Regional Innovation in the Innovation Unionec.europa.eu/research/innovation-union/pdf/regional...Regional Innovation in the Innovation Union Executive Summary Innovation Innovation

Research and Innovation

Regional Innovationin the Innovation Union

EUROPEAN COMMISSION

Directorate-General for Research and InnovationDirectorate C — Directorate for Research and InnovationUnit C6 — Economic analysis and indicators

Contact: Adrian Pascu

European Commission

E-mail: [email protected] [email protected]

EUROPEAN COMMISSION

Prepared byHenning Kroll, Thomas Stahlecker, Viola Peter and

Lorena Rivera Leon of Fraunhofer ISI, Technopolis Belgium

Cartography by Stephen Siering

Directorate-General for Research and Innovation Capacities: Support for the Coherent Development of

Research Policies FP7 2012 EUR 25191 EN

Regional Innovation in the Innovation Union

Project financed by the 6th Framework Programme for Research, for the implementation of the specific programme

“Strengthening the Foundations of the European Research Area”

(Invitation to tender n° DG RTD 2005 M 02 02)

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ISBN 978-92-79-22820-9ISSN 1831-9424doi 10.2777/59218

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Table of Contents Executive Summary ..................................................................................................................................................................................................... 6�

I � Introduction ....................................................................................................................................................................................................... 11�

A Broader View on Innovation – Conceptual Background ................................................................................................................................... 11�

Europe 2020, the Innovation Union and Regional Implications ............................................................................................................................ 14�

II� Policy Intelligence on Regional Innovation ..................................................................................................................................................... 16�

III� The Regional Distribution of Innovation Activities in Europe ......................................................................................................................... 18�

Methodological Approach...................................................................................................................................................................................... 19�

The Regional Distribution of Business R&D Intensity.......................................................................................................................................... 20�

The Regional Distribution of Product and Process Innovation Activities in SME According to CIS Data .......................................................... 21�

The Regional Distribution of Management and Organisational Innovation Activities in SME According to CIS Data ...................................... 22�

IV� Framework Conditions Expected to be Conducive to Innovation .................................................................................................................... 23�

General Framework................................................................................................................................................................................................ 23�

Overall Economic Performance (GDP per capita) ................................................................................................................................................. 24�

Specialised Critical Mass (Employment Cluster Stars) ......................................................................................................................................... 25�

Quality of Governance (DG Regio EU QoG Index) .............................................................................................................................................. 26�

Pre-competitive Knowledge Generation (Public Research)................................................................................................................................... 27�

Pre-competitive Knowledge Generation (Higher Education) ................................................................................................................................ 28�

Human Resources (HRSTC per Econ. Active Population).................................................................................................................................... 29�

Human Resources (High-tech Industry and KIS Employment)............................................................................................................................. 30�

Access to Public Finance (7th Framework Programme)......................................................................................................................................... 31�

Access to Public Finance (SF Allocations for Core RTDI) ................................................................................................................................... 32�

Access to Public Finance (SF Alloc. for Business Innovation) ............................................................................................................................. 33�

Networked Specialisation / Smart Specialisation................................................................................................................................................... 34�

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V� Relations between Framework Conditions and Innovative Performance ......................................................................................................... 38�

Determinants of Market Oriented R&D (BERD per Inhabitant) ........................................................................................................................... 39�

Determinants of Innovative Activities According to the CIS ................................................................................................................................ 43�

Determinants of Product and Process Innovation .................................................................................................................................................. 45�

Determinants of Management and Organisational Innovation............................................................................................................................... 48�

VII Summary and Policy Conclusions ....................................................................................................................................................................... 51�

Summary ................................................................................................................................................................................................................ 51�

Policy Conclusions ................................................................................................................................................................................................. 52�

Annex ......................................................................................................................................................................................................................... 53�

References .................................................................................................................................................................................................................. 61�

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List of Tables and Figures Figure 1: Interactive model of the innovation process with feedback loops and interaction ........................................................................................................................... 12 Figure 2: Concepts to Measure Innovation ...................................................................................................................................................................................................... 18 Figure 3: Dimensions to Differentiate Types of Innovation ............................................................................................................................................................................ 19 Figure 4: The regional distribution of business R&D intensity 2007............................................................................................................................................................... 20 Figure 5: The regional distribution of product and process innovation activities in SME according to 2006 CIS data.................................................................................. 21 Figure 6: The regional distribution of management and organisational innovation activities in SME according to 2006 CIS data............................................................... 22 Figure 7: Measurable Framework Conditions of Innovation ........................................................................................................................................................................... 23 Figure 8: GDP per capita (2006)...................................................................................................................................................................................................................... 24 Figure 9: Intensity of employment clustering in region: Number of ‘Cluster Stars’ in all sectors (2005 data) .............................................................................................. 25 Figure 10 Quality of governance (EUQoG) (2009/2010 survey)...................................................................................................................................................................... 26 Figure 11 Government expenditure on R&D (GOVERD) in EUR per inhabitant (2006) ............................................................................................................................... 27Figure 12 Higher education expenditure on R&D (HERD) in EUR per inhabitant (2006) ............................................................................................................................. 28Figure 13: The regional distribution of HRSTC as share of active population (2006) ...................................................................................................................................... 29 Figure 14: Employment in high tech industries and knowledge intensive services (share of total employment) (2006) .................................................................................. 30 Figure 15 Amount of FP7 Funding in the 2007-2013 support period (up until early 2009) ............................................................................................................................ 31 Figure 16: SF allocations to core RTDI, Euro per inhabitant (2007-2013 support period)............................................................................................................................... 32Figure 17: SF allocations to business innovation, Euro per inhabitant (2007-2013 support period) ................................................................................................................. 33 Figure 18: Technological specialisation in regions (based on 2003-2007 data) ............................................................................................................................................... 34 Figure 19: Intensity of technology clustering in region: Number of ‘Cluster Stars’ in all sectors (based on 2005-07 data) ............................................................................ 35 Figure 20: Share of Co-Patenting in total Patenting (2006) ............................................................................................................................................................................... 36 Figure 21: Share of SME collaborating for innovation according to 2006 CIS data......................................................................................................................................... 37 Figure 22: Factor Analysis of Determinants of Market Oriented R&D (BERD)............................................................................................................................................... 39 Figure 23: Cross Tabulation of Market Oriented R&D (BERD per Inhabitant) vs. GDP per Inhabitant........................................................................................................... 40 Figure 24: Cross Tabulation of Market Oriented R&D (BERD per Inhabitant) vs. HERD per Inhabitant........................................................................................................ 40 Figure 25: Cross Tabulation of Market Oriented R&D (BERD per Inhabitant) vs. Quality of Governance (EUQOG Index).......................................................................... 41 Figure 26: Cross Tabulation of Market Oriented R&D (BERD per Inhabitant) vs. Employment in hightech and KIBS sectors as share of total............................................ 41 Figure 27: Analysis of Factors relevant for Market Oriented R&D (BERD)..................................................................................................................................................... 42 Figure 28: Cross Tabulation of Share Product & Process Innovators (CIS) vs. Share Management & Organisational Innovators (CIS)......................................................... 43 Figure 29: Cross Tabulation of Share Product & Process Innovators (CIS) vs. Firms Co-operating for Innovation (CIS)............................................................................... 43 Figure 30: Factor Analysis of Determinants of Innovation according to CIS.................................................................................................................................................... 44 Figure 31: Cross Tabulation of Share Product & Process Innovators (CIS) vs. GDP per Inhabitant ................................................................................................................ 45 Figure 32: Cross Tabulation of Share Product & Process Innovators (CIS) vs. BERD per Inhabitant.............................................................................................................. 45 Figure 33: Cross Tabulation of Share Product & Process Innovators (CIS) vs. Quality of Governance (EUQoG Index)................................................................................. 46 Figure 34: Cross Tabulation of Share Product & Process Innovators (CIS) vs. SF Expenditure on RTDI per Inhabitant ................................................................................ 46 Figure 35: Analysis of Factors relevant for Product and Process Innovation (According to CIS data) ............................................................................................................. 47

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Figure 36: Cross Tabulation of Share Management & Organisational Innovators (CIS) vs. GDP per Inhabitant............................................................................................ 48 Figure 37: Cross Tabulation of Share Management & Organisational Innovators (CIS) vs. BERD per Inhabitant ......................................................................................... 48 Figure 38: Cross Tabulation of Share Management & Organisational Innovators (CIS) vs. Quality of Governance (EUQOG Index) ........................................................... 49 Figure 39: Cross Tabulation of Share Management & Organisational Innovators (CIS) vs. Quality of Governance (EUQOG Index) ........................................................... 49 Figure 40: Analysis of Factors relevant for Management and Organisational Innovation (According to CIS data) ......................................................................................... 50

Figure Annex 1: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. HERD per Inhabitant ...................................................................................................... 53�Figure Annex 2:� Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. HRSTC per Economically Active Population................................................................. 53�Figure Annex 3: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Nr. of Interregional Co-Patent Applications Filed in Region ......................................... 53�Figure Annex 4:� Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Total Number of EU FP7 Projects with Regional Participation ..................................... 53�Figure Annex 5: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Co-patents as share of Total Regional Patent Applications ............................................ 54�Figure Annex 6: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Total Amount of EU FP7 Funding per Inhabitant .......................................................... 54�Figure Annex 7: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Total Amount of SF for Business Innovation per Inhabitant .......................................... 54�Figure Annex 8: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Total Amount of SF for Core RTDI per Inhabitant ........................................................ 54�Figure Annex 9: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Intensity of Employment Clustering in the Region......................................................... 55�Figure Annex 10: Cross Tabulation of Market Oriented R&D (BERD per Inh.) vs. Intensity of Technology Clustering in the Region .......................................................... 55�Figure Annex 11: Cross Tabulation of Product & Process Innovator Share vs. GOVERD per Inhabitant ........................................................................................................ 55�Figure Annex 12:� Cross Tabulation of Product & Process Innovator Share vs. HERD per Inhabitant.............................................................................................................. 55�Figure Annex 13: Cross Tabulation of Product & Process Innovator Share vs. Employment in Hightech and KIBS Sectors as Share of Total .............................................. 56�Figure Annex 14:� Cross Tabulation of Product & Process Innovator Share vs. HRSTC per Economically Active Population ........................................................................ 56�Figure Annex 15: Cross Tabulation of Product & Process Innovator Share vs. Total Amount of SF for Business Innovation per Inhabitant.................................................. 56�Figure Annex 16: Cross Tabulation of Product & Process Innovator Share vs. Number of Interregional Co-Patent Applications Filed in Region ......................................... 56�Figure Annex 17: Cross Tabulation of Product & Process Innovator Share vs. Share of Co-Patents in Total Regional Patent Applications ................................................... 57�Figure Annex 18: Cross Tabulation of Product & Process Innovator Share vs. Total Amount of EU FP7 Funding per Inhabitant .................................................................. 57�Figure Annex 19: Cross Tabulation of Product & Process Innovator Share vs. Total Number of EU FP7 Projects with Regional Participation............................................. 57�Figure Annex 20: Cross Tabulation of Product & Process Innovator Share vs. Intensity of Employment Clustering in the Region ................................................................ 57�Figure Annex 21: Cross Tabulation of Product & Process Innovator Share vs. Intensity of Technology Clustering in the Region.................................................................. 58�Figure Annex 22: Cross Tabulation of Man. and Org. Innovator Share vs. GOVERD per Inhabitant .............................................................................................................. 58�Figure Annex 23:� Cross Tabulation of Man. and Org. Innovator Share vs. HERD per Inhabitant .................................................................................................................... 58�Figure Annex 24: Cross Tabulation of Man. and Org. Innovator Share vs. Employment in Hightech and KIBS Sectors as Share of Total..................................................... 58�Figure Annex 25: Cross Tabulation of Man. and Org. Innovator Share vs. HRSTC per Economically Active Population .............................................................................. 59�Figure Annex 26: Cross Tabulation of Man. and Org. Innovator Share vs. Total Amount of SF for Business Innovation per Inhabitant ........................................................ 59�Figure Annex 27: Cross Tabulation of Man. and Org. Innovator Share vs. Total Amount of SF for Core RTDI per Inhabitant ...................................................................... 59�Figure Annex 28: Cross Tabulation of Man. and Org. Innovator Share vs. Number of Interregional Co-Patent Applications Filed in Region ............................................... 59�Figure Annex 29: Cross Tabulation of Man. and Org. Innovator Share vs. Share of Co-Patents in Total Regional Patent Applications ......................................................... 60�Figure Annex 30: Cross Tabulation of Man. and Org. Innovator Share vs. Total Number of EU FP7 Projects with Regional Participation ................................................... 60�Figure Annex 31: Cross Tabulation of Man. and Org. Innovator Share vs. Intensity of Employment Clustering in the Region ...................................................................... 60�Figure Annex 32: Cross Tabulation of Man. and Org. Innovator Share vs. Intensity of Technological Clustering in the Region .................................................................... 60�


Executive Summary Innovation � Innovation - Making Sense of the Term in Regional Analyses

In this final booklet of the five-year Regional Key Figures project, the contractors set out to bring together evidence from prior book-lets, as well as novel findings, to determine how the innovation po-tentials of regions can best be measured in order to demonstrate and illustrate the regional differentiation of innovative activities in the European Union.

The illustration of regional patterns is not only an aim in itself, but also provides more general insights. Not only does it enable the reader to understand and visualise how heterogeneous the often talked about process of “innovation” really is, in addition it also helps the analyst to understand which framework conditions differ-ent types of innovation are associated with.

With a view to other projects funded by the European Union, e.g., the Regional Innovation Scoreboard or the Regional Innovation Monitor, this booklet will therefore not add to the manifold classifi-cations of ‘innovative regions’ already available. Instead, it focuses on a number of selected indicators to contrast both ends ??? of the continuum of ‘types of innovation’ found in the Union.

Innovation can have different connotations, depending on who is talking about it. This booklet will thus question whether it is mean-ingful to speak about innovation in a uniform sense. In the authors’ view, ambiguous interpretations have caused much confusion and at times led to seemingly irreconcilable policy statements caused by using one term while talking about different issues.

In this booklet, the authors have sought to corroborate that at a time when the EU is setting out to become an Innovation Union, the ques-tion cannot be “whether” to support innovation, but “how”.

Instead, the issue is what type of research and innovation should be addressed by a specific type of policy – to enable each region to contribute to the Innovation Union according to its potential.

Conceptual Background

A survey of conceptual literature sources provides the background of the authors’ key tenet that there is not one uniform process of in-novation. In the past three to four decades, a plethora of sources provides ample evidence of the diversity of actions that can be and have been subsumed under the heading of “innovative activities”.

First of all, innovations can have an incremental or a break-through character. It is worthwhile remembering that the original notion of innovation was one related to products new to a specific market, without necessarily implying technological novelty. Later in the booklet, it will be argued that while it is correct to address a broad range of activities as “innovative”, it will be necessary to dif-ferentiate between their determining factors.

Secondly, innovation can occur in different sectors and is neither always related to the development of new products and proc-esses, nor necessarily dependent on formalised research and devel-opment. In both the service and the manufacturing sectors, innova-tions may result from learning by doing and management and organisational innovations play a central role.

