POLICY BRIEF
The potential knowledge divides among Member States as a
consequence of the simultaneous implementation of Major EU
policies, including Horizon 2020
Disclaimer: The information and views set out in this policy brief are those of the authors and do not
necessarily reflect the official opinion of the Commission. The Commission does not guarantee the
accuracy of the data included in this study. Neither the Commission nor any person acting on the
Commission's behalf may be held responsible for the use which may be made of the information
contained therein
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Authors: Christian Hartmann, Marie Therese Eysselt, Cornelia Sterner
Title of the contract: PotentialKnowledge (SC-04) The potential knowledge divides among Member
States as a consequence of the simultaneous implementation of major EU policies, including Horizon
2020
Contract number: 30-CE-0496943/00-66
Client service: European Commission, DG Research and Innovation, Directorate C, Unit C2
Abstract:
The overall objective of this policy brief is to support the work of ERIAB by supplying additional
qualitative or quantitative analysis and exploratory work in order to make it possible for ERIAB to
formulate substantiated opinions in face of potential knowledge divides that may arise as a
consequence of the implementation of Horizon 2020. A hierarchical cluster analysis for European
regions was performed in order to get a deep understanding of specific policy needs with respect to the
knowledge and innovation divide. Based on these statistical clusters a need analysis has been
performed for research and innovation policy interventions in order to pin down RDI policy
intervention needs and associated potential interventions. In addition a policy analysis was carried out
in order to screen and structure potential policy responses in the upcoming programming period. A
special focus was put thereby at the analysis of potential synergies between Horizon 2020 and ESIF.
Finally the results of the needs analysis were matched with the outcomes of the policy analysis and
conclusions were drawn o the basis of this assessment.
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Table of Contents
1
2
3
Executive Summary ......................................................................................................................... 5
1.1.
Objectives ................................................................................................................................ 5
1.2.
Analyses ................................................................................................................................... 5
1.3.
Conclusions.............................................................................................................................. 6
Introduction..................................................................................................................................... 8
2.1.
Objectives and Scope of the brief ........................................................................................... 8
2.2.
Do regional disparities and knowledge divides really matter? ............................................... 8
The EU knowledge and innovation divide – trends and structures ................................................ 9
3.1.
Characterising the EU knowledge divide ................................................................................. 9
3.1.1
Few regions have already reached the EU target of 3.0% .............................................. 9
3.1.2
Regional R&D expenditure increases slowly ................................................................. 10
3.2.
Regional private sector R&D investments concentrate in Centre-North Europe ................. 11
3.2.1
3.3.
4
Regional innovation capacity varies strongly across European regions........................ 13
Characterising European regions and policy challenges ....................................................... 15
3.3.1
The results of the cluster analysis ................................................................................. 15
3.3.2
Needs analysis for the identified clusters ..................................................................... 18
Analysis of policy responses .......................................................................................................... 20
4.1.
Horizon 2020 and the CSF for Cohesion Policy ..................................................................... 20
4.2.
Key actions addressing the knowledge and innovation divide ............................................. 20
4.2.1
Horizon 2020 – “Stairways to excellence”..................................................................... 20
4.2.2
CSF for Cohesion Policy – Regional Smart Specialisation Strategies ............................. 21
4.3.
Policy Interaction between ESIF and Horizon 2020 .............................................................. 23
4.3.1
Diachronic interaction between instruments ............................................................... 23
4.3.2
Synchronic interaction between instruments ............................................................... 23
4.4.
Synthesis – Identification of potential gaps and synergies ................................................... 25
5
Conclusions.................................................................................................................................... 27
6
References ..................................................................................................................................... 29
7
Annex ............................................................................................................................................. 30
7.1.
Methodological Notes ........................................................................................................... 30
7.1.1
Dataset and Indicators .................................................................................................. 30
7.1.2
Cluster Analysis.............................................................................................................. 31
7.1.3
Qualitative Analysis ....................................................................................................... 31
7.2.
Complementary Tables.......................................................................................................... 33
3
7.3.
Complementary Figures ........................................................................................................ 41
4
1 Executive Summary
1.1. Objectives
The overall objective of this policy brief is to support the work of ERIAB by supplying additional
qualitative or quantitative analysis and exploratory work in order to make it possible for ERIAB to
formulate substantiated opinions in face of potential knowledge divides that may arise as a
consequence of the implementation of Horizon 2020. This policy brief at hand policy brief is needed
in view of a potential knowledge divide that can arise among Member States, since calls under
Horizon 2020 will be competitive in nature, and may represent for some Member States of the
European Union a significant proportion of the total available public research and innovation budget.
Accordingly the specific objectives are:
• To provide an assessment of the complementarities among the major EU policies as a
consequence of the prospective implementation of Horizon 2020. An assessment is necessary
especially with a view on the structural and cohesion funds.
• To identify which regions of Europe might suffer from negative impacts on their innovation
capacities as a consequence of the implementation of Horizon 2020.
• To provide policy relevant conclusions for research and innovation policy, especially with a
view on how apparent complementarities could be strengthened by anticipative policies.
1.2. Analyses
A hierarchical cluster analysis for European regions has been performed in order to get a deep
understanding of specific policy needs with respect to the knowledge and innovation divide. With the
exemption of overseas territories all European regions at NUTS2 level have been covered by the
analysis. In total 5 clusters have been identified by our hierarchical cluster analysis:
Cluster 1 “Catching up regions”: are characterized on average by moderate levels of R&D intensity
and R&D output in terms of EPO patent applications. Their regional R&D capacities and knowledge
base are somewhat stronger than the “Inbetweeners” (Cluster2) but they do possess on average a
weaker economic basis and are less densely populated than the latter.
Cluster 2 “Inbetweeners”: display on the one hand on average the highest levels of GRP per Capita
and population density after “Cities and Agglomerations”. While being economically strong they show
on the other hand rather weak values for R&D in- and output. Regions belonging to “Inbetweeners”
are to some extent former old industrial areas but do also consist of capital city regions not belonging
to Cluster 5 because of a less strong regional R&D endowment.
Cluster 3 “Lagging behind regions” are characterised on average by modest levels of economic
output and low population density. Regions in this cluster do display on average the lowest values for
R&D intensity, Human Resources in Science and Technology and EPO patent applications. in which
one measures a low knowledge and innovation intensity, entrepreneurship, creativity, a high
attractiveness and a high innovation potentials, that can be considered as local pre-conditions enabling
the acquisition of external innovation.
Cluster 4 “Frontrunners”: are characterised by strong knowledge and innovation producing
capacities, which are represented by the highest average values for R&D intensity and EPO patent
applications of all clusters. “Frontrunner” regions specialize in Key Enabling Technologies, with a
high generality and originality of science-based regional knowledge, and a high R&D endowment.
“Frontrunners” do have a strong regional industry base and do have good capabilities in basic and
applied research.
Cluster 5 “Big Cities and Agglomerations”: show the highest regional economic output and
population density of all five clusters. Equally the highest values for HRST as % of labour force are
5
reached on average in this cluster. This reflects the fact that large cities do host public administration
and headquarters of enterprises. For “Big Cities and Agglomerations” average values for R&D
intensity and EPO patenting are comparatively high. The regions in this cluster are stemming from two
groups: one the one hand they belong to the big European capital cities1, on the other hand also big
agglomerations are represented here. The cluster can be thus characterised by strong R&D capacities
with critical mass in specific science fields and technology domains in particular in basic research.
Based on these statistical clusters a need analysis has been performed for research and innovation
policy interventions in order to pin down RDI policy intervention needs and associated potential
interventions. In addition a policy analysis has been carried out in order to screen and structure
potential policy responses in the upcoming programming period. A particular focus was put thereby
and the analysis of potential synergies between Horizon 2020 and ESIF. Finally the results of the
needs analysis were matched with the outcomes of the policy analysis and conclusions were drawn o
the basis of this assessment.
1.3. Conclusions
Conclusion No 1: The heterogeneity of Europe’s regions requires place-based strategies to boost
R&D investments
Regional policies focusing on the increase of R&D investments should take into account the large
diversity of European regions and the need for adopting place-based approaches. For many regions,
the 3% headline target may indicate more the necessary direction of development rather than a fixed
and reachable target. The cluster analysis has shown that in particular “Lagging behind regions”
display such low levels of R&D investment that it would be wishful thinking to expect them to come
even close to the headline target within the next programming period.
Conclusion No 2: “Lagging behind regions” (Cluster 3) should focus on enhancing the
innovation capacity of domestic firms instead of building cathedrals in the desert
“Lagging behind regions” should in first place focus on the promotion of growth and new jobs. Thus –
as the identification of potential gaps and synergies has shown - priority should be given to fostering
innovation in regional enterprises and to the promotion of entrepreneurial activity instead of trying to
build public regional R&D capacities at any rate. The understanding of innovation should be
pragmatic and based on the existing regional potentials. The current concept of innovation being
promoted and targeted in European policy is still dominated by high-tech activities. Broadening the
definition of ‘innovation’ will be critical to ensure that these regions can ‘buy-in’ to smart growth
goals in place-appropriate ways. Knowledge-and service innovation (for example in agri-food, ecotourism etc.) could be suitable concepts to enhance regional innovative capacity. ‘Bottom-up’
innovative potential in Europe, potentially a significant source of growth for ‘lagging regions’ requires
attention and support. A place-specific and broader definition of innovation should be developed as a
priority.
Conclusion No 3: The new policy mix offered by Horizon 2020 and ESIF seems to be most
appropriate for “Catching-up regions” (Cluster 1) and “In-Betweeners” (Cluster 2)
“Catchting-up regions” and “Inbetweeners” share the potential for expanding their R&D and
innovation capacity at the same time. They should be the main target regions for the “staircase to
excellence” and a smart mix of Horizon 2020 and ESIF policy instruments. Both types of regions have
the need to tap into global flows of knowledge and to strengthen all parts of the regional innovation
system in order to increase the diffusion and absorption of new scientific and technological
knowledge. While policy challenges are much related for both types of regions the initial levels for
future development efforts do vary: “Inbetweeners” need apart from widening their regional R&D
capacities in particular to strengthen their innovation output. The region specific policy mix thus needs
1
Due to the NUTS 2 categorisation of territories some big cities that would be part of the cluster (like
Stockhom or Helsinki) can be found in the “Frontrunners Cluster”
6
to gear new knowledge into new products and to foster firm academic spin-offs in order to strengthen
the regional base of innovative firms. “Catching-up regions” are somewhat stronger in this respect that
“Inbetweeners”.
Conclusion No 4: “Frontrunners” and Cities & Agglomerations may benefit in particular from
KETs based policy actions
Regional smart specialisation strategies may provide for “Frontrunners” and “Cities &
Agglomerations” adequate means of contributing to the EU ambitions on smart growth in terms of
R&D expenditure. Currently, there are a number of high-performing KETs based economic clusters in
parts of Europe characterised by specialised facilities and critical mass of actors. These should be
supported to enable them compete globally but there should not be an expectation that all parts of
Europe promote KETs based economic activities as a source of future growth. Targeted actions for
these specific parts of the European space must be developed and implemented with this in mind.
