innovacion regional 2012 regions europa

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Regional

InnovationScoreboard2012

Enterprise

and Industry


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More inormation on the European Union is available on the Internet (http://europa.eu)

Cataloguing data can be ound at the end o this publication.

ISBN 9789279263088

doi: 10.2769/55659

Cover picture: iStockphoto_16961307 Tibor Nag

European Union, 2012

Reproduction is authorised provided the source is acknowledged.

Printed in Belgium

PRINTED ON CHLORE FREE PAPER

Legal notice:

The views expressed in this report, as well as the inormation included in it, do not necessaril reect the

opinion or position o the European Commission and in no wa commit the institution.

This report was prepared by:

Hugo Hollanders, Maastricht Economic and Social Research Institute on Innovation and technolog (UNUMERIT)

Lorena Rivera Lon & Laura Roman, Technopolis Group.

With inputs rom:

Cambridge Econometrics, Centre or Science and Technolog Studies (CWTS Leiden Universit), Joint Research Centre

Institute or the Protection and Securit o the Citizen.

Coordinated by:

DirectorateGeneral or Enterprise and Industr

Directorate B Sustainable Growth and EU 2020

Unit B3 Innovation Polic or Growth

Acknowledgements

The authors are grateul to the CIS Task Force members or their useul comments on previous drafs o the RIS report

and the accompaning Methodolog report. In particular we are grateul to all Member States which have made available

regional data rom their Communit Innovation Surve. Without these data, the construction o a Regional Innovation

Scoreboard would not have been possible.

Europe Direct is a service to help you nd answers

to your questions about the European Union

Freephone number (*):

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RegionalInnovationScoreboard 2012

This report is accompanied b the Regional Innovation Scoreboard 2012 Methodolog report

available on Europa: http://ec.europa.eu/enterprise/policies/innovation/index_en.htm

The ear 2012 in this edition o the Regional Innovation Scoreboard reers to the ear in which the analtical work was completed.


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TABLE OF CONTENTS

6 EXECUTIVE SUMMARY

8 1 INTRODUCTION

9 2 INDICATORS AND DATA AVAILABILITY

9 2.1 Indicators

9 2.2 Data availability

11 2.3 Regional coverage

12 3 REGIONAL INNOVATION PERFORMANCE

12 3.1 Innovation perormance analysis Regional Innovation Index

17 3.2 A urther renement o the cluster groups

19 3.3 Comparison with the Regional Competitiveness Index

22 3.4 Relative perormance analysis

25 4 METHODOLOGY

25 4.1 Imputation o missing data

26 4.2 Composite indicators

28 5 REGIONAL RESEARCH AND INNOVATION POTENTIAL THROUGH EU FUNDING,

28 5.1 Introduction

28 5.2 The use o EU unding at regional level

30 5.3 Indicators and data availability

30 5.3.1 Data sources

30 5.3.2 Indicators

31 5.4 Methodology

32 5.5 Regional absorption and leverage o EU unding

35 5.5.1 Matching leverage and absorption capacity to innovation perormance

36 5.5.2 Changing leverage, absorption capacity o EU unding and innovation perormance

36 5.6 Regional research and innovation potential through EU unding: conclusions

37 6 CONCLUSIONS

38 ANNEX 1: RIS indicators explained in detail

42 ANNEX 2: Regional innovation perormance group membership

47 ANNEX 3: Regional data availability

49 ANNEX 4: Perormance maps per indicator

61 ANNEX 5: Normalised data per indicator by region

71 ANNEX 6: Use/absorption o EU unding and regional innovation perormance:

2000-2006 vs. RIS2007

73 ANNEX 7: Use/absorption o EU unding and regional innovation perormance:

2000-2006 vs. RIS2012


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Regional InnovationScoreboard 20126

Executive summaryThis edition o the European Regional Innovation

Scoreboard (RIS) provides a comparative assessmento innovation perormance across NUTS 1 and NUTS2 regions o the European Union, Croatia, Norwayand Switzerland. As the regional level is importantor economic development and or the design andimplementation o innovation policies, it is important tohave indicators to compare and benchmark innovation

perormance at regional level. Such evidence is vital to

inorm policy priorities and to monitor trends.

The 2012 Regional Innovation Scoreboard replicatesthe methodology used at national level in theInnovation Union Scoreboard (IUS), using 12 o the24 indicators used in the IUS or 190 regions acrossEurope.