Thirdly, there is evidence that the advantages of localised co-operation for innovation apply to all forms of innovation, even if sometimes for different reasons. For technology-driven product innovation as much as for learning-by-doing and management innovation, the regional dimension often plays a central role.

In summary, both the conceptual literature and a number of empiri-cal studies suggest that the diverse activities subsumed under the term “innovation” are indeed such that it makes little sense to con-sider “innovation policy” as a seamless, uniform approach.

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The fact that many innovative activities are not based on formal re-search and development is one obvious reason why a differentiated perspective has to be taken on innovation policy. A second one is the fact that product and process innovation depend on different inputs and framework conditions from management and organisational in-novation. Following the outline of the smart specialisation strategy, this booklet aims to analytically reconcile different approaches to measure innovation with the target of generating evidence for inte-grated policymaking at both the regional and the inter-regional level.

Measuring Innovation

This booklet illustrates the differences between the multiple types of innovative activities relevant for European regions. Given that the booklet aims to illustrate the extremes of the items subsumed under the term of “innovation”, a somewhat less inclusive approach was chosen than, for example, in the Regional Innovation Scoreboard or similar exercises. Since our approach aims to illustrate contrasts, overlaps have to be avoided and only the most concise indicators were included.

Despite the fact that innovative activities only partially depend on formalised processes of research and development, the regional dis-tribution of business expenditures on research and developmentis used as a core indicator. The innovative activities reflected by this indicator are highly concentrated on a number of leading Mem-ber States and, even within those, on a number of leading re-gions.

The innovative activities reflected by the regional prevalence of product and process innovation in SMEs, as measured by the Community Innovation Survey (CIS), follow a remarkably differ-ent regional pattern. Obviously, innovative activities in SMEs are neither as concentrated on certain Member States nor as unequally distributed within nations as monetary inputs into formal business R&D activities are.

Concerning marketing and organisational innovation in SMEs, a third, distinctive pattern was observed: there is a higher degree of spatial concentration than the one found for SMEs' product and process innovation However, it remains far less focused on particu-lar regions than that of business expenditures on R&D.

The analysis thus confirms that the regional pattern of innovation in the Innovation Union depends on which aspect of innovation one decides to measure.

Framework Conditions for Innovation

Moreover, the booklet analyses the regional distribution of frame-work conditions potentially conducive to those different aspects of innovation – limited by the availability of indicators.

Firstly, this relates to a number of framework conditions for which the literature suggests an indirect influence on innovation:

� overall economic performance, � specialised critical mass (pot. localised specific demand), � quality of governance.

Secondly, this relates to a number of framework conditions which in the literature are considered to be direct input factors to innovation:

� pre-competitive knowledge creation, � human capital, � access to (public) finance, � networked specialisation (access to extra-regional knowledge).

Nonetheless, with a view to the introductory chapter, this booklet takes a systemic approach to considering the factors constituting the basis of innovation – of whatever type. All influences are considered as mutually dependent and will be analysed as such.

Regional innovation is shaped by a complex interplay of factors.

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Regional Pattern of Framework Conditions The following regional patterns were found for indirect factors:� GDP per capita is concentrated on a Y-shaped core including

on its left side southern Ireland, southern Scotland, England, the Netherlands, parts of Belgium, France and Luxembourg, on its right side most of Sweden, southern Finland, and Denmark. The lower part then stretches via Germany, Austria, down to Lazio.

� Specialised critical mass, and with it demand, is more concen-trated. Specialised agglomerations are evident in the national capitals and in their vicinity, as well as in the key economic cen-tres of some countries besides the capital cities (e.g. Catalunya).

� Quality of governance is very high in Swedish, Danish, and Dutch regions, most UK regions, as well as in selected Belgian, German, Austrian, Italian and Spanish regions. In contrast, it is low in most New Member States, as well as in Greece and southern Italy. Intra-national differences are common.

The following regional patterns were found for direct factors:� Investment in pre-competitive public research is fairly con-

centrated, in parts following the mentioned Y-shape comple-mented by additional hot spots of activity in southern France.

� Human capital, in contrast, is more broadly spread across Euro-pean regions, irrespective of whether the level of education (HRSTC) or the factual employment in high-tech and KIBS firms is concerned.

� Access to public finance differs substantially, depending on the programme tapped. Funding from the Framework Programme is the most regionally concentrated of all indicators, while struc-tural funding for RTDI mainly benefits the periphery.

� With regard to networked specialisation, we find that both en-hanced networking and specialisation are often high in peripheral regions, which, however, often do not possess much critical mass, while clusters in the centre exist that are hard to detect with the indicators available.

Summary

Following a brief illustrative comment on the regional distribution of the factors of influence across the European Union, the data regard-ing the different aspects of innovation have been analytically related to the different factors of potential relevance. Firstly, a factor analysis confirmed that the different aspects of innovation are associated with different framework conditions as well. The pattern of correlation between certain regional framework conditions, i.e. the pattern of their tendency to jointly occur in re-gions, suggests that there are two main types of environment: one for public R&D-driven innovation and one for applied SME-driven innovation. Remarkably, neither of them was automatically associated with the prevalence of innovative collaborations involv-ing regional SMEs. Apparently, there is room for improvement in both types of environment. Moreover, the findings confirm that, be-yond the general level of development, which is a key factor in terms of both economic performance and quality of governance, there are manifold factors of influence and the ideal point of lever-age differs according to the type of innovation that should be region-ally encouraged. One central finding of this booklet is that publicly supported R&D-driven innovation, as measured by business R&D intensity, is far more difficult to achieve than applied SME-driven innovation, as reflected in CIS data. With respect to publicly supported R&D-driven innovation, it has been found for nearly all individual factors of influence that one fac-tor alone may be necessary, but not sufficient to raise the level of R&D-driven innovation.The opposite is the case for applied SME-driven innovation. It is prevalent in many regions in many different contexts for many dif-ferent reasons. Most factors of influence, therefore, were found to be adequate yet not necessary conditions.

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Policy Conclusions

By juxtaposing information on innovative activities as well as on relevant framework conditions, evidence was found that only some regions are characterised by technology-oriented publicly supported R&D-driven innovation while many others focus on applied SME-driven innovation. Both require different types of policy support.Consequently, the findings suggest firstly that funding from the Framework Programme may be overly focused on existing areas of strength in Europe, as well as secondly not necessarily on those ac-tivities which are most relevant to enable Europe to become an In-novation Union. This, however, is no argument against FP funding as such, as in the context of a smart specialisation strategy it can serve as a valuable tool to increase the leading regions’ ability to act as seedbeds for genuinely new technologies.Nonetheless, it implies that a complement is needed. An obvious candidate for this role is structural funding for RTDI. Many of the measures thus supported, however, remain focused on creating RTDI infrastructure in less developed areas and on standardised in-terventions. While these are necessary steps, they are insufficient to enable cohesion policy to play the above mentioned role. For that, a more adapted and strategic approach would be needed. In brief, we come to the following conclusions: � The smart specialisation strategy should be continued and

further developed, it is well aligned to the realities of divers re-gional patterns of innovation in Europe,

� A continued ‘division of tasks’ between Framework Pro-gramme and Structural Funds appears to serve the different needs of regions and should as such not be called into question,

� However, both approaches should be more closely coordi-nated, to better integrate different types of innovation potentials,

� This applies both to the integration across the Innovation Union as well as to the integration within the same region.

Policy Conclusions in the Light of Earlier Booklets’ Findings

The final booklet’s findings are in the tradition of the findings of prior booklets’– particularly in the most recent thematic ones.

Firstly, the notion of an Innovation Union characterised by region-ally specific types of innovation is in line with an earlier booklet’s findings that all cluster policies need to identify the specific dor-mant potentials of a region to unlock them in the best possible way. In this context, it illustrated that cluster policies need to be adapted to both the stage of development and the potential for fur-ther development of local clusters. As suggested by the smart spe-cialisation strategy, it was found that, with a view to global competi-tion, Europe needs to develop a number of selected, leading-edge innovation clusters, whilst the remaining ‘second tier’ clusters have to be tied into an international network of knowledge ex-change across the EU27.

With that in mind, another booklet on inter-regional networking found that, currently, the exchange of knowledge between Euro-pean regions rests on a ‘network of the strong’ – established be-tween the key centres of publicly supported R&D-driven innova-tion. In many cases, however, transfers of knowledge from the cen-tres of technological R&D to regions focused on applied SME-driven innovation were contained within national borders. If a strat-egy of smart specialisation is to be pursued successfully across Eu-rope, then this booklet suggests that future networks need to be extended across national borders.

In a third booklet it was underlined that since 2000 the Structural Funds, notably the ERDF, have become a key instrument sup-porting RTDI in European regions. It illustrated how the Struc-tural Funds have become the dominant funding source for innova-tion policy in many convergence regions, particularly in the New Member States. It suggested that this will have major consequences for the design and implementation of regional RTDI measures, in-

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fluenced by ERDF guidelines and also by an increasing emphasis on the need to ensure synergies with other EU support instruments.

On the other hand, however, the booklet illustrated that regions tend to follow their own distinct paths and they adapt the available SF instruments and their thematic scope to their specific needs. As a decentralised EU instrument, structural funding allows the regions to channel the support according to their strategic outlooks, political objectives as well as programming and implementation capacity. Finally, the recent booklet on knowledge-intensive business services found that the differentiation into service industries and manu-facturing industries is blurring and that non-technological inno-vation is playing an increasing role. It concluded that future sup-port measures for innovation should not only address the service sector, but focus on innovative service activities in all kinds of firms. Based on this booklet’s findings on the possible heterogeneity of the processes of innovation found in regions, this can only be vig-orously underlined. In summary, the Regional Key Figures project has accompanied the political debate from a focus on research in regions and the rele-vance of the national 3% target for regional policymakers, up to the recent debate on flexible and smart specialisation. In nearly all book-lets, however, the authors came to a twofold conclusion. Firstly, the regional landscape of innovation in Europe is diverse and characterised by manifold idiosyncrasies that are difficult to capture with the present available data. It is thus necessary to exer-cise caution when following national or European policy objectives at the regional level. In most cases, a translation rather than a mere transfer of objectives is needed. Secondly, regions, even more than nations, are open systems that have to be seen in context. Obviously, many regions will not be able to become innovative hubs. Their ability to absorb and to fruit-fully apply what is known elsewhere, however, may be just as cru-

cial for establishing an Innovation Union as the development of leading-edge centres. All this underlines that a truly place-based regional innovation policy has to be adaptive to the specific local framework conditionsand refrain from nonreflective ‘best-practice learning’ from oth-er regions in which many of the framework conditions can be quite different.Against this background, a broad and reliable data basis – and thus, the further development and effort to collect regional data – will be a central prerequisite for future efforts to improve the effectiveness and efficiency of regional innovation policy.

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I Introduction It is almost universally accepted that technological change, and in-novations in particular, are the most important sources of productiv-ity growth, employment and material welfare.

Innovations are a major cause of destroying old jobs as well as creat-ing new employment (Edquist 1997). Innovations are new creations of economic significance. Innovations may be of various kinds: technological and organisational, completely new or a combination of existing elements. With regard to the innovation process, a high degree of complexity can be observed, especially with a view to technology-based innovations: the emergence and diffusion of knowledge elements appears to be as important as the translation of science- and technology-based results into concrete products and processes.

As a reflection of the increased complexity of innovation processes, the systems of innovation approaches – emphasising, among others, interactive relations involving science, technology, learning, produc-tion, policy, and demand – came to the fore in the mid 1990s and, by providing an appropriate framework for the understanding of inno-vations in their contexts, became highly relevant from an innovation policymaking viewpoint .

With a view to the regional level, these new approaches have been adapted and further developed and complemented by research on innovative milieus, regional innovation systems and industrial dis-tricts. Especially Michael Porter’s research on the significance of the regional environment as a home base for global enterprises resulted in a growing interest in the regional level from science and politics. Thus, from an innovation perspective the regional level is particu-larly appropriate to analyse the interaction between producers, users and mediators of knowledge who not only share the same territory, but also the same culture, values and common references.

Despite significant progress in innovation research in the last 15-20 years, no overall and consistent theoretical framework exists to ex-plain innovation activities. Instead, various different theories and approaches have been advanced (e.g. neoclassical, evolutionary models). However, on the basis of today’s state-of-the-art in innova-tion research, evolutionary innovation models are considered to ex-plain the dynamic and cumulative innovation process much more adequately than other models (Grupp 1997). Evolutionary innova-tion models try to explain innovation processes within the context of behavioral theory and according to biological evolution (Nelson/ Winter 1982). The essential features are the natural selection among the market participants, the dynamic and irreversibility of economic processes, the limited rationality of actors and path-dependant tech-

Against the background that innovation has recently been “placed at the heart of the Europe 2020 strategy” (European Commission 2010), this final Regional Key Figures booklet will focus on using the indicators made available in the course of this project to report on the level of innovation activities in Europe as well as on relevant framework conditions.

A Broader View of Innovation – Conceptual Background

Economists generally agree that innovations are crucial for eco-nomic growth. The significance of innovations lies not only in their role as a primary engine of national economic growth, but also in stimulating regional and local economic development. The role that high-technology and innovative firms play regarding long-term na-tional and regional economic growth has ignited much competition in recent years between countries and regions for a share of knowl-edge-based jobs. Given the significance of innovations for the eco-nomic development of countries and regions, policymakers' efforts focus on identifying locational attributes expected to be conducive to innovation and improving the respective attributes or framework conditions.

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nological development (Koschatzky 2001). Especially institutional economic research (Williamson 1965) focused on the institutional structures of the economy (e.g. companies, branches, research sys-tem) and their impact on shaping the institutional framework condi-tions. The essential objective of the different approaches is to ex-plain and measure technological change and to analyse the functions and interactions of the diverse actors in the innovation process. Here, the focus is put on technological development, the interfaces be-tween research and the manufacturing sector, and the exchange be-tween supply and demand of technologies (Koschatzky 2001).

For a long time, thinking about technological change and innovation was dominated by linear models – in the 1950s and 1960s by the technology-push and then the demand-pull model. In the former, the development, production and marketing of new technology was as-sumed to follow a well defined time sequence, which began with basic and applied research activities, involved a product develop-ment stage, and then led to production and possibly commercialisa-tion (Fischer 1999). In the second model, this linear process empha-sised demand and markets as the source of ideas for R&D activities. Despite the appealing logic of such conceptualisations, these models came under increasing attack, particularly due to new and different innovation processes occurring in the post-Fordist era.

