-
R&D Investments
and Structural Changes
in Sectors
Quantitative and Qualitative
Analysis
Policy Recommendations
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EUROPEAN COMMISSION
Directorate-General for Research & Innovation
Directorate A — Policy Development and Coordination Unit A4—
Analysis and monitoring of national research policies Contact:
VANKALCK Stephane
E-mail: [email protected]
[email protected] European Commission B-1049
Brussels
mailto:[email protected]
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EUROPEAN COMMISSION
R&D Investments and
Structural Changes in Sectors
Quantitative and Qualitative Analysis
Policy Recommendations
Final Report
Edited by Intrasoft International S.A.
under FP7-RTD/DirC/C3/2010/Si2.569213
Directorate-General for Research and Innovation 2016 EN
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LEGAL NOTICE
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however it reflects the views only of the authors, and the
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Luxembourg: Publications Office of the European Union, 2016.
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4
Table of Contents
TABLE OF CONTENTS
.....................................................................................................
4
TABLE OF TABLES
.........................................................................................................
5
TABLE OF
FIGURES........................................................................................................
6
EXECUTIVE SUMMARY
....................................................................................................
7
1 INTRODUCTION
...................................................................................................
22
2 PART I: APPROACH AND METHODOLOGY
................................................................
24
2.1 Approach
...................................................................................................
24
2.2 Study Objectives
........................................................................................
25
2.3 Coverage of sectors and data time series
....................................................... 25
2.4 Foundations for the quantitative and qualitative sectoral
analyses: conceptual framework
.................................................................................................
27
2.4.1 Dimensions of the sectoral analysis
.................................................. 27
2.4.2 Basic structure of reasoning and analysis
.......................................... 27
2.4.3 Indicators..
...................................................................................
30
2.5 Approach to cross sector analysis
.................................................................
30
2.6 Approach to the collaboration and networking analysis
.................................... 31
2.7 Approach to the structural change analysis
.................................................... 32
2.8 Approach to the qualitative sector appraisal
................................................... 33
3 PART II: R&D INVESTMENT AND STRUCTURAL CHANGE – AN
OVERARCHING ANALYSIS
...........................................................................................................
36
3.1 Cross sector analysis
...................................................................................
36
3.2 Globalization versus regionalization of R&D activities
....................................... 41
3.3 The common relevance of fields of technology in sectors
................................. 43
3.4 Innovation behaviour and patterns of innovation
............................................ 46
3.5 Networking and collaboration
.......................................................................
50
3.5.1 Patterns of collaboration
.................................................................
50
3.5.2 Determinants of cooperation
........................................................... 50
3.5.3 Impacts of cooperation
...................................................................
54
3.6 Structural change
.......................................................................................
55
3.6.1 Patterns of structural change
.......................................................... 55
3.6.2 Drivers of structural change: Productivity, R&D, other
factors ............. 59
3.7 Meta sectors (clusters)
................................................................................
62
3.7.1 Introduction
..................................................................................
62
3.7.2 Clusters based on R&D intensity
...................................................... 63
3.7.3 Clusters based on Globalization versus regionalization of
R&D
activities 64
3.7.4 Clusters based on technological commonalities
.................................. 65
3.7.5 Clusters based on innovation behaviour
............................................ 67
3.7.6 Clusters based on technology cycles
................................................. 68
3.7.7 Clusters based on R&D challenges and knowledge
transfer ................. 70
3.7.8 Clusters based on RDI costs as a driver
............................................ 71
3.8 Policy Instruments and Structural Change
...................................................... 72
4 PART III: SECTOR SYNTHESES
..............................................................................
77
4.1 Manufacture of food products and beverages and manufacture
of machinery for these products (15 + 29.53)
........................................................................
77
-
5
4.2 Manufacture and sales of textiles and manufacture of
machinery for these products (17 + 29.54 + 51.41/2 + 51.83 +
52.41/2) ...................................... 80
4.3 Reproduction of recorded media and related manufactured
goods (22.3 + 24.64/5)
....................................................................................................
82
4.4 Manufacture of basic chemicals and manufacture of paints,
varnishes and
similar coatings, and glues and gelatines (24.1 + 24.3 + 24.62)
...................... 84
4.5 Manufacture of pharmaceuticals (24.4)
......................................................... 85
4.6 Manufacture of plastic products (25.2)
.......................................................... 87
4.7 Manufacture of other non-metallic mineral products (26)
................................. 89
4.8 Manufacture of general purpose machinery and machine tools
(29.1 + 29.2 + 29.4)
........................................................................................................
91
4.9 Manufacture of office machinery and computers (30)
...................................... 94
4.10 Manufacture of electrical motors, generators and
transformers (31.1) ............... 96
4.11 Manufacture of electricity distribution and control
apparatus; manufacture of insulated wire and cable; manufacture of
accumulators, primary cells and primary batteries; electricity,
gas, steam and hot water supply (31.2 +31.3 + 31.4 + 40)
.................................................................................................
98
4.12 Manufacture of electronic valves and tubes and other
electronic components
(32.1) 100
4.13 Manufacture of medical and surgical equipment (33.1)
.................................. 102
4.14 Manufacture of instruments and appliances for measuring,
checking, testing, navigating and other purposes, industrial
process control equipment and optical instruments and photographic
equipment (33.2 + 33.3 + 33.4) ..................... 104
4.15 Manufacture of motor vehicles, manufacture of parts and
accessories for motor vehicles and their engines (34.1 + 34.3)
..................................................... 105
4.16 Manufacture of aircraft and spacecraft (35.3)
............................................... 107
4.17 Recycling (37)
..........................................................................................
108
4.18 Collection, purification and distribution of water (41 +
45. 24) ....................... 110
4.19 Construction (45 except 45.24)
..................................................................
112
4.20 Cargo handling and storage (63.1)
.............................................................
114
4.21 Telecommunications (64.2)
........................................................................
117
4.22 Services for computer and related activities (72 except
72.5) ......................... 120
4.23 Manufacture of lighting equipment and electric lamps (31.5)
.......................... 123
4.24 Manufacture of television and radio transmitters and
receivers (32.2 + 32.3) ... 126
4.25 Services fo r research and development (73)
............................................... 129
Table of Tables
Table 1: Main structural change indicators for 25 RTDS sectors
2000-2009 ......................... 13
Table 2: Analysed Sectors NACE Rev 1.1
.........................................................................
26
Table 3: Overview – Estimated likelihood of collaboration per
sector ................................... 52
Table 4: Main structural change indicators for 25 RTDS sectors
2000-2009 ........................ 58
Table 5: Clustering R&D intensity and embodied R&D
....................................................... 63
Table 6: Clustering Globalization versus regionalization of
R&D activities ............................. 64
Table 7: Clustering – Common significance of fields of
technology ...................................... 65
Table 8: Clustering – Propensity to Innovation Outputs
..................................................... 67
Table 9: Clustering Technology Cycle and Speed of Innovation
.......................................... 69
Table 10: Clustering - Challenges of RDI and Knowledge Transfer
........................................ 70
Table 11: Clustering – Drivers of RDI Costs
.......................................................................
71
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6
Table of Figures
Figure 1: Comparison of R&D intensity and national
differences of R&D (boxplot) .................... 9
Figure 2: The role of embodied R&D diffusion compared to own
R&D (in terms of R&D intensity 2007)
.............................................................................................................
10
Figure 3: Increasing global standardization and competition of
R&D locations ....................... 11
Figure 4: Comparison of the state and average annual growth
(2000-2007) of BERD intensity versus the relative economic
importance (share of value added in total value added) in EU
countries
....................................................................................................
12
Figure 5: Propensity to innovation in products (goods, boxplot)
........................................... 17
Figure 6: Propensity to innovation in services products
(boxplot) ......................................... 17
Figure 7: Number of sector specific versus total number of
measures .................................. 18
Figure 8: Number of (sector specific) measures per country (910
measures) ........................ 19
Figure 10: Literature review as guiding element of the study
approach. ................................. 24
Figure 11: The basic logic of this study
..............................................................................