The data available at regional level remainsconsiderably less than at national level. Due to theselimitations, the 2012 RIS does not provide an absoluteranking o individual regions, but ranks groups oregions at broadly similar levels o perormance. Themain results o the grouping analysis are summarised inthe map above, which shows our perormance groupssimilar to those identied in the Innovation UnionScoreboard, ranging rom Innovation leaders to Modestinnovators. Within each o the 4 perormance groups 3urther subgroups could be identied leading to a total

o 12 regional innovation perormance groups.

There is considerable diversity in regional

innovation perormances

The results show that most European countrieshave regions at dierent levels o perormance.For 2011 we observe at least one region ineach o the 4 broader perormance groups inFrance and Portugal. Czech Republic, Finland,Italy, Netherlands, Norway, Spain, Sweden andthe UK have at least one region in 3 dierentperormance groups. This regional diversity in

innovation perormance also calls or regional

The EU Member StatesCyprus, Estonia, Latvia,Lithuania, Luxembourg andMalta are not included inthe RIS analysis. Groupmembership shown is thato the IUS 2011(Cyprus,Estonia and Luxembourg areinnovation ollowers, Maltais a moderate innovator andLatvia and Lithuania are

modest innovators). Mapcreated with Region MapGenerator.


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7Regional InnovationScoreboard 2012

innovation support programmes better tailored to

meet the needs o individual regions.

The most innovative regions are typically

in the most innovative countries

Most o the regional innovation leaders and innovationollowers are located in the country leaders andollowers identied as such in the Innovation UnionScoreboard (IUS) 2011. The results do highlightseveral regions in weaker perorming countries beingmuch more innovative:

Praha (CZ01) is an innovation leader within the Czech

Republic (a moderate innovator); Attiki (GR3) is an innovation ollower where Greece is

a moderate innovator;

Kzp-Magyarorszg (HU1) is the most innovativeregion in Hungary;

Mazowieckie (Warsaw) (PL12) ) is the most innovativeregion in Poland;

Lisboa (PT17) is an innovation leader in Portugal (amoderate innovator).

Bucuresti Ilov (RO32), a moderate innovator, is muchmore innovative than any other Romanian region;

East o England (UKH) and South East (UKJ) areinnovation leaders within the UK. Northern Ireland

(UKN) lags behind being a moderate innovator andall other regions are innovation ollowers.

In Croatia (a moderate innovator), SjeverozapadnaHvratska (Zagreb) (HR01) is an innovation ollower.

Regions have dierent strengths and

weaknesses

Three groups o regions can be identied based on theirrelative perormance on Enablers, Firm activities andOutputs. The majority o innovation leaders and highperorming innovation ollowers are characterised by a

balanced perormance structure whereas the majority othe moderate and modest innovators are characterisedby an imbalanced perormance structure. Regionswishing to improve their innovation perormance shouldthus pursue a more balanced perormance structure.

Regional perormance appears relatively

stable

Between 2007 and 2011 regional perormance isquite stable with only a relatively small number oregions moving rom one broader perormance group

to the other. More changes are observed at the level

o the 12 subgroups and 8 regions have demonstrated

a continuous improvement by moving to a highersubgroup in both 2009 and 2011: Niedersachsen(DE9), Bassin Parisien (FR2), Ouest (FR5), Calabria(ITF6), Sardegna (ITG2), Mazowieckie (PL12), Lisboa(PT17) and Ticino (CH07).

Regional research and innovation

potential through EU unding

There are remarkable dierences in the use o EUunds across EU regions. There are 4 typologieso regions absorbing and leveraging EU unds:

Framework Programme leading absorbers,Structural Funds leading users, ull users/absorbers but at low levels, and low users/absorbers.

The results suggest that Structural Funds and FPare complementary types o unding targeting arather speciic, but comparatively dierent set oregions. Whereas capital regions in the EU15 arelargely FP leading absorbers or low users/absorbersin both periods, there is no much dierentiationbetween capital regions and all other regions in theEU12. The latter were mainly low users/absorbers inthe period 2000-06 (96%) and ull users/absorbers

(50%) in 2007-13.