Current thinking about innovation processes emphasises their com-plexity in terms of searching, learning, information processing and interaction (Feldman 1994). As a generic example for the translation of this complexity into a model, the innovation model of Kline/ Ros-enberg has been intensively reviewed since the mid 1980s. Point of origin of their reflections is the uncertainty in the innovation proc-ess. The reduction of these uncertainties is the main objective of the relevant actors, for instance, while feeding in test experiences into the innovation process or shortening the development process in the form of parallel development stages (Koschatzky 2001). For Kline and Rosenberg, the main innovation impulses are the market needs

as well as learning in the course of the production. Special emphasis is put on the tacit and non-codifiable nature of technology, the im-portance of learning-by-doing, and the cumulative nature of learn-ing. Learning is now widely accepted as a central element in the process of innovation. Figure 1 presents the interactive model of the innovation process which is now commonly referred to as the “chain-linked model”. The innovation process is portrayed as a set of activities that are linked to one another through complex feedback loops. Problems arising during the process of designing and testing new products and production processes often require links to sci-ence, and especially engineering disciplines in academia (Fischer 1999).Figure 1: Interactive model of the innovation process with feedback loops

and interaction

Source: based on Kline/Rosenberg 1986

In addition to reviewing the theoretical literature on the innovation process, it is important for the purpose of this contribution to provide a working definition of innovation in a broader view. As pointed out above, innovation is an evolutionary, cumulative, interactive and feedback process in terms of information transfer, implicit and ex-

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plicit knowledge in alterations of a technical and organisational character. According to Koschatzky (2001), this definition of inno-vation includes explicitly socio-cultural factors which significantly influence the ability and intensity to interact among the different ac-tors in the innovation process. For Hall (1986), innovation is gener-ally defined as the activities of developing and commercialising new products and processes. Two major types of innovation activities can be distinguished: fundamental innovations, which involve the crea-tion and utilisation of a piece of new scientific, technological or or-ganisational knowledge; and incremental innovations, which con-cern product and process improvements based on existing knowl-edge (Freeman 1986).

Although the afore mentioned two types of innovation constitute no spatial implications, conclusions regarding the importance of spatial proximity in the innovation process are, however, important. Ac-cording to Koschatzky (2001), spatial and cultural proximity among the innovation related actors are particularly important if

� the innovation process contains a particularly high degree of un-certainty; above all this applies to new technological paradigms and systems in the early phase, particularly with a view to radical innovations;

� the innovations involve technical fields that are characterised by substantial science linkages, i.e. close interaction between aca-demic and industrial research; these kind of linkages are particu-lar pronounced in new technologies (e.g. biotechnology, nano-technology etc.);

� knowledge is only available in a non-codified and implicit shape and information codes are complex and flexible; hereby knowl-edge can only be transferred via direct processes (face-to-face contact, observations);

� knowledge and information are connected to single spatial units and problem-solving is only possible by using this specific knowledge; and

� interaction and learning processes require close cooperation be-tween producers and users of technologies; this applies above all to innovations which meet the demands of (potential) users.

The impact of these determinants regarding the necessity of spatial proximity between innovating actors is further influenced by the corporate absorption capacity. In the case of a high absorption ca-pacity, the need to interact in spatial proximity is either high, or own abilities to generate and process information substitute spatial prox-imity.

Analogous to the above mentioned factors, spatial and cultural prox-imity is of subordinate importance in the following cases:

� incremental innovations with less or no uncertainties;

� standardised technologies constitute the basis for innovations;

� the technologies are characterised by minor scientific linkages;

� a minor necessity exists regarding a close interaction between producers and users of technologies (e.g. in the case of incre-mental innovations for mass consumption goods); and

� process innovations dominate, especially at the end of the product life cycle.

Although from a different scientific perspective, but very much in line with the determinants influencing innovation processes, the concept of regional innovation systems (RIS) formulated an analyti-cal framework concept which went beyond the scope of the empiri-cal evidence of single case study results and delivered insights into the systemic elements regarding innovation activities in regions (Koschatzky 2001). According to Cooke (1996), regional innovation

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systems are understood as “geographical distinctive, interlinked or-ganisations supporting innovation and those conducting it, mainly firms”. Thus the concept underlines that the region and the spatial environment play a significant role in the innovation and develop-ment process of companies and other innovation-oriented actors.

In a regional innovation system, organisations operate and shape the system in the course of mutual interactions and their linkages with other innovation systems. Especially with a view to interactions and linkages, the concept of regional innovation systems shows overlaps with Michael Porter’s cluster concept (and its focus on concentration and specialisation of technologies and branches). Essential elements of a (regional) innovation system are innovative or technology-oriented companies, universities and non-university research centres, technology transfer organisations, VC companies and other innova-tion financing organisations, as well as organisations providing training and qualification.

According to Koschatzky (2001), the RIS concept not only addresses the regional analysis, but also shows a policy orientation which is based on the central hypothesis that growth and competitiveness of a region are determined by the innovation and network ability of its companies. In drawing conclusions, deficits in innovation and coop-eration have to be addressed by policy measures which aim to inten-sify cooperation (Cooke et al. 1996). According to the criteria of an ideal-typical RIS, a fragmentation of the system can be overcome by intensifying the intra-regional cooperation in order to mobilise new innovation or technological potentials.

Europe 2020, the Innovation Union and Regional Implications

Against the background that Europe is facing severe challenges, like increasing global competition, major demographic changes, mainte-nance of a high level of employment, climate change, social security etc., innovation has been “placed at the heart of the Europe 2020 strategy” (European Commission 2010). The European Commission

considers innovation the best means to successfully tackle major so-cietal challenges, such as climate change, energy and resource scar-city, and health and ageing. Furthermore, a situation of “innovation emergency” has been stated, particularly in terms of a paucity in re-search and development (R&D) and innovation investment. Cur-rently, Europe is spending 0.8% of GDP less than the USA and 1.5% less than Japan every year on R&D. In addition, other countries like China and South Korea are catching up fast. Further challenges refer to weaknesses regarding the transfer of knowledge and technologies or the essential results of R&D into new and better products and ser-vices.

With the concept of the ‘Innovation Union’, the European Commis-sion set out an integrated strategic approach with the main aim of exploiting and leveraging European strengths, particularly focussing on an increase of R&D and innovation investment, better integration of EU and national research and innovation systems, modernisation of education systems, support for the movement of knowledge (par-ticularly incorporated in researchers and innovators), simplification of the access to EU programmes, better linking of the academic and business community, and removing barriers for entrepreneurs to bring ”ideas to market”.

According to the Communication on the Innovation Union published on 6 October by the Commission, the development of Europe’s own distinctive approach to innovation has been put forward, namely to

� focus on innovations that address the major societal challenges identified in Europe 2020 (e.g. leadership in key technologies in order to enhance EU competitiveness);

� pursue a broad concept of innovation, both research-driven inno-vation and innovation in business models, design, branding and services that add value for users and where Europe has unique talents;

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� involve all actors and all regions in the innovation cycle (large companies as well as SMEs in all sectors, high-tech regions as well as rural and economic weak regions).

The Innovation Union is among one of the seven so-called flagship initiatives which constitute the priorities of the Europe 2020 strategy for a smart, sustainable and inclusive economy. Within each initia-tive (e.g. digital agenda for Europe, resource efficient Europe, agen-da for new skills and jobs, Innovation Union), both the EU and na-tional authorities have to coordinate their efforts so that they are mu-tually reinforcing (http://ec.europe.eu/europe2020/).

The Innovation Union contains over thirty detailed action points with the following aims to

� turn Europe into a world-class science performer;

� remove obstacles to innovation – like expensive patenting, market fragmentation, slow standard-setting and skills shortages – which currently prevent ideas getting quickly to market; and

� revolutionise the way public and private sectors work together, notably through innovation partnerships between the European institutions, national and regional authorities and business.

The actions are grouped into thirteen chapters, ranging from the promotion of excellence in education and skills development, to the generation or improvement of funding/financing instruments to spreading the benefits of innovation and increasing benefits. The dif-ferent chapters and actions are highlighted with Innovation Union commitments which contain concrete (but not quantified) objectives.

However, rather than at the level of concrete policy actions, the pro-gress towards the Innovation Union will be measured by the identifi-cation of new indicators which “would best reflect R&D and innova-tion intensity, avoiding duplication with the 3% R&D investment target focussing on outputs and impacts and ensuring international

comparability” (European Commission 2010). Within this context, the Commission will - in the near term – monitor the overall pro-gress on innovation performance on the basis of the Research and Innovation Union scoreboard. In the long run, however, new indica-tors – measuring the share of fast-growing innovative companies, for instance – have to be developed.

With regard to European regions, the Innovation Union strategy formulates no specific objectives or commitments addressing re-gional innovation systems as a possible area of action. However, given the settlement structure in Europe, with densely populated ar-eas – among them regions equipped with a critical mass of technol-ogy-oriented and innovative firms, a diverse public research infra-structure and complementary institutions – as well as large rural ar-eas, some of them showing structural weaknesses in terms of infra-structure and economic/technology potentials, it appears obvious, that selected regional innovation systems in Europe are particularly important for the technological, innovative and economic capability of whole countries, or even the EU as a whole.

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II Policy Intelligence on Regional Innovation

The growing interest in regional innovation at the European level has been accompanied by a growing demand for evidence in the form of data and indicators as well as more qualitative information.

In order to obtain policy intelligence for the further development of regional innovation policies, the European Commission uses several approaches to obtain and diffuse relevant and useful information. Projects in this context have been undertaken by a number of differ-ent EC Directorates, leading to manifold approaches adapted to the specific needs and missions of the respective units.

The following overview is broadly divided by two characteristics, namely, quantitative and qualitative information.

Firstly, a broad range of regional indicators relevant for studies on innovation is available at Eurostat, based on both centralised surveys and harmonised national data from a plethora of sources. One obvi-ous example in this regard are the regional statistics on R&D expen-ditures in the business, the higher education as well as the govern-ment sector. The legal basis for collecting regional S&T data is pro-vided by EC Regulation 753/2004 and the most current NUTS sys-tematic in force. In general, the compilation of regional data is coor-dinated and often led by Eurostat in cooperation with the national statistical offices. Moreover, a broad array of regional patent statis-tics is developed in co-operation with the European Patent Office.

Finally, regional data can to a certain degree be obtained from the Community Innovation Survey (CIS) data. Based on Regulation 1450/2004, the CIS is carried out every four years. While it provides the only broad-based source of information on innovation at firm level, it was not really designed for region-specific analyses so that regional data are unavailable for many countries (DE, IE, NL, SE).

In addition to these quantitative exercises, a large number of moni-toring initiatives with a regional perspective have been launched.

DG Enterprise unites its initiatives related to regional innovation under the roof of ProInno Europe and Europe INNOVA. Many of these monitoring activities are based on networks of regional experts who provide answers to a given set of specific questions.

The Inno-Policy TrendChart initiative is based on such a network. It provides country briefs and an inventory of innovation support measures mainly at national, but also at regional level.

Under the roof of Inno-Metrics, the Regional Innovation Score-board (RIS) has been launched as a sub-activity of the Innovation Union Scoreboard, IUS (formerly European Innovation Scoreboard (EIS)). It is produced about every four years, starting in 2006. The 2009 edition used 16 of the 29 EIS/IUS indicators.

The European Cluster Observatory, launched in 2007 within Eu-rope-INNOVA is another initiative aiming to provide both quantita-tive information on clusters as well as qualitative information on cluster initiatives at national and regional level.

The Regional Innovation Monitor (RIM) is a recently launched initiative that aims to provide the reference framework and tools for the development of more effective and efficient regional innovation policies. It offers regional briefs and an inventory of innovation pol-icy measures, documents and organisations. Moreover, it provides thematic papers on issues of regional innovation policy as well as regular annual reports summarising the findings of both quantitative and qualitative surveys. It can be seen in the tradition of the former “Regions of Knowledge” website, but with a much broader mission.

Additionally, there are some initiatives that do not take a specifically regional perspective, but take some regional aspects into account at times. Examples of this are the European Competitiveness Report, Sectoral Innovation Watch and the SME Performance Review.

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The main source of policy intelligence with regard to RTDI-oriented regional policy under the roof of the Structural Funds and thus DGRegional Development is the InfoRegio website, which not only documents all ERDF regional operational programmes in great detail as well as the detailed amount of funding allocated to them, but also presents a plethora of good practice examples of innovative and suc-cessful SF interventions from a large number of regions.

Moreover, DG Regional Development regularly commissions a number of studies regarding specific topics of relevance for Cohe-sion Policy. Increasingly, these studies cover issues related to Lis-bon-oriented measures and thus regional innovation policy1.

The largest quantitative activity under the responsibility of DG Re-gional Development is the European Spatial Planning Observa-tion Network – ESPON, a research programme that is now in its second programme period (2006-2013). Its budget of €14 million is used for applied, transnational projects dealing with territorial issues at EU, national, and regional level. The supported projects do not necessarily have an immediate link to innovation, but the work on typologies, indicators and cooperation networks provide important background information, or are even directly usable. Additionally, a number of studies are regularly commissioned by the ESPON Net-work. Insofar applicable, the quantitative results of these studies are made available on the ESPON database2.

Regions for Economic Change is a learning platform that aims to highlight good practice in urban and regional development with a particular focus on innovation. It aims to support the challenging endeavour of exchanging good practice among European regions by means of a policy learning database with case studies. Moreover, since 2008, the initiative has presented the annual RegioStarsawards.

1 http://ec.europa.eu/regional_policy/sources/docgener/studies/study_en.htm 2 www.espon.eu/main/Menu_Projects/Menu_ScientificPlatform/espondatabase2013.html

DG Research and Innovation is traditionally more involved in re-search policy and used to put a lesser emphasis on regional issues. In the course of the past decade, however, this gradually changed. In-creasingly, DG RTD started to commission studies and targeted re-search projects that involved an analysis of the regional aspects of research and innovation as at least part of the objective.

An important activity in this regard has been the incorporation of an analysis of the regional dimension of research and research policy in the ERAWATCH inventory. Based on a more comprehensive un-derstanding of innovation, and with the aim to better coordinate re-search and innovation policies, it will in the near future be merged with the Inno-Policy TrendChart activities. United, both activities will ultimately bring together an extensive wealth of information concerning research and innovation at national and regional level.

Additionally, the seven year Regional Key Figures project plays a central role by establishing an online database focused on indicators relevant for studies of regional innovation and by developing a series of ten analytical booklets of which this one is the last.

Additionally, the recent establishment of a specific unit dealing with the regional dimension of innovation underlines the emphasis that DG Research and Innovation now puts on the regional perspective.

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III Regional Distribution of InnovationActivities in Europe

As the preceding chapter illustrated, innovation is a complex phe-nomenon which cannot be equated with research and development efforts alone. Even if the original Schumpeterian definition of inno-vation as “something new to the market” is specified by focusing on those products, processes and services that involve genuine novelty beyond their immediate market context, it becomes clear that inno-vation in a broader sense is difficult to capture by means of statisti-cal indicators. In contrast to research and development activities, sta-tistics on innovation itself are not and cannot easily be collected.

In trying to capture innovation by means of indicators, the analyst faces the choice between reliability and validity.

On the one hand, indicators like business R&D intensity are readily available and comparatively reliable. They, however, only capture a segment of all innovation activities, i.e. those that require a clearly identifiable technological effort to which dedicated resources are committed. Business R&D intensity is thus an indicator with limited validity when innovation is to be measured in a broader sense.

On the other hand, attempts have been made to capture innovation in a holistic sense with the Community Innovation Surveys (CIS). While the relevant question ‘whether innovative activities have been performed’ suggests a high degree of validity, it remains open to discussion if the resulting context-specific data can be considered inter-reliable. Moreover, reliability is challenged by statistical issues resulting from the limited number of cases covered by the CIS.

This booklet, therefore, will take a two pronged approach including both approaches. Based on the conceptual findings presented in the introduction, the approach taken will suggest that both are important elements – since the elusive notion of “Regional Innovation in the Innovation Union” cannot be captured by one indicator alone.

Figure 2: Concepts to Measure Innovation

Source: Own Figure

As Figure 2 illustrates, consideration of different types of data al-lows the analyst to reflect on the broad notion of innovation better than with one type of data alone. Unfortunately for the analysis of this booklet, however, the different indicator types cannot simply be combined, as both regional coverage and regional systematics differ.

Moreover, it has to be borne in mind that no set of indicators, how-ever sophisticated, will be able to fully capture the broad notion of innovation utilised in the Europe 2020 strategy.

Nonetheless, the indicators available suffice to provide a robust and well-founded overview of the EU27’s regional pattern of innovation.

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Methodological Approach

In contrast to other exercises already performed under a number of contracts for the European Union, it is not the ambition of this book-let to add to the numerous classifications of regions according to their local innovation potential already available.

Among others, two recent and elaborate classifications have been developed in the framework of the Regional Innovation Scoreboard (with the aim to rank regions) and for the latest Annual Report of the Regional Innovation Monitor (with the aim to characterise regions).

Instead, this booklet will aim to analyse relations between different types of innovation intensity found in regions and the framework conditions for which theory suggests a potential relevance.

In this regard, two main conceptual dimensions can be thought of to differentiate between different types of innovation.

� Firstly, technological (i.e. product- and process-oriented) innova-tions can be differentiated from management or organisational innovations.

� Secondly, incremental innovation resulting from small-scale learning by doing can be differentiated from large-scale R&D-based planned efforts – often aiming at basic innovation.

In this booklet, therefore, the main conceptual guideline when se-lecting and focusing on certain indicators was to identify those that reflect the extremes of the continuum formed by those two dimen-sions (cf. Figure 3). As far as possible, we aimed to base our analysis on indicators that are conceptually distinct with regard to the type of innovation they reflect.

This characteristic is needed later on in the later analysis aimed at identifying differences with regard to the systemic embeddedness of different types of innovation activity, as well as differences with re-spect to the specific framework conditions relevant for them.

Figure 3: Dimensions to Differentiate Types of Innovation

Source: Own Figure

In order to be able to differentiate clearly between the findings, as little conceptual overlap as possible should exist between the indica-tors suggested to measure innovation.

Moreover, as the indicators selected to measure innovation will be used as dependent variables, it is important that they display as few conceptual overlaps as possible with other indicators that are aimed to reflect framework conditions (i.e. independent variables).

In summary, this booklet aims to perform a type of analysis that is different from other studies, so that different criteria have to be ap-plied to the selection of relevant indicators.

While a rich set of indicators characterised by multiple conceptual overlaps is desirable to develop a classification of regions according to their innovation potential, it is unsuitable for the analysis aimed at in the framework of this booklet.

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Regional Distribution of Business R&D Intensity

The intensity of research and development in the business sector is a relevant indicator, not only because it is available, but also because, to a large extent, it reflects those R&D activities with at least a mid-term market orientation. It is thus the standard indicator that comes closest to reflecting innovation, even if only partially, so that it dis-plays clear characteristics of a proxy variable.

As expected, the R&D intensity in the business sector is highest in the technologically and economically leading areas, including many Scandinavian, southern German, British as well as some Austrian and French regions. They involve known leaders such as Cam-bridgeshire, Frankfurt, Göteborg, Helsinki, København-Malmö, Midi-Pyrénées, Munich, Stockholm, and Stuttgart but also a number of less intuitive cases such as Greater Manchester, Upper Austria as well as regions of northern and eastern Germany (cf. Figure 4).

A middle level of R&D intensity in the business sector can be found in much of remaining, France, Germany, Scandinavia, the UK as well as in the Czech Republic, Estonia, Ireland, Northern Italy, Cata-lunya, the Basque country, Navarra and in the capitals of southern, eastern and south-eastern Europe.

In the remaining parts of southern, eastern and south-eastern Europe as well as in southern Italy, southern Portugal, Corsica, the Extre-madura and certain areas in Scotland and Wales, the level of R&D intensity in the business sector remains rather low.

Overall, a clear dichotomy with regard to innovation activities is thus apparent between most western, central and northern European Member States, on the one hand, and most southern, eastern and south-eastern European States on the other. Nonetheless, the re-gional distribution of R&D intensity in the business sector is distrib-uted quite unevenly, not only between, but also within most coun-tries, even within most leading Member States.

Figure 4: Regional distribution of business R&D intensity 2007

Source: Regional Key Figures

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Regional Distribution of Product and Process Innovation Activi-ties in SMEs according to CIS Data

To get a more holistic view of the pattern of regional innovation in Europe, the regional pattern of R&D intensity in the business sector is complemented by data from the Community Innovation Survey. While this data has clear limitations, it also yields important addi-tional insights with regard to the prevalence of product and process innovation among European small and medium-sized enterprises. For statistical reasons related to the small size of the CIS sample, regionally disaggregated data are not available for Germany, Ireland, the Netherlands, and Sweden. Moreover, the authors have reserva-tions regarding the reliability of the data from Austria as well as parts of Portugal and Greece, since the figures appear unduly high3.Beyond these obvious irregularities, the CIS data (Figure 5) reflect a pattern quite distinct from that found for R&D intensity in the busi-ness sector. While in Spain and Italy the distribution appears intui-tive, with a higher degree of activity in the northern regions, it ap-pears less obvious why product and process innovations should be more widespread in eastern Poland, eastern Romania, Wales or Cornwall. A possible explanation for these surprising findings is that the Community Innovation Survey is primarily focused on small and medium-sized firms’ innovation activity, so that a number of leading regions in which many innovation activities are performed by large corporations (e.g. Île-de-France) will not be reflected as such. In general, the distribution of activities follows the pattern known from the intensity of business R&D in that activities are more preva-lent in northern, western and central European countries than in eastern European countries and in that there are substantial intra-national differences. In general, however, the differences are less pronounced and peripheral regions are lagging less substantially.

3 Data for Belgium are only suspicious for this but less so for other CIS indicators.

Figure 5: Regional distribution of product and process innovation activities in SMEs according to 2006 CIS data

Note: CIS data for Germany, Ireland, the Netherlands, and Sweden are not available in regional disaggregation for statistical reasons. Data for Austria, Greece and Portugal that will be excluded from later considerations are included for illustrative purposes. Source: Regional Innovation Scoreboard, 2009

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Regional Distribution of Management and Organisational In-novation Activities in SMEs according to CIS Data

So far the pattern of regional innovation in European regions has been described with a view to product and process innovation or its basis in terms of BERD. Beyond those, data from the Community Innovation Survey provide insights with regard to the prevalence of management and organisational innovation in European SMEs.

Again, data are not available for Germany, Ireland, the Netherlands, and Sweden. Moreover, the inconsistencies with regard to data from Austria, Greece and Portugal are even more evident than in the case of product and process innovation. Consequently, they will be ex-empted from the discussion.

In general, the overall pattern of the regional distribution of man-agement and organisational innovation in SMEs (cf. Figure 6) is similar to that for product and process innovations. There is, how-ever, a clearer focus on many capital regions, e.g. Île-de-France, Lazio, Madrid, Bucharest or Mazowieckie. Remarkably, this is not the case for London. Again, however, this may be due to the fact that many of the management and organisational innovations for which London is renowned are introduced by larger firms rather than by SMEs.

To a certain extent, moreover, the pattern of management and organ-isational innovation in SMEs stands between that of product and process innovation in SMEs and that of business R&D intensity. Other than in the case of SME product and process innovation, for example, there is a clear divide between south-western and south-eastern Europe as well as between France and Spain. The internal differentiation of the intensity of business R&D expenditure in Italy, however, is different to that of SME management and organisational innovation due to the strong capital effect. Likewise, most peripheral regions are performing somewhat better in managerial and organisa-tional than for product and process innovations.

Figure 6: Regional distribution of management and organisational innovation activities in SMEs according to 2006 CIS data

Note: CIS data for Germany, Ireland, the Netherlands, and Sweden are not available in regional disaggregation for statistical reasons. Data for Austria, Greece and Portugal that will be excluded from later considerations are included for illustrative purposes. Source: Regional Innovation Scoreboard, 2009

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IV Framework Conditions Expected to be Conducive to Innovation

General Framework

A large array of findings in the literature on innovation systems sug-gests that many factors and framework conditions come to play at a multiple number of levels (regional, national, technological) when it comes to explaining innovation. In the context of this project, how-ever, the analysis has to focus on those factors and framework condi-tions that can at least be proxied by indicators available at the re-gional level. What cannot be measured will in this analysis have to be omitted, which does not imply any assessment of its relevance.

Evidently, moreover, innovation is not a homogeneous phenomenon. Consequently, it is impossible to establish explanatory factors in a mathematical sense without clearly defining which aspect and what type of innovation the particular analysis is to focus on.

Innovation in the sense of the Europe 2020 strategy and the aim to move towards an ‘Innovation Union’ is a rather broad phenomenon. While the overall policy objective to make economic activity in the European Union more innovation-oriented is clearly defined, its pol-icy implications at the regional level will differ strongly according to the different region’s starting conditions.

Even with the same overall aim in mind, policymakers will have to address different aspects and types of innovation. Hence, it is not to be expected that ‘the relevant factors’ for innovation can statistically be identified, simply because the innovation potentials addressed in a peripheral region are not like those in a leading region, and the rel-evance of individual factors of influence will differ accordingly.

Nonetheless, findings of the innovation systems literature suggest that a number of central factors are relevant in all cases – so that it appears justified to juxtapose them with innovative activities.

Figure 7: Measurable Framework Conditions of Innovation

Source: Own concept

For the purpose of this booklet, the following available indicators have been selected to capture relevant framework conditions.

Firstly, through the inclusion of three general factors that impact on all activities and interactions relevant for innovation indirectly.

� overall economic performance, � specialised critical mass (pot. localised specific demand), � quality of governance.

Secondly, through the inclusion of four general aspects that can be considered to directly reflect the basis for processes of innovation:

� pre-competitive knowledge creation, � human capital, � access to (public) finance, � networked specialisation.

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Overall Economic Performance (GDP per capita)

The overall economic performance of a region is one of the most relevant indicators to put innovative activity in perspective. While, on the one hand, innovation constitutes the foundation for future wealth, current wealth provides the basis for R&D and innovation. With a view to explaining innovation, it has an indirect character as it is neither necessary nor sufficient for innovation – even though often associated and arguably constitutive.

The map of European regions shows a Y-shaped core in terms of the regions achieving a GDP per capita above €30,000 (cf. Figure 8).This “Y” includes on its left side southern Ireland, southern Scot-land, southern England, the Netherlands, Brussels and Vlaams Bra-bant, the Île-de-France and Luxembourg, on its right side most of Sweden, southern Finland, and Denmark and its lower part then stretches via several southern German, Austrian, and Italian regions such as Bolzano and Emilia Romagna down to Lazio. Within this “Y” there are three city regions with a GDP per capita well above all other regions. On the left hand side of this “Y” the French and west-ern Spanish regions display a GDP per capita in the range of €20-30,000. Regions in south-west Spain or Portugal achieve on average between €10-20,000 GDP per capita. On the left-hand side of the central “Y” the bordering German, Austrian and Italian regions, but also Prague and Bratislava are in the range of €20-30,000.

All New Member States, in contrast, are in the two lowest catego-ries. Only a few like Slovenia, Malta, Cyprus, Polish Mazowieckie and Hungarian Közép-Magyarország (both hosting the capital cities) reach a GDP per capita between €10-20,000, the majority remains below. In terms of growth, however, many of these follower regions realise the highest growth rates. For most of these regions, GDP per capita increased between 2000-2006 at a two-digit level between 10-

15% with the exception of most Polish and Hungarian regions, (5-7% for Poland and 8-9% for most of Hungary). Figure 8: GDP per capita (2006)


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Specialised Critical Mass (Employment Cluster Stars)

The picture found with regard to GDP per capita is underlined by findings regarding the agglomeration of economic activities in spe-cific sectors which can be illustrated based on a detailed analysis of the data of the European Cluster Observatory. These data illustrate the extent to which economic activity in a certain region has achieved ‘specialised critical mass’. It assigns 0, 1, 2 or 3 ‘cluster stars’ to each region, depending on both the degree of specialisation and the size of the sectoral agglomeration4.

‘Specialised critical mass’ is interesting as a framework condition for innovation, as it is the best proxy available of a region’s potential to develop a localised specific demand which at the national level has often been found to drive innovation. When taking as a given that final consumer demand cannot meaningfully be attributed to a regional level, local demand driving innovation would have to be intermediate demand based on localised value chains. Although the underlying assumption that the sectorally related firms in a region with ‘specialised critical mass’ cannot generally be taken for grant-ed, evidence suggests that it holds in many cases.

In terms of the regional pattern found, specialised agglomerations are evident in the national capitals and their vicinity as well as in some countries’ key economic centres outside the capitals (e.g. Ba-varia, Catalunya, northern Italy). Beyond those expectable peaks, however, sectoral activities appear just as prone to cluster in regional agglomerations with at least some critical mass in much of the EU27 periphery as they do in the centre. Examples for those are regions in Romania, southern Italy and eastern Poland with more than two cluster stars, although those could mainly relate to specialisation.

4 It is not the objective of this booklet to discuss whether a sectoral agglomera-tion with ‘specialised critical mass’ can be considered a ‘cluster’. In any case, the ECO approach does not cover information on local cooperation, inter-regional networks or any other aspects often associated with regional clusters.

Figure 9: Intensity of employment clustering in region: Number of ‘Cluster Stars’ in all sectors (2005 data)

Note: Analyses of data from the European Cluster Obeservatory, Count refers to cluster stars in aerospace, automotive, biopharma, chemical, communications, instruments, IT, and the medical sector. Source: Regional Key Figures

25


Quality of Governance (DG Regio EU QoG Index)

To develop a proxy for the quality of governance, individual items have to be measured, based on surveys, and compiled in an index.

For this booklet the authors analysed the quality of governance in-dex, recently developed in the study “Measuring the Quality of Gov-ernment and Subnational Variation”5. The EU-QoG Index devel-oped and used in this study is based on four pillars: ‘rule of law’, ‘government effectiveness’, ‘control of corruption’ as well as ‘voice & accountability’. There is no data available for Finland, the Baltic states, Slovenia, Cyprus, Malta, Luxembourg, and Ireland. Where data is available, the study illustrates that, with a view to the quality of governance, the situation among the EU regions is rather diverse.