27
Figure 12: Sector Performance and Change with data sources
applied ................................... 30
Figure 13: Approach to collaboration and networking analysis
............................................... 32
Figure 14: Composition of survey panel (organisation type)
.................................................. 34
Figure 15: Composition of survey responses (organisation type)
........................................... 34
Figure 16: Comparison of R&D intensity and national
differences of R&D (boxplot) .................. 37
Figure 17: Comparison of the state and average annual growth
(2000-2007) of BERD intensity versus the relative economic
importance (share of value added in total value added) in EU
countries
....................................................................................................
38
Figure 18: The role of embodied R&D diffusion compared to
own R&D (in terms of R&D intensity 2007)
.............................................................................................................
40
Figure 19: Increasing global standardization and competition of
R&D locations ....................... 42
Figure 20: Regional adaptive R&D close to local market
(needs)15 ........................................ 42
Figure 21: Cost-driven relocation of R&D to emerging
countries ............................................ 43
Figure 22: Expert judgement - Relevance of the KETs
Nanotechnology, Micro and Nanoelectronics and Photonics for
innovation (0=not relevant; 10= highly relevant)
....................... 44
Figure 23: The Relevance of the KETs Advanced Materials and
Advanced Manufacturing for sectoral innovation (0=not relevant; 10=
highly relevant) .................................... 45
Figure 24: The Relevance of the KET Biotechnology for sectoral
innovation (0=not relevant; 10=highly relevant)
.........................................................................................
46
Figure 25: Propensity to innovation in products (goods, boxplot)
........................................... 47
Figure 26: Propensity to innovation in services products
(boxplot) ......................................... 47
Figure 27: Expert judgement - Shortening of technology life
cycles or time to market in 25 sectors (0= don’t agree; 10= fully
agree) ...........................................................
48
Figure 28: Comparison of thematic priorities of FP4 to Horizon
2020 ..................................... 49
Figure 29: Number of (sector specific) measures per country (910
measures) ........................ 73
Figure 30: Number and financial volume of measures (overall
budget per year in MEUR) per sector
.............................................................................................................
75
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7
Executive summary
There is no doubt that in the long run investments in
innovation, and more specifically in research and development, are
beneficial at the level of companies, sectors and countries. But as
soon as we start to dig deeper into the ways in which this works we
enter into a world with complex and heterogeneous developments. In
most cases these insights are fragmentary, based on a specific
viewing angle. This report presents the results of an effort to
contribute to a more comprehensive
view based on looking at R&D from several different
perspectives. It looks for example at economic sectors, companies
and their interactions, technologies, countries, policies at
national and European levels, etc.
More specifically, the study on which this report is based aimed
at (i) carrying out a quantitative and qualitative analysis of
R&D investments and structural changes in sectors and (ii)
delivering policy recommendations. This includes:
- Identifying strength and weaknesses of knowledge production
and use in different industrial sectors of the European
economy.
- Identifying factors (including effectiveness of policies) that
either accelerate or reduce structural adjustment processes.
- Analysing the effect of intra-EU investment in R&D on the
sectoral structure of the EU economy.
The overarching goal is to provide a basis of evidence for
improved understanding and
effectiveness of European R&D policies and to a certain
extent the wider innovation policies, and their impact on the
sectoral structure of the European economy. It looks in particular
at policies which directly or indirectly have the capacity of
interacting with strategic choices in private sector R&D and
act as a “leverage” to stimulate developments of broader societal
and socioeconomic significance.
The study is part of a broader effort of the European Commission
to develop an evidence-based ERA monitoring system based on
indicators. This framework of indicators must enable analysis
of
the evolution of national research and innovation systems in
Europe, their interconnectedness and performance, as well as their
impact on structural change towards a more knowledge-intensive
economy. In addition to monitoring progress in the realization
of the ERA, such a framework of indicators must facilitate the
analysis of the impact of research, research policies and
programmes on the competitiveness of Europe and of the contribution
of research to growth and to addressing societal and global
challenges.
Sectors and sectoral systems
The core units of analysis in the study are sectors and sectoral
systems. A sector is characterized as a set of activities around
related products for an existing or future demand, sharing a
certain knowledge base. A sectoral system is composed of a set of
agents engaged in market and non-market interactions for the
creation, production and sale of the related sectoral products. In
this sense sectors present an economic, statistical and political
reality, despite all the problems which
we encounter in applying the concept in our analysis.
The selection of sectors included in the study has some
limitations; not all sectors of the economy are included and not
all sectors are defined at the same level of (statistical)
categorisation; many sectors, even under the same NACE digit level,
still appear very heterogeneous. But all in all the
twenty-five chosen sectors provide a relevant cross-section of
the economy as a whole, of very different R&D performing
sectors, and can be seen as more than sufficient to address the
full complexity of the problem at hand.
In general it can be concluded that the focus on sectors and
sectoral systems has led to an improved understanding of the
complexities and of the dynamics of R&D and R&D policies,
the conditions under which they operate, and how they interact. The
improved understanding of the concept of “sector” furthermore
allowed a more careful framing and targeting of policy
measures.
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8
Changing patterns and structures in sectors: investment,
performance and other characteristics
A wide range of literature shows that at the firm-level positive
relationships exist between innovativeness, conceptualized as a
firm’s ability to change and adopt innovations, and
profitability
measures (ROI, ROA, and ROS1). There is a positive relationship
between product and process innovation and performance of the firm
as measured in terms of overall sales, sales per employee, and
employment growth. R&D intensity, market share, and
concentration are the most relevant causal factors for this.
Innovative performance is affected by R&D expenditures, timing
(first/early movers in innovation), innovation targets (new
products in new markets, vs. mature markets) and process-related
R&D vs. product-related R&D.
A good number of commonalities and interdependencies in sectors
is generally seen as sufficient to
make sectors a relevant unit of analysis and policymaking, even
with the observed wide variety at the firm level within sectors.
The basic idea here is that R&D investment, innovation
dynamics, and productivity are influenced by a broad range of
factors which in its consequences can be rather sector specific. It
needs to be underlined that next to the availability and size of
R&D investments there can be several other key
determinants.
Against this background the study focused on how sectors produce
knowledge, how they succeed
in putting knowledge to use, and on the longer term outcomes in
terms of sector performance and structural changes at the level of
sectors and between sectors.
Sector performance: R&D intensity and sectors
R&D intensity is often used as a key indicator to
characterise the R&D-related profile of a sector. But the level
and growth of R&D intensity also is a problematic indicator to
compare different sectors. A subgroup of those sectors classified
as high technology by the OECD is clearly an outlier according to
high levels of R&D investment. Other sectors differentiate to a
smaller extent from
each other, whereby sectoral differences are frequently clouded
by national differences.
Our results indicate that the ‘R&D services sector’, which
includes R&D institutes and services, shows the highest level
of R&D intensity. Apart from that, the highest R&D
intensities can be found in aerospace, the manufacture of
television and radio equipment, and in the pharmaceutical sector.
However, national R&D intensities in sectors with relatively
low average R&D intensity can significantly exceed those of on
average high R&D intensive sectors.
1 Return on Investment, Return on Assets, Return on Sales,
respectively.
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9
Figure 1: Comparison of R&D intensity and national
differences of R&D (boxplot)2
Source: Eurostat R&D Statistics, RTDS calculations
There is considerable heterogeneity of annual growth rates of
R&D intensity among the sectors of interest between countries.
Within individual sectors some countries increased R&D
intensity while others showed considerable decrease.