We nd a relatively even distribution o shares o high,medium and low innovators in low absorber/user regionsand ull absorber/user regions. A majority o FP leadingabsorbers in FP6 were innovation leaders or innovationollowers in 2007 and 2011. In contrast, a majority oall SF leading user regions in the period 2000-06 werealso modest innovators in 2007 and 2011. The resultsshow a lack o common characteristics/patterns linkinginnovation perormance and the use o EU unds inregions across time.

There is a need or more disaggregated analyses othe impact o EU unding on innovation perormanceand that such analyses need to be built arounda model that takes into account a broad set opotential variables aecting perormance overa longer time period. Moreover and needless tosay, the SFs are an instrument that is signiicantlyeasier to control by the regions than FP. In practice,the SF can und activities normally unded byresearch programmes thus supporting researchexcellence objectives without the obligation to

orm international research consortia as in FP.


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1. IntroductionInnovation is a key actor determining productivity

growth. Understanding the sources and patterns oinnovative activity in the economy is undamentalto develop better policies. The Innovation UnionScoreboard (IUS) benchmarks on a yearly basis theinnovation perormance o Member States, drawingon statistics rom a variety o sources, including theCommunity Innovation Survey. It is increasingly used asa reerence point by innovation policy makers acrossthe EU.

The IUS benchmarks perormance at the level oMember States, but innovation plays an increasing

role in regional development, both in the Lisbonstrategy and in Cohesion Policy. Regions areincreasingly becoming important engines o economicdevelopment. Geographical proximity matters inbusiness perormance and in the creation o innovation.Recognising this, innovation policy is increasinglydesigned and implemented at regional level. However,despite some advances, there is an absence o regionaldata on innovation indicators which could help regionalpolicy makers design and monitor innovation policies.

The European Regional Innovation Scoreboard (RIS)addresses this gap and provides statistical acts

on regions innovation perormance. In 2002 and2003 under the European Commissions EuropeanTrend Chart on Innovation two Regional InnovationScoreboards have been published. Both reportsocused on the regional innovation perormance o theEU15 Member States using a more limited number oindicators as compared to the European InnovationScoreboard (EIS). In 2006 a Regional InnovationScoreboard was published providing an update o bothearlier reports by using more recent data and alsoincluding the regions rom the New Member States butwith an even more limited set o data as regional CIS

data were not available.

Following the revision o the EIS in 2008, the 2009 RISwas using as many o the EIS indicators at the regionallevel or all EU Member States and Norway includingregional data rom the Community Innovation Survey(CIS) where available. The 2009 RIS paid more attentionto wider measures o innovation including amongothers non-R&D and non-technological innovation. Forthe 2009 RIS or the rst time regional CIS data havebeen collected (directly rom most but not all MemberStates) on a large scale.

This 2012 RIS report provides both an update o

the 2009 RIS report and it resembles the revisedInnovation Union Scoreboard (IUS) at the regionallevel. Regions are ranked in our groups o regionsshowing dierent levels o regional innovationperormance. These peer groupings are derived romregional data and do not directly correspond to thecountry groupings in the IUS.

For all regions we will identiy regions withcomparable perormance patterns within each o theclusters. The purpose o this analysis is to provideregions with additional inormation about their

relative strengths and weaknesses.

The European Regional Competitiveness Index (RCI)maps economic perormance and competitiveness atthe NUTS 2 regional level or all EU Member States.Innovation is a key driver o competitiveness and wewill establish a link between regions perormance inthe RIS and RCI using correlation analyses.

In section 2 we will briey discuss the availabilityo regional data, the indicators that are availableor the RIS and the regions or which regional CISdata are available. Section 3 presents two sets o

results, one identiying groups o regions with similarlevels o innovation perormance and the otheridentiying groups o regions with similar relativepatterns o innovation perormance. For each regiongroup membership or both the absolute and relativeperormance analysis is provided in ull detail inAnnex 1. Section 4 summarizes the methodologyor calculating regional composite indicator and orimputing missing data. Section 5 concludes.

Section 6 provides a separate analysis on therelationship between the use o two main EU

unding instruments and innovation perormance:the Framework Programmes or Research andTechnological Development (FP6, FP7) and theStructural Funds.