All Swedish, Danish, and Dutch regions, most regions in the UK, Belgian Flanders, German Thüringen, the Austrian Burgenland, the small Italian regions Valle d’Aosta, Bolzano, Trento, and Veneto as well as the Basque country reach levels equal or above 1 (Figure10). Of the remaining regions in Spain, Germany, as well as northern Italy most reach a mark between 0 and 1. Most Portuguese, southern Italian but also French regions only reach levels between -1 and 0 (including Ile de France). A similarly low quality of governance, ev-idenced by index levels below zero, can be found in most New Member States, e.g. Hungary, Poland, and the Czech Republic. The Romanian Nord-Vest is the only EU12 region with a positive index. Greece, Bulgaria and the Slovak Republic range at the end of the list with index levels below -1, partially below -2.

Another interesting finding is the different degree of intra-country variation. While the quality of governance is quite similar among e.g. Swedish and Polish regions, it differs starkly within Italy, Spain, and Belgium, but also, at a generally low level, within Bulgaria.

5 DG Regio, 2010; prepared by the Quality of Government Institute, Department of Political Science, University of Gothenburg Sweden

Figure 10 Quality of governance (EUQoG) (2009/2010 survey)

Note: Based on the Annexes to the study: http://ec.europa.eu/regional_policy/sources/ docgener/studies/pdf/2010_government_1.pdf Source: Regional Key Figures

26


Pre-competitive Knowledge Generation (Public Research)

In many European regions, the public sector is the dominant R&D performer. Most funding is in general spent via the universities or public research institutes. While running a research university may be associated with similar costs for most EU regions, costs related to research institutes vary greatly, depending on the institutes’ size and thematic focus. An institute for physics, for example, may cost 10-20 times more than as a social sciences institute. That many Eastern European countries have high absolute shares of GOVERD is often a result of the significant budgets of their academies of sciences.

Moreover, GOVERD per inhabitant in a region varies according to the structure of the institutes supported, but also according to the population of a region. Thus the sparsely populated northern regions of Finland and Sweden look rather impressive (cf. Figure 11).

Given that salaries of the R&D personnel constitute the largest share of public expenditure, the general discrepancies found between the New Member States and the western European countries do not come as a surprise. Moreover, we find that public research plays an above average role in Germany, where GOVERD per capita is high in many regions, particularly in the east.

Beyond that, however, GOVERD per inhabitant displays an above average degree of regional concentration. In France, Midi-Pyrénées is the region with the highest per capita spending, followed by Paris and regions bordering the Mediterranean. In Spain and Italy, public research is focused in the capital regions. In several countries, how-ever, this is misleading, as national research councils in charge of many to most public research centres are often headquartered in the capitals. To some extent, this also applies to Paris as headquarter of CNRS and Upper Bavaria (Munich) as headquarter of the Max Planck Society and Fraunhofer. GOVERD per capita is also above €200,000 in Flevoland, the UK region of Gloucestershire, Wiltshire and Bristol/Bath area, as well as Italy’s Friuli-Venezia Giulia.

Figure 11 Government expenditure on R&D (GOVERD) in EUR per inhabitant (2006)


27


Pre-competitive Knowledge Generation (Higher Education)

Universities are the key research performing institutions in many Member States and their relative weight as drivers of national and regional innovation systems is substantial. In western Europe, the level of HERD per capita generally exceeds the level of GOVERD per capita. In the New Member States, in contrast, the focus on the academies of sciences results in a lower per capita investment in higher education research than in other government research.

Nonetheless, research expenditure in government research institutes and higher education institutions follows a generally similar pattern. In a number of central regions, such as Upper Bavaria, Île-de-France or Stockholm, a high level of GOVERD and a high level of HERD per capita coincide (cf. Figure 12). Other regions, in contrast, are more clearly oriented to one type of public research. In particular, more peripheral regions tend to be characterised by either a relevant level of HERD or a relevant level of GOVERD per capita.

Even though somewhat less pronounced than in the case of GOVERD per capita, the variance of HERD per capita in European regions remains substantial.

At the top of the list, more than €250 per capita is spent on HERD in a number of regions, going up to almost €1,000 in Aberdeen and more than €720 in Inner London. Berkshire, Bucks and Oxfordshire follows with €363 and East Anglia with €281. In the Netherlands, Utrecht stands out with €293 (2003). Moreover, four Swedish re-gions spent between €290 (Sydsverige) and €490 (Övre Norrland) Steiermark (€260), Tirol (€ 331), and Vienna (€478) are the regions with the highest HERD intensity in Austria, just as Gießen is the re-gion with the highest level of HERD per capita in Germany (€ 271). Some of these examples, however, illustrate that the overall popula-tion density of a region needs to be borne in mind before interpreting HERD per capita as a framework condition for regional innovation (e.g. Aberdeen, Övre Norrland, Gießen).

Figure 12 Higher education expenditure on R&D (HERD) in EUR per inhabitant (2006)


28


Human Resources (HRSTC per Econ. Active Population)

As illustrated above, educated researchers and engineers provide a key pillar for innovation, in particular for product and process inno-vation in the manufacturing sectors. In statistical terms, this can be measured by ‘Human Resources in Science and Technology (Core)’, individuals with formal qualifications in S&T-related occupations – like the R&D personnel in firms and the public sector.

This data is available at NUTS-2 level for most regions of the EU27 (Figure 13). Firstly, it is interesting that very high shares of 20-24% of S&T educated workforce in the economically active population are found in the rather thinly populated northern parts of Finland and Sweden, but given the very low absolute figures, this finding should not be over-interpreted. On the contrary, it is well worth looking at the more densely populated industrial regions of which only a small number display a share of above 25% knowledge workers among the economically active population: Stockholm, inner London and the Belgian province of Brabant Wallon lead with a share of 28% each, followed by Utrecht (27%), Ile de France and Vlaams Brabant (26%), and the Basque Country (25%).

Given that the majority of the regions attain shares between 10 and 20%, it is worth looking at the outliers in the higher and lower lev-els. While the top is limited to seven regions in six countries, shares between 20-24% are attained in at least one NUTS2 region in 15 EU Member States. The lowest shares below 10% are documented for peripheral regions, e.g. in Portugal (all but Lisbon), Romania (all but Bucharest), Italy (Sardinia, Puglia, Bolzano and Veneto) and Greece (Ionian islands). Likewise, however, low shares are found in more central regions in Austria, the Czech Republic and Slovakia.

The central result of the analysis thus is that shares of HRTSC are high in many of the northern and central Member States (except Germany), whereas their share in the southern and eastern Member States is low, with only the capitals constituting notable exceptions.

Figure 13: Regional distribution of HRSTC as share of active population (2006)

Note: All regions 2006 data with the exception of data for BG: 2002; UKM1, UKM4, FR9: not available. FR83 (Corsica) only obtained 24% in 2006. For all other available years since 2005, the HRSTC share is below 10%.


29


Human Resources (High-tech Industry and KIS Employment)

Another way to proxy the local availability of human resources is to measure the share of employment in high-tech industries and knowl-edge-intensive services (KIS) in total employment.

This indicator complements the geography of skills (HRSTC) de-scribed above, as it covers a much broader basis. While it has the advantage of only measuring human resources actually ‘at work,’ it has the disadvantage of covering all employees of high-tech and KIS firms, including secretaries and caretakers. Consequently, both indi-cators have to be considered as equally relevant.

In most EU regions, the share of high-tech and KIS employment is quite low. Those regions displaying high shares of employment in the high-tech and KIS sectors are often capital regions or other eco-nomic centres of the country in question: Stockholm (9.3%), Île de France (8.8%), Upper Bavaria (8.5%) and Comunidad de Madrid (7.2%). Only six regions in the EU had a share of more than 8% of total employment in 2006, led by Berkshire, Buckinghamshire and Oxfordshire (UKJ1) with 11% of total employment in 2006, as well as Stockholm, Île de France, Dresden (8.7%), Upper Bavaria (8.5%), and Közép-Magyarország (HU10, 8.4%).

Changing employment patterns provide an indication of important transformations in the economic structures of, most notably, the New Member States. In two of the top regions the share of high-tech and KIS employment has increased significantly since the year 2000: Dresden has increased its share by 81%, and Közép-Magyarország by 35%. Other regions with high increases in the last ten year period can be found in Romania, Slovakia, and Hungary as well as in Spain and Portugal. In contrast, the share of high-tech in-dustrial and KIS employment has decreased substantially in several regions of the United Kingdom and France, e.g. in North Yorkshire (by 52%), Devon (41%) and Poitou-Charentes (39%).

Figure 14: Employment in high-tech industries and knowledge-intensive services (share of total employment) (2006)


30


Access to Public Finance (7th Framework Programme)

In a nationally fragmented framework of multi-level sources of pub-lic finance, it is impossible to document the availability of finance or even public finance as such. Often, however, access to public project funding is both central to the realisation of innovative projects and in line with basic funding priorities. It can thus serve as a proxy indica-tor. One indicator in this field is data from the 7th Framework Pro-gramme launched in 2007. Figure 15 is based on an early 2009 data retrieval from the ECORDA database.

In general, the regional pattern of allocations reflects the location of those research units that are able to obtain funding in a competitive procedure. In brief, FP7 support contributes to the existing strengths in the ‘Innovation Union’. Interestingly, however, some positive out-liers can be found at the EU 27’s periphery, including Malta, Cy-prus, Lisbon, Crete and the Bulgarian Yugoiztochen.

Nonetheless, the key recipients of funding are located in the centre of Europe on a line starting in the north of Sweden and leading down to Lazio in Italy. Due to the high level of funding allocated to UK and Belgian regions, the “Y” identified with a view on GDP per cap-ita can be observed for the allocation of FP7 funding, with an exten-sion to south-west France and north-east Spain.

This picture of a strengthening of strengths is confirmed by the fact that those regions with a high level of FP7 funding that are not part of the ‘Y’ found for GDP per capita display other strengths in terms of HERD and GOVERD per capita. The Basque country, for exam-ple, the only Spanish region in which HERD per capita is above €100, is quite successful with a view to obtaining FP7 funding, and with €44 per capita even reaches a slightly higher level of allocation than the national capital of Madrid (€42). A similar correlation can be identified with a view to Austria where the regions of Vienna (€93), Steiermark (€51), and Tirol (€33) lead the list for HERD per capita as well as for FP7 funding.

Figure 15 Amount of FP7 Funding in the 2007-2013 support period (up until early 2009)

Note: Data originally retrieved from the E-CORDA Database Source: Regional Key Figures

31


Access to Public Finance (SF Allocations for Core RTDI)

In recent years, Cohesion Policy has, at least nominally, become the most important EU instrument funding research and innovation at the regional level. From the onset of any analysis, however, we have to bear in mind that structural funding mainly aims to build RTDI capacities in lagging regions as well as to connect them to the core of the ‘Innovation Union’. While the Structural Funds also support RTDI in leading regions under the competitiveness and employment objective, their objectives remain complementary to those of the Framework Programme. Mostly, SF support is not aimed at R&D projects as such, but either at creating R&D infrastructure or a vari-ety of models to improve interaction in the innovation system.

The map shows clear differences in allocations between the EU12 and the EU15 (€175 vs. €49 respectively), mostly resulting from the differences between convergence and RCE regions (€147 vs. €28). Bratislavský Kraj (SK01) is the region with the highest subsidies to core RTDI with €384 per inhabitant, followed by regions in Slovenia (€359) and the Czech Republic (€280), Estonia (€267) and Slovakia (€224). All top regions are from the EU10 and nearly all of them (with the exception of Bratislavský Kraj) are convergence regions. When compared to the period of 2000-2006, per capita subsidies to core RTDI in regions in the New Member States were considerably increased from about €14 in 2000-2006 to €175 in 2007-2013.

In the regions of the former EU15, support increased from at least €28 to €49 per inhabitant. The leading thematic category of core RTDI investments is R&TD infrastructure with 31% of total alloca-tions, followed by R&TD activities in research centres and assis-tance to R&TD particularly in SMEs (both with 18% each). In line with the above said, this shows a conventional approach to support and stimulate R&D investment in regions (i.e. prevalence of invest-ments in R&D infrastructure and equipment), by focusing on the improvement of the overall framework conditions for RTDI.

Figure 16: SF allocations to core RTDI, euro per inhabitant (2007-2013 support period)

Note: Includes ERDF as well as ESF allocations Source: Regional Key Figures

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Access to Public Finance (SF Alloc. for Business Innovation)

Covering a wider spectrum than allocations to ‘core RTDI’, SF allo-cations to ‘business innovation’ are aimed to develop new ways of doing business in order to improve regional productivity and per-formance. Allocations to business innovation accounted for €38.5 billion or 11% of the total SF budget for the period 2007-2013.

As is the case with regard to core RTDI allocations, all regions in the New Member States allocated more than €314m of structural fund-ing to business innovation compared to only €106m in regions of the former EU15. When compared to the 2000-2006 period, when around three times more was spent in the EU15 than in the EU10 regions (€88 million and €27 million, respectively), this distribution of allocations represents a major shift in focus. However, the EU15 regions spent an average of 15% of their total SF allocations on business innovation, compared to only 13% in EU12 regions. Con-vergence regions allocated on average €203 compared to €35 in RCE regions; representing more than a doubling for convergence regions compared to 2000-06 (€91 spent by Objective 1 regions); while no change is shown on average annual allocations for RCE regions compared with expenditures in 2000-06. All three top re-gions were Portuguese: Região Autónoma dos Açores (PT20) with a total subsidy for the period of €949 per inhabitant, followed by Alentejo (PT18, €554) and Norte (PT11, €388) In total, only nine European regions allocated structural funding of more than €350 per capita to business innovation.

Looking at the thematic priorities of business innovation allocations, as was the case of core RTDI allocations, structural funding is most-ly used to improve framework conditions and infrastructure in sup-port of businesses. The top priority was the category “other invest-ment in firms” with 36% of all allocations – a rather general cate-gory that could include investment of all types, including physical capital that may or may not be innovative.

Figure 17: SF allocations to business innovation, euro per inhabitant (2007-2013 support period)

Note: Includes ERDF as well as ESF allocations Source: Regional Key Figures

33


Networked Specialisation / Smart Specialisation

First Aspect: Degree of Technological Specialisation

The concepts of “networked specialisation” as well as the currently much discussed concept of “smart specialisation” are both based on the notion that it matters whether innovative activities in a region are adapted to regional needs and focused on areas that appear promis-ing with regard to future growth (Benneworth, 2010).

This issue, however, is not identical to the degree of technological specialisation as such, which can have multiple reasons, among which the most obvious is the presence of one strong player in an otherwise weak region. Nonetheless, it appears worth analysing if more innovative regions tend to be technologically specialised or if they are characterised by a rather broadly based technological pro-file. To do so, Gini coefficients were calculated across a number of technology classes that were developed for the WIPO (Schmoch, 2008) to structure the large array of existing IPC classes. A high Gini coefficient thus indicates that regional patent activities are fo-cused on a small number of technological fields.

The findings illustrate that strong technological specialisations are quite often a characteristic of more peripheral regions e.g. Scotland, Latvia and parts of Hungary and Finland with coefficients above 0.70 as well as central Sweden, central Spain, central Portugal and Bretagne with coefficients above 0.55. Most regions, in contrast, display a coefficient of technological specialisation ranging between 0.40 and 0.55. In this group, innovative and less innovative regions are represented on equal terms (e.g. Bavaria and Sicily, Andalucia and Midi-Pyrenées). Likewise, an even lower degree of technologi-cal specialisation (< 0.40) is not associated with lower or higher in-novation capacity per se. In a number of comparatively centralised Member States, it is characteristic for the technologically leading capital regions (e.g. Île de France, Berlin or London), but also for some lagging regions like Basilicata or Puglia.

Figure 18: Technological specialisation in regions (based on 2003-2007 data)

Note: A 5-year period of aggregation is needed to reach figures high enough to calculate meaningful Gini coefficients, as coefficients in regions with a very low overall level of patenting cannot meaningfully be interpreted. No coefficients were calculated where n<50. Source: Regional Key Figures

34


Second Aspect: Critical Mass (Technology Cluster Stars)

As in the case of economic activity, ‘specialised critical mass’ plays a relevant role to develop an understanding of a region’s potential to develop a self-standing regional innovation system in a sectoral field e.g. – in the framework of smart specialisation – to become a source of generic, multi-purpose technologies that can be deployed in and adapted by other regions with less potential to generate them.

‘Specialised critical mass’ was identified, based on an own analysis following the approach of the European Cluster Observatory, i.e. assigning each region 0, 1, 2 or 3 ‘cluster stars’. As the analysis is based on patents rather than on employment, clusters are identified for groups of IPC classes rather than for groups of NACE fields.

With a view on ‘specialised critical mass’ in technology, only two regions have been assigned 10 or more “cluster stars”: Île de Francewith 11, and Düsseldorf with 10. All regions which received more than 5 cluster stars are EU15 regions located in only ten Member States: France, Germany, the Netherlands, Belgium, Finland, Swe-den, Denmark, Italy, the United Kingdom, and Spain.

The most evident concentrations of ‘specialised critical mass’ are found in a number of French and German regions in the top ranks. Another centre of ‘specialised critical mass’ is found in northern It-aly. To support this potential, the governments of France and Ger-many have launched national cluster initiatives that stipulate a cer-tain level of pre-existing excellence and are thus clear cases of de-velopment- and performance-oriented policies (e.g. pôles de compé-titivité in France, leading-edge clusters in Germany).

Due to the limited overall level of patenting in the New Member States, in contrast very little evidence of ‘specialised critical mass’ is found in eastern Europe. The highest ranking EU12 region with a view to regional technology cluster starts is Prague with 3 stars in the biopharma (2) and IT (1) sectors.

Figure 19: Intensity of technology clustering in region: Number of ‘Cluster Stars’ in all sectors (based on 2005-07 data)

Note: Fraunhofer ISI analysis of the EPO Worldwide Patent Statistical Database (PATSTAT);Count refers to cluster stars in aerospace, automotive, biopharma, chemical, communications, instruments, IT, and the medical sector.

35



Third Aspect: Degree of Inter-regional “Networkedness” Another central aspect associated with “networked specialisation” and “smart specialisation” is the inter-regional network aspect, i.e. the capability to absorb external knowledge. One possible proxy to operationalise technology-related knowledge exchange between re-gions are patent statistics. The share of co-patents in regional patent-ing can be used as an indicator of “networkedness”, because the joint mention of two inventors in a patent suggests that two or more actors committed a joint effort to the development of a new technology.

As a general finding, the analysis of patent data suggested that the relative share of cooperative activities in all activities is highest in peripheral countries and regions. For example, the highest shares of co-patenting are found in regions of the New Member States as well as in Portugal and regions of southern Italy. In all of these cases, more than four out of five EPO patent applications were based on a collaboration of a regional and a non-regional inventor, indicating a certain degree of dependency. In contrast, common characteristics of regions with a lower share of co-patenting activities are more diffi-cult to identify. While the degree of networkedness is generally low-er in the EU15, low shares occur in leading (Bavaria, Ile de France) as well as in lagging (Extremadura, parts of Wales) regions.

Moreover, the degree of networkedness differs strongly within the individual countries. In Germany, for example, the share of co-patenting is higher in the eastern than in the western Länder, in the case of Italy it is higher in the Mezzogiorno than the north – where it is particularly low, even from a European perspective.

Our findings thus underline that co-patenting practice and patterns of knowledge exchange are contingent on the institutional framework within and between countries (cf. Kroll/Mallig, 2009), which can look very different from nation to nation. Likewise, the propensity to co-patent differs according to the technological focus of a region.

Figure 20: Share of Co-patenting in total Patenting (2006)

Note: Data originally retrieved from the EPO Worldwide Patent Statistical Database (PATSTAT), by means of custom made queries Source: Regional Key Figures

36


Final Aspect: General Openness to Collaborate for Innovation

A final aspect associated with “networked specialisation” and “smart specialisation” is the intra-regional network or strategic anchoring aspect (Benneworth, 2010). Even when a region is suitably special-ised and many of its firms are well connected to the outside world, successful innovation often remains contingent on interactive proc-esses at the local level. In this booklet, CIS data is used to map the openness of firms to collaborate for innovation even though these data are not limited to regional collaboration.

As in the case of other CIS data, data are not available for Germany, Ireland, the Netherlands, and Sweden. Apparently, firms in Cyprus, Finland, Austria and Greece are most prone to collaborate. A second group of regions includes Flanders (72%), Estonia (71%), Zahodna Slovenija (69%), Luxembourg (63%), Strední Cechy, Denmark (62%), and Brussels (61%). In contrast, firms in regions of the New Member states, particularly the technologically less developed, co-operate least for innovation. For example, the region with the lowest share of firms collaborating is Nord-Vest Romania (5%), followed by regions in Bulgaria, Italy and Spain (e.g. Illes Baleares, 7%).

Despite the limited coverage and validity of regional CIS data that severely restricts our ability to reliably interpret them for individual cases, some general conclusions can be drawn.

Apparently, the high degree of inter-regional networkedness in many peripheral regions is not matched by an equally high general propen-sity to cooperate, particularly with a view to SMEs. Generally, the propensity to collaborate seems to be higher in technologically more advanced economies and regions. Against this background, the high level of networkedness in peripheral regions identified above should be read as an indicator of dependency, possibly related to the activi-ties of larger firms. It is thus by no means evidence of good practice or objectives achieved. Put positively, it is a starting point to recog-nize and build on in novel strategies of smart specialisation.

Figure 21: Share of SMEs collaborating for innovation according to 2006 CIS data

Note: CIS data for Germany, Ireland, the Netherlands, and Sweden are not available in regional disaggregation for statistical reasons. Data for Austria, Greece and Portugal that will be excluded from later considerations are included for illustrative purposes. Source: Regional Key Figures

37


V Relations between Framework Conditions and Innovative Performance

As outlined above, carrying out analysis of the outcomes and deter-minants of regional innovation processes constitutes a challenging task, as few suitable indicators are available to measure ‘innovation’ directly. So far, a number of relevant indicators have been compiled, but it has not yet been made clear how they can be interpreted so that meaningful policy conclusions can be drawn.

The main challenge results not only from the breadth of the term as such, but also from the complexity of regional innovation systems, which can only be reflected by many, often highly interrelated proxy variables, especially in the framework of this booklet which aims to take as broad a view of innovation as possible.

Moreover, due to the different regional classifications used and the high number of missing cases for the latter, business R&D data and CIS data on SME innovation will have to be discussed separately.

To handle these challenges, a three-step approach will be taken to disentangle, analyse and interpret the complex structures and causal interrelations within regional innovation systems.

Firstly, factor analyses will be conducted to reveal the internal struc-ture of regional innovation systems. A factor analysis reduces the information reflected in a broad number of proxy variables to a smaller set of non-observable, synthetic variables, thus limiting re-dundancy and conceptual overlap. Each synthetic variable combines the information contained in highly correlated variables, so that the resulting factors have a low correlation amongst each other. From a conceptual point of view, each factor can thus be understood as re-flecting an essential aspect of regional innovation systems. Our first step thus serves to summarise the information contained in the origi-nal set of indicators, with the aim of reflecting the underlying struc-ture of regional innovation systems in Europe.

Following the factor analysis, individual, bivariate relations between indicators will be illustrated and the issue will be discussed in some more detail, based on which of those indicators regional innovation activities can best be predicted. For selected cases, the nature of the relationships will be discussed against the background of the con-ceptual approach developed in earlier sections. Based on a systemic understanding of regional innovation systems, the second step will seek to illustrate in what sense individual framework conditions or input factors can be understood to exert an impact on the level of innovation activities. Moreover, with a view to the later policy con-clusions, we will point out the nature of these relations separately for each type of innovation considered.

Finally, with a view to policymakers' likely interest in levers to raise the level of regional innovation activities a stepwise regression mod-el has been run with references to each of the three aspects of inno-vation discussed in this booklet. These regression models are a suit-able approach to identify a set of key factors which enable an analyst to predict the regional level of the aspect of innovation in question. While not necessarily to be understood as direct ‘causes for innova-tion’, the discussed factors inevitably highlight which preconditions and/or inputs a certain aspect of innovation is most dependent on.

While for the sake of methodological soundness, business R&D data and CIS data on SME innovation will be discussed in separate sub-sections, a joint discussion and interpretation will be provided in the final summary of the booklet’s findings.

38


Determinants of Market-oriented R&D (BERD per Inhabitant)

With a view to the broader set of regions for which data on business R&D intensity is available, the following results have been found:

A first factor, which could be referred to as public R&D component,is formed by the intensities of R&D expenditure in the higher educa-tion and the government sector, the share of human resources in S&T in the economically active population, the amount of FP fund-ing per inhabitant, the absolute number of FP projects in the region as well as GDP per capita.

A second factor, that could be referred to as private R&D compo-nent, is formed by the share of high-tech and KIBS employment in total employment, the number of co-patents applied for, the intensity of business R&D expenditures as well as the amount of FP funding per inhabitant, the absolute number of FP projects in the region.

A third factor, which could be designated general level of develop-ment component, is formed by the quality of governance index, GDP per capita as well as the intensity of business R&D expendi-tures. Additionally, some indicators are negatively correlated with development. These include the amount of structural funding for RTDI and business innovation that a region receives, as well as the share of co-patents in total patent applications.

In general, however, the possibly most interesting finding is that there is no clear-cut differentiation between the different factors. For example, both types of R&D components are associated with FP funding. Moreover, a high level of GDP per capita is part of the pri-vate R&D element as well as the public R&D component, possibly reflecting their widespread joint occurrence in capital regions.

This interrelatedness of even these synthetic variables is in line with the propositions on the systemic nature of the regional framework for innovation laid out in the conceptual section. Nonetheless, the findings underline that distinct subsystems can clearly be identified.

Figure 22: Factor Analysis of Determinants of Market-oriented R&D (BERD)

1 2 3HERD per capita 0.898 0.131 0.295GOVERD per capita 0.898 0.131 0.295FP funding per inhabitant 0.760 0.462 0.155HRSTC per econ. active population 0.613 0.365 0.154Number of FP projects in region 0.607 0.656 -0.055BERD per capita 0.379 0.561 0.491Hitech and KIBS emp in tot emp 0.344 0.681 0.138Number of Co-Patents in Region 0.227 0.733 0.232GDP per capita 0.558 0.295 0.676Quality of Governance (Index) 0.300 0.018 0.804SF for core RTDI per inhabitant -0.050 -0.547 -0.580SF for bus. Innov. per inhabitant -0.052 -0.626 -0.618Share of Co-Patents in all Patents -0.148 -0.176 -0.731

The figure below indicates how ‘close’ variables are in terms of mutual correlation.

sq_hesq_govsq_gdp

eucog

sq_fp_pisq_berd

sq_hrstc

1,01,0

sq_fp_nr

-,5

sq_empsq_nrcop

0,0

,5,5

,5

1,0

sq_sf_rdsq_sf_bu

0,00,0

sq_shcop

-,5-,5

Source: own calculation, based on Regional Key Figures

39


Cross tabulation of relevant factors The following section will illustrate and comment on some central bivariate correlations in detail. All other bivariate correlations are documented in the Annex.

Firstly, a certain relation between the level of GDP per capita and the level of business R&D intensity in a region appears obvious. In general, the relationship appears to be of a quadratic rather than of a proportional nature. Moreover, it appears to an extent one-sided. While it appears clear that only a certain level of general economic development opens up the opportunity for substantial investments in business R&D, it is just as evident that this opportunity is not real-ised even in some of Europe’s wealthiest regions. To some extent, this may be explained by the differing prevalence of industrial and service sector activities among Europe’s leading regions. Figure 23: Cross Tabulation of Market-oriented R&D (BERD per Inhabitant)

vs. GDP per Inhabitant

GDP per Inh_2006

100000800006000040000200000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


Secondly, a juxtaposition of the level of business R&D intensity and the level of higher education R&D intensity shows that both follow a comparatively different logic.

Apparently there is neither a generally positive nor a negative rela-tion. Not surprisingly, there are a number of regions that display ei-ther very high levels of business R&D intensity or very high levels of higher education R&D intensity. There are no regions with very high values for both indicators.

A similar statement can be made with respect to the correlation be-tween the level of business R&D intensity and the level of govern-ment sponsored R&D intensity (cf. Annex). In that case, it is even more evident that there is no clear relation between the two factors. Other than in the case of higher education R&D intensity, however, high values with respect to the indicators are not mutually exclusive. Figure 24: Cross Tabulation of Market-oriented R&D (BERD per Inhabitant)

vs. HERD per Inhabitant

HERD per Inh_2006

10008006004002000

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07

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2000

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40


Thirdly, a closer look was taken at the relation between good gov-ernance and the level of business R&D intensity. What the bivariate correlation suggests is that the opportunity to spend more intensively on business R&D comes with better governance. As the relation is unidirectional, i.e. better governance does not automatically imply a higher level of business R&D intensity, a causal relation in the sug-gested sense appears plausible. Apparently, good governance consti-tutes something like a necessary condition, as none of the regions with poor governance reach a significant level of business R&D in-tensity. Once more, however, the results underline the systemic na-ture of the regional framework for innovation, in which good gov-ernance constitutes one necessary, but by itself not sufficient condi-tion for innovation. Figure 25: Cross Tabulation of Market-oriented R&D (BERD per Inhabitant)

vs. Quality of Governance (EUQOG Index)

euqogindex

3210-1-2-3

BER

D p

e In

h_20

07

2500

2000

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Finally, a similar statement can be made with respect to the share of employment in the high-tech and knowledge-intensive business ser-vice (KIBS) sectors. As in the case of good governance, it is evident that regions without a certain share of employment in high-tech and KIBS sectors do not reach a substantial level of business R&D in-tensity. Again, however, it is just as evident that a high share of em-ployment in high-tech and KIBS sectors does not make for a high level of business R&D intensity. In fact, the level of business R&D intensity was found to be lower in some regions with a high-tech and KIBS employment share of as high as 8% than in other regions with a share of below 4%. Beyond the systemic nature of the framework conditions, this finding may in part be due to the fact that certain re-gions are characterised by assembly in high-tech sectors. Figure 26: Cross Tabulation of Market-oriented R&D (BERD per Inhabitant)

vs. Employment in high-tech and KIBS sectors as share of total

EMP hitech_KIBS per tot emp

121086420

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D p

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41


Regression analysis While the factor analysis illustrated the overall systemic nature of the regional framework for innovation, the question remains whether we can identify a set of variables that allow us to predict the distri-bution of business R&D intensity with a certain degree of precision.

For that purpose, we perform a stepwise regression analysis which aims, from a large array of indicators, to identify a small set which can best be used to satisfactorily predict the dependent variable. The process is stopped when the addition of an additional variable no longer improves the explanatory power of the model sufficiently.

In this specific case, the distribution of business R&D intensity can be explained by a set of eight variables by more than two thirds. The figure below the model indicates that the highest share by far of the explanatory power of the model is contributed by the first two indi-cators: GDP per capita and the number of co-patents. A less substan-tial role is played by the share of employment in high-tech and KIBS sectors, the quality of governance, the degree of FP funding per in-habitant, a low total number of FP projects as well as both techno-logical and employment clustering (‘specialised critical mass’).

In summary, the model illustrates that the intensity of business R&D activities is likely to be higher in regions that are wealthy and inter-change knowledge actively. Moreover, it helps when there is a high share of employment in high-tech and KIBS sectors, a high quality of governance, funding from the Framework Programme and when activities are clustered. That the number of FP projects is inversely related may be due to the fact that this number is typically highest in public R&D institutions, which as known from the factor analysis, constitute a different systemic aspect than business R&D.

In summary, the findings underline the importance of the general framework and flexible specialisation, as well as the fact that good governance and public support are conducive.

Figure 27: Analysis of Factors relevant for Market-oriented R&D (BERD)

Model Stepwise Regression – 8 Iterations SS df MS

Regression 13864.1 8 1733.0Residuals 6374.8 227 28.1total 20238.8 235

R R-Squared AdjustedR-Squared

0.8277 0.6850 0.6739

Standardised Coefficients SignificanceBeta t

(Constant) -1.4472 0.14921 GDP p cap 0.1652 2.2811 0.02352 Nr of Co-Patents 0.2383 3.6329 0.00033 Emp. High-Tech & KIBS (share) 0.1543 3.0336 0.00274 Governance Quality Index 0.1740 3.0939 0.00225 FP funding per Inhabitant 0.3613 4.6007 0.00006 Number of FP projects -0.2659 -3.4782 0.00067 Technological Clustering 0.1441 2.6110 0.00968 Employment Clustering 0.1069 2.0044 0.0462

Note: square root transformation was applied to improve normality of the data

Figure illustrates to what extent variables contribute to explanatory power of model

0

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R�Squared

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42


Determinants of Innovative Activities According to the CIS

Before a more in-depth analysis of the CIS data on SME innovation is presented, a few general remarks are due regarding the opportuni-ties offered and challenges posed by the use of this data.

On the one hand, CIS data constitute the only source of information that reflects the elusive issues of SME product and process innova-tion as well as SME management and organisational innovation at a European level. Consequently, they have to be taken into account by any thorough analysis of regional innovation in Europe.

Unfortunately, however, there are also a substantial number of qual-ity issues and thus challenges associated with their use.

Firstly, coverage is limited, as regional data for Germany, Ireland, the Netherlands and Sweden are not available as the number of cases in many regions is too low to allow the analyst to draw meaningful conclusions. In other countries, a regional systematic different from the NUTS 2 level had to be used. In sum, the coverage of regional CIS data is limited and it inevitably misses elements of the overall “regional logic of innovation” in Europe when countries such as Germany and Sweden cannot be considered.

Secondly, the data themselves have a tendency to follow an own dis-tinctive ‘CIS logic’. Figure 28 and Figure 29 indicate that figures for SME management and organisational innovation, product and proc-ess innovation as well as innovation collaboration correlate strongly. If an overall correlation analysis is conducted, CIS indicators ‘stand for themselves’ with little correlation to other variables, raising sub-stantial doubts if this finding is fact-based or due to data reliability. Hence, it was decided to drop the values from those countries which in the descriptive section had been found to be suspect and counter-intuitive (AT, GR and PT). As a result, the CIS indicators no longer form a separate section in correlation/factor analyses so that further analyses can be expected to yield more robust findings.

Figure 28: Cross Tabulation of Share Product & Process Innovators (CIS) vs. Share Management & Organisational Innovators (CIS)

CIS_PuP_2006

1,0,8,6,4,20,0-,2

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0

Source: own calculation, based on Regional Key Figures, including AT, GR, PT Figure 29: Cross Tabulation of Share Product & Process Innovators (CIS)

vs. Firms Co-operating for Innovation (CIS)

CIS_collaboration_2006

1,0,8,6,4,20,0

CIS

_MuO

_200

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,8

,6

,4

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Source: own calculation, based on Regional Key Figures, including AT, GR, PT

43


With a view to the more limited and thus reduced set of regions for which CIS data are available, the following results were found:

A first factor, which could be referred to as the high-tech/public R&D component, is formed by the absolute number of FP projects in the region, the share of high-tech and KIBS employment in total employment, the amount of FP funding per inhabitant, the intensities of R&D expenditure in the government sector, the share of human resources in S&T per economically active population, the number of co-patents applied for, as well as the intensities of R&D expenditure in the higher education and the business sector and GDP per capita.

A second factor, that could be referred to as the SME applied inno-vation component, is formed by the quality of governance index, the share of SMEs with product and process as well as management and organisational innovations, the intensities of R&D expenditure in the higher education and the business sector, and GDP per capita. More-over, the indicators for SF allocated to the regions and the share of co-patenting in total patenting are mostly inversely associated with this factor (although the association particularly of the SF indicators with the first factor is nearly just as notable).

A third factor is formed by the propensity of SME to cooperate for innovation. Apparently, this central element of a regional innovation cannot be predicted based on any of the other variables.

Again, the factor analysis confirmed that the regional framework for innovation is of a systemic nature and that its different aspects can-not be regarded as unambiguously distinct. In detail, it could be demonstrated that high-tech/public R&D and SME-based applied innovation can be regarded as different aspects, even though both are subject to the overall logic of development as reflected in GDP per capita and the level of SF allocations. Likewise, a high level of business R&D intensity is associated with both factors. Moreover, SME innovation cooperation was found to be context-independent.

Figure 30: Factor Analysis of Determinants of Innovation according to CIS

1 2 3Number of FP projects in region 0.8953 0.2557 -0.0432Hi-tech and KIBS employment in total emp. 0.8177 -0.0361 0.3109FP funding per inhabitant 0.8069 0.4340 0.0388GOVERD per capita 0.7795 0.2938 -0.0807HRSTC per econ. active population 0.7326 0.1283 0.0787Number of Co-Patents in Region 0.7205 0.3124 0.0140BERD per capita 0.6996 0.5592 0.1798HERD per capita 0.6502 0.5963 -0.0750GDP per capita 0.6139 0.7074 -0.0610CIS: share of SME with man. & org. innov. 0.3820 0.6003 0.2769CIS: share of SME with prod. & proc. innov. 0.1975 0.8125 0.3169Quality of Governance (Index) 0.0540 0.8346 0.0321Number of Co-Patents in Region -0.2266 -0.7230 0.1508SF for core RTDI per inhabitant -0.4877 -0.4242 0.4940SF for business innovation per inhabitant -0.5040 -0.5961 0.3498CIS: share of SME with innov. cooperations 0.2121 0.1821 0.8558

The figure below indicates how ‘close’ variables are in terms of mutual correlation.

sq_emp

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44


Determinants of Product and Process Innovation The following section will discuss and comment on some of the most relevant bivariate correlations in some more detail. All other bivariate correlations are documented in the Annex.

Firstly, a certain relation between the level of GDP per capita and the prevalence of SME product and process innovation in a region appears obvious. However, the relationship appears to be of a one-sided nature. While a high level of GDP per capita inevitably entails at least an intermediate prevalence of SME product and process in-novation, high shares of SME performing product and process inno-vations can also be found in less wealthy regions. Overall, a joint occurrence of both characteristics cannot be denied, but the findings do not provide a basis to assume a causal relationship. Figure 31: Cross Tabulation of Share Product & Process Innovators (CIS)

vs. GDP per Inhabitant

GDP per Inh_2006

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Secondly, the relation between the level of business R&D intensity and the prevalence of SME product and process innovation is even less clear-cut than that between the level of GDP per capita and the prevalence of SME product and process innovation. While regions with high business R&D intensities are, with one exception, also characterised by a high prevalence of SME product and process in-novation, the opposite relation cannot even be tentatively suggested. In fact, there are a large number of regions in which the level of business R&D intensity is low or even close to zero, but in which the CIS has nonetheless found a high share of SMEs introducing product and process innovations. In some cases, the share of innovating SMEs in regions without substantial business R&D intensity amount to over 80%, even though values of around 40% are more common. Figure 32: Cross Tabulation of Share Product & Process Innovators (CIS)

vs. BERD per Inhabitant

BERD per Inh_2007

1400120010008006004002000

CIS

_PuP

_200

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,8

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45


A third important issue can be raised in view of the question whether or not good governance is conducive to the prevalence of SME product and process innovation. What we find is that, indeed, this seems to be the case. Of the bivariate relations considered so far, that between SME product and process innovation and the EU-QoG In-dex of quality of governance is by far the least ambiguous. In gen-eral, there are no regions with poor governance and high shares of innovating SMEs just as there are no regions with good governance and a low share of innovating SMEs. While it is always difficult to derive causal effect from mere correlations, it does in this case seem fair to suggest that good governance seems more than likely to have a conducive effect on SME product and process innovation. Figure 33: Cross Tabulation of Share Product & Process Innovators (CIS)

vs. Quality of Governance (EUQOG Index)

euqogindex

210-1-2-3

CIS

_PuP

_200

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1,0

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Finally, since the descriptive analysis showed that a number of less developed regions display comparatively high shares of SMEs with product and process innovation activities, it appears worthwhile to take a closer look at the relation between product and process inno-vation and Structural Funds allocations on ‘core RTDI’. What the findings illustrate is that the prevalence of innovation in SMEs var-ies quite broadly among the regions that receive little structural funding, i.e. have reached at least an intermediate level of develop-ment, so that they do not fall into the convergence objective. Among those regions where more than €100 per inhabitant is spent, how-ever, there is a tendency that more SF allocations for RTDI are asso-ciated with a higher prevalence of innovation in SMEs. Figure 34: Cross Tabulation of Share Product & Process Innovators (CIS)

vs. SF Expenditure on RTDI per Inhabitant

SF_RTDI per Inh_2007

4003002001000

CIS

_PuP

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46


Regression analysis, While the factor analysis has illustrated the overall systemic nature of the regional framework for innovation, the question remains if we can identify a set of variables that allow us to predict the distribution of product and process innovation in SMEs (according to CIS data) with a certain degree of precision. Again, a stepwise regression analysis has been performed which aims, from a large array of indi-cators, to identify a small set which can best be used to satisfactorily predict the dependent variable: product and process innovation.

In the case of SME product and process innovation, seven key vari-ables are identified that explain more than half of the regional varia-tion in the data. Remarkably, the main factors of importance are quite different from those that were identified earlier as key explana-tory variables to predict the intensity of business R&D innovation.

While GDP per capita is initially found to yield substantial explana-tory power, it is later found that a better model results when it is re-placed by a set of other variables. Finally, quality of governance is identified as the most important factor, together with FP funding per inhabitant and (inversely) the number of co-patents. Additionally, a low share of HRSTC per economically active population and a low intensity of structural funding for business innovation, as well as a high intensity of structural funding for RTDI, help to explain the dis-tribution of SME product and process innovation across Europe.

With remarkable clarity, the results indicate that good governance and targeted support (FP funding as well as structural funding for RTDI) are conducive to SME product and process innovation. That the number of co-patents and HRSTC appears to be negatively re-lated, in contrast, can be seen as evidence that SME product and process innovation can be, and typically is, prevalent outside the main RTDI centres as long as the above conditions are given. Additionally, differentiated findings with regard to structural fund-ing suggest that it does succeed in serving its different objectives.

Figure 35: Analysis of Factors relevant for Product and Process Innovation (According to CIS data)

Model Stepwise Regression – 6 Iterations SS df MS

Regression 1.9021 6 0.3170Residuals 1.3299 98 0.0136total 3.2320 104


0.7672 0.5885 0.5633

Standardised Coefficients SignificanceBeta t

(Const.) 3.8823 0.00022 Quality of Governance (Index) 0.3762 4.8045 0.00003 Nr. of Co-Patents in Region -0.1497 -1.7090 0.09064 FP funding per inhabitant 0.4833 4.4309 0.00005 HRSTC per econ. act. pop. -0.1953 -2.2446 0.02706 SF for core RTDI per inh. 0.3397 3.4831 0.00077 SF for business inn. per inh. -0.2970 -2.6207 0.0102

Note: square root transformation was applied to improve normality of the data Figure illustrates to what extent variables contribute to explanatory power of model

Source: own calculation, based on Regional Key Figures/Regional Innovation Scoreboard

47


Determinants of Management and Organisational Innovation

The following section discusses and comments on the central bivari-ate correlations in some more detail. All other bivariate correlations are documented in the Annex.

Firstly, a quite clear correlation can be identified between the level of GDP per capita and the share of SMEs introducing management and organisational innovations in a given region. While, once more, the relationship could be characterised as somewhat unidirectional, with some regions reporting a high prevalence of SME management and organisational innovations despite low levels of GDP per capita, the number of the exceptions is far lower than in the case of SME product and process innovations, i.e. the relationship is closer. Figure 36: Cross Tabulation of Share Management & Organisational

Innovators (CIS) vs. GDP per Inhabitant

GDP per Inh_2006

8000070000

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Secondly, the relationship with business R&D intensity is found to be a little less ambiguous than in the case of SME product and proc-ess innovations, but still quite unidirectional. Again, a high level of business R&D intensity inevitably comes with a rise in the preva-lence of SME management and organisational innovation, a relation-ship which in fact appears even more clear-cut than in the case of SME product and process innovations. Nonetheless, there are a large number of regions which report significant levels of SME manage-ment and organisational innovation in the absence of any substantial level of business R&D intensity. Apparently, a high level of business R&D intensity is one compelling factor that can induce SME man-agement and organisational innovations. In the proposed systemic framework, however, it plausible that it is far from the only one. Figure 37: Cross Tabulation of Share Management & Organisational

Innovators (CIS) vs. BERD per Inhabitant

BERD per Inh_2007

1400120010008006004002000

CIS

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48


Thirdly, and differing from the case of SME product and process innovations, the juxtaposition of the prevalence of management and organisational innovation in SME and the EUQOG-index of good governance does not yield any clear evidence of a specific relation. While, with some audacity, it could be claimed that the relation ap-pears positive rather than negative, it is generally quite fuzzy, so that a clear conclusion cannot be drawn. While these findings do not in any way prove that good governance does not have a conducive in-fluence on SME management and organisational innovation, they do indeed bear witness to the fact that other factors appear to be more important as immediate determinants. To a degree, this finding can be considered in line with the findings from the descriptive section that in all countries, irrespective of the (often country-specific) qual-ity of governance, SME management and organisational innovation tends to be most prevalent in the capital regions. Figure 38: Cross Tabulation of Share Management & Organisational

Innovators (CIS) vs. Quality of Governance (EUQOG Index)

euqogindex

210-1-2-3

CIS

_MuO

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6

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Finally, a more detailed look was cast on the influence of the level of support from the Framework Programme. Apparently, the preva-lence of management and organisational innovation in SMEs is posi-tively affected by this factor, which technically appears as a suffi-cient yet not necessary condition. Many regions are characterised by a high prevalence of SME management and organisational innova-tion without receiving substantial support from the Framework Pro-gramme, whereas those regions that do receive substantial Frame-work Programme support are without exception characterised by high levels of SME management and organisational innovations. Nonetheless, these results have to be interpreted with caution against the background that both SME management and organisational in-novations and FP support have earlier been found to be concentrated in capital regions. While a beneficial effect of co-location thus seems likely, it appears inadequate to derive a direct causality. Figure 39: Cross Tabulation of Share Management & Organisational

Innovators (CIS) vs. Quality of Governance (EUQOG Index)

TotalFP per Inh_2009

140120100806040200

CIS

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49


Regression analysis, While the factor analysis illustrated the overall systemic nature of the regional framework for innovation, the question remains if we can identify a set of variables that allow us to predict the distribution of management and organisational innovation in SMEs with a cer-tain degree of precision. Again, a stepwise regression analysis is per-formed which from an array of variables identifies a small set that can best be used to satisfactorily predict the dependent variable.

In the case of SME management and organisational innovation, four key variables are identified that explain nearly half of the regional variations in the data. As to be expected, based on the descriptive findings of chapter 2, they are distinct from both those suitable to predict the intensity of business R&D innovation and those suitable to predict the distribution of SME product and process innovation.

As in the former cases, the results indicate that GDP per capita is the most powerful factor when aiming to explain the regional pattern of SME management and organisational innovation. Other than in the case of SME product and process innovation, the model does not find other factors suitable to replace GDP per capita. Additionally, the amount of FP funding per inhabitant, as well as the intensity of business R&D expenditure and a low intensity of HRSTC per eco-nomically active population, are found to be meaningful explanatory factors. As in the case of the intensity of business R&D expenditure, the figure illustrates that GDP per capita alone explains much of the variation, but in this case substantial explanatory power can be add-ed by the three additional variables.

Summing up, the results suggest a stronger orientation towards the centres than in the case of SME product and process innovation. The negative relation with HRSTC per economically active population, however, suggests that this orientation is one towards the centres of business rather than of public R&D and education. Nonetheless, the intensity of FP funding is found to be conducive.

Figure 40: Analysis of Factors relevant for Management and Organisational Innovation (According to CIS data)

Model Stepwise Regression – 4 Iterations

SS df MSRegression 0.71 4 0.18Residuals 0.83 100 0.01total 1.54 104


0.6775 0.4590 0.4374

Standardised Coefficients Significance Beta t

(Const.) 9.6086 0.00001 GDP per capita 0.3025 2.2569 0.02622 FP funding per inhabitant 0.3439 2.5041 0.01393 HRSTC per econ. act. pop. -0.3118 -3.1292 0.00234 BERD per capita 0.2644 1.9908 0.0492

Note: square root transformation was applied to improve normality of the data Figure illustrates to what extent variables contribute to explanatory power of model

Source: own calculation, based on Regional Key Figures/Regional Innovation Scoreboard

50


VII Summary and Policy Conclusions Summary

In summary, our findings are solidly in line with the systemic ap-proach laid out in the conceptual section. This result is robust, irre-spective of whether business R&D intensity or the prevalence of product and process or management and organisational innovation in the SME sectors are taken as a point of reference.

By means of factor analysis, we were able to demonstrate that the regional framework for innovation is characterised by a number of subsystems that exert a differentiated influence on regional innova-tion. For example, it was empirically corroborated that public R&D and private R&D follow a different logic, but that both are important for the general level of development and market-oriented R&D in-tensity. Additionally, it was confirmed that within the regional proc-ess of innovation itself, different types of activities can be identified: public R&D-driven innovation and applied SME-driven innovation. Remarkably, none of the general characteristics of the regional framework was closely associated with the regional SMEs’ decision to collaborate for innovation. Apparently, some elements of regional innovation systems remain very difficult to capture, even when the general institutional framework is taken into account.

Furthermore, the findings confirm that beyond the general level of development, which is a key factor in terms of both economic per-formance and quality of governance, there are manifold factors of influence and the ideal point of leverage differs according to the type of innovation that should be regionally encouraged.

One central finding, of possibly highest interest for policymakers, is that publicly supported R&D-driven innovation, measured by busi-ness R&D intensity, is far more difficult to achieve than applied SME-driven innovation, as reflected in CIS data.

With respect to publicly supported R&D-driven innovation it was found for nearly all individual factors of influence that one factor alone may be necessary but not sufficient to raise the level of R&D-driven innovation.

While, for example, good governance or employment in the high-tech and KIBS sectors are characteristics without which no region can reach a high level of R&D-driven innovation, in no case are they sufficient on their own. Our empirical analysis illustrates that raising the level of R&D-driven innovation is an ambitious goal that needs to be tackled systemically.

With respect to applied SME-driven innovation, the opposite is the case. Applied SME-driven innovation is prevalent in many regions in many different contexts for many different reasons. Most factors of influence, therefore, were found to be sufficient yet not necessaryconditions.

In brief, many different ways appear possible to raise the prevalence of applied SME-driven innovation and each way may on its own be sufficient. While, naturally, these findings cannot serve as a pretext to discard a systemic approach entirely, they emphasise that less ambitious goals can successfully be reached by less developed re-gions when a suitable policy focus is directed at the regional subsys-tem with the highest potential. In a sense, this is in line with the cur-rent focus on smart specialisation.

According to our findings, less developed regions may be facing the best prospects when aiming to raise the local prevalence of product and process innovations in SMEs that appear less structurally bound to economic and administrative centres than the prevalence of SME management and organisational innovations. Remarkably, among less developed regions, the level of Structural Funds allocations to RTDI projects was found to be associated with a higher share of re-gional SME introducing product and process innovation.

51


Policy Conclusions

The overarching finding of our analysis is that while the general thrust of the Europe 2020 Strategy as well as the notion of the Inno-vation Union are clearly defined at the European level, European policymakers should bear in mind that the actions necessary to pur-sue this overall objective at the regional level can be very different. By juxtaposing information on innovative activities as well as on relevant framework conditions, we found evidence that innovative regions in Europe are characterised by different forms of innovation that require different types of support. Only some are characterised by technology-oriented publicly supported R&D-driven innovation, while many others focus on applied SME-driven innovation.These findings are in line with the recent European strategies under the headings of smart specialisation/networked specialisation which suggest that there are a limited number of regions that can act as sources of technology in a European context, whereas most others are to a greater extent focused on the adaptation and demand-led ad-aptation of existing or at least piloted technologies. Moreover, our findings underline the established impression that funding from the Framework Programme tends to strengthen exist-ing strengths of the European Research Area, possibly overly so. While in the context of a smart specialisation strategy, this appears welcome, as pre-competitive research increases the leading regions’ ability to act as seedbeds for and sources of genuinely new tech-nologies, an over-polarisation has to be avoided. Structural fund allocations for ‘core RTDI and business innovation’, in contrast, remain focused on less developed areas. Moreover, they remain to an overly pronounced extent focused on standard RTDI infrastructure investment. That they have been found associated with high levels of SME innovation in convergence regions, however, suggests that they can play a constructive role in increasing follower regions’ absorptive capacity in a system of “smart specialisation”.

On the basis of the above said, the following key policy conclusions result from the analysis carried out for this final booklet within the framework of the Regional Key Figures project.

� The “smart specialisation” strategy, which stresses that not all regions should strive to attain the same type of innovation, is well adapted to the diverse reality of innovative capabilities in Euro-pean regions, it should be maintained and further developed.

� The differentiation between regions pioneering new technologies and those focusing on adaptation and application, should not be interpreted as a simple classification, it describes the extremes of a continuum and is as such unsuitable as a criterion for funding.

� Instead, EU support for innovation should, wherever possible, be allocated based on flexible, performance-oriented rather than fixed, pre-determined criteria, to enable approaches adapted to the different aspects of innovation potential present in regions.

� A continued ‘division of tasks’ between Framework Programme and Structural Funds Support appears commendable and should not as such be called into question for the coming support period, the certainly needed closer coordination of both should be based on complementarities to avoid blurring the individual strengths of the respective approaches.

� While it could be shown that FP funding in principle serves its purpose to strengthen the most capable research performers, the findings also underline that public research capacities, which it often supports, do not automatically translate into private sector innovation, continuous efforts should be made to bridge this gap by supporting science-industry collaboration under the FP.

� While there are some indications of the suitability of current Structural Funds Support, it appears necessary to reconsider the array of actions supported in a strategic manner and reduce the relative weight of measures focused on infrastructure provision.

52


AnnexFigure Annex 1: Cross Tabulation of Market-oriented R&D (BERD p Inh.)


GOVERD per Inh_2006

3002001000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0

Source: Regional Key Figures Figure Annex 2: Cross Tabulation of Market-oriented R&D (BERD p Inh.)

vs. HRSTC per Economically Active Population

HRSTC per econ act pop

3020100

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


Figure Annex 3: Cross Tabulation of Market-oriented R&D (BERD p Inh.) vs. Nr. of Interregional Co-patent Applications Filed in Region

Nr_CoPatents_2006

5004003002001000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0

Source: Regional Key Figures Figure Annex 4: Cross Tabulation of Market-oriented R&D (BERD p Inh.)

vs. Total Number of EU FP7 Projects with Regional Participation

TotalNrFP_2009

1400120010008006004002000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


53


Figure Annex 5: Cross Tabulation of Market-oriented R&D (BERD per Inh.) vs. Co-patents as Share of Total Regional Patent Applications

Share_CoPatents_2006

1,0,50,0

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0

Source: own calculation, based on Regional Key Figures Figure Annex 6: Cross Tabulation of Market-oriented R&D (BERD per Inh.)

vs. Total Amount of EU FP7 Funding per Inhabitant


140120100806040200

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


Figure Annex 7: Cross Tabulation of Market-oriented R&D (BERD per Inh.) vs. Total Amount of SF for Business Innovation per Inhabitant

SF_Business per Inh_2007

10008006004002000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


vs. Total Amount of SF for Core RTDI per Inhabitant


4003002001000

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


54


Figure Annex 9: Cross Tabulation of Market-oriented R&D (BERD per Inh.) vs. Intensity of Employment Clustering in the Region

Stars per Region 2005 (EMP)

1614121086420

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


vs. Intensity of Technology Clustering in the Region

Stars per Region (Patent) 2006

121086420

BER

D p

e In

h_20

07

2500

2000

1500

1000

500

0


Figure Annex 11: Cross Tabulation of Product & Process Innovator Share vs. GOVERD per Inhabitant

GOVERD per Inh_2006

3002001000

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Annex 12: Cross Tabulation of Product & Process Innovator Share


HERD per Inh_2006

4003002001000

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure

55


Figure Annex 13: Cross Tabulation of Product & Process Innovator Share vs. Employment in High-tech and KIBS Sectors as Share of Total

hitech KIBS emp per tot emp

1086420

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


vs. HRSTC per Economically Active Population

HRSTC per econ act pop_2006

3020100

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


Figure Annex 15: Cross Tabulation of Product & Process Innovator Share vs. Total Amount of SF for Business Innovation per Inhabitant


4003002001000

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


vs. Number of Interregional Co-patent Applic. Filed in Region

Nr_CoPatents_2006

4003002001000

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


56


Figure Annex 17: Cross Tabulation of Product & Process Innovator Share vs. Share of Co-patents in Total Regional Patent Applications


1,21,0,8,6,4,20,0

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


vs. Total Amount of EU FP7 Funding per Inhabitant


140120100806040200

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


Figure Annex 19: Cross Tabulation of Product & Process Innovator Share vs. Total Number of EU FP7 Projects with Regional Participation

TotalNrFP_2009

1400120010008006004002000

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Figure Annex 20: Cross Tabulation of Product & Process Innovator Share

vs. Intensity of Employment Clustering in the Region


121086420

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0


57


Figure Annex 21: Cross Tabulation of Product & Process Innovator Share vs. Intensity of Technology Clustering in the Region


121086420

CIS

_PuP

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Annex 22: Cross Tabulation of Man. and Org. Innovator Share

vs. GOVERD per Inhabitant

GOVERD per Inh_2006

3002001000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


Figure Annex 23: Cross Tabulation of Man. and Org. Innovator Share vs. HERD per Inhabitant

HERD per Inh_2006

4003002001000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Figure Annex 24: Cross Tabulation of Man. and Org. Innovator Share

vs. Employment in High-tech and KIBS Sectors as Share of Total

hitech KIBS emp per tot emp

1086420

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


58


Figure Annex 25: Cross Tabulation of Man. and Org. Innovator Share vs. HRSTC per Economically Active Population

HRSTC per econ act pop_2006

3020100

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Annex 26: Cross Tabulation of Man. and Org. Innovator Share vs.

Total Amount of SF for Business Innovation per Inhabitant


4003002001000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


Figure Annex 27: Cross Tabulation of Man. and Org. Innovator Share vs. Total Amount of SF for Core RTDI per Inhabitant


4003002001000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure Figure Annex 28: Cross Tabulation of Man. and Org. Innovator Share vs.

Number of Interregional Co-patent Applications Filed in Region

Nr_CoPatents_2006

4003002001000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


59


Figure Annex 29: Cross Tabulation of Man. and Org. Innovator Share vs. Share of Co-patents in Total Regional Patent Applications


1,0,8,6,4,20,0

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


Total Number of EU FP7 Projects with Regional Participation

TotalNrFP_2009

1400120010008006004002000

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0

Source: Regional Key Figures Figure

Figure Annex 31: Cross Tabulation of Man. and Org. Innovator Share vs. Intensity of Employment Clustering in the Region


121086420

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


Intensity of Technological Clustering in the Region


121086420

CIS

_MuO

_200

6

1,0

,8

,6

,4

,2

0,0


60


61

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European Commission

EUR 25191 — Regional Innovation in the Innovation UnionLuxembourg: Publications Office of the European Union

2012 — II, 64 pp — 17.6 x 25 cm

ISBN 978-92-79-22820-9ISSN 1831-9424doi 10.2777/59218

How to obtain EU publications

Free publications:• viaEUBookshop(http://bookshop.europa.eu);• attheEuropeanUnion’srepresentationsordelegations.YoucanobtaintheircontactdetailsontheInternet(http://ec.europa.eu)orbysendingafaxto+3522929-42758.

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This is the final booklet of a series of ten booklets on regional innovation. It analyses evidence from prior booklets as well as new findings to determine how innovation potentials of regions can best be measured in a suitable way to illustrate the regional differentiation of innovative activities in the European Union. It shows, by means of factor analysis, that the different aspects of innovation are associated with different framework conditions.

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