Conclusion No 5: Innovation oriented competence development (i.e. education and training)
needs to complement measures to stimulate R&D and innovation investments in all types of
regions
Regional innovation capacity does not exclusively stem from strong investments in research and
development. Education and training are equally important to enhance the absorptive capacities of
enterprises for innovations and to broaden the regional knowledge base. Thus the role of universities
appears critical in encouraging and supporting the innovation agenda in Europe. Education and
training should be part of the innovation policy mix in all regions. Nevertheless the specific focus may
vary between the different types of regions: while “Catching-up regions” and “Inbetweeners” could
benefit most from an increase of the regional knowledge base “lagging behind regions” could take
most out of targeted entrepreneurial training actions fostering firm formation rates and contributing to
the growth of the local population of enterprises.
Conclusion No 6: Reduced administrative burdens are needed to attract and mobilise firms to
regional innovation policy actions
One of the main obstacles for successful participation of firms in innovation projects in the current
structural funds programming period is the existence of high administrative burdens. Thus regulatory
complexity can put, as experiences from regional funding agencies show, severe constraint to regional
innovation policy programming. This applies generally to all projects funded under Structural Funds
regulations, especially when State aid is involved, but in particular also to innovations projects, which
due to their complex, risky character and lack of tangible outputs do not fit very well into the current
regulatory environment. As detailed funding rules for ESIF will be under the command of member
states there is for the next programming period again an inherent risk of “gilding” simplified EU rules.
Thus it will be essential to ensure that simplified rules will come really into effect also at the level of
regions implementing ESIF measures.
Conclusion No 7: Regional/national policy coordination will need to improve significantly to
allow for synergies between Horizon2020 and the Cohesion Fund
The aspired synergies between Horizon 2020 and ESIF do imply a strongly increased need for smart
policy coordination as policy interactions may reach down to project level. As experience from recent
RIS3 development processes has shown this will be a very challenging task for all member states and
regions. New smart governance models will be needed at national and regional level to cope with the
new possibilities that funding rules will allow in the upcoming programming period. Adequate
organisational development efforts will be needed to overcome existing administrative silos and to
break down barriers between the administrational entities involved. Particular attention should thus be
paid to the provision of specific EU support for capacity building and training. In addition support
could be given by external experts and peers from other regions to accompanying the organisational
development processes.
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2 Introduction
2.1. Objectives and Scope of the brief
The overall objective of this policy brief is to support the work of ERIAB by supplying additional
qualitative or quantitative analysis and exploratory work in order to make it possible for ERIAB to
formulate substantiated opinions in face of potential knowledge divides that may arise as a
consequence of the implementation of Horizon 2020. This policy brief at hand policy brief is needed
in view of a potential knowledge divide that can arise among Member States, since calls under
Horizon 2020 will be competitive in nature, and may represent for some Member States of the
European Union a significant proportion of the total available public research and innovation budget.
Accordingly the specific objectives are:
• To provide an assessment of the complementarities among the major EU policies as a
consequence of the prospective implementation of Horizon 2020. An assessment is necessary
especially with a view on the structural and cohesion funds.
• To identify which regions of Europe might suffer from negative impacts on their innovation
capacities as a consequence of the implementation of Horizon 2020.
• To provide policy relevant conclusions for research and innovation policy, especially with a
view on how apparent complementarities could be strengthened by anticipative policies.
2.2. Do regional disparities and knowledge divides really matter?
Albeit the objective of this policy brief is to assess the potential knowledge divide among European
regions it is far from self-evident why such disparities must lead to intervention needs for public
policy. The most well-known arguments based on economic theory for challenging policies addressing
regional disparities do come from the neoclassical approach. According to this school of economic
thought, regional disparities as a rule should vanish over time. The neoclassical arguments for
vanishing disparities between nations or regions have also been the basis for the convergence literature
(Armstrong 1995, Barro / Sala-i-Martin 1995). But also empirical analyses of regional disparities in
the territories of the European Union do reveal that in terms of economic development, disparities
between European countries have been reduced over the last two decades, showing a clear
convergence between countries (NORDREGIO et al. 2007).
So why should temporarily growing disparities (e.g. knowledge divides) matter? A first argument is
also stemming from recent empirical research addressing regional disparities. Firstly, disparities
between regions within countries have increased in almost all EU-27 countries over the last two
decades. This phenomenon is mainly due to the strong performance of the capital regions and other
metropolitan regions. Secondly the distribution of economic activities across the European territory is
increasingly polarised towards the larger agglomerations and most advanced regions that enjoy higher
rates of economic growth (NORDREGIO et al. 2007).
But there are also a clearly expressed political will at European level to put forward interventions
reducing regional disparities. Legal foundations have been laid with the single act of 1985: The first
major Treaty revision constitutionalised Cohesion policy by introducing the specific title of Economic
and Social Cohesion (Manzanella 2009). The policy objective was defined as promoting the “overall
harmonious development” of the Community and “strengthening economic and social cohesion”,
particularly by “reducing disparities between the various regions and the backwardness of the least
favoured regions” (Article 130a). Since its reform at the end of the 1980s cohesion policy of the
European Union (EU) has become the second most important item of the EU budget after the
Common Agricultural Policy (Eiselt 2007).
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3 The EU knowledge and innovation divide – trends and
structures
3.1. Characterising the EU knowledge divide
3.1.1 Few regions have already reached the EU target of 3.0%
Only 37 European regions met the 3% target in 2009. Regions with higher R&D expenditure as share
of GDP are mostly concentrated along few corridors, for instance Styria (Austria) to England and
Denmark to Finland (ESPON 2012).
Figure 1: Overall R&D expenditure as percentage of GDP, 2009
Source: ESPON Atlas 2012
9
Urban areas are strong in R&D, but the largest metropolitan areas and capital cities are not necessarily
the strongest regions. Example of regions with high R&D expenditure shares, beyond first tier
metropolitan cities are e.g. Midi-Pyrénées (France), Oulu (Finland), Saxony (Germany), and Styria
(Austria). R&D intensity of the latter is a result of a concentration of knowledge intensive industries
(e.g. automotive and machine-building). Such structural effects of specific industrial concentrations
may be observed also in other regions such as Wolfsburg in Germany, Västsverige in Sweden or Oulu
in Finland (ESPON 2012). There is no fixed territorial pattern visible: top- and bottom-ranked places
may be neighbours. Some regions with very high R&D investment shares are located next to regions
of the lowest investment shares. In the UK for example, Shropshire and Staffordshire directly border
Cheshire, a region with a very high rate of R&D investments. This may be a result of effective
clustering putting the neighbouring regions at a disadvantage. Most Eastern and Southern regions are
lagging behind. These lagging behind places are located mostly in Eastern Europe and in Southern
parts of Italy, Portugal and Spain (ebda). But there are regions in other parts of Europe with low levels
of R&D expenditure such as Galicia and the Scottish Highlands. Regions with R&D expenditure
lower than 0.2% of GDP are located in Bulgaria, Poland, and Romania (ebda).
3.1.2 Regional R&D expenditure increases slowly
The level of R&D expenditure for the entire EU has increased during the past decade. The pace at
which this change is occurring is however comparatively low as compared to other countries and
regions of the world. The territorial pattern of this low increase is heterogeneous, but most regions
have achieved little progress. Although innovation is not always mirrored in R&D expenditures, this
development indicates that achieving a smart economy is a challenging task (ESPON 2012).
Many regions do show with R&D expenditure shares for the last decade. 17% of the European regions
have not made any progress or have even faced a reduction of the share of GDP spent on R&D. These
regions are spread across the EU territory without any clear territorial pattern. Among them are a few
regions with particularly high levels of R&D expenditure, for instance Braunschweig, Upper Bavaria
and West Sweden (ESPON 2012).
Some regions which were especially well ranked in 2009 (Figure 1) have increased their R&D
expenditure level considerably in previous years. Among these regions are Pohjois-Suomi and LänsiSuomi in Finland and Midi-Pyrénées in France. This is related to the fact that returns from R&D are
likely to occur in regions with an already critical mass of R&D efforts. In Pohjois-Suomi this positive
trend results mainly from collaboration activities of the University of Oulu with the private sector
(ESPON 2012).. Other examples are the Irish regions, which have considerably increased their R&D
expenditures. In Cork and Limerick biotechnology/NBIC investments have considerably contributed
to this development by drawing on the regional universities and institutes of technology and attracting
foreign investments (ebda).
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Figure 2: R&D expenditure as percentage of GDP, 2003-2009
Source: ESPON Atlas 2012
3.2. Regional private sector R&D investments concentrate in
Centre-North Europe
Innovation is related to R&D. Although the precise link between innovation and R&D has been
debated, there is consensus that R&D in many cases is a pre-condition for innovation. Innovation
refers to new or highly improved products and processes, which may be achieved with or without
R&D. Innovation without prior regional R&D activities may occur by exploiting regionally existing
knowledge, or by using knowledge from outside the region (ESPON 2013).
11
As recognised in the ‘Innovation Union’ flagship initiative, innovation is difficult to measure at
national or regional level. This flagship initiative suggests measuring innovation by means of R&D
indicators, including private expenditures in R&D (BERD) (Map 2.5), since they may give at least
some corresponding indication. The ‘Innovation Union’ flagship initiative emphasises the need for
closing the innovation divide between the most innovative and other European regions. It asks for a
better tailoring of innovation policies to the relative strengths of individual regions (ESPON 2013).
Figure 3: Private expenditure on R&D as percentage of GDP, 2007 to 2009
Source: ESPON Atlas 2013
High levels of private R&D expenditure can be spotted mainly in Centre-North Europe. The
“corridors” with particular high private expenditure on R&D as share of GDP are similar to those of
overall R&D expenditure. They are located between Copenhagen (DK) and Pohjois-Suomi (FI), MidiPyrenèes (FR) and Bavaria (DE) and most parts along the corridor from Austria to Southeast England
(ESPON 2013).
12
Top regions in private R&D expenditure are related to specialised industries and tertiary institutions.
Some of the most specialised regions in Germany, such as Stuttgart and Braunschweig, which are
dominated by the automobile industry, show high levels of private investments in R&D. In South-East
England the proximity to tertiary institutions such as the University of Cambridge indicates the
importance of universities for creating spin-off effects in particular in the Biotechnology Sector and
thereby enhancing BERD. Regions with low private R&D expenditure are concentrated in East and
South of Europe. Most regions in Eastern and Southern Europe, in particular in Greece, Bulgaria,
Romania, Southern Italy and the South and North-West of Spain display only low private R&D
expenditures (ESPON 2013).
The territorial pattern of overall (combining both private and public) R&D expenditure (Figure 1) is
strongly related to private R&D expenditure (Figure 2). This indicates that for the most innovative
regions, private R&D activities are a key driver. However, there are also regions where R&D
expenditure of the public sector and institutions of higher education account for most expenditure on
R&D. This is e.g. the case in national capitals (Berlin, Wien or Madrid) and some regions in Germany,
Sweden, the Netherlands and Southern France (ESPON 2013).