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2. Indicators and data availability2.1 Indicators

The Regional Innovation Scoreboard (RIS) includesregional data or 12 o the 24 indicators used inthe IUS. For the other IUS indicators regional dataare not available. The denition o the indicators isidentical to the IUS or 7 o these indicators, whileor 5 indicators there is some dierence as shownin Table 1. The indicator measuring the educationalattainment o the population uses a broader agegroup, the CIS indicators on non-R&D innovation

2.2 Data availabilityOverall data availability depends on the availabilityo regional CIS data. As highlighted in Annex 3, mosto the missing data are CIS data. In particular orCroatia, Denmark, Germany, Ireland, the Netherlandsand Switzerland data availability is poor as orthese countries regional CIS data are not available.Regional CIS data requests were made to 20countries in April-May 20101 and 16 countriesprovided regional in May-June 20112. For Croatia,

Denmark and Switzerland a regional CIS datarequest was not submitted as at the time o ling

expenditures and the sales share o new innovativeproducts reer to SMEs only and the IUS indicator onemployment in knowledge-intensive activities hasbeen replaced with an indicator capturing employ-ment in medium-high and high-tech manuacturingand knowledge-intensive services. The indicators areexplained in detail in Annex 1.

these requests it was thought that these countrieswould not be included in the RIS.

Overall data availability is perect or Belgium,Czech Republic, Romania and Slovakia, very goodor Bulgaria, Finland, Poland, Portugal, Sloveniaand Spain, good or Austria, France, Hungary andUK, relatively good or Italy, Norway and Sweden,relatively poor or Germany, Greece, Ireland and

the Netherlands and poor or Croatia, Denmark andSwitzerland.

1 Austria, Belgium, Bulgaria, Czech Republic, Finland, France, Greece, Hungary, Ireland, Italy, Netherlands, Norway, Poland, Portugal, Romania, Slovakia,

Slovenia, Spain, Sweden and UK.2 Austria, Belgium, Bulgaria, Czech Republic, Finland, France, Hungary, Italy, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain and Sweden.


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Table 1: A comparison o the indicators included in IUS and RIS

Innovation Union Scoreboard Regional Innovation Scoreboard

ENABLERS

Human resources1.1.1 New doctorate graduates (ISCED 6) per 1000 population aged 25-34 No regional data available

1.1.2 Percentage population aged 30-34 having completed tertiary educationPercentage population aged 25-64 having

completed tertiary education

1.1.3 Percentage youth aged 20-24 having attained at least upper secondary level education No regional data available

Open, excellent and attractive research systems

1.2.1 International scientic co-publications per million population No regional data available

1.2.2 Scientic publications among the top 10% most cited publications worldwide as % o totalscientic publications o the country

No regional data available

1.2.3 Non-EU doctorate students as a % o all doctorate students No regional data available

Finance and support

1.3.1 R&D expenditure in the public sector as % o GDP Identical

1.3.2 Venture capital (early stage, expansion and replacement) as % o GDP No regional data available

FIRM ACTIVITIES

Firm investments

2.1.1 R&D expenditure in the business sector as % o GDP Identical

2.1.2 Non-R&D innovation expenditures as % o turnover Similar (only or SMEs)

Linkages & entrepreneurship

2.2.1 SMEs innovating in-house as % o SMEs Identical

2.2.2 Innovative SMEs collaborating with others as % o SMEs Identical

2.2.3 Public-private co-publications per million population Identical

Intellectual assets2.3.1 PCT patent applications per billion GDP (in PPS)

EPO patent applications per billion regional

GDP (PPS)

2.3.2 PCT patent applications in societal challenges per billion GDP (in PPS) No regional data available

2.3.3 Community trademarks per billion GDP (in PPS) No regional data available

2.3.4 Community designs per billion GDP (in PPS) No regional data available

OUTPUTS

Innovators

3.1.1 SMEs introducing product or process innovations as % o SMEs Identical

3.1.2 SMEs introducing marketing or organisational innovations as % o SMEs Identical

3.1.3 High-growth innovative rms indicator not yet included No regional data available

Economic eects

3.2.1 Employment in knowledge-intensive activities (manuacturing and services) as % o totalemployment

Employment in knowledge-intensive services

+ Employment in medium-high/high-tech

manuacturing as % o total workorce

3.2.2 Medium and high-tech product exports as % total product exports No regional data available

3.2.3 Knowledge-intensive services exports as % total service exports No regional data available

3.2.4 Sales o new to market and new to rm innovations as % o turnover Similar (only or SMEs)

3.2.5 License and patent revenues rom abroad as % o GDP No regional data available


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Table 2: Regional coverage

3 In the IUS 2011 Cyprus, Estonia and Luxembourg are innovation ollowers, Malta is a moderate innovator and Latvia and Lithuania are modest innovators.