Taking account of the international division of labour and
embodied R&D diffusion via the value chain provides a
complementary view on R&D investment. It is found that even
sectoral segments
with low levels of own R&D at national levels can benefit
considerably from knowledge-intensive inputs (e.g. complex machines
or systems which embody R&D) along the value chain. A sectoral
grouping on the basis of commonalities and differences according to
embodied R&D then clearly deviates from the groupings defined
by the usual OECD high-tech/low-tech classification.
Sectors with a high level of R&D activity strongly benefit
from embodied R&D diffusion, primarily via domestic
knowledge-intensive trade, but also through international trade.
This is for example the case for the ‘electrical and optical
equipment sector’ and also for lighting equipment. And it is
also found for the sector groups ‘basic chemicals and
pharmaceutical products’ and ‘transport equipment’.
The study results indicate relatively high shares of R&D
diffusion embodied in value chain linkages for lesser R&D
active countries, particularly with respect to the ‘electrical and
optical equipment sector’, the ‘transport equipment sector’, the
‘machinery sector’, and the ‘plastics sector’.
2 Explanation of the Boxplot: The ‘red line” shows the
statistical median, which provides a benchmark for sectoral
comparison. The ‘green box” marks the range of R&D intensities
for the 25% of countries in a sector above the median and below the
median. The ‘black point” marks the Interquartile Range, an
indicator for the range of outliers. The upper end of the line
(maximum) shows the value of the country with the highest R&D
intensity in the sector.
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10
Figure 2: The role of embodied R&D diffusion compared to own
R&D (in terms of R&D intensity 2007)
Source: WIOD (World Input Output Database); EUROSTAT, OECD
R&D Statistics; RTDS calculations
The sectors in the study show differences in patterns of
internationalization and location of R&D investments. A small
group of sectors shows a strong tendency towards international
standardization and open competition among R&D locations. In
other sectors the demand for regionally adapted products is more
dominant. Expert surveys and interviews show the following
picture:
There are diverging views with respect to the various issues of
internationalization in sectors such as non-metallic mineral
products, in electrical motors, generators and transformers,
and in instruments and appliances for measuring, checking,
testing. This divergence of views seems to reflect internal
diversity of the sectors concerned.
There is a relatively high agreement on the various issues of
internationalization in sectors such as recorded media, in office
machinery and computers, and in television and radio.
Generally high agreement that R&D investments are located
close to production sites in
sectors such as non-metallic mineral products, handling and
storage, and television and radio.
0%
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Electrical and opticalequipments
Basic chemicalsa andPharma
Machinery Transport equipments Plastic products Non-metallic
minerals Food products Textile Industry Recorded media Recycling
Watercollection Construction Telecommunications Services for
R&D Cargo
High technologymanufacturing
Medium high technology manufacturing Medium low technology
Low-technology manufacturing Electricity, Gas andWater Supply
Construction Knowledge-intensivehigh-technology
services
Knowledge-intensivemarket services
Less-knowledge-intensive market
services
EU27 CountriesR&D embodied in imported (from 40 countries)
capital goods
R&D embodied in domestic capital goods
R&D embodied in imported (from 40 countries) inputs
R&D embodied in domestic inputs
Own R&D
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11
Figure 3: Increasing global standardization and competition of
R&D locations3
Source: RTDS – Sectoral Experts Online Survey 2013
Combining the performance measures value added and R&D
investment over the period 2000-2007 produces the following picture
of 24 sectors.
3 The sectoral experts involved gave their judgments concerning
the relevance of each scenario for the respective sector. The
figure shows the mean of all judgments (0:= not relevant; 10=
highly relevant).
-
12
Figure 4: Comparison of the state and average annual growth
(2000-2007) of BERD intensity versus the relative economic
importance (share of value added in total value
added) in EU countries4
It can be seen that at the sector level the dynamics of long
term R&D growth can deviate considerably from changes in
economic significance of the sector as measured by value added (an
indicator of structural change).
For example, the manufacture of medical and surgical equipment
(sector 13) gained most significantly in relative economic
importance despite showing decreases in R&D intensity. The
manufacture of computer related services (sector 22), the
manufacture of instruments and
appliances for measuring (sector 14) and the construction sector
(sector 19) showed positive development of R&D intensities and
economic growth. Another (small) group of sectors showed
significant annual increases in R&D intensity. The manufacture
of electronic valves and tubes (sector 12) has been negatively
affected by globalization and relocation activities over the last
decade. The sector’s gain in R&D intensity may partially be
explained by decreases in the value added originating in Europe and
other industrialized countries.
The manufacturing sectors with the highest R&D intensities,
aerospace (sector 16) and the manufacture of radio and television
(sector 24), as well as the medical device sector (sector 13)
didn’t show positive growth.
4 The size of circles in bright blue mark R&D intensity as
measured by business expenditure in R&D (BERD) per value added,
the circles in dark blue mark the relative economic importance as
measured by the value added of the sector in total value added.
Sector 25 (R&D) is an extreme case and is therefore not
presented in figure 4.
-
13
A data intensive econometric analysis supports this picture. It
demonstrates significant differences in the relationship between
sector R&D intensity and productivity performance (the
strongest
predictor of structural change over time), both across sectors
and within individual sectors. The study has found extensive
differences in terms of sector performance and in terms of
R&D
intensity, not only across countries and sectors but also within
single sectors.
Hence, we conclude from these observations that there is no
clear and unique direct relationship between change in R&D
intensity and structural sectoral change. While a strong
relationship does exist (as also shown by this project), the
relationship appears to be moderated by several other socioeconomic
factors both at the macro and micro levels.
Hence, we can conclude at the sector level (i.e., based on
sector aggregated data) that is difficult to infer an unequivocal
relationship between changes in R&D intensity and structural
change. While
a strong relationship does exist (as also shown by this
project), the relationship appears to be moderated by several other
socioeconomic factors both at the macro and micro levels. This
strong relationship and the importance of other contextual factors
strongly indicate that R&D is a necessary but not sufficient
condition for growth and structural change.
The Table below provides a detailed overview of R&D
intensity and performance of the sectors
during the ten-year time period examined by the study.
Table 1: Main structural change indicators for 25 RTDS sectors
2000-2009
SECTORS VARIABLES
Sector name OECD classification EU position
VA Employment
Labour Producti
vity
Concentration
Research
Intensi
ty
Pharmaceuticals
High-Tech manufacturing
Leading + + + Yes Outperformed
Aircraft High-Tech manufacturing
Leading + + - Yes
Medical surgical instruments
High-Tech manufacturing
Second - - - = Yes
Measurement instruments
High-Tech manufacturing
Second - - - = Outperformed
Office machinery
High-Tech manufacturing
Trail + = - - - Outperformed
Electronics High-Tech manufacturing
Trail - - - Outperformed
TV and radio High-Tech manufacturing
Trail -
-
- - - Outperformed
Telecommunications
Knowledge intensive service
Leading + - + Yes
Computer related
services
Knowledge intensive service
Leading + + - Outperformed
R&D services Knowledge intensive service
Leading + + - -
-
14
SECTORS VARIABLES
Sector name OECD classification EU
position
VA Employm
ent
Labour
Productivity
Concentra
tion
Resear
ch Intensi
ty
Chemicals Medium-High Tech manuf.
Leading - - - - Yes
General purpose machinery
Medium-High Tech manuf.
Leading - + -
Electrical Motors transformers
Medium-High Tech manuf.
Leading - - + Yes
Automotive Medium-High Tech manuf.
Leading - - +
Plastics Medium-low Tech manuf.
Leading - - - -
Non-metallic Medium-low Tech manuf.
Leading -
-
- - Yes
Recorded media
Medium-low Tech manuf.
Trail -
-
- - +
Lighting Equipment
Medium-low Tech manuf.
Data limitations
+ -
Electric utilities/ gas
Medium high-medium low manuf.
Leading + - - -
Food Low-medium high manuf.
Leading - - - -
Textiles Low-medium high manuf.
Leading -
-
- - -
Construction Low Tech manuf. Leading - - - -
Recycling Low Tech manuf. Data limitations
- + - -
Water Low Tech manuf. Data limitations
- - - Yes
Cargo and handling storage
Low Tech manuf. Data limitations
- -
Note: + positive growth; - negative growth; = stagnant; -- long
term negative growth; positive trend;
negative trend; positive trend in earlier period of decade and
negative later; blank spaces indicate no
available data.
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15
Networking and Cooperation
Considering the patterns of cooperation and networking the
analysis shows that,
Innovative firms are strongly oriented towards collaboration:
almost 35% of firms reporting innovative activities also
collaborate. High- or mid-tech industries collaborate
relatively
more than the average.
There is not much “horizontal” collaboration with competitors,
the main focus is on “vertical” cooperation with suppliers and
clients.
Most of cooperation is in the same country and intra-EU, and
there seems to be a correlation between the reference geographical
markets and the geographical scope of cooperation. High-tech
industries tend to cooperate with overseas partners.
In respect to the factors determining cooperation, we see
that:
The larger the firm size, the higher the propensity to
collaborate
Being part of an enterprise group makes it more likely to engage
in institutional and (notably) internal cooperation.
Internationalization is also found to positively influence
collaboration in general
Public subsidies exert a strong positive effect on
collaboration
Impacts
Horizontal cooperation affects negatively intramural R&D and
positively non-R&D innovation activities
Vertical collaboration has a positive effect on extramural
R&D and on non-R&D activities
Institutional collaboration is associated positively and
significantly with both types of R&D – intramural and
extramural.
Institutional cooperation, however, is negatively associated
with non-R&D activities. It also seems to relate predominantly
with process innovation, and has a negative effect on new-
to-firm product innovation.
Finally, internal cooperation appears to be associated with
extramural R&D and non-R&D
activities (but not with intramural R&D), and has a positive
effect on imitative product innovation and on innovative sales.
The results obtained suggest that even though (general)
collaboration has a significant positive direct effect on
innovation inputs and outputs, these impacts do not vary much over
sectors as far as innovation outputs and intramural R&D are
concerned. In contrast, the effects on extramural
R&D and on non-R&D activities do vary. Specifically,
with regard to extramural R&D, in most sectors the impact of
collaboration is significantly higher than in “services for
R&D”.
Our findings point to a policy “divide” across different levels
of government: local and (to a somewhat lesser extent) national
policy initiatives seem to “drive” firms towards “home-country”
partners (and hence to more inward-looking cooperation), whereas EU
policy initiatives are associated, by definition, with cross-border
collaboration. Given the growing importance of regions
and local authorities in innovation policy, this “divide”
certainly suggests a need for stronger coordination of innovation
policy formulation and delivery across levels of government.
Clusters of Sectors, Meta-sectors
For policy purposes it is useful to group sectors with similar
characteristics. Our extensive review of the literature clearly
showed that sectoral aggregation has more meaning if dedicated to
specific policy questions and fields of intervention and therefore
must be based on the specific set of indicators taken into
account.
The study found the following ways of clustering sectors to be
relevant:
- Clusters based on R&D intensity
- Clusters based on technological commonalities
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16
- Clusters based on innovation behaviour
- Clusters based on technology cycles
- Clusters based on R&D challenges and knowledge
transfer
- Clusters based on RDI costs as a driver
The identified clusters/meta-sectors in each approach can be
used as a basis for a focused policy intervention approach,
avoiding clustering which puts too heterogeneous sectors into one
single
category.
R&D and structural change
Drivers and patterns of structural change
From our analysis, the following main results emerge:
a) Europe is relatively specialized and maintains an advantage
mainly in what standard classifications label as ‘medium and
medium-high technology manufacturing sectors’, complemented by a
few high technology sectors and knowledge-intensive service
sectors.
Among the sectors in which Europe leads, one can observe
relative differences in
performance in the sense that in some it maintains dynamism
while in others it looks less dynamic if not stagnating.
b) A strong accelerating effect of R&D intensity on total
factor productivity (TFP) growth has been found in the aggregate
estimation across countries and sectors. This effect gains even
more strength when we look at very developed economies at the
productivity frontier
(Germany, Netherlands, US). Such countries, one may argue, can
better capitalize on their R&D effort.
c) The effect of R&D on productivity occurs over time,
rather than at once. After an initial growth period it diminishes
over time.
d) R&D is important to both high and lower technology sector
classes but the effect of R&D on productivity growth is much
stronger in the case of high R&D-intensity sectors.
e) Sectors with a high level of R&D activity strongly
benefit from embodied R&D diffusion
through value chains, reflecting both domestic and international
knowledge-intensive trade. Follower countries show relatively high
shares of R&D diffusion embodied in value chain linkages. Yet,
there are extensive inter-sectoral differences when sectors are
classified in
terms of combination of own R&D expenditures and embodied
R&D gains through value chains.
f) Experts identify the following factors affecting structural
change and the international division of labour: (a) increasing
product standardization across geographical markets (some
sectors); (b) internationalizing product development (much
broader set of sectors); and (c) cost driven relocation of R&D
to emerging countries (many sectors but not all). There is lesser
agreement on the relocation of R&D activities to
R&D-intensive countries. Again, there are extensive
inter-sectoral differences.
g) In a number of sectors there is a strong tendency towards
shorter technology life cycles and time-to-market. This is
especially the case in high and medium-high technology sectors.
Less R&D-intensive sectors appear to be less affected by
this tendency.
h) Firm level (CIS) data used to estimate innovation system
efficiency (ISE) indicate that a significant number of EU countries
can be regarded as comparatively efficient in producing and using
knowledge across various sectors but still are less effective in
exploiting innovation in the market and profiting from it.
i) A comparison of the propensities to innovate across sectors
on the basis of firm level (CIS) data also shows extensive national
heterogeneities. The heterogeneity is larger with respect
to manufacturing products and process innovations than in
service innovations or innovations in distribution. The general
expectation that sectors with higher R&D intensities are also
likely to have higher propensities to innovate is supported.
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17
Figure 5: Propensity to innovation in products (goods,
boxplot)5
Source: Eurostat – Community Innovation Survey, RTDS
calculations
Figure 6: Propensity to innovation in services products
(boxplot)
Source: Eurostat – Community Innovation Survey, RTDS
calculations
j) Across sectors, the most important drivers of R&D
investment for companies include the expectation of increased
market shares, public funding, environmental impact, and firm size.
The most important barriers reported at the firm level include the
lack of market information
and uncertainty with respect to the demand for innovative goods
and services, funding gaps, lack of information technology, and
difficulty in identifying co-operation partners. Product market
regulation is found to have an important negative effect on all
sectors and especially on the low technology sector group.
The role of EU policy instruments with regard to structural
change
The main findings in terms of sector specific policies can be
summarized as follows:
a) Sector-relevant R&D and innovation policy measures can be
found in most European
countries. Only seven countries reported that they had no
explicit sector-specific policy measures in place. These included
the three largest European economies: France, Germany, and the
United Kingdom. In almost all countries non-sector specific policy
measures greatly outnumber sector specific measures, reflecting a
general reluctance of current R&D&I policies toward using
more specific interventions.
5 See Footnote 12, whereas in this figure the ‘white circle’
marks the Interquartile Range.
-
18
Figure 7: Number of sector specific versus total number of
measures
Source: JIIP, Joint Inventory of Policy Measures (ERAWATCH and
INNO-Policy TrendChart)
b) In line with this, the bulk of policy measures do not
specifically address or favour individual sectors. Only 12% (117)
of the identified national active measures are sector specific. Of
these, 87 relate to the sectors looked at in this study. Almost all
are only relevant to one
sector. Sixty measures relate to precompetitive research and/or
applied industrial research. Twenty-seven measures can be
classified as broader innovation oriented.
803
2087
No specific sector Other sectors RTDS specific sectors
-
19
Figure 8: Number of (sector specific) measures per country (910
measures)
Source: JIIP, Joint Inventory of Policy Measures (ERAWATCH and
INNO-Policy TrendChart)
c) Only about half of the twenty-five sectors included in the
study are covered by one or more sector-specific policy measures
somewhere.
d) Some sectors account for a relatively large number of sector
specific measures – e.g.
‘computer and related activities’ has 8 while the sectors water
supply, aerospace and pharmaceuticals have 5. The R&D services
sector, with 50 sector specific measures, is clearly an outlier as
it comprises R&D institutes, which may be addressed by as
generic support for R&D and not with a sector-specific view. In
terms of overall budget, the aerospace and the pharmaceuticals
sectors come on top. They are followed at some distance by
telecommunications and R&D services.
e) The most frequently observed policy priorities are in the
categories “Governance and horizontal research and innovation
policies” and “Enterprises”. The least frequently observed policy
priorities are “Human Resources” and “Markets and innovation
culture”.
0 10 20 30 40 50 60 70
Rep. of Korea
Fyrom
Romania
Slovakia
Greece
Russian Federation
Cyprus
Luxembourg
Bulgaria
Iceland
Slovenia
Croatia
Italy
Estonia
Israel
Spain
China
Denmark
Hungary
Portugal
Germany
Switzerland
Czech Republic
Latvia
Japan
Malta
Netherlands
Sweden
Turkey
United States
Norway
Finland
Ireland
France
United Kingdom
Austria
Poland
Lithuania
Belgium
3
8
11
8
11
4
12
12
14
11
13
14
12
12
13
14
17
16
19
19
22
21
16
8
19
23
25
26
28
26
22
31
35
38
38
37
41
35
69
1
2
3
17
1
1
1
1
3
5
3
2
3
6
15
5
1
2
3
1
3
2
14
1
1
2
1
11
1
1
2
5
1
2
1
Not sector specific
RTDS sector specific
Other sectors
-
20
f) Industry perceives that EU funding has a significant function
in limiting the risk of precompetitive research and in supporting
technology-driven innovation.
g) EU funding has a higher significance for SMEs than for large
companies. Yet, small firms are disadvantaged in terms of
transaction costs for learning about and using such programmes
and procedures. SMEs are also disadvantaged in that they often
have short-term, close-to-market objectives while EU programmes
tend to be designed more in line with the longer term, upstream
research objectives of large firms.
h) There is moderate agreement on the appropriateness of the
thematic continuity of EU funding instruments. In general all
sectors agree that EU R&D funding is a source of high
reputation.
i) A number of sector specific EU initiatives are seen as key
drivers for innovation. Examples
include the Innovative Medicine initiative (IMI), the Water
Framework Directive, and the regulation on Energy Efficiency in
buildings. Apparently well targeted specificity plays a role.
j) The EU KETS initiative has been a major step in supporting
R&D and innovation in Europe. Experts view the relevance of the
different KETS to vary across the twenty-five RTDS sectors.
Importantly, taken as a set, the sector range of KETS relevance is
very broad.
If a more general conclusion has to be drawn from these
findings, it points in a direction that –
largely due to the relatively small number of sector specific
policies - sector specific impacts of both EU policies and national
policies are rather limited. They are mostly confined to sectors
with a strong public interest and/or public good character.
Policy consequences and recommendations
The cross-sector analysis leads to a set of strong findings:
1) Promoting R&D is not a choice for Europe - it is a
necessity. R&D intensity is positively and significantly linked
to productivity growth, a necessary step towards competitiveness
and output growth.
2) A strong indication of a success-breeds-success phenomenon
results from strong indications that high productivity countries
and knowledge-intensive sectors can capitalize better on their
R&D effort.
3) Long term efforts regarding R&D expenditure will be
rewarded. The effect of R&D on productivity occurs over time
and also diminishes over time.
4) A more significant involvement of industry stakeholders in
the definition of R&D and Innovation policies together with
academia will produce benefits for industrial sectors. In
particular it should build on a more fine-grained segmentation
of sectors identified by the NACE classification, according to
specific characteristics and intrinsic innovation and operation
patterns.
5) At the same time, structural change and the international
division of labour are affected by phenomena such as increasing
product standardization across geographical markets,
internationalizing product development, cost-driven relocation of
R&D from home bases, and
shortening technology life cycles and time-to-market. Simply
put, things happen much faster today than too narrowly targeted
policies generally can address.
6) There seems to be room for policy action in raising the
ability of EU Member States’ industry to exploit an innovative
activity in the market and profit from it.
Only about half of the twenty-five sectors are covered by one or
more sector-specific policy
measures somewhere in Europe. Moreover, there is wide variation
in terms of the number, type, and weight (budget size) of specific
sector policy measures these sectors benefit from. The policy
implications of this finding will be heavily influenced by the
general political position of a public administration in favour or
against specific sectoral policy intervention.
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21
The cross-sectoral comparison leads to a number of conclusions
with direct consequences for R&D policymaking:
a) R&D is clearly a driver for innovation, but national
R&D intensities in many sectors with lower medians of R&D
intensity can significantly exceed those of supposedly high
R&D
performing sectors according to R&D intensity. In other
words, in many countries R&D in medium or even low-tech sectors
is maybe more relevant for policies aiming at the improvement of
the innovation system than a focus on a very small proportion of
high-tech, high R&D performing companies.
b) Some R&D intensive sectors show relatively low
contributions to economic growth in some countries.
c) There is evidence of a gradual shift of the European economy
towards knowledge-intensive
activities. In this respect, it is also interesting to note the
strong growth of R&D in an enabling industry such as
‘manufacture of instruments and appliances for measuring’. In value
added growth, however, this sector mostly follows the overall
economy.
d) An analysis focusing on transfers between sectors (value
chain approach) clearly shows that sectors with a high level of
R&D activity also strongly benefit from embodied R&D
diffusion,
primarily via domestic knowledge-intensive trade relations, but
also via international trade.
In these cases high tech knowledge is integrated via
intermediate products in finally technically competitive products.
In other words, from an innovation policy perspective ‘traded
technology’ should be seen as highly relevant.
e) The analysis of localization of R&D (along the lines of
increasing global standardization and competition of R&D
locations, cost-driven relocation of R&D to emerging countries,
and regional adaptive R&D close to local market needs) reveals
a small but interesting group of sectors which seems to be less
affected by the trends of internationalization, e.g. non-
metallic mineral products, general machinery or machine tools
sector, recycling and instrument appliances to measuring testing
and checking. A closer look at these sectors may help to deepen the
insights in the mechanisms of R&D location and provide guidance
for nationally/regionally-oriented R&D&I policy.
Overall, the analysis cautions against a policy approach which
focuses on sector-specific policies. It suggest a policy approach
which complements sector-oriented policies with policies
oriented
toward the specific characteristics of groups of industries as
they can be found in regional
specialisations. Higher level policy making (e.g. national
and/or EU) can provide the more general conditions and incentive
structures for such specific policies. This could help to bridge
differences “within” sectors and “between” countries as such
differences are often larger than those found between sectors.
Advances in the field of sectoral analysis
What can policymaking expect from the evidence-base this kind of
analysis can deliver? What is the context in which the results have
to be interpreted? What are the main contributions and limitations
of the analysis? The following key issues have been encountered and
are at various points discussed in the full reports:
a) What is the relevance of the sector-concept and thus of the
sectoral analysis itself?
The cross-sector analysis clearly shows that in many cases
intra-sectoral differences in the research and innovation relevant
behaviour of companies are as large, or even larger than
the difference between sectors. The same is true for the
countries/sectors comparison: due to the fact that countries often
tend to specialise in subsectors or niches, the comparative
data on sector-behaviour between countries can show larger
differences than those between the behaviour of all sectors in one
country. The key factor here is that a sector is to a large extent
determined by the characteristics of its products. Over time,
however, the manufacturing of these products has become distributed
over sometimes very long, complex and geographically distributed
value chains (from materials to components to subsystems to
final products).
b) Does the data allow us to develop the full picture? To a very
limited extend. General problems concerning the availability of
data, limited or broken time series, but also comparability across
countries and over time considerably
-
22
constrain the possibilities for empirical analysis at a level
necessary for evidence-based policy making.
c) Potential alternatives, and what data problems do they
incur?
Promising and currently much advocated alternative approaches to
sector analysis and
sector policy such as a value-chain based approach and analysis
tend to encounter even more severe problems of data availability
and comparability than the traditional sector-based approaches.
Completely different approaches based on “big data” analysis are in
very early stages of development, but point in promising
directions.
In conclusion:
Even though the analyses do bring deeper insight, one should be
careful in drawing too uniform policy conclusions. One should be
aware in particular of the internal heterogeneity
of traditional statistical groupings.
Use a differentiated approach to clustering of sectors which has
been developed in this study; different policy problems require
different kinds of clustering and different data analysis.
Seek alternatives and provide additional approaches to
traditional statistics (e.g. full text analysis, ‘interactive’
data). Foster the conceptual frame for empirical and statistical
analysis
(e.g. an accepted taxonomy of ‘fields of technology’
complementary to the existing taxonomies of fields of science).
During the analysis strong indications were encountered that new
approaches would offer promising, if not necessary, avenues for
research and for policymaking. Such new approaches would focus on
value chains and the building of eco-systems to support such value
chains and on the respective specialization patterns of groups of
firms and of countries along these value chains. Our discussion of
the value chain approach and the data found on the importance of
innovation
based on the transfer of embodied R&D between firms and
sectors lead to an increased focus on manufacturing, or more in
general, on the production of tradable goods and services. R&D
and innovation performance should become more strongly viewed in
the light of their impacts on exports and trade balance as
determinants of growth and competitiveness.
But for the time being these approaches suffer from a lack of
detailed and comparable empirical evidence. The provision of the
statistical basis for such types of analysis for the purpose of
policymaking requires policy action.
1 INTRODUCTION
Private investments in R&D are major drivers of innovation,
competitiveness and sustainable economic growth. Increasingly,
private investments in R&D are becoming essential for
addressing the grand challenges our society faces, like ageing
populations, climate change and food supplies.
The effect of private investments in R&D is not limited to
the company that does these investments to create long term
sustainable competitiveness, on the contrary, very often the
broader social effects are more important.6 In other words, not
only the company itself benefits through increased efficiencies
and/or the creation of new markets, but also society at large
through new high quality jobs and the use of new products and
processes. And in between we also often see benefits with supplying
and client companies in the value networks in which R&D
investors operate.
For this purpose, the European Union puts in place policies
targeted at increasing private R&D investments in the general
context of the Lisbon Strategy and more recently Europe 2020, in
order
to favour the development of a knowledge-based economy. These
policies need to be based on a sector-specific understanding of
factors driving or hampering the increase in private R&D
investments. For this purpose this study provides qualitative and
quantitative evidence to support European policies and instruments
aimed at strengthening the EU research and innovation system
6 See for an overview of analyses U. Muldur e.o: “A new deal for
an effective European Research Policy: the design and impacts of
the 7th Framework Programme”, Springer 2007.
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23
and provide input and feedback to the strategies for
specialisation and the conditions to increase competitiveness.
The study provides a comprehensive qualitative and quantitative
picture and analysis of the factors influencing the production and
use of knowledge at the sectoral level, constructing and
applying
indicators to measure the specific characteristics of each
sector, of their approach to research and development, the specific
structural changes occurring and those related to knowledge
production and use. The analysis also measures the structural
differences between sectors and the way they have evolved over
time. The results of the analysis are to be used in improving the
understanding of the relevant strategies and policies to be
implemented. They should provide input for the optimisation of
Community actions to strengthen the European research and
innovation system.
For the partners participating in this project these are already
good and sufficient reasons to take
care of a policy environment that accommodates and stimulates
private sector R&D investments. But there is more. In the long
run the nature of the R&D done in Europe is expected to
influence the structure of economy and society. The successive
editions of the EU Industrial R&D Scoreboard and other analyses
have shown that the sector composition of the European economy and
of the R&D done in these sectors differs considerably from the
main competitor world regions. And even if the individual companies
are competitive compared to their counterparts, we should ask
what
the effects of the sector composition of R&D are on the
longer-term distribution of economic activities and the impact
thereof on the competitiveness of Europe. This is something which
of course is very difficult for individual companies to address,
and thus should be considered as a topic for European policymaking.
This requires a very good understanding of the dynamics of sectoral
R&D and the interactions with the social, economic and policy
environment (including publicly funded research).
Other reasons to look in more detail at R&D in sectors are
changes in the structures and character
of research itself. The growing complexity of the issues that
need to be addressed leads to new forms of collaboration (e.g.,
outsourcing), users/consumers starting to play an important role in
several research domains (e.g. demand driven research,
crowd-sourcing), the phase of transferring research results to its
application environments becoming more important (translational
research), growing experimentation in several areas, the
distinction between fundamental and applied research getting
increasingly hard to make, etc.7 We may expect to find different
patterns and timing of these changes in different sectors, partly
in relation to the
dominant technological trajectories in these sectors. This has
implications for policy making, because it changes drivers and
incentives.
This report summarizes the main findings of the RTDS study. It
does so in three parts. Part I starts with the objectives and the
general analytical approach and methodology of the study. Part II
follows this with inter-sector comparisons, the discussion on
structural change, and the experimental effort to cluster the
results in terms of meta-sectors. This Part is intended to
stand
alone as a brief exposition of the core results of the RTDS
study. Part III provides summaries of stylized facts about sector
performance over the previous decade (available data) and future
prospects (expert opinion) for each of the twenty-five RTDS
sectors. This Part is intended to indicate a possible standardized
way of briefing a non-expert reader over a very complex sectoral
environment.
We urge the interested reader to obtain the umbrella reports of
the various analytical tasks on which the present document is based
in order to form a more rounded picture of the very
extensive and diverse quantitative and qualitative analysis that
underpins the results reported herein.
7 Jos Leijten The future of RTOs: a few likely scenarios;
Contribution to the DG Research expert group on the future of key
actors in the European Research Area, working paper, 2007,
Directorate-General for Research Cooperation EU 22962 EN
(ftp://ftp.cordis.europa.eu/pub/foresight/docs/thefutureofkeyactors-working-papers_en_09_web.pdf)
ftp://ftp.cordis.europa.eu/pub/foresight/docs/thefutureofkeyactors-working-papers_en_09_web.pdf
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24
2 PART I: APPROACH AND METHODOLOGY
2.1 Approach
The study approach was developed on the basis of literature
review of existing research that served as main input for defining
the overarching conceptual framework and detailed methodology of
the analysis carried out. The diagram below pictures the
relationship between the different
elements.
Figure 9: Literature review as guiding element of the study
approach.
The remaining of this part aims to provide a short overview of
the study, and then give some details of the approaches applied to
1) quantitative and qualitative sectoral analysis, 2) cross sector
analysis and 3) collaboration and networking analysis.
This study is part of the effort to develop an evidence-based
ERA monitoring system based on
indicators. This framework of indicators enables analysis of the
evolution of national research and innovation systems in Europe,
their interconnectivity and performance, as well as their impact on
structural change towards a more knowledge-intensive economy. In
addition to monitoring progress in the realization of the ERA, such
a framework of indicators must facilitate the analysis of the
impact of research, research policies and programmes on the
competitiveness of Europe and of the contribution of research to
growth and to addressing societal and global challenges.
One of a set of several complementary studies, this study
concentrates on the current strength and weaknesses of knowledge
production and use in different industrial sectors of the European
economy, the identification of factors affecting structural
adjustment processes, and the analysis of the effect of intra-EU
investment in R&D on the sectoral structure of the European
economy.
Accordingly, the study is about sectors, structural adjustment,
knowledge production and use across differential sector
environments, and the role of intra-EU R&D investment in this
process. The ultimate goal is to create an evidence-based framework
that will facilitate both the monitoring
of the ERA and the analysis of the impact of R&D and related
policies on European growth and competitiveness.
Literature Review
Literature Review
Conceptual Framework
Detailed Methodology per WP
-
25
2.2 Study Objectives
The study aimed at (i) carrying out a quantitative and
qualitative analysis of R&D investments and structural changes
in sectors and (ii) delivering policy recommendations. Specific
study objectives
include:
Identify strength and weaknesses of knowledge production and use
in different industrial sectors of the European economy
Identify factors (including effectiveness of policies) that
either accelerate or reduce structural adjustment processes
Analyse the effect of intra-EU investment in R&D on the
sectoral structure of the EU economy
The study provides a comprehensive qualitative and quantitative
picture and analysis of the factors influencing the production and
use of knowledge, constructing and applying indicators to measure
the specific characteristics of each sector, of their approach to
research and development, of the specific structural changes
occurred and those related to knowledge production and use. The
analysis also measures the structural differences between sectors
and the way they have evolved over time. The results will be used
to improve the understanding of the relevant strategies and
policies to be implemented and will provide input for the
optimisation of Community actions to strengthen the European
research and innovation system.
The study aimed at identifying the key factors of sectoral
structural change and at constructing sets of indicators to
benchmark performance across European countries and major global
competitors. The general purpose of this and other studies launched
by the European Commission is to develop an evidence-based ERA
monitoring system to verify the developments against the framework
of long-term objectives for the realisation of the ERA.
The study focused on a broad range of selected sectors. It
examined, for each of these sectors, the intrinsic characteristics
in a static and dynamic perspective and highlights structural
evolution and the development of drivers regarding investment,
production, procurement, transfer and use of research. The study
also investigated and highlighted complex relationships within the
sectors – looking at industrial, economic, and employment features
– as well as inter-sectoral and geographical relationships.
Specific care was placed in the analysis of the relationships
between structure, patterns and behaviour, and related
policies.
2.3 Coverage of sectors and data time series
The study covers twenty-five sectors, twenty-two of which were
predefined by the Commission. The remaining three (3) sectors were
selected by the study team from a longer list of sectors provided.
The sectors were defined as a combination of various NACE Rev 1
sectors at 2, 3 and 4 digit levels.
The time line of data to be analysed was set to 7 years,
counting back from the start of the study in 2010, and subject to
data availability for the latter years. A main challenge has been
the transition from NACE Rev1 to NACE Rev 2 in the middle of the
time series, and in particular the not corresponding new
definitions of many of the sectors.
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The table below provides the description of the sectors for the
NACE Rev 1.1 data.
Table 2: Analysed Sectors NACE Rev 1.1
Sector originally defined in NACE Rev 1.1
1 Manufacture of food products and beverages and manufacture of
machinery for these products (15 + 29.53)
2 Manufacture and sales of textiles and manufacture of machinery
for these products (17 + 29.54 + 51.41/2 + 51.83 + 52.41/2)
3 Reproduction of recorded media and related manufactured goods
(22.3 + 24.64/5)
4 Manufacture of basic chemicals and manufacture of paints,
varnishes and similar coatings, and glues and gelatines (24.1 +
24.3 + 24.62)
5 Manufacture of pharmaceuticals (24.4)
6 Manufacture of plastic products (25.2)
7 Manufacture of other non-metallic mineral products (26)
8 Manufacture of general purpose machinery and machine tools
(29.1 + 29.2 + 29.4)
9 Manufacture of office machinery and computers (30)
10 Manufacture of electrical motors, generators and transformers
(31.1)
11
Manufacture of electricity distribution and control apparatus;
manufacture of insulated wire
and cable; manufacture of accumulators, primary cells and
primary batteries; electricity, gas, steam and hot water supply
(31.2 +31.3 + 31.4 + 40)
12 Manufacture of electronic valves and tubes and other
electronic components (32.1)
13 Manufacture of medical and surgical equipment (33.1)
14 Manufacture of instruments and appliances for measuring,
checking, testing, navigating and other purposes, industrial
process control equipment and optical instruments and photographic
equipment (33.2 + 33.3 + 33.4)
15 Manufacture of motor vehicles, manufacture of parts and
accessories for motor vehicles
and their engines (34.1 + 34.3)
16 Manufacture of aircraft and spacecraft (35.3)
17 Recycling (37)
18 Collection, purification and distribution of water (41 + 45.
24)
19 Construction (45 except 45.24)
20 Cargo handling and storage (63.1)
21 Telecommunications (64.2)
22 Services for computer and related activities (72 except
72.5)
23 Manufacture of lighting equipment and electric lamps
(31.5)
24 Manufacture of television and radio transmitters and
receivers (32.2 + 32.3)
25 Services for research and development (73)
The Twenty-five sectors cover part of the economy, but together
provide a good cross-cutting
picture, because the sector list has large diversity, includes
growing and declining sectors, economically strong and weaker
sectors, etc.
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2.4 Foundations for the quantitative and qualitative sectoral
analyses:
conceptual framework
2.4.1 Dimensions of the sectoral analysis
Our conceptual framework is centred on the sector and recognizes
that:
Sectors experience differential rates of growth (or decline)
over time, manifested in structural adjustment.
Both supply and demand factors are responsible for structural
adjustment, in particular technological progress and output demand
elasticity.
Sectors with a high rate of technological progress and
productivity tend to gain in importance over sectors with a low
rate of technological progress and productivity which tend to lose
ground in terms of employment and value added.
Individual organizations (firms) play a core role in promoting
and adapting to technological
progress, but their behaviour and performance are critically
influenced by sectoral competitive environments and by the socio
economic and institutional context.
Sectoral contingency – persistent and significant differences
between sectors with respect to average factor intensities,
dominant firm strategies, firm entry and survival rates, etc. – and
firm-level variety – heterogeneity of behaviour – coexist.
Sectors are inter-dependent in terms of both markets and
scientific and technological knowledge.
2.4.2 Basic structure of reasoning and analysis
This study targets the sector (meso) level. As the previous
paragraphs and our detailed literature review make evident,
however, it is impossible to analyse sectors without extensive
reference to both the firm (micro) and the national/regional
(macro) levels. Our analytical framework thus
takes into consideration all three levels of analysis, while
maintaining focus on sectors and sector performance.
Figure 10 illustrates our conceptualization of the causality
path to sector performance.
Figure 10: The basic logic of this study
Drivers of
Innovation
& Change
Sector
Characteristics
Innovative
Behaviour
Innovation
Performance
Economic
Performance
Structural
Change
Cross sector structural long-term influences to the meso
landscape
Structural long-term influences to the firm behaviour
Sec
tor
1
Sec
tor
2 Sec
tor
3 Sec
tor
n
Cros
s sec
tor dy
nami
cs
Mes
o-e
conom
icla
ndsc
ape
Drivers of
Innovation
& Change
Sector
Characteristics
Innovative
Behaviour
Innovation
Performance
Economic
Performance
Structural
Change
Cross sector structural long-term influences to the meso
landscape
Structural long-term influences to the firm behaviour
Sec
tor
1
Sec
tor
2 Sec
tor
3 Sec
tor
n
Cros
s sec
tor dy
nami
cs
Drivers of
Innovation
& Change
Sector
Characteristics
Innovative
Behaviour
Innovation
Performance
Economic
Performance
Structural
Change
Cross sector structural long-term influences to the meso
landscape
Structural long-term influences to the firm behaviour
Sec
tor
1
Sec
tor
2 Sec
tor
3 Sec
tor
n
Cros
s sec
tor dy
nami
cs
Mes
o-e
conom
icla
ndsc
ape
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The core of this schematic is the set of sectors indicated by
the grey areas. The sectors are related vertically – serve
different markets, feed each other products/services (supply chain)
and
information – or horizontally – serve the same or similar
markets, exchange information voluntarily or involuntarily (spill
overs). These cross-sector dynamics are conditioned by the
macroeconomic framework conditions (upper left-hand side of the
figure) that include macroeconomics proper (development stage of
national economy, GDP growth, exports, imports, factor conditions,
etc.) as well as the socio-cultural environment and the
characteristics of the national/regional innovation system. Ongoing
structural change is fuelled by changing demand conditions (demand
elasticity of the sector output) and by supply-side conditions
(such as technological opportunities, rate of technological
advance, firm competencies and rivalry). Structural change implies
that over time some sectors grow faster than others and take
prominent
positions in national economies, some decline and lose ground in
terms of employment and value-added, some disappear or merge, and
new sectors spring up. This continuous movement is illustrated in
the right-hand side of the figure. Finally, to point to the fact
that sector performance reflects the performance of the companies
operating in it, the front part of the figure shows that within
each sector micro level forces combine with those meso and macro
forces to create the drivers for innovation at the firm level. In
combination with sector characteristics and other
structural long-term influences they will affect firm innovative
behaviour, resulting in a certain level of innovative and economic
performance. Each of the blocks of the micro (intra-sector)
level
dynamics has its own logic, approaches and bodies of literature.
And in turn each depends on a set of indicators, some of which are
broadly available whereas others are less developed.
The set of relationships that guides hypothesis-building at the
micro-meso level is outlined below:
Innovation Behaviour and Innovation Performance of for-profit
companies are two key variables to be explained. Innovation
Behaviour affects Performance.
Firm-level factors affect Innovation Behaviour. Demand,
inter-sector trade and competition, as well as institutions, also
play a role.
Sector characteristics frame Behaviour and together with firm
level factors influence Behaviour. Sectoral effects on Behaviour
can be direct or indirect (mediated by firm-level characteristics.)
Sector characteristics directly affect Performance (e.g.
technological opportunities, appropriability regime,
regulation).
Universities and other public research organizations are
important players in the innovation
systems with several knowledge-intensive sectors.
Knowledge flows and influences occur not only within sectors but
also across sectors. Both flows feed into the overall landscape of
structural change across the economy.
Feedback effects occur and in the long run structural change is
the result of the complex interaction of institutional, supply and
demand factors. In turn, structural adjustment will affect sector
characteristics. New conditions are created in the new structure of
sectors and
can in turn become new drivers of change.
The long lasting tension in empirical analyses between the micro
and the meso levels of observation is fully appreciated in this
study. Time and again micro data on innovation and performance
indicate significant heterogeneity in firm behaviour. Similarly,
sectoral data persistently point at significant differences between
sectors with respect to average factor intensities, technological
opportunities, firm strategies, and so forth. Rather than
contradictory, we consider these complementary and co-existing
(Peneder, 2010). Several attempts to reconcile the
two levels of analysis in the form of various sector taxonomies
have been presented in our literature review. The more appreciative
approach of evolutionary economics8 comes closer to the complexity
of the issues and has underlined the development of the systems of
innovation literature, also including the thinking on sectoral
systems of innovation. Sectoral innovation
system concepts that will help guide hypothesis building at this
level are outlined below.
8 Evolutionary theory perceives economic development as a
dynamic, cumulative, open-ended process. Historical contingencies
introduce path-dependencies. Economic agents are heterogeneous,
face uncertainty, and behave under bounded rationality. Innovations
frequently refer to incremental improvements of products and
processes on established technological trajectories. Radical
innovations appear infrequently and open up fundamentally new
technological paradigms. Structural change is inevitable because
innovation affects various industries differently. (Krueger, 2008,
p.344).
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29
According to Malerba (2004), a sector is a set of activities
unified by some related product group that share some basic
knowledge. A sectoral system is composed of a set of agents engaged
in
market and non-market interactions for the creation, production
and sale of sectoral products. This system has a knowledge base,
technologies, inputs and existing or potential demand. The
agents are individuals or organizations at various levels of
aggregation, with specific learning processes, competencies,
organizational structure, beliefs, objectives and behaviours. A
sectoral system undergoes change and evolution through the
co-evolution of its various elements.
A sectoral system has three building blocks:
Knowledge and Technology – A sector can be characterised by a
specific knowledge base, technologies and inputs. Sector specific
learning regimes depend on the accessibility, opportunity and
cumulativeness of the relevant knowledge. The specificities of
technological
regimes and knowledge base restrict the patterns of firm
learning, competencies, behaviour and organization of innovative
and productive activities.
Actors and Networks – A sector is composed of heterogeneous
agents, including both individuals and organizations. Firm
heterogeneity is key. Agents are connected through market and
non-market interactions.
Institutions – Included are norms, routines, common habits,
established practices, rules,
laws, standards, and so on. National institutions may have major
effects on sectoral systems as, for instance, the patent system,
property rights, and antitrust regulations.
The elements of a sectoral system, including demand, technology,
knowledge base, learning processes, organizations, and
institutions, co-evolve. Macro level factors (e.g. overall economic
growth/decline characteristics) also impact significantly sector
characteristics and performance and, thus, affect the intensity of
structural adjustment. They will be part of the sector appraisal in
this study. Macro-level factors are taken as exogenous in this
study. One of the reasons is that
there is no good theory to support the analysis; the linkage
between the meso and the macro level is not well developed in
mainstream economic theory.
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30
IndicatorsFigure 11 replicates Figure 10 with the various cells
now indicating the sources of empirical data in Europe. In addition
the study extensively made use of sector expert interviews
and information from an online survey.
Figure 11: Sector Performance and Change with data sources
applied
While the study dealt with long lists of sector characteristics,
seven indicator components have been chosen as especially relevant
in summarizing sector economic characteristics and knowledge
production/utilization:
Value Added and Growth in Value Added
Employment and Employment Growth
Labour Productivity and Growth in Labour Productivity
Birth and Death rates of business enterprises
Research Intensity
Innovation System Efficiency
(Main) Drivers and Barriers of Innovation
2.5 Approach to cross sector analysis
The RTDS study collected an enormous set of quantitative and
qualitative information and experiences concerning the value and
weakness of the data availability at the sectoral level. The
sectoral analysis increased awareness about the necessity of a
multi-dimensional view on sectors, about logic relations between
relevant factors explaining R&D and about the
intra-sectoral
heterogeneity of sub-activities within sectors and the
heterogeneity of distribution of this sub-activities across
countries (which partially explained national heterogeneity within
sectors according to different indicators, e.g. R&D intensity).
All this has been done in an isolated manner
without a broader attempt to compare all sectors
systematically.
The cross sectoral analysis collects the various findings and
serves as a complementary contribution to the quantitative and
qualitative analyses. The analysis focuses on the
commonalities and differences across sectors in terms of
patterns of R&D investment and innovation, and furthermore on
the common factors shared by sectors that catalyse or hamper
investment in the production and transfer of knowledge. The
objective is to make a cross-comparison of the sectors along the
following lines:
Characterisation (from an R&D&I perspective) of the
sectors
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31
Comparison of performance in R&D investment and use of the
different sectors (from traditional to high technology sectors)
Inspired by seminal work on comparative analyses of innovation
at the sectoral level (Pavitt 1984, Marsili et al. 2001, Malerba et
al 1997, Castellacci 2008), commonalities and differences
across
sectors were discussed. The ma