3.2.1 Regional innovation capacity varies strongly across European
regions
The Regional Innovation Scoreboard 2012 classifies European regions into four innovation
performance groups, similarly to the Innovation Union Scoreboard: there are 41 regions in the first
group of "innovation leaders", 58 regions belong to the second group of "innovation followers", 39
regions are "moderate innovators" and 52 regions are in the fourth group of "modest innovators" (see
figure 4).
The results show that there is considerable diversity in regional innovation performance not only
across Europe but also within the Member States. Most of the European countries have regions at
different levels of innovation performance. The most pronounced examples are France and Portugal:
in both countries the performance of regions (including overseas territories) ranges from innovation
leaders to modest innovators. Other countries with wide variations in performance are Czech
Republic, Finland, Italy, the Netherlands, Norway, Spain, Sweden and the United Kingdom: all have
at least one region in 3 different innovation performance groups. The most homogenous countries are
the moderate innovators Greece, Hungary, Poland and Slovakia, where all regions except one each are
also moderate innovators. The situation is similar in Romania and Bulgaria where most or even all
regions are modest innovators (European Commission 2012a).
The most innovative regions in the EU are typically in the most innovative countries: Sweden,
Denmark, Germany and Finland. In Germany, 12 out of 16 regions are innovation leaders. In Finland
3 out of 5 regions and in Sweden 5 out of 8 regions are innovation leaders. Only in Denmark, the
majority of the regions are innovation followers, and 2 out of 5 regions are innovation leaders,
including the capital region of Hovestaden (Copenhagen) and Midtjylland (European Commission
2012a).
Since 2007, the regional performance has been relatively stable. Most European regions seem to
maintain their innovation potential and activity. However, there are clear upward movements. The
number of innovation leaders increased by 7 regions between 2007 and 2011. Four regions improved
from moderate or modest innovators to the category of innovation followers. 8 regions are
continuously improving their innovation performance scoring higher in each of the three Scoreboards
(2007, 2009, 2012): Niedersachsen (DE), Bassin Parisien and Ouest (FR), Calabria and Sardegna (IT),
Mazowieckie (PL), and Lisboa (PT) (European Commission 2012a).
13
Figure 4: Innovation performance by regions
Source: Regional Innovation Scoreboard, 2012
Most of the moderate and modest innovation regions barely use Framework Programme funds but they
are usually high users of Structural Funds for business innovation. Several innovation leaders, on the
other hand, are very successful in attracting grants under the Research & Development Framework
Programme: More than 90% of leading FP absorbers are the regional innovation leaders. The Regional
Innovation Scoreboard 2012 shows that at this stage there is a lack of common pattern linking
innovation performance and the use of EU funds in regions across time. For example, some of the
most dynamic upward movers like Bassin Parisien and Ouest (FR) were low users of EU funds. At the
same time, in the case of Calabria, Sardegna (IT) and Mazowieckie (PL) the steady increase in
innovation performance happened during a period of increased use of EU funds (European
Commission 2012a).
14
3.3. Characterising European regions and policy challenges
3.3.1 The results of the cluster analysis
We have performed a hierarchical cluster analysis for European regions in order to get a deeper
understanding of specific policy needs with respect to the knowledge and innovation divide2. With the
exemption of overseas territories all European regions at NUTS2 level have been covered by the
analysis. The following dataset has been used3:
1. Gross Regional Product per Capita (GRP) in PPP reflects the overall level economic activity
of a region. Thus there may be regions that do have weak R&D capacities but are nevertheless
strong in economic terms.
2. Population density is being used as an indicator to assess a region’s potential for critical mass
for R&D and enterprises and its geographical position (i.e. periphery).
3. Gross Expenditure on R&D as share of GRP is being used an input indicator for the
characterisation of a region’s R&D capacities.
4. Human Resources in Science and Technology as share of the total labour force is being used
as a second input indicator characterising the regional knowledge base.
5. EPO patent applications per 1 Mio inhabitants is being used as an output indicator for R&D
activities and indicates in how far regional innovation systems have been able to transform
new scientific and technological knowledge into products.
Table 1: Average values for the clusters
BRP/Capita in PPP
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
27.163,33
78.522,00
15.977,74
58.527,23
131.095,16
Population Density
131,71
702,01
85,06
301,09
2.647,01
HRST as % of EPO patent
apllications per 1
GERD as % regional
labour force Mio. Inh.
of GRP
1,19
35,43
76,72
1,06
32,12
31,82
0,39
23,89
4,33
3,09
40,91
247,69
2,01
47,05
137,37
Source: JR-POLICIES
In total 5 clusters have been identified by our hierarchical cluster analysis; the average values are
displayed in table 1 while a brief discussion of each cluster is provided below:
Cluster 1 “Catching up regions”: are characterized on average by moderate levels of R&D intensity
and R&D output in terms of EPO patent applications. Their regional R&D capacities and knowledge
base are somewhat stronger than the “Inbetweeners” (Cluster2) but they do possess on average a
weaker economic basis and are less densely populated than the latter. “Catching up regions” can be
found in particular in the EU15 countries (with the exemption of Portugal) but also among new
member countries such as Estonia, Latvia, Slovenia, Croatia and the Czech Republic.
Cluster 2 “Inbetweeners”: display on the one hand on average the highest levels of GRP per Capita
and population density after “Cities and Agglomerations”. While being economically strong they show
on the other hand rather weak values for R&D in- and output. Regions belonging to “Inbetweeners”
are to some extent former old industrial areas like Pas de Calais (FR) or Greater Manchester (UK), but
do also consist of capital city regions not belonging to Cluster 5 because of a less strong regional R&D
endowment (e.g. Attiki (GR) or Közép-Magyarország (HU).
Cluster 3 “Lagging behind regions” are characterised on average by modest levels of economic
output and low population density. Regions in this cluster do display on average the lowest values for
R&D intensity, Human Resources in Science and Technology and EPO patent applications. in which
2
For a more detailed discussion of limits and details of the methodology and datasets used please consult section
7 in the annex.
3
We have used average values for the years 2001-2009 for our analysis.
15
one measures a low knowledge and innovation intensity, entrepreneurship, creativity, a high
attractiveness and a high innovation potentials, that can be considered as local pre-conditions enabling
the acquisition of external innovation. Most of these regions are situated in eastern European member
states such as Latvia, Poland, Slovakia, Hungary, Romania and Bulgaria, but also in Greece, Southern
Italy, Southern Spain and Portugal.
Figure 5: Geographical distribution of the clusters
NUTS2
Classification
Cluster 1: Catching-up Regions
Cluster 2: Inbetweeners
Cluster 3: Lagging behind Regions
Cluster 4: Frontrunners
Cluster 5: Cities and Agglomerations
·
0
205
410
820
1.230
1.640
2.050 kilometers
GIS-processing: Dipl.-Ing. Clemens Habsburg-Lothringen, MAS
Sources:
Eurostat; JR-POLICIES
Source: EUROSTAT, JR-POLICIES
Cluster 4 “Frontrunners”: are characterised by strong knowledge and innovation producing
capacities, which are represented by the highest average values for R&D intensity and EPO patent
applications of all clusters. “Frontrunner” regions specialize in Key Enabling Technologies, with a
high generality and originality of science-based regional knowledge, and a high R&D endowment.
“Frontrunners” do have a strong regional industry base and do have good capabilities in basic and
applied research. These regions are mostly located in three corridors reaching across EU territory: the
first corridor is reaching from Styria (AT), via Germany, the Netherlands and Belgium to the Southern
England, the second one ranges from Pais Vasco (ES) and Southern France to North Eastern Italy. In
addition “Frontrunner” regions can be found in Nordic countries starting in Hovestaden (DK) and
reaching to North Finland.
Cluster 5 “Big Cities and Agglomerations”: show the highest regional economic output and
population density of all five clusters. Equally the highest values for HRST as % of labour force are
reached on average in this cluster. This reflects the fact that large cities do host public administration
and headquarters of enterprises. For “Big Cities and Agglomerations” average values for R&D
intensity and EPO patenting are comparatively high. The regions in this cluster are stemming from two
groups: one the one hand they belong to the big European capital cities4, on the other hand also big
agglomerations are represented here. The cluster can be thus characterised by strong R&D capacities
4
Due to the NUTS 2 categorisation of territories some big cities that would be part of the cluster (like
Stockhom or Helsinki) can be found in the “Frontrunners Cluster”
16
with critical mass in specific science fields and technology domains in particular in basic research.
Cities regions do also to some degree show a bias in favour of public R&D capacities. These regions
are spread all across Europe with Vienna (AT) or London (UK) as typical examples for cities and the
Randstad (NL) or the Ruhr area (DE) as typical representatives for big agglomerations.
17
3.3.2 Needs analysis for the identified clusters
Cluster
RDI policy challenges
Policy intervention needs
Relevant policy level
Beneficiaries
1 (‘Catching up
regions’)
•
•
Establish global pipelines for new
knowledge by fostering networks of
excellence
Support for the development of R&D
infrastructure and academic training
facilities
Thematic/regional orientation of
innovation funding, in order to
strengthen the regional knowledge
base through co-operation with strong
external partners in sectors with
strong specialization
Support of technology transfer and
diffusion
Regional / EU
Local firms, Universities,
Research Centres, RTOs
Establish links to European research
network in relevant domains
Support for the development of R&D
infrastructure and academic training
facilities
Create early stage start-up
programmes using both non-financial
and financial instruments to support a
viable long-term market for
innovation finance
Support of technology transfer and
diffusion
Regional / EU
Local firms, MNCs,
Universities, RTOs, R&D
infrastructures
Introduce entrepreneurship key
competence development at all levels
of education and training (curriculum,
teacher training, assessment)
Achieve minimum necessary levels of
Regional
Local SMEs, Local
Entrepreneurs, Education and
Training Institutions
•
•
•
•
Catching up with
“Frontrunner” regions
Tying in into European
knowledge networks
Broadening regional R&D
capacities
Broadening regional
innovation capacities
Smart diversification of
existing economic strengths
•
•
•
2 (‘Inbetweeners’)
•
•
•
•
•
3 (‘Lagging behind
regions’)
•
•
Moving up the development
ladder to cluster 1
Tying in into European
knowledge networks
Insufficient public sector
R&D capacities
Insufficient entrepreneurial
capacities for R&D and
innovation
Technological upgrading of
the regional production
system
•
Development of a regional
basis for growth and
employment
Poorly developed R&D
infrastructure
•
•
•
•
•
18
•
•
•
4 (‘Frontrunners’)
•
•
•
•
•
5 (‘Big Cities and
Agglomerations’)
•
•
•
•
Lack of R&D personnel and
knowledge base
Lack of critical mass of
enterprises
Distinct regional competences
in fields of activity far from
science and technology (e.g.
agriculture, tourism)
•
•
Keeping the momentum of
past success
Maintaining firm’s production
and R&D capacities in the
region
Shortage of qualified
personnel
Uptake of innovations at
higher Technology readiness
levels (i.e. in the domain of
Key enabling technologies)
Development of European
interregional clusters
•
Competing with other big
cities for highly skilled people
Mismatch between public and
private R&D capacities
Private sector R&D is
performed by headquarters
that may be subject to global
relocations
Integration of the creative
sector into the regional
innovation system
•
•
•
•
•
•
•
absorptive capacity for innovation
within local firms
Support of technology transfer and
diffusion with low thresholds
Promote non technological
innovations
Reach critical mass in R&D activities
through concentration of public
support
Priority to triangular projects by
Universities-RTOs-Enterprises
Support to knowledge and
technological transfer mechanisms to
related sectors
Thematical/ regional orientation of
R&D funding in KETs
EU / National
Big local enterprises, MNCs,
hidden champions, Universities,
Research Centres, RTOs
Reach a critical mass in R&D
activities through concentration of
public support with a particular
priority for basic research
Priority to triangular projects by
Universities-RTOs-Enterprises
Support to knowledge and
technological transfer mechanisms to
related sectors
Support to research in HRST and
translational research
EU / National
Big local enterprises, MNCs,
hidden champions, Research
Centres, Universities, RTOs
Source: JR-POLICIES, ESPON KIT
19
4
Analysis of policy responses
4.1. Horizon 2020 and the CSF for Cohesion Policy
Both funding instruments have shared objectives linked to the smart growth objective of Europe 2020,
but have different characteristics and focus:
Horizon 2020 focuses on excellence in research and innovation, and science and technology-driven
research and development, and increasingly targets its investments towards addressing societal
challenges and fostering the competitiveness of industry, with special attention paid to SMEs. Horizon
is under centralised direct management mode. Its work programmes have been designed at EU level
and will evolve annually with a planning perspective of 1-2 years. Projects will be selected at EU level
through independent and excellence based peer review stemming mainly from competitive calls for
proposals. There is no pre-defined geographical distribution of funding,. The overwhelming majority
of projects have a trans-national perspective (except fundamental research (ERC) and parts of the new
SME instrument).
Cohesion Policy (CP) focuses on applied research and innovation for the purpose of regional socioeconomic development, on innovative SMEs and how to build capacities for innovation and growth
through the promotion of innovation friendly business environments. Programmes are designed in
shared management with national / regional authorities and with a medium to long term planning
perspective (3-10 years). The financial distribution is place-based, with a defined financial envelope
and a larger allocation to less developed regions. Projects are selected on the basis of economic, social
and territorial impact criteria and mainly involve actors from one Member State or region (except for
territorial cooperation). The new regulatory provisions for thematic concentration ensure that the
thematic objective for R&I is part of the minimum 80% concentration for ERDF funds in more
developed regions (50% in less developed regions).
The crucial difference between the funds is that while the amount of structural funds in a region can be
predicted as regions are given a seven-year envelope, Horizon 2020 funding will be competitive and
thus funding for projects is less certain. So while there is more funding for research and innovation in
both programmes, the key for research clusters is how to maximise their access to this funding.
4.2. Key actions addressing the knowledge and innovation divide
4.2.1 Horizon 2020 – “Stairways to excellence”
Horizon 2020 pays special attention to closing the research and innovation divide in Europe, by
promoting a more balanced development of the European Research Area. The European Commission
has set out a strategy for the creation of “stairways to excellence” through ERA chairs, teaming and
twinning initiatives, and stimulating cross-border science networks.
• The objective of 'ERA Chairs' is to attract outstanding academics to institutions with a clear
potential for research excellence, in order to help these institutions fully unlock this potential and
hereby create a level playing field for research and innovation in the European Research Area.
This will include institutional support for creating a competitive research environment and the
framework conditions necessary for attracting, retaining and developing top research talent within
these institutions.
• The objective of ‘teaming’ is to establish, reinforce and develop partnerships between countries
and regional research actors and international leading counterparts. Teaming initiatives under
Horizon 2020 will leverage support from the Cohesion Funds to help countries and regions build
critical mass from existing pockets of excellence and move to a positive spiral of development in
research and innovation. Teaming will support the planning of larger-scale activities aimed at
creating or significantly enhancing an international centre of excellence. It will, inter alia, enable
existing partnerships between regional institutions and international research partners to move to a
20
•
•
new level of integration and excellence and/or the establishment, through partnership, of new
centres of excellence of significant scale.
‘Twinning’ facilitates partnerships between regional research entities which have a substantial
potential for excellence and capacity to innovate and international leading counterparts. Typically,
these initiatives will be of limited scale (e.g. with the aim of strengthening a defined field of
research) and/or will support the formation of viable new partnerships; they will involve activities
such as staff exchanges, expert visits, conferences and training. Both twinning and teaming should
allow for a variety of partnership models and configurations, adapted to the regional and scientific
realities.
Cross-border science networks can be stimulated through COST5. Within Horizon 2020, COST
should further bring together "pockets of excellence" and play a mobilising role not only for the
less participating countries but also for the enlargement countries and the European
neighbourhood policy countries. COST could make a significant contribution to the development
of a 'staircase to excellence' for research organisations across Europe.
4.2.2 CSF for Cohesion Policy – Regional Smart Specialisation
Strategies
The European Commission's Cohesion Policy aims to reduce differences between regions in Europe
and to ensure growth across Europe. Structural Funds are among the main tools to implement the
policy and it is within this framework that Smart Specialisation was introduced.
The intellectual origins of the smart specialisation can be traced to a number of complementary
sources. It builds partly on the work undertaken by Dominic Foray and the Knowledge for Growth
expert group in the framework of the European Research Area (ERA). This group explored why
Europe was lagging behind the U.S. in competitiveness with a particular focus on research and
development (R&D) intensity and dissemination of new technologies to explain growth differentials.
The group identified that research investment in Europe was overly fragmented, lacking in coordination of research and innovation (R&I) investment between stakeholders, and lacking critical
mass. It noted a clear 'me-too' syndrome in that regions made investments in areas that were too
similar and fashionable, such as information and communication technologies (ICT), nano- and biotechnologies. Its recommendation was to support structural change and enable the emergence of new
activity sectors or industries by investing in R&I in areas of strategic potential in each of Europe's
regions, acknowledging that these differ with respect to areas of strength and potential.
Box 1: Smart Specialisation in a Catching up Region – the Moravia-Silesia Region (CZ)
Diversifying regional industry and catching up with high ambition
The Moravia-Silesia Region is mainly an industrial region with a long tradition. Its key industries include
mining (brown coal extraction), metallurgy, engineering, energy, automotive industry, IT, electronics and newly
also biotechnology. Strategic vision of the region: To become progressive innovative region utilising existing
know-how for tackling new challenges and belonging to 25 most innovative regions in Europe by 2020. The
global objective is to improve the competitiveness of the economy of Moravian-Silesian Region in global
markets through the following horizontal key actions: (1) Technology transfer, (2) Human resources (3)
Internationalization. Five major sectors do represent the vertical priorities for the upcoming Programming
period:
– Engineering and metallurgy (e.g. modern materials)
– Energy (e.g. energy savings, co-generation units)
– Automotive (e.g. modular electric drives, low-cost automation)
– Biotechnology (e.g. regenerative medicine)
– IT + electrical engineering (e.g. mobile technologies, measuring and testing systems, smart grids…
Key tool for supporting research, development and innovation activities will be grant programmes, innovation
vouchers, grants for jobs created in research and development in companies, support for international scientific
teams, a fund for micro loans, etc. administered by the Moravia-Silesia Region.
5
COST is a bottom-up, open networking mechanism that encourages international exchanges and co-operation
of researchers within Europe and beyond. Joint activities such as conferences, short-term scientific exchanges
and publications are supported.
21
In the present funding period, the average Cohesion Fund spending on R&I across Europe as a whole
is 25%. In the ongoing discussions around the design for the forthcoming programming period (2014–
2020), it is suggested that within developed and transition regions, 80% of investments should be
channelled through energy efficiency, renewables, competiveness of SMEs and R&I. In lessdeveloped regions, this target is 50%. At the same time, to receive funding from the European
Regional Development Fund (ERDF), a Research and Innovation strategy for Smart Specialisation
(RIS3) must be developed. For many regions in Europe, more efficient use and management of the
structural funds is a crucial factor to overcome the economic crisis, reinforce their capacities and
create well-being for their citizens. The RIS3 does not equal the Operational Programme for the
implementation of the funding, but is a strategic framework that should guide and align European,
national, regional and private investments in the regions or member states.
The ideas around Smart Specialisation are well in line with the Commission's overall growth strategy,
EU 2020, and its response to the ongoing economic crisis. These include a focus on identifying niche
areas of competitive strength, solving major societal challenges (bringing in a demand-driven
dimension), innovation partnerships emphasizing greater co-ordination between different societal
stakeholders and aligning resources and strategies between private and public actors of different
governance levels. Smart specialisation was also found to be an appropriate strategy to counteract EU
R&D investment being amassed in a few northern regions and as a way for southern regions to find
their strengths, develop their innovation potential and access these funds.
Box 2: Smart Specialisation in a Lagging behind Region – the Balearic Islands (ES)
Focus on technology and innovation related to tourism
The tourism sector represents almost the half of the regional GDP (43%), and generates about 30% of the
employment in the region. Tourism is the main economic engine of the islands and has important linkages with
other sectors.
Tourism related research and innovation activities will also form the key priority in the new Smart
Specialisation Strategy building on past actions. Already the Science, Technology and Innovation Plan of the
Balearic Islands 2009-2012 has prioritized its actions in areas of technology, markets and sectors where the
existing clusters in the region work. Key tool for supporting regional innovation is RDI-cluster based policy.
Regional clusters positioned in the fields of ICT for tourism, media, music, maritime technologies, and
Biohealth & will be promoted further so as to contribute to enhance technology domains in tourism and
improved to competitive position of the region.
The concept of Smart Specialisation builds on the accumulated knowledge from working with
Regional Innovation Strategies (RIS and RITTS). This indicates that it is a fruitful concept, but some
alterations are needed. In addition to the challenges identified by Foray and his group, which have also
been recognized by the practitioners in these processes, it has been observed that in many regions the
process was driven by external consultants rather than regional stakeholders, which created problems
for appropriation and engagement. Furthermore, there was too strong a focus on technology supply
and R&D, which led to a failure to recognize other important areas for innovation, such as demand
stimulation, market access, social and service innovation, and calls for greater integration of policies.
Box 3: Smart Specialisation in an Inbetweener Region – the Attiki Region (GR)
Technological Development and upgrading driven by public demand
The Attiki region is the Capital-Region of Greece The most populous and most densely populated region in the
country, the producer of 45% of the National GDP and of 57% of National Tertiary Sector, the place of
employment of 37% of National Workforce, the place of operation of 35% of National Enterprises, the main
exporter, main transportation hub, main university and research centre. Vision for the region: “Transforming
our Present Weakness into Future Opportunity: Attica as a Centre of Excellence for Sustainable Waste
Management and Waste Economic Exploitation”. Goal is the development of a state-of-the-art and modern
infrastructure for solid and liquid waste treatment and the formation of a whole new sector related to all aspects
of waste treatment (including supporting services), as well as sub-products’ processing and exploitation.
Function as interregional centre for urban or farming waste treatment for the production of biofuel and
fertilisers. Policy actions should thus target the development of a state-of-the-art and modern infrastructure for
solid and liquid waste treatment and the formation of a whole new sector related to all aspects of waste
treatment (including supporting services), as well as sub-products’ processing and exploitation.
22
Furthermore, in previous strategies, there was a lack of governance structures incorporating
entrepreneurs or an entrepreneurial viewpoint in the development, priority setting and implementation
of the strategy. In the RIS3 strategy, the bottom-up perspective is heavily emphasized and although it
is a multi-level approach, there is a re-emphasis on the regional ownership of the process. Stakeholder
involvement in this sense must mean much more than just consultation in the traditional manner in
which stakeholders have been involved in the Nordic countries. It also means that the traditional nodes
of power must be reconsidered and even expanded, because they may form a relational lock-in
situation obscuring future potential, which would need public support and encouragement.
4.3. Policy Interaction between ESIF and Horizon 2020
4.3.1 Diachronic interaction between instruments
Synergies between the two instruments shall be set free on two different roads: both upstream and
downstream actions are considered as key actions under the current Community Strategic Framework
for Cohesion Policy. Upstream actions are those to prepare regional R&I players to participate in
Horizon 2020 projects. Key actions are:
• Capacity-building in Member States and regions for R&I excellence and technological
change, by investing in innovative solutions and research infrastructures and equipment, in
particular those of European interest in the context of Joint Programming Initiatives, the ESFRI
('European Strategy Forum on Research Infrastructures') research infrastructures, the development
of the Regional Partner Facilities and within the Strategic Energy Technology Plan. This includes
support for “satellite infrastructures” linked to the ESFRI-related research infrastructures,
national/regional research facilities and technology centres, competence centres and science parks,
with a clear focus on enhancing applied research, through reinforced cooperation with industry to
leverage private R&I investment.
• The upgrading of smaller research partnering facilities of regional importance into research
excellence.
• The modernisation of universities and higher education institutions and research
organisations, including the development of post-graduate studies.
Downstream actions should provide through enabling ESIF projects the means to exploit and diffuse
swiftly R&I results stemming from Horizon 2020- funded basic research into the market, with
particular attention to creating innovation-friendly market conditions and business environment, in
particular for SME. Key actions are:
• Innovation in enterprises includes the dissemination and adoption of new technologies, in
particular key enabling technologies, through cooperation with actors in the world of research and
education, technology transfer, applied research, technology development and demonstration
facilities, in order to help companies develop more innovative products, processes, marketing and
services and diversify the national/regional economy through new high-growth activities;
• Capacity-building for the swift economic exploitation of new ideas stemming from research
and innovation (R&I). This includes support for clusters, cooperative partnerships between
research, education and innovation actors, business R&I infrastructures, promotion of R&I
business advisory services, also in the field of services, creative hubs, cultural and creative
industries and social innovation, pilots and demonstration activities, and creating more demand for
innovative products through public procurement of innovation.
4.3.2 Synchronic interaction between instruments
Apart from sequential interactions between policy instruments a policy mix leading to higher levels of
R&D investment can also follow a simultaneous approach. According to Art 55(8) CPR an operation
may receive support from one or more European Structural and Investment Funds or from one or more
programmes and from other Union instruments, provided that the expenditure item included in a
request for payment for reimbursement by one of the ESI Funds does not receive support from another
23
Fund or Union instrument, or support from the same Fund under another programme. Three different
scenarios for synergies between Horizon 2020 and ESIF are possible:
ESIF and Horizon 2020 used in the same project: ESIF and Horizon 2020 funding may be
combined in the same project (cumulative funding). The possible form of ESIF investments for this
purpose could be funding of specific parts or expenditure items within an Horizon 2020 project
(equipment, staff cost for R&D&I activity, …). In this case the synchronisation of the funding
decisions of Horizon 2020 and ESIF is crucial. Thus strong ex ante communication needs to all parties
involved should be anticipated.
Parallel use of funds: ESIF and Horizon 2020 funding are running in parallel (under separate
project/grant contracts) and are mutually supportive. The possible form of ESIF investments for this
purpose would be funding of specific parts of a R&D&I project of which elements are co-funded
under Horizon 2020. Of particular importance will be in this scenario the timing and the preparation of
a potential proposal: Given that Horizon 2020 Work Programmes will be biannual, it may be easier
than in FP7.
Alternative funding through ESIF: projects that were positively evaluated in Horizon 2020 (i.e. are
put on the reserve list) are being reoriented for submission to ESIF. The possible form of ESIF
investments for this purpose could be funding of applied research, development and demonstration
activities, etc. This scenario will imply considerable communication and coordination efforts:
At the strategic level structured and timely exchange of information between Horizon 2020 and
respective Managing Authorities (MA) will be needed.
• On the Horizon 2020 side: design of the H2020 guide for participants, in particular templates
for standard letters to inform H2020 applicants (“ESRs”- Evaluation Summary Report), task
description for National Contact Points (NCPs), project databases, etc.
• On the MA side: identification of needs and design for suitable support schemes under OPs,
contacts with universities and other research actors in the territory, etc.) ensuring cooperation
between the MA and NCPs on the ground.
At the level of individual projects the following coordination needs between regional / national level
and Horizon 2020 will be present:
• On the Horizon 2020 side: digest information on successful R&I projects to make it attractive
for industry and other innovation actors to take it up;
• On the MA side: inform H2020 project participants on the ESIF support possibilities, possibly
foresee in the Operational Programme the possibility to give them vouchers for proof ofconcept or other support, etc.
24
4.4. Synthesis – Identification of potential gaps and synergies
Cluster
RDI policy
challenges
Horizon 2020 – “Stairways to Excellence”
ERA
Chairs
1 (‘Catching
up regions’)
2 (‘In
betweeners’)
Teaming
Twining
Crossborder
science
networks
ESIF – Upstream actions
capacitybuilding for R&I
excellence and
technological
change
upgrading of
smaller
research
partnering
facilities
Modernisation of
universities
and higher
education
institutions
ESIF – downstream
actions
Innovation
in enterprises
Capacitybuilding for
the swift
economic
exploitation
of new ideas
ESIF & Horizon 2020 used
simultaneously
Parallel /
cumulative
funding of
R&D projects
Tying in into
European knowledge
networks
Broadening regional
R&D capacities
Broadening regional
innovation capacities
Smart diversification
of existing economic
strengths
Tying in into
European knowledge
networks
Insufficient public
sector R&D capacities
Insufficient
entrepreneurial
capacities for R&D
and innovation
Technological
upgrading of the
regional production
system
25
Substitutional use of
funding in
R&D projects
3 (‘Lagging
behind
regions’)
Development of a
regional basis for
growth and
employment
Poorly developed
R&D infrastructure
Lack of R&D
personnel and
knowledge base
Lack of critical mass
of enterprises
Distinct regional
competences in fields
of activity far from
science and
technology (e.g.
agriculture, tourism)
Source: JR-Policies
Legend: relevance of key action for regional policy challenge
High relevance
Moderate relevance
Low relevance
26
5 Conclusions
Conclusion No 1: The heterogeneity of Europe’s regions requires place-based strategies to boost
R&D investments
Regional policies focusing on the increase of R&D investments should take into account the large
diversity of European regions and the need for adopting place-based approaches. For many regions,
the 3% headline target may indicate more the necessary direction of development rather than a fixed
and reachable target. The cluster analysis has shown that in particular “Lagging behind regions”
display such low levels of R&D investment that it would be wishful thinking to expect them to come
even close to the headline target within the next programming period.
Conclusion No 2: “Lagging behind regions” (Cluster 3) should focus on enhancing the
innovation capacity of domestic firms instead of building cathedrals in the desert
“Lagging behind regions” should in first place focus on the promotion of growth and new jobs. Thus –
as the identification of potential gaps and synergies has shown - priority should be given to fostering
innovation in regional enterprises and to the promotion of entrepreneurial activity instead of trying to
build public regional R&D capacities at any rate. The understanding of innovation should be
pragmatic and based on the existing regional potentials. The current concept of innovation being
promoted and targeted in European policy is still dominated by high-tech activities. Broadening the
definition of ‘innovation’ will be critical to ensure that these regions can ‘buy-in’ to smart growth
goals in place-appropriate ways. Knowledge-and service innovation (for example in agri-food, ecotourism etc.) could be suitable concepts to enhance regional innovative capacity. ‘Bottom-up’
innovative potential in Europe, potentially a significant source of growth for ‘lagging regions’ requires
attention and support. A place-specific and broader definition of innovation should be developed as a
priority.
Conclusion No 3: The new policy mix offered by Horizon 2020 and ESIF seems to be most
appropriate for “Catching-up regions” (Cluster 1) and “In-Betweeners” (Cluster 2)
“Catchting-up regions” and “Inbetweeners” share the potential for expanding their R&D and
innovation capacity at the same time. They should be the main target regions for the “staircase to
excellence” and a smart mix of Horizon 2020 and ESIF policy instruments. Both types of regions have
the need to tap into global flows of knowledge and to strengthen all parts of the regional innovation
system in order to increase the diffusion and absorption of new scientific and technological
knowledge. While policy challenges are much related for both types of regions the initial levels for
future development efforts do vary: “Inbetweeners” need apart from widening their regional R&D
capacities in particular to strengthen their innovation output. The region specific policy mix thus needs
to gear new knowledge into new products and to foster firm academic spin-offs in order to strengthen
the regional base of innovative firms. “Catching-up regions” are somewhat stronger in this respect that
“Inbetweeners”.
Conclusion No 4: “Frontrunners” and Cities & Agglomerations may benefit in particular from
KETs based policy actions
Regional smart specialisation strategies may provide for “Frontrunners” and “Cities &
Agglomerations” adequate means of contributing to the EU ambitions on smart growth in terms of
R&D expenditure. Currently, there are a number of high-performing KETs based economic clusters in
parts of Europe characterised by specialised facilities and critical mass of actors. These should be
supported to enable them compete globally but there should not be an expectation that all parts of
Europe promote KETs based economic activities as a source of future growth. Targeted actions for
these specific parts of the European space must be developed and implemented with this in mind.
27
Conclusion No 5: Innovation oriented competence development (i.e. education and training)
needs to complement measures to stimulate R&D and innovation investments in all types of
regions
Regional innovation capacity does not exclusively stem from strong investments in research and
development. Education and training are equally important to enhance the absorptive capacities of
enterprises for innovations and to broaden the regional knowledge base. Thus the role of universities
appears critical in encouraging and supporting the innovation agenda in Europe. Education and
training should be part of the innovation policy mix in all regions. Nevertheless the specific focus may
vary between the different types of regions: while “Catching-up regions” and “Inbetweeners” could
benefit most from an increase of the regional knowledge base “lagging behind regions” could take
most out of targeted entrepreneurial training actions fostering firm formation rates and contributing to
the growth of the local population of enterprises.
Conclusion No 6: Reduced administrative burdens are needed to attract and mobilise firms to
regional innovation policy actions
One of the main obstacles for successful participation of firms in innovation projects in the current
structural funds programming period is the existence of high administrative burdens. Thus regulatory
complexity can put, as experiences from regional funding agencies show, severe constraint to regional
innovation policy programming. This applies generally to all projects funded under Structural Funds
regulations, especially when State aid is involved, but in particular also to innovations projects, which
due to their complex, risky character and lack of tangible outputs do not fit very well into the current
regulatory environment. As detailed funding rules for ESIF will be under the command of member
states there is for the next programming period again an inherent risk of “gilding” simplified EU rules.
Thus it will be essential to ensure that simplified rules will come really into effect also at the level of
regions implementing ESIF measures.
Conclusion No 7: Regional/national policy coordination will need to improve significantly to
allow for synergies between Horizon2020 and the Cohesion Fund
The aspired synergies between Horizon 2020 and ESIF do imply a strongly increased need for smart
policy coordination as policy interactions may reach down to project level. As experience from recent
RIS3 development processes has shown this will be a very challenging task for all member states and
regions. New smart governance models will be needed at national and regional level to cope with the
new possibilities that funding rules will allow in the upcoming programming period. Adequate
organisational development efforts will be needed to overcome existing administrative silos and to
break down barriers between the administrational entities involved. Particular attention should thus be
paid to the provision of specific EU support for capacity building and training. In addition support
could be given by external experts and peers from other regions to accompanying the organisational
development processes.
28
6 References
Armstrong, H. W. (1995). Convergence among Regions in the European Union, 1950 – 1990,
Regional Science, Vol. 74, pp. 143-152.
Barro, R. J., Sala-i-Martin, X. (1995), Economic Growth, Cambridge: MIT Press
Borsi, B., Papanek, G. (2008), Regional Innovation and Research Policy Outlook, policy practices in
eight European regions, Budapest
Charles, D. et al. (2010), ‘Smart Specialisation and Cohesion Policy – a Strategy for all regions,
Glasgow
ESPON (2007), Territorial Futures - Spatial scenarios for Europe, Luxembourg
ESPON (2012), KIT - Knowledge, Innovation, Territory, Luxembourg
ESPON (2013a), SIESTA - Spatial Indicators for a ‘Europe 2020 Strategy’ Territorial Analysis,
Luxembourg
ESPON (2013b), Territorial Dimensions of the Europe 2020 Strategy, ESPON Atlas, Luxembourg
European Commission (2011a), Synergies between FP7, the CIP and the Cohesion Policy Funds,
Brussels
European Commission (2011b), Horizon 2020 - The Framework Programme for Research and
Innovation, COM(2011) 808 final, Brussels
European Commission (2012a), Regional Innovation Scoreboard, Brussels
European Commission (2012b), Elements for a Common Strategic Framework 2014 to 2020 for the
European Regional Development Fund the European Social Fund, the Cohesion Fund, the European
Agricultural Fund for Rural Development and the European Maritime and Fisheries Fund, European
Commission Staff Working Document, Brussels
Halkier H. et al. (eds.) (2010), Knowledge Dynamics, Regional Development and Public Policy,
Aalborg
Krieger-Boden, C., Morgenroth, E., Petrakos G. (eds) (2008), The Impact of European Integration on
Regional Structural Change and Cohesion, Routledge Studies in the European Economy, Abington
Kroll, H., Stahlecker Th. (2009), Europe’s regional research systems: current trends and structures,
Brussels
Krugman, P. (1991), Increasing Returns and Economic Geography, The Journal of Political Economy,
99, pp.483-499
Manzella, G.P. (2009), The turning points of EU Cohesion policy, Working Paper, European
Investment Bank, Luxembourg
Mazura, M. (2013), Draft - Synergies between European Structural and Investment Funds, Horizon
2020 and other EU programmes related to innovation, Guide for policy-designers and implementers,
29
7 Annex
7.1. Methodological Notes
7.1.1 Dataset and Indicators
We have used EUROSTAT data on NUTS2 level for our analysis. Overseas territories (such as
Azores, La Reunion, Ceuta etc.) have been excluded from our analysis. To control for fluctuations
(e.g. economic cycles, major single investment in a particular regions) we have calculated average
values for the years 2001-2009 for all datasets used. The following indicators have been selected for
our analysis:
•
Gross Regional Product per Capita (GRP) in PPP reflects the overall level economic
activity of a region. Thus there may be regions that do have weak R&D capacities but are
nevertheless strong in economic terms. When measuring regional economic performance GDP
per capita is problematic from the point of view of not taking into account the commuting that
occurs across regional boundaries. Regions with higher in- than out-commuting receive higher
per capita values simply because the denominator in this case is smaller than would be the
case if all employed persons within the region was utilised. This is most often the case for
European regions containing larger cities. Similarly, regions with higher out- than incommuting populations attain lower per capita values because their population “produces”
their value-added in a neighbouring region. This is, in the European context, often the case for
smaller regions surrounding large metropolises.
•
Population density is being used as an indicator to assess a region’s potential for critical mass
for R&D and enterprises and its geographical position (i.e. periphery). Population density is
the ratio between (total) population and surface (land) area. This ratio can be calculated for
any territorial unit for any point in time, depending on the source of the population data.
•
Gross Expenditure on R&D as share of GRP (GERD) is being used an input indicator for
the characterisation of a region’s R&D capacities. It is constructed by adding together the
intramural expenditures on research and development (R&D) as reported by the performing
sectors. As a term used by OECD Member countries, it is defined as "total intramural
expenditure on R&D performed on the national territory during a given period. GERD
includes R&D performed within a country and funded from abroad but excludes payments for
R&D performed abroad". Regional data on GERD may show distortions caused by the fact
that R&D investments are accounted for the headquarters of R&D performing institutions.
•
Human Resources in Science and Technology as share of the total labour force is being used
as a second input indicator characterising the regional knowledge base. Human resources in
science and technology (HRST) are defined according to the Canberra Manual (OECD and
Eurostat, 1995) as persons having graduated at the tertiary level of education or employed in a
science and technology occupation for which a high qualification is normally required and the
innovation potential is high. While tertiary level graduates give a measure of supply, demand
for HRST is better gauged by -occupations.
•
EPO patent applications per 1 Mio inhabitants is being used as an output indicator for
R&D activities and indicates in how far regional innovation systems have been able to
transform new scientific and technological knowledge into products. Patent counts can
provide a measure of invention and innovation and a time series of data is available for an
analysis by region. However, care should be taken interpreting the data as not all inventions
are patented and patent propensities vary across activities and enterprises; furthermore,
patented inventions vary in technical and economic value. Patent applications tend to be
30
clustered geographically in a limited number of regions and this is especially true for hightech activities.
No data has been available for the German NUTS2 regions Niederbayern and Oberpfalz for the
indicators Gross Expenditure on R&D as share of GRP, Human Resources in Science and Technology,
and EPO patent applications per 1 Mio inhabitants. These two regions have been thus excluded from
the analysis.
7.1.2 Cluster Analysis
In order to identify statistical clusters a hierarchical cluster analysis has been carried out with
MCLUST. MCLUST is a software package for cluster and discriminant analysis written in Fortran and
interfaced to the S-PLUS commercial software package and the freely available R language which has
a similar look and feel. It implements parameterized Gaussian hierarchical clustering algorithms and
the EM algorithm for parameterized Gaussian mixture models with the possible addition of a Poisson
noise term. MCLUST also includes functions that combine hierarchical clustering, EM and the
Bayesian Information Criterion in a comprehensive clustering strategy. Methods of this type have
shown promise in a number of practical applications.
To control for the results achieved with MCLUST also a hierarchical cluster analysis with Wards
methods has been applied leading to a very similar set of clusters.
7.1.3 Qualitative Analysis
Needs analysis
A needs analysis has been conducted for the 5 clusters that have been identified in the cluster analysis.
A table for the needs analysis has been constructed on the basis of the work carried out in the KIT
(Knowledge, Innovation, Territory) project (ESPON 2012). For each cluster the following dimensions
have been analysed qualitatively: (1) RDI policy challenges, (2) Policy intervention needs, (3) relevant
policy level, and (4) beneficiaries. Relevant information was collected by semi structured interviews
and an informal focus group with regional innovation policy actors.
Policy analysis
A policy analysis has been carried out in order to screen and structure potential policy responses in the
upcoming programming period. In a first step the following EU policy documents have been analysed:
• European Commission (2011b), Horizon 2020 - The Framework Programme for Research and
Innovation, COM(2011) 808 final, Brussels
• European Commission (2012b), Elements for a Common Strategic Framework 2014 to 2020
for the European Regional Development Fund the European Social Fund, the Cohesion Fund,
the European Agricultural Fund for Rural Development and the European Maritime and
Fisheries Fund, European Commission Staff Working Document, Brussels
In a second step a more in depth desk research was conducted related to Smart Specialisation
Strategies. As a result micro case studies were prepared each for a “lagging behind region”, a
“catching-up region” and an “inbetweener region”.
A third step had then a particular focus on potential synergies between Horizon 2020 and ESIF.
Building upon analyses for the programming period 2007-2013 (i.e. European Commission 2011a), a
review of potential interactions between instruments for the next period was done with the help of the
draft guide on Synergies between European Structural and Investment Funds, Horizon 2020 and other
EU programmes related to innovation (Mazura 2013).
31
Synthesis of needs and policy responses
Finally the results of the needs analysis were matched with the outcomes of the policy analysis. In
order to do so first a synthesis table was constructed by the project team. For each cluster and its
specific RDI policy challenges the following groups of policy interventions have been assessed
qualitatively with regard to their relevance:
• Policy instruments in the category Horizon 2020 – “Stairways to Excellence”
• Policy instruments in the category “ESIF downstream actions”
• Policy instruments in the category “ESIF downstream actions”
• Policy instruments in the category “ESIF & Horizon 2020 used simultaneously”
The assessment was done in a first step by the project team alone; in a second step also the informed
opinions of members of regional innovation policy agencies were collected and integrated into the
final version of the synthesis table.
32
7.2. Complementary Tables
Table 2: Regions in cluster 1 “Catching up Regions”
Region
GRP
Abruzzo
Pop.
Dens.
GERD
HRST
Patents
26180,111
122,133
1,020
31,067
38,676
887,111
17,356
0,163
40,767
60,954
Alsace
42561,333
218,267
1,570
38,000
170,384
Aquitaine
68877,000
75,078
1,555
35,833
51,245
Aragón
31165,444
26,500
0,830
38,933
38,949
Auvergne
27249,889
51,222
2,312
34,367
102,200
Basse-Normandie
29071,889
82,700
1,025
33,511
61,078
Border, Midland and Western
24496,778
34,711
1,278
30,956
70,857
Bourgogne
34815,222
51,567
0,992
31,589
72,387
Brandenburg
49652,000
86,844
1,253
43,200
94,816
Bretagne
65636,889
113,156
1,700
37,744
117,844
5328,889
76,057
0,628
29,856
76,384
Canarias (ES)
39950,444
261,825
0,586
29,756
4,147
Cantabria
12451,444
105,225
0,702
38,244
13,280
Castilla y León
53594,111
26,363
0,979
35,978
14,671
Åland
Burgenland (AT)
Centre (FR)
54348,667
64,089
1,560
33,744
97,795
Champagne-Ardenne
29932,222
52,311
0,743
29,833
56,564
Chemnitz
30392,500
239,700
1,490
37,522
64,325
Comunidad Foral de Navarra
16929,000
56,750
1,643
43,100
80,042
8891,667
145,878
0,216
32,600
36,286
Cumbria
10106,000
72,767
0,522
32,433
46,677
Devon
23282,778
165,411
0,776
33,700
39,215
Dorset and Somerset
26304,778
199,089
0,794
35,133
62,557
Drenthe
11131,667
183,178
0,678
36,889
74,437
East Wales
26706,000
141,000
1,676
39,044
59,900
East Yorkshire and Northern Lincolnshire
18496,222
252,467
0,572
29,611
57,104
Eastern Scotland
50300,222
108,138
2,224
41,400
89,257
Eesti
18286,444
31,067
0,988
40,733
12,851
Franche-Comté
24010,444
70,967
2,253
33,678
118,308
Friesland (NL)
15244,667
191,511
0,695
37,100
54,922
Friuli-Venezia Giulia
33340,667
159,689
1,254
32,056
118,016
Galicia
51707,556
92,200
0,880
33,967
9,371
Groningen
21152,000
245,422
1,677
42,656
82,230
Haute-Normandie
39987,111
147,056
1,448
33,700
110,772
Herefordshire, Worcestershire and
Warwickshire
Highlands and Islands
28182,222
211,567
1,854
36,756
122,656
8813,889
11,180
0,764
37,389
36,744
Hrvatska (Croatia)
14266,667
79,326
0,893
26,400
16,173
Itä-Suomi
33800,000
7,700
1,581
36,300
58,792
Jihovýchod
26091,556
120,033
1,159
33,778
14,719
Kärnten
13281,667
59,729
2,388
31,811
115,359
Cornwall and Isles of Scilly
33
Kassel
31772,333
150,989
1,125
37,122
128,176
Koblenz
33168,778
187,978
0,670
37,938
166,953
Kypros
15716,889
81,533
0,387
39,267
13,456
La Rioja
7455,556
59,413
0,800
35,044
25,030
Languedoc-Roussillon
48780,889
91,522
2,280
37,011
54,014
Leicestershire, Rutland and
Northamptonshire
Leipzig
40942,556
329,000
1,574
34,733
81,842
21569,000
247,850
1,888
40,567
57,171
Lietuva
40356,444
54,467
0,749
36,211
2,763
Liguria
40417,667
299,200
1,216
36,267
65,396
Limousin
14512,000
43,089
0,828
32,100
53,571
Lincolnshire
12969,000
114,322
0,400
30,022
47,848
Lorraine
46868,556
99,167
1,162
32,311
61,307
Lüneburg
31760,556
109,378
0,765
37,467
150,693
Luxemburg
26937,111
180,522
1,637
40,878
185,040
Malopolskie
33072,667
215,075
0,911
27,244
5,811
Marche
38973,000
163,750
0,624
29,789
67,555
Mecklenburg-Vorpommern
30503,778
73,900
1,453
36,344
46,033
9386,778
5,222
0,763
38,067
79,455
Mellersta Norrland
Midtjylland
31736,889
93,960
1,500
40,433
212,550
Moravskoslezsko
18187,667
234,122
0,838
30,233
7,745
Niederösterreich
36394,444
83,471
1,082
33,656
133,307
Nordjylland
14244,222
72,880
2,250
35,533
105,705
Norra Mellansverige
19599,333
12,911
1,258
34,767
121,369
North Eastern Scotland
15233,667
61,240
1,648
42,500
37,184
North Yorkshire
17954,667
92,922
1,742
38,311
81,950
Northern Ireland (UK)
35950,556
122,956
1,172
33,878
32,354
Northumberland and Tyne and Wear
30010,000
252,011
1,160
32,233
53,373
Oberfranken
27359,778
152,211
1,318
35,478
238,777
Oberösterreich
38729,111
119,300
2,092
31,889
214,101
Overijssel
28288,111
333,589
1,447
38,900
109,869
Övre Norrland
12947,778
3,300
2,558
38,667
114,972
Picardie
36991,889
97,533
1,320
30,867
75,177
Poitou-Charentes
34932,556
66,578
0,828
31,200
52,217
Principado de Asturias
21646,889
99,988
0,787
38,311
14,577
Prov. Hainaut
22831,111
342,111
1,135
37,811
70,912
Prov. Liège
20745,111
270,456
1,468
41,800
98,504
Prov. Limburg (BE)
18132,889
340,133
1,000
39,800
100,914
Prov. Luxembourg (BE)
4735,556
58,156
0,420
40,044
109,759
Prov. Namur
8566,444
125,222
1,153
41,578
64,142
Prov. West-Vlaanderen
28927,333
364,644
1,015
40,400
109,643
Provincia Autonoma di Bolzano/Bozen
15944,000
65,200
0,438
29,567
84,455
Provincia Autonoma di Trento
14418,667
81,267
1,306
32,878
55,945
Saarland
25941,444
407,433
1,128
36,511
146,515
Sachsen-Anhalt
44879,444
121,389
1,203
35,056
42,920
Salzburg
16749,778
74,314
1,034
33,944
169,144
34
Schleswig-Holstein
64522,222
178,967
1,185
39,822
147,031
Severovýchod
22136,889
121,500
0,962
29,900
14,932
Shropshire and Staffordshire
30007,222
242,744
0,504
33,044
48,156
Sjælland
16433,222
112,240
2,665
40,633
116,724
Småland med öarna
19726,111
24,033
1,098
34,144
110,391
South Western Scotland
53931,444
174,988
0,976
38,256
33,027
Strední Cechy
19179,000
107,744
2,579
30,144
13,385
Syddanmark
29872,889
97,700
0,800
37,033
142,832
Tees Valley and Durham
21472,111
380,767
1,128
31,322
62,949
Thüringen
42122,333
144,789
1,913
38,911
109,534
Tirol
20325,000
55,357
2,304
31,833
171,355
Trier
10983,333
104,444
0,858
38,863
90,936
Umbria
19842,111
104,344
0,860
31,722
43,154
3747,222
38,111
0,436
27,911
60,091
17348,222
89,144
0,873
28,822
37,950
Valle d'Aosta/Vallée d'Aoste
Vzhodna Slovenija
Weser-Ems
55930,000
164,722
0,565
34,322
119,207
West Wales and The Valleys
31248,222
143,144
0,724
31,578
30,958
Yugozapaden
27522,111
104,067
0,905
39,971
5,196
Zahodna Slovenija
21569,222
115,544
2,029
39,578
59,914
Zeeland
10082,222
211,789
0,712
36,789
65,031
Source: EUROSTAT, JR-POLICIES
35
Table 3: Regions in cluster 2 “Inbetweeners”
Region
GRP
Pop.
Density
GERD
HRST
Patents
per Cap.
Andalucía
136609,778
89,438
0,853
29,811
7,250
Attiki
102718,222
1048,611
0,763
35,656
12,818
Bucuresti - Ilfov
41278,222
1267,467
1,147
39,278
5,037
Campania
88512,000
431,000
1,186
27,089
13,735
Cataluña
187022,444
215,538
1,380
36,144
66,143
Comunidad Valenciana
97559,444
199,925
0,910
31,733
22,267
Greater Manchester
60138,667
1993,278
1,100
35,078
46,205
Közép-Magyarország
65442,889
413,844
1,390
40,878
33,672
Lazio
152609,889
316,511
1,770
36,556
38,908
Lisboa
69095,444
949,500
1,479
30,167
11,325
Malta
7324,556
1279,089
0,476
27,489
14,152
Mazowieckie
95842,111
145,150
1,163
35,844
6,926
Merseyside
28971,556
2091,800
1,744
33,289
64,324
Nord - Pas-de-Calais
79580,778
323,633
0,730
33,833
40,613
Norte
52490,444
174,711
0,779
15,267
7,879
Pays de la Loire
76677,889
106,900
1,000
33,400
64,937
Puglia
64699,111
211,356
0,688
26,011
12,282
Região Autónoma da Madeira (PT)
5439,667
298,333
0,291
15,789
2,655
Sicilia
78538,111
197,100
0,826
27,622
12,627
Slaskie
59749,667
380,088
0,370
28,900
2,729
South Yorkshire
25548,444
829,289
1,126
31,067
41,351
106750,111
84,200
1,319
38,567
61,503
Toscana
93929,333
159,056
1,110
31,644
78,318
West Midlands
61917,778
2881,867
1,324
31,800
44,856
West Yorkshire
51521,111
1058,822
0,746
33,689
53,778
Southern and Eastern
Source: EUROSTAT, JR-POLICIES
36
Table 4: Regions in cluster 3 “Lagging behind regions”
Region
GRP
Pop.
Dens.
GERD
HRST
Patents
Alentejo
12475,778
24,256
0,788
17,089
3,026
Algarve
8119,000
82,544
0,282
17,544
4,369
Anatoliki Makedonia, Thraki
9187,444
43,189
0,353
20,422
2,033
Basilicata
10063,111
61,156
0,588
27,022
9,027
Calabria
30729,556
136,089
0,416
28,878
6,045
Castilla-la Mancha
34306,667
23,975
0,484
27,322
8,764
Centro (PT)
35270,222
84,078
0,828
14,544
6,916
Centru
21232,778
75,244
0,187
20,311
0,950
Corse
5608,111
33,511
0,285
31,867
9,953
Dél-Alföld
13042,000
73,644
0,761
25,511
9,296
Dél-Dunántúl
9579,111
68,756
0,414
26,500
5,601
Dolnoslaskie
35760,222
144,850
0,444
28,200
4,165
Dytiki Ellada
11384,778
66,356
0,687
21,856
5,456
Dytiki Makedonia
5464,111
31,844
0,140
22,322
6,791
Észak-Alföld
13976,444
86,533
0,812
26,400
7,480
Észak-Magyarország
11369,000
94,044
0,361
25,467
4,852
Extremadura
16532,000
25,975
0,682
27,167
2,270
Illes Balears
25058,667
197,513
0,287
26,767
6,127
Ionia Nisia
4415,222
96,689
0,113
16,178
4,501
Ipeiros
5380,000
38,000
0,637
23,967
3,355
Jihozápad
18923,556
69,389
0,771
31,478
8,673
Kentriki Makedonia
33063,778
101,522
0,593
28,511
7,535
Közép-Dunántúl
14194,667
99,611
0,538
25,300
5,444
Kriti
11684,222
72,267
0,877
22,544
10,452
Kujawsko-Pomorskie
21401,889
115,075
0,302
22,744
2,521
Latvija
24892,556
36,944
0,508
33,278
5,698
Lódzkie
27929,778
141,488
0,551
27,078
5,182
Lubelskie
17764,444
86,763
0,473
25,567
2,425
Lubuskie
10419,111
72,113
0,118
25,156
5,701
Molise
6113,556
73,400
0,430
28,156
6,039
Nord-Est
20748,778
103,467
0,241
14,867
0,470
Nord-Vest
21516,778
81,489
0,258
19,167
0,822
Notio Aigaio
7541,000
57,456
0,110
17,022
5,275
Nyugat-Dunántúl
14114,556
88,533
0,440
24,989
5,901
Opolskie
10116,556
111,238
0,164
24,911
2,748
Peloponnisos
10469,667
38,556
0,230
18,211
3,679
Podkarpackie
17189,000
117,600
0,349
24,967
2,923
Podlaskie
10538,111
59,425
0,253
26,533
1,122
Pomorskie
25446,222
120,275
0,482
28,733
3,061
Região Autónoma dos Açores (PT)
3918,111
103,933
0,424
14,089
3,690
Región de Murcia
25606,000
118,200
0,732
29,878
9,868
Sardegna
29891,000
69,144
0,652
25,900
9,738
Severen tsentralen
5970,444
63,900
0,126
27,857
1,686
37
Severoiztochen
7241,111
69,156
0,225
25,557
1,974
Severozápad
16157,778
133,344
0,244
25,411
3,919
Severozapaden
5852,333
50,867
0,125
26,386
1,052
Sterea Ellada
11435,889
36,111
0,143
17,167
2,869
Stredné Slovensko
15239,667
83,167
0,328
26,756
1,708
Strední Morava
17213,444
135,744
0,832
29,367
10,805
Sud-Est
20203,667
91,422
0,153
17,744
0,446
Sud-Vest Oltenia
15224,000
80,978
0,193
16,033
0,553
Sud - Muntenia
23033,444
99,767
0,400
15,333
0,227
Swietokrzyskie
11693,111
109,725
0,142
24,367
1,870
Thessalia
12303,889
52,667
0,290
25,078
4,288
Vest
18228,111
61,500
0,206
20,411
1,070
Voreio Aigaio
3471,222
52,900
0,407
24,000
4,888
Východné Slovensko
15693,000
99,700
0,303
24,233
4,372
Warminsko-Mazurskie
12709,889
59,025
0,240
25,511
0,762
Wielkopolskie
41970,333
113,138
0,492
24,578
3,693
Yugoiztochen
8326,667
57,667
0,144
24,771
0,722
Yuzhen tsentralen
9619,667
70,267
0,185
22,943
1,087
Zachodniopomorskie
18321,667
74,013
0,206
29,044
2,253
Západné Slovensko
24788,556
124,400
0,449
26,378
4,122
Source: EUROSTAT, JR-POLICIES
38
Table 5: Regions in cluster 4 “Frontrunners”
Region
GRP
Pop.
Dens.
GERD
HRST
Patents
Arnsberg
89.281,33
469,78
1,44
35,72
184,72
Bedfordshire and Hertfordshire
47.677,11
570,94
3,20
41,60
150,84
Berkshire, Buckinghamshire and
Oxfordshire
Bratislavský kraj
74.853,56
374,53
3,31
44,39
225,05
19.747,33
294,94
0,94
49,43
19,48
Braunschweig
40.568,56
203,67
7,33
38,97
193,24
Cheshire
24.891,56
428,90
5,12
39,32
140,01
134.685,78
506,38
3,22
46,43
375,80
Derbyshire and Nottinghamshire
46.437,44
425,02
2,17
33,59
94,77
Detmold
51.386,00
316,52
1,54
36,33
252,07
Dresden
33.990,00
210,12
3,67
40,99
144,32
East Anglia
54.756,67
180,01
5,58
35,19
216,54
131.660,00
195,70
1,27
32,49
174,31
Essex
36.688,00
452,31
3,62
33,04
95,57
Etelä-Suomi
26.100,00
58,10
3,57
48,10
305,81
7.992,22
257,21
3,65
41,37
61,15
Freiburg
55.517,89
233,16
2,49
39,49
457,14
Gelderland
49.089,00
396,28
1,84
42,13
128,79
Gießen
25.256,89
196,89
2,01
40,31
224,82
Gloucestershire, Wiltshire and Bristol/Bath
area
Hampshire and Isle of Wight
63.297,89
298,86
3,00
40,31
164,68
46.627,56
437,91
3,40
38,81
154,49
Hannover
54.168,67
238,83
2,19
41,98
226,08
Hovedstaden
54.790,11
642,98
5,32
54,10
319,02
Karlsruhe
80.832,56
393,88
3,93
43,56
491,92
Kent
35.448,22
434,01
2,45
34,10
72,39
Köln
117.818,11
591,84
2,89
45,23
300,60
Lancashire
29.182,78
467,64
2,46
34,62
45,40
Länsi-Suomi
30.960,11
22,88
3,59
39,41
231,29
Limburg (NL)
29.710,67
526,01
1,84
38,60
181,93
Darmstadt
Emilia-Romagna
Flevoland
Midi-Pyrénées
61.235,11
60,77
3,89
39,71
112,84
Mittelfranken
50.142,89
235,71
2,94
41,61
486,38
Münster
57.949,11
379,02
0,95
37,87
175,49
Noord-Brabant
71.028,78
490,32
2,82
41,10
723,68
156.540,56
241,47
4,60
48,21
570,17
Östra Mellansverige
36.035,33
39,36
4,09
40,73
244,78
País Vasco
61.502,78
292,70
1,62
50,32
53,30
115.745,11
173,91
1,71
30,82
128,78
Oberbayern
Piemonte
Pohjois-Suomi
25.500,00
4,50
5,01
38,52
181,90
Prov. Antwerpen
52.968,00
604,52
2,25
42,60
174,78
9.545,11
335,01
7,24
57,10
315,50
Prov. Oost-Vlaanderen
33.352,44
471,64
1,97
43,98
136,98
Prov. Vlaams-Brabant
29.491,00
497,02
3,35
52,16
213,07
110.571,56
152,11
1,93
35,91
100,10
Prov. Brabant Wallon
Provence-Alpes-Côte d'Azur
39
Rheinhessen-Pfalz
48.491,89
294,38
2,92
41,91
411,51
149.552,00
136,99
2,61
39,87
235,48
Schwaben
47.674,89
178,28
1,14
37,96
322,73
Steiermark
29.295,22
73,84
3,50
31,13
178,57
Stockholm
72.351,89
291,17
4,11
55,30
367,16
125.223,56
378,27
5,58
43,78
633,31
Surrey, East and West Sussex
71.966,67
478,00
1,37
44,44
159,74
Sydsverige
32.682,00
94,59
4,49
42,40
346,00
Tübingen
49.141,89
201,57
4,09
41,52
499,55
Unterfranken
34.953,67
156,87
2,01
37,13
392,74
Västsverige
48.306,44
61,62
4,80
41,79
266,32
133.625,00
268,08
0,75
29,93
122,35
10.709,00
142,83
1,37
32,42
437,51
Rhône-Alpes
Stuttgart
Veneto
Vorarlberg
Source: EUROSTAT, JR-POLICIES
Table 6: Regions in cluster 5 “Big Cities and Agglomerations”
Region
GRP
Pop.
Dens.
GERD
HRST
Patents
Berlin
78.425,889
3.815,789
3,655
49,044
186,278
Bremen
23.366,778
1.638,511
2,428
40,022
92,224
Comunidad de Madrid
177.830,111
741,375
1,830
46,556
37,966
Düsseldorf
151.903,333
988,033
1,725
39,889
268,490
Hamburg
76.933,333
2.311,322
1,988
46,956
208,168
Île de France
448.818,889
954,689
3,140
51,933
261,428
Inner London
214.636,778
9.233,733
1,178
57,567
92,906
Lombardia
293.629,556
412,300
1,184
34,200
146,855
Noord-Holland
88.269,889
974,567
1,695
49,589
113,859
Outer London
107.612,778
3.623,833
0,724
44,956
43,394
Praha
44.321,444
2.447,933
2,081
54,556
25,880
Rég. Bruxelles / Brussels Gewest
53.710,889
6.329,233
1,326
50,678
114,407
Utrecht
41.702,556
849,567
1,935
52,667
154,562
Wien
61.500,111
4.160,514
3,610
41,944
179,882
103.765,000
1.223,733
1,707
45,156
134,317
Zuid-Holland
Source: EUROSTAT, JR-POLICIES
40
7.3. Complementary Figures
Figure 6: Gross Regional Product per Capita (PPP) in NUTS2 Regions, Average Values 2001-2009
NUTS2
BRP
887 - 15.000
15.001 - 30.000
30.001 - 45.000
45.001 - 75.000
75.001 - 125.000
125.001 - 200.000
200.001 - 300.000
300.001 - 448.819
·
0
205
410
820
1.230
1.640
2.050 kilometers
GIS-processing: Dipl.-Ing. Clemens Habsburg-Lothringen, MAS
Sources:
Eurostat; JR-POLICIES
Source: EUROSTAT, JR-POLICIES
Figure 7: EPO Patent Applications per 1 Mio Inhabitants in NUTS2 Regions, Average Values 2001-
NUTS2
Patent
0 - 50
51 - 100
101 - 200
201 - 350
351 - 500
501 - 724
·
0
205
410
820
1.230
1.640
2.050 kilometers
GIS-processing: Dipl.-Ing. Clemens Habsburg-Lothringen, MAS
Sources:
Eurostat; JR-POLICIES
2009
Source: EUROSTAT, JR-POLICIES
41
Figure 8: Population Density in NUTS2 Regions, Average Values 2001-2009
NUTS2
Populationdensity
3 - 50
51 - 100
101 - 250
251 - 500
501 - 1.000
1.001 - 9.234
·
0
205
410
820
1.230
1.640
2.050 kilometers
GIS-processing: Dipl.-Ing. Clemens Habsburg-Lothringen, MAS
Sources:
Eurostat; JR-POLICIES
Source: EUROSTAT, JR-POLICIES
Figure 9: Human Resources in Science and Technology as Share of the total Labor Force, in NUTS2
Regions, Average Values 2001-2009
NUTS2
HR
14,1
20,1
30,1
40,1
50,1
·
0
- 20,0
- 30,0
- 40,0
- 50,0
- 57,6
205
410
820
1.230
1.640
2.050 kilometers
GIS-processing: Dipl.-Ing. Clemens Habsburg-Lothringen, MAS
Sources:
Eurostat; JR-POLICIES
Source: EUROSTAT, JR-POLICIES
42