2.3 Regional coverageBased on regional data availability the analysis willcover 190 regions or 21 EU Member States, Croatia,Norway and Switzerland at dierent NUTS levelswith 55 NUTS 1 level regions and 135 NUTS 2 level

Country NUTS Regions

1 2

Austria 3 Oststerreich (AT1), Sdsterreich (AT2), Weststerreich (AT3)

Belgium 3 Rgion de Bruxelles-Capitale / Brussels Hoodstedel ijk Gewest (BE1), Vlaams Gewest (BE2), Rgion Wallonne (BE3)

Bulgaria 2 Severna i iztochna Bulgaria (BG3), Yugozapadna i yuzhna tsentralna Bulgaria (BG4)

Croatia 3 Sjeverozapadna Hrvatska (HR01), Sredisnja i Istocna (Panonska) Hrvatska (HR02), Jadranska Hrvatska (HR03)

Czech Republic 8Praha (CZ01), Stredn Cechy (CZ02), Jihozpad (CZ03), Severozpad (CZ04), Severovchod (CZ05), Jihovchod (CZ06),Stredn Morava (CZ07), Moravskoslezsko (CZ08)

Denmark 5 Hovedstaden (DK01), Sjlland (DK02), Syddanmark (DK03), Midtjylland (DK04), Nordjylland (DK05)

Finland 1 4 It-Suomi (FI13), Etel-Suomi (FI18), Lnsi-Suomi (FI19), Pohjois-Suomi (FI1A), land (FI2)

France 9le de France (FR1), Bassin Parisien (FR2), Nord - Pas-de-Calais (FR3), Est (FR) (FR4), Ouest (FR) (FR5), Sud-Ouest (FR)(FR6), Centre-Est (FR) (FR7), Mditerrane (FR8), French overseas departments (FR) (FR9)

Germany 16Baden-Wrttemberg (DE1), Bayern (DE2), Berlin (DE3), Brandenburg (DE4), Bremen (DE5), Hamburg (DE6), Hessen(DE7), Mecklenburg-Vorpommern (DE8), Niedersachsen (DE9), Nordrhein-Westalen (DEA), Rheinland-Palz (DEB),Saarland (DEC), Sachsen (DED), Sachsen-Anhalt (DEE), Schleswig-Holstein (DEF), Thringen (DEG)

Greece 4 Voreia Ellada (GR1), Kentriki Ellada (GR2), Attiki (GR3), Nisia Aigaiou, Kriti (GR4)

Hungary 1 6Kzp-Magyarorszg (HU1), Kzp-Dunntl (HU21), Nyugat-Dunntl (HU22), Dl-Dunntl (HU23), szak-Magyarorszg (HU31), szak-Alld (HU32), Dl-Alld (HU33)

Ireland 2 Border, Midland and Western (IE01), Southern and Eastern (IE02)

Italy 21

Piemonte (ITC1), Valle d'Aosta/Valle d'Aoste (ITC2), Liguria (ITC3), Lombardia (ITC4), Provincia Autonoma Bolzano/Bozen (ITD1), Provincia Autonoma Trento (ITD2), Veneto (ITD3), Friuli-Venezia Giulia (ITD4), Emilia-Romagna (ITD5),

Toscana (ITE1), Umbria (ITE2), Marche (ITE3), Lazio (ITE4), Abruzzo (ITF1), Molise (ITF2), Campania (ITF3), Puglia (ITF4),Basilicata (ITF5), Calabria (ITF6), Sicilia (ITG1), Sardegna (ITG2)

Netherlands 12Groningen (NL11), Friesland (NL) (NL12), Drenthe (NL13), Overijssel (NL21), Gelderland (NL22), Flevoland (NL23), Utrecht(NL31), Noord-Holland (NL32), Zuid-Holland (NL33), Zeeland (NL34), Noord-Brabant (NL41), Limburg (NL) (NL42)

Norway 7Oslo og Akershus (NO01), Hedmark og Oppland (NO02), Sr-stlandet (NO03), Agder og Rogaland (NO04), Vestlandet(NO05), Trndelag (NO06), Nord-Norge (NO07)

Poland 16Ldzkie (PL11), Mazowieckie (PL12), Malopolskie (PL21), Slaskie (PL22), Lubelskie (PL31), Podkarpackie (PL32),Swietokrzyskie (PL33), Podlaskie (PL34), Wielkopolskie (PL41), Zachodniopomorskie (PL42), Lubuskie (PL43),Dolnoslaskie (PL51), Opolskie (PL52), Kujawsko-Pomorskie (PL61), Warminsko-Mazurskie (PL62), Pomorskie (PL63)

Portugal 2 5Norte (PT11), Algarve (PT15), Centro (PT) (PT16), Lisboa (PT17), Alentejo (PT18), Regio Autnoma dos Aores (PT)(PT2), Regio Autnoma da Madeira (PT) (PT3)

Romania 8Nord-Vest (RO11), Centru (RO12), Nord-Est (RO21), Sud-Est (RO22), Sud - Muntenia (RO31), Bucuresti - Ilov (RO32),Sud-Vest Oltenia (RO41), Vest (RO42)

Slovakia 4 Bratislavsk kraj (SK01), Zpadn Slovensko (SK02), Stredn Slovensko (SK03), Vchodn Slovensko (SK04)

Slovenia 2 Vzhodna Slovenija (SI01), Zahodna Slovenija (SI02)

Spain 2 17

Galicia (ES11), Principado de Asturias (ES12), Cantabria (ES13), Pas Vasco (ES21), Comunidad Foral de Navarra(ES22), La Rioja (ES23), Aragn (ES24), Comunidad de Madrid (ES3), Castilla y Len (ES41), Castilla-la Mancha (ES42),Extremadura (ES43), Catalua (ES51), Comunidad Valenciana (ES52), Illes Balears (ES53), Andaluca (ES61), Regin deMurcia (ES62), Ciudad Autnoma de Ceuta (ES) (ES63), Ciudad Autnoma de Melilla (ES) (ES64), Canarias (ES) (ES7)

Sweden 8Stockholm (SE11), stra Mellansverige (SE12), Smland med arna (SE21), Sydsverige (SE22), Vstsverige (SE23),Norra Mellansverige (SE31), Mellersta Norrland (SE32), vre Norrland (SE33)

Switzerland 7Rgion lmanique (CH01), Espace Mittelland (CH02), Nordwestschweiz (CH03), Zrich (CH04), Ostschweiz (CH05),Zentralschweiz (CH06), Ticino (CH07)

UK 12North East (UK) (UKC), North West (UK) (UKD), Yorkshire and The Humber (UKE), East Midlands (UK) (UKF), WestMidlands (UK) (UKG), East o England (UKH), London (UKI), South East (UK) (UKJ), South West (UK) (UKK), Wales (UKL),Scotland (UKM), Northern Ireland (UK) (UKN)

regions (c. Table 2). The EU Member States Cyprus,Estonia, Latvia, Lithuania, Luxembourg and Maltahave not been included as there are no separateregions in these countries3.


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3. Regional innovation perormanceCluster analysis is used to identify regions that share similar innovation systems4. Two

approaches are taken. The first method searches for similarities in absolute performance,or regions that display similar strengths and weaknesses in innovation (Section 3.1).

The second method searches for similarities in the pattern of strengths and weaknesses

(Section 3.3). For example, a region that performed twice as well as another region on every

composite index would have an identical pattern of strengths and weaknesses. In order to

remove the effect of absolute performance in the cluster analysis of similar patterns, the

sum of performance across all composite indices is set to the same value for all regions.

Both approaches have different uses for policy.

The ranking in perormance across the 4 perormancegroups is also observed or the separate compositeindicators or Enablers, Firm activities and Outputs

But whereas there is no overlap in overall innovationperormance between the 4 perormance groups, thereis an overlap in perormance in Enablers, Firm activitiesand Outputs (c. Figure 1). E.g. part o the innovation

Hierarchical cluster analysis using Wards method

distinguishes 4 perormance groups5

based on the overallRegional Innovation Index (RII). For these 4 perormancegroups we nd (over the 3 observation periods 2007,2009 and 2011, i.e. 570 observations or 190 regions)113 innovation leaders, 165 innovation ollowers, 121moderate innovators and 171 modest innovators.

(c. Table 4). Innovation leaders also perorm best ineach o the 3 main innovation groups whereas theModest innovators perorm worst.

ollowers perorm better than several innovationleaders on Enablers and the worst perorming Moderateinnovator perorms worse than the worst perormingModest innovator.

Table 3: A comparison o number o regions between the IUS and RIS perormance groups

Regions

LEADERS FOLLOWERS MODERATE MODESTTOTAL NUMBER

OF REGIONS

Country

group

Leaders 77 39 7 0 123

Followers 32 67 28 2 129

Moderate 4 58 81 133 276

Modest 0 1 5 36 42

Total number o regions 113 165 121 171

Table 4: Perormance characteristics or the 4 perormance groups

LEADERS FOLLOWERS MODERATE MODEST

RII 0.621 0.494 0.395 0.269

Enablers 0.631 0.522 0.407 0.317

Firm activities 0.606 0.469 0.362 0.234

Outputs 0.632 0.506 0.432 0.280

4 Hierarchical clustering with Wards method was used or all cluster analyses.5 The dierence in coeicients values as provided in the agglomeration schedule was used to identiy the optimal number o solutions.

The IUS 2011 innovation leader and innovation ollower

countries include 252 regions whereas there are 286 regionalleaders and ollowers (c. Table 3). Most o the regional lead-ers and ollowers are ound in IUS country innovation leadersand ollowers although we also observe 62 cases o regionalleaders and ollowers in IUS moderate innovator countriesand 1 case in IUS modest innovator countries.

3.1 Innovation perormance analysis Regional Innovation Index


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Figure 1: Distribution o perormance or the 4 perormance groups

Maps o the regional perormance groups areshown in Figure 2. For 2007, 2009 and 2011 themaps show group membership or each o the 190regions covered in the RIS. Most o the regionalinnovation leaders and ollowers are ound in Austria,Belgium, Denmark, France, Germany, Finland, Ireland,Netherlands, Sweden, Switzerland and UK but wealso observe regional innovation ollowers in partso Czech Republic, Italy, Norway and Spain and inindividual regions in Croatia, Greece, Hungary, Poland,Portugal, Romania and Slovakia.

Most o the moderate and modest innovators areound in Eastern and Southern Europe, with mosto the moderate innovators in Czech Republic, Italy,Portugal and Spain, and most o the modest innovatorsin Bulgaria, Hungary, Italy, Poland, Portugal, Romania,Slovakia and Spain.


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Figure 2: RIS perormance group maps

The EU Member States Cyprus, Estonia, Latvia, Lithuania, Luxembourg and Malta are not included in the RIS analysis. Group membership shown isthat o the IUS 2011(Cyprus, Estonia and Luxembourg are innovation ollowers, Malta is a moderate innovator and Latvia and Lithuania are modestinnovators). Maps created with Region Map Generator.

2011

2007 2009


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Figure 3: RIS and IUS perormance group maps

The EU Member States Cyprus, Estonia, Latvia, Lithuania, Luxembourg and Malta are not included in the RIS analysis.Group membership shown is that o the IUS 2011(Cyprus, Estonia and Luxembourg are innovation ollowers, Malta is amoderate innovator and Latvia and Lithuania are modest innovators). Maps created with Region Map Generator.

By comparing regional group membership in 2011with country group membership (c. Figure 3) weobserve the ollowing:

Praha (CZ01) is an innovation leader within theCzech Republic and 3 more Czech regions areinnovation ollowers.

Denmark is an innovation leader mainly by thestrong perormance o Hovedstaden (DK01) andMidtjylland (DK04). The other Danish regions areinnovation ollowers.

12 o the 16 German NUTS-1 regions are innovationleaders. 4 Regions are innovation ollowers are

ound in Eastern and Northern Germany. Attiki (GR3) is an innovation ollower where Greece

is a moderate innovator and the other Greekregions are modest innovators.

Spain is a moderate innovator but there is alarge variance in innovation perormance with 8modest innovators, 6 moderate innovators and 5innovation ollowers.

In France (an innovation ollower), le de France(FR1) and Centre-Est (FR7) are innovation leaders.4 French regions are innovation ollowers, 2 aremoderate innovators and 1 region is a Modest

innovator.

In Italy (a moderate innovator) 12 regions are alsomoderate innovators, 7 regions are innovationollowers and 2 regions are Modest innovators.

Kzp-Magyarorszg (HU1), Hungarys capