, G08G, G09B, H01B11, H01J(11/, 13/, 15/, 17/, 19/, 21/, 23/, 25/, 27/, 29/, 31/,
33/, 40/, 41/, 43/, 45/), H01L]. 

2.6 High-tech industries and knowledge based service

Enterprises in high-tech industries and knowledge-intensive services

Indicators on enterprises in high-tech industries and knowledge-intensive services are extracted and aggregated on
the basis of the NACE (see general information) using data from the Structural Business Statistics — SBS.

These data are available for EU-27 Member States, candidate countries, Norway and Switzerland at the national level.
The data are aggregated using the definition of high-tech industries and knowledge-intensive services based on NACE
rev. 1.1 at 3-digit level (see General information).

Definition of indicators

Value added at factor cost is the gross income from operating activities after adjusting for operating subsidies and
indirect  taxes.  It  can  be  calculated  from  turnover,  plus  capitalised  production,  plus  other  operating  income,  plus  or
minus the changes in stocks, minus the purchases of goods and services, minus other taxes on products which are
linked to turnover but not deductible, minus the duties and taxes linked to production. Value added at factor cost is
calculated "gross", as value adjustments (such as depreciation) are not subtracted. 

Labour productivity refers to the value added at factor cost per person employed.

Production value measures the amount actually produced by the unit, based on sales, including changes in stocks
and the resale of goods and services. The production value is defined as turnover, plus or minus the changes in stocks
of finished products, work in progress and goods and services purchased for resale, minus the purchase of goods and
services  for  resale,  plus  capitalised  production,  plus  other  operating  income  (excluding  subsidies).  Income  and
expenditure classified as financial or extra-ordinary in company accounts is excluded from production value. Included
in purchases of goods and services for resale are services purchased in order to be rendered to third parties in the
same condition.

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Gross  investment  in  tangible  goods is  defined  as  investment  in  all  tangible  goods  during  the  reference  period.
Included are new and existing tangible capital goods, whether bought from third parties or produced for own use (i.e.
Capitalised production of tangible capital goods), having a useful life of more than one year including non-produced
tangible goods such as land. Investment in intangible and financial assets is excluded.

Gross investment in machinery and equipment covers machinery (office machines etc.), special vehicles used on
the  premises,  other  machinery  and  equipment,  all  vehicles  and  boats  used  off  the  premises,  i.e.  motor  cars,
commercial vehicles and lorries as well as special vehicles of all types, boats, railway wagons, etc. acquired new or
second hand during the reference period. Machinery and equipment acquired through restructuring (such as mergers,
take-overs,  break-ups,  split-offs)  are  excluded.  Also  included  are  all  additions,  alterations,  improvements  and
renovations  which  prolong  the  service  life  or  increase  the  productive  capacity  of  these  capital  goods.  Current
maintenance costs are excluded.

Venture capital investment

Venture Capital Investment (VCI) is defined as private equity raised for investment in companies. Management buy-
outs, management buy-ins, and venture purchase of quoted shares are excluded. 
Data are broken down into two investment stages: 

- Early stage (seed + start-up) and 
- Expansion and replacement (expansion and replacement capital).

Venture capital is expressed as a percentage of GDP (Gross domestic product at market prices), which is defined in
accordance with the European System of national and regional Accounts in the Community (ESA 95).

The data cover EU-15, EU-27 Member States (except for Bulgaria, Estonia, Cyprus, Latvia, Lithuania, Luxembourg,
Malta and Romania), Norway and Switzerland. 

The  basic  data  are  provided  by  the  European  Private  Equity  and  Venture  Capital  Association  (EVCA).  For  more
information on venture capital, please refer to: http://www.evca.com 

Definition of indicators

Seed is  defined  as  financing  provided  to  research,  assess  and  develop  an  initial  concept  before  a  business  has
reached the start-up phase.

Start-up  is  defined  as  financing  provided  for  product  development  and  initial  marketing,  manufacturing,  and  sales.
Companies may be in the process of being set up or may have been in business for a short time, but have not sold
their product commercially. 

Expansion is defined as financing provided for the growth and expansion of a company which is breaking even or
trading  profitably.  Capital  may  be  used  to  finance  increased  production  capacity,  market  or  product  development,
and/or provide additional working capital. It includes bridge financing for the transition from private to public quoted
company, and rescue/turnaround financing. 

Replacement capital is defined as purchase of existing shares in a company from another private equity investment
organisation or from another shareholder or shareholders. It includes refinancing of bank debt. 

High-tech trade

Indicators  on  high-tech  trade  are  extracted  and  aggregated  on  the  basis  of  the  Standard  International  Trade
Classification (SITC Rev3) using data from COMEXT and from COMTRADE databases. 

These data are available for EU-27 Member States, candidate countries, Iceland, Norway, Switzerland, China, Japan
and the United States. There are no data for Luxembourg and Belgium separately before 1999. Hence, both countries
are treated together previous to that year. EU aggregates exclude intra-EU trade.

High technology groups of products are defined according to the R&D intensity of products. Nine SITC Rev3 groups
of products are considered as high-tech. These are: 

- Aerospace, 
- Computers-Office machinery, 
- Electronics-Telecommunications,
- Pharmacy, 
- Scientific instruments, 
- Electrical machinery, 
- Chemistry, 
- Non-electrical machinery and 
- Armament.

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Employment in high-tech industries and knowledge-intensive services

Data on employment in high-tech industries and knowledge-intensive services are extracted and aggregated on the
basis of the NACE (see General information) using data from the Community Labour Force Survey — CLFS.

These  data  are  available  for  EU-27  Member  States,  candidate  countries,  Iceland,  Norway  and  Switzerland  both  at
national level and at regional NUTS level 2 (see General information). These are aggregated using the definition of
high-tech  industries  and  knowledge-intensive  services  based  on  NACE  rev.  1.1  at  2-digit  level  (see  General
information).

2.7 The 2006 EU industrial R&D investment scoreboard

The 2006 EU industrial R&D investment scoreboard has been jointly prepared by the Directorate-General for Research
(DG-RTD) and the Joint Research Centre (JRC). It reports on the worldwide research and development of 2 000 top
companies. The Scoreboard was compiled from companies' annual reports and accounts with the reference date of
1st  August  of  each  year.  In  order  to  maximise  completeness  and  avoid  double  counting,  the  consolidated  group
accounts  of  the  ultimate  parent  company  are  used.  Companies  which  are  subsidiaries  of  another  company  are  not
listed  separately.  Where  consolidated  group  accounts  of  the  ultimate  parent  company  are  not  available,  however,
subsidiaries are included.

Definitions of indicators

1. Research and Development (R&D) investment in the Scoreboard is the cash investment funded by the companies
themselves. It excludes R&D undertaken under contract for customers such as governments or other companies. It
also excludes the companies' share of any associated company or joint venture R&D investment. Being that disclosed
in the annual report and accounts, it is subject to the accounting definitions of R&D. For example, a definition is set
out in International Accounting Standard (IAS) 38 “Intangible assets” and is based on the OECD “Frascati” manual. 

Research is defined as original and planned investigation undertaken with the prospect of gaining new scientific or
technical knowledge and understanding. Expenditure on research is recognised as an expense when it is incurred.

Development is the application of research findings or other knowledge to a plan or design for the production of new
or substantially improved materials, devices, products, processes, systems or services before the start of commercial
production or use. Development costs are capitalised when they meet certain criteria and when it can be demonstrated
that the asset will generate probable future economic benefits. Where part or all of R&D costs have been capitalised,
the additions to the appropriate intangible assets are included to calculate the cash investment and any amortisation
eliminated. 

2. Sales follow the usual accounting definition of sales, excluding sales taxes and shares of sales of joint ventures &
associates. For banks, sales are defined as the “Total (operating) income” plus any insurance income. For insurance
companies, sales are defined as “Gross premiums written” plus any banking income. 

3. R&D intensity is the ratio between R&D investment and net sales of a given company or group of companies. At
the aggregate level, R&D intensity is calculated only by those companies for which data exist for both R&D and net
sales in the specified year. The calculation of R&D intensity in the Scoreboard is different from that in official statistics,
e.g. BERD, where R&D intensity is based on value added instead of net sales. 

4. Operating profit is calculated as profit (or loss) before taxation, plus net interest cost (or minus net interest income)
and government grants, less gains (or plus losses) arising from the sale/disposal of businesses or fixed assets. 

5. One-year growth is simple growth over the previous year, expressed as a percentage: 1yr growth = 100*((C/B)-1);
where C = current year amount, and B = previous year amount. 1yr growth is calculated only if data exist for both the
current and previous year. At the aggregate level, 1yr growth is calculated by aggregating only those companies for
which data exist for both the current and previous year. 

6. Three-year growth is the compound annual growth over the previous three years, expressed as a percentage: 3
yr growth = 100*(((C/B)^(1/t))-1); where C = current year amount, B = base year amount (where base year = current
year - 3), and t = number of time periods (= 3). 3yr growth is calculated only if data exist for the current and base years.
At  the  aggregate  level,  3yr  growth  is  calculated  by  aggregating  only  those  companies  for  which  data  exist  for  the
current and base years. 

7. Capital expenditure (Capex) is expenditure used by a company to acquire or upgrade physical assets such as
equipment, property, industrial buildings. In accounts capital expenditure is added to an asset account (i.e. capitalised),
thus increasing the asset's base. It is disclosed in accounts as additions to tangible fixed assets 

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8. Number of employees is the total consolidated average employees or year end employees if average not stated.

9.  R&D  per  employee is  the  simple  ratio  of  R&D  investment  over  employees.  At  the  aggregate  level,  R&D  per
employee and the other non-growth statistics are calculated by aggregating only those companies for which data exist
for both the numerator and the denominator. 

10. R&D employees is the number of employees engaged in R&D activities as stated in the annual report. 

11.  Market  capitalisation is  the  share  price  multiplied  by  the  number  of  shares  issued  at  a  given  date.  Market
capitalisation  data  have  been  extracted  from  both  the  Financial  Times  London  Share  Service  and  Reuters.  These
reflect  the  market  capitalisation  of  each  company  at  the  close  of  trading  on  4  August  2006.  The  gross  market
capitalisation amount is used to take account of those companies for which not all the equity is available on the market.
Companies not listed on a recognised stock exchange have been distinguished separately by the use of italics. 

12. Market Spread details sales by destination, distinguishing between Europe, North America (USA and Canada) and
the Rest of the World. The definition of Europe is subject to the definitions adopted by the individual companies. In
cases in which companies have defined a market spread area as EMEA (Europe, Middle East, Africa), this has been
allocated to Europe. When a company has not clearly disclosed the turnover region North America but Americas, this
has been allocated to North America. 

13. Industry sectors in are based on the ICB Industry Classification System. The level of disaggregation is generally
the three-digit level unless indicated otherwise.

More information is available at http://iri.jrc.es/research/scoreboard_2006.htm.

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Annexes

Abbreviations & Symbols

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Statistical symbols and abbreviations

© ........................................................................................................................................................................Copyright 

® ......................................................................................................................................................................Registered 

% .....................................................................................................................................................................Percentage

-..................................................................................................................Not applicable or real zero or zero by default

: ....................................................................................................................................................................Not available

0 ........................................................................................................................................Less than half of the unit used

1000s ...............................................................................................................................................................Thousands

1999-2004  ............................................................Period of several calendar years (e.g. from 1.1.1999 to 31.12.2004)

b  ...............................................................................................................................................................Break in series

:c.................................................................................................................................................................... Confidential

e  .........................................................................................................................................................................Estimate

f  ..........................................................................................................................................................................Forecast

i .............................................................................................................................More information in explanatory notes

p  ......................................................................................................................................................................Provisional

r  ...........................................................................................................................................................................Revised 

s ............................................................................................................................................................Eurostat estimate

u  .......................................................................................................................................................................Unreliable

:u.............................................................................................................................................. Extremely unreliable data

Abbreviations

A 

AAGR....................................................................................................................................Annual average growth rate

AGR  ...................................................................................................................................................Annual growth rate

AVI  ...................................................................Aviation (high-tech group, based on International Patent Classification)

B

BERD .......................................................................................Expenditure on R&D in the bBusiness enterprise sector

BES ........................................................................................................................................Business enterprise sector

C

CAB  ........................................................................................................Computer and automated business equipment 
(high-tech group, based on International Patent Classification)

CBSTII .......................................................................Common basis for science, technology and innovation indicators

CDH.....................................................................................................................................Careers of doctorate-holders

CD-ROM .......................................................................................................................Compact disc read-only memory

CEC  .............................................................................................................Commission of the European Communities

CeSTII....................................................................................Centre for Science, Technology and Innovation Indicators

CIP ..........................................................................................Competitiveness and Innovation Framework Programme

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CIS ....................................................................................................................................Community Innovation Survey

CTE .......................................................................................................................................Communication technology 
(high-tech group, based on International Patent Classification)

CV ...........................................................................................................................................................Curriculum vitae

D

DG  ....................................................................................................................................................Directorate-General

DG-RTD  ......................................................................................................................Directorate-General for Research 

DVD........................................................................................................................................................Digital video disc

E

EC ............................................................................................................................European Community/Communities

ECU/EUR........................................................................................................Ecu up to 31.12.1998/Euro from 1.1.1999

EEA30 ...............................................................................................European Economic Area (EU-27 plus IS, LI, NO)

EFRD .............................................................................................................European Fund for Regional Development

EFS................................................................................................................................................European Social Fund

EFTA ............................................................................................................................European Free Trade Association

EIS................................................................................................................................European Innovation Scoreboard

EIT ................................................................................................................................European Institute of Technology

EP....................................................................................................................................................European Parliament

EPC.....................................................................................................................................European Patent Convention

EPO  ............................................................................................................................................European Patent Office

ERA...........................................................................................................................................European Research Area

EU LFS ................................................................................................................European Union Labour Force Survey

EU-15  ..............................................................................................................................European Union (15 countries)

EU-25  ..............................................................................................................................European Union (25 countries)

EU/EU-27 .........................................................................................................................European Union (27 countries)

EU-CC ...............................................................................................................................................Candidate countries

EUR  ..........................................................................................................................................................................Euro

Eurostat  .................................................................................................Statistical Office of the European Communities

EVCA ....................................................................................................................European Venture Capital Association

F

FAPESP...............................................................................Fundacão de Amparo à Pesquisa do Estado de São Paulo 
- State of São Paulo Research Foundation

FOS ..........................................................................................................................................................Field of science

FP  ...............................................................................................................................................Framework Programme

FP6 ............................................................................................Sixth EU Research Framework Programme 2002-2006

FP7.......................................................................................Seventh EU Research Framework Programme 2007-2013

FSI...............................................................................................................................................Frank Stronach Institute

FTE  ...................................................................................................................................................Full-time equivalent

FTSE .............................................................................................................................Financial Times Stock Exchange

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G

G7..................................................................Group of Seven (France, Germany, Italy, Japan, United Kingdom, United
States of America)

G8 .............................................................................. Group of Eight (Canada, France, Germany, Italy, Japan, Russia, 
the United Kingdom and United States)

GBAORD  ...................................................................................Government budget appropriations or outlays on R&D

GDP ............................................................................................................................................Gross domestic product

GERD  ....................................................................................................................Gross domestic expenditure on R&D

GISCO  ........................................................................Geographical information system for the Commission - Eurostat

GOV ...................................................................................................................................................Government sector

GPS..........................................................................................................................................Global positioning system

GUF  ...........................................................................................................................................General university funds

H

HC ..................................................................................................................................................................Head count

HES  ............................................................................................................................................Higher education sector

HRST  ........................................................................................................Human resources in science and technology

HRSTC ...........................................................................................Human resources in science and technology - Core

HRSTE ...................................................................................Human resources in science and technology - Education

HRSTO  ................................................................................Human resources in science and technology - Occupation

HRSTU  ..............................................................................Human resources in science and technology - Unemployed

I

IBCS ...........................................................................................................Integrated Business Characteristics Strategy

IBGE .........................................................................................................Brazilian Institute of Geography and Statistics

ICB..............................................................................................................................Industrial classification benchmark

ICT................................................................................................................Information and communication technology

ILO ...............................................................................................................................International Labour Organisation

IPC  ..............................................................................................................................International Patent Classification

IPR .............................................................................................................................................Intellectual property right

IRI...................................................................................Commission’s Industrial Research and Innovation Programme

ISBN  ........................................................................................................................International standard book number

ISCED  ..............................................................................................International Standard Classification for Education

ISCO ................................................................................................International Standard Classification of Occupation

ISIC .................................................................International Standard Industrial Classification of all Economic Activities

IT  ..................................................................................................................................................Information technology

J

JPO  .............................................................................................................................................Japanese Patent Office

JRC................................................................................................................................................Joint Research Centre

K

KIC.......................................................................................................................Knowledge and innovation Community

KIS  ....................................................................................................................................Knowledge-intensive services

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L

LFS  .................................................................................................................................................Labour Force Survey

LKIS ...........................................................................................................................Less knowledge-intensive services

LSR  ...................................................................Lasers (high-tech group, based on International Patent Classification)

M

MGE  ....................................................................Micro-organism and genetic engineering (high-tech group, based on
International Patent Classification)

Mio  .........................................................................................................................................................................Million

MSTI ...............................................................................................Main Science and Technological Indicators - OECD

N

NABS  .................................................................................................Nomenclature for the analysis and comparison of 
science budgets and programmes

NAC.......................................................................................................................................................National currency

NACE  ..........................................................................................General industrial classification of economic activities 
in the European Community

NewCronos  .......................................................................................................Eurostat's statistical reference database

NHRSTU  ....................................................................................................................................Unemployed non-HRST

NUTS  .......................................................................................................Nomenclature of Territorial Units for Statistics

O

OECD  ..................................................................................Organisation for Economic Cooperation and Development

OHIM.......................................................................................................Office of Harmonisation for the Internal Market

P

p.a. ..................................................................................................................................................Per year (per annum)

PATSTAT .............................................................................................Patent statistics database (provided by the EPO)

PCT .........................................................................................................................................Patent Cooperation Treaty

PNP ............................................................................................................................................Private non-profit sector

PPS  ......................................................................................................................................Purchasing power standard

PSL....................................................................................................................................................................Personnel

R

R&D ......................................................................................................................................Research and development

RFID...................................................................................................................................Radio frequency identification

S

SBS .....................................................................................................................................Structural Business Statistics

SE ..............................................................................................................................................Scientists and engineers

S&E  ..........................................................................................................................................Science and engineering

SII ...........................................................................................................................................Summary Innovation Index

SITC ..............................................................................................................Standard International Trade Classification

SMC  ..........................................................................................................Semi-conductors (high-tech group, based on 
the International Patent Classification)

SME ..........................................................................................................................Small and medium-sized enterprise

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S&T  ............................................................................................................................................Science and technology

T

TUG ...................................................................................................................................Graz University of Technology

U

UIS ...................................................................................................................................UNESCO Institute for Statistics

UN .............................................................................................................................................................United Nations

UNESCO .....................................................................United Nations Educational, Scientific and Cultural Organisation

UOE ............................................................................................................................................Unesco/OECD/Eurostat

USPTO  ........................................................................................................United States Patent and Trademark Office

V

VCI  .......................................................................................................................................Venture capital investments

vs.  ..........................................................................................................................................................................Versus

W

WIPO  ................................................................................................................World Intellectual Property Organisation

Countries

EU-27

BE ........................................................................................................................................................................Belgium

BG  .......................................................................................................................................................................Bulgaria

CZ  ...........................................................................................................................................................Czech Republic

DK  ......................................................................................................................................................................Denmark

DE ......................................................................................................................................................................Germany

EE .........................................................................................................................................................................Estonia

IE  ...........................................................................................................................................................................Ireland

EL  .........................................................................................................................................................................Greece

ES ............................................................................................................................................................................Spain

FR  .........................................................................................................................................................................France

IT  ................................................................................................................................................................................Italy

CY  .........................................................................................................................................................................Cyprus

LV ............................................................................................................................................................................Latvia

LT  .......................................................................................................................................................................Lithuania

LU  .................................................................................................................................................................Luxembourg

HU .......................................................................................................................................................................Hungary

MT ............................................................................................................................................................................Malta

NL ..................................................................................................................................................................Netherlands

AT  ..........................................................................................................................................................................Austria

PL  ..........................................................................................................................................................................Poland

PT ........................................................................................................................................................................Portugal

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RO ......................................................................................................................................................................Romania

SI  ........................................................................................................................................................................Slovenia

SK  .......................................................................................................................................................................Slovakia

FI  ..........................................................................................................................................................................Finland

SE ........................................................................................................................................................................Sweden

UK ...........................................................................................................................................................United Kingdom

Candidate countries

FYROM ............................................................................................................Former Yugoslav Republic of Macedonia

HR..........................................................................................................................................................................Croatia

TR  ..........................................................................................................................................................................Turkey

Other countries

AU........................................................................................................................................................................Australia

CA  ........................................................................................................................................................................Canada

CH ..................................................................................................................................................................Switzerland

CN  ...........................................................................................................................................................................China

IL...............................................................................................................................................................................Israel

IN ...............................................................................................................................................................................India

IS  ..........................................................................................................................................................................Iceland

JP ............................................................................................................................................................................Japan

KR.........................................................................................................................................................Republic of Korea

LI  .................................................................................................................................................................Liechtenstein

NO  ........................................................................................................................................................................Norway

RU ..........................................................................................................................................................................Russia

TW..........................................................................................................................................................................Taiwan

US  ...............................................................................................................................................................United States

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European Commission

Science, technology and innovation in Europe

Luxembourg: Office for Official Publications of the European Communities

200  — 233 pp. — 21 x 29.7 cm

8

ISBN 978-92-79-07801-9
ISSN 1830-754X

Price (excluding VAT) in Luxembourg: EUR 25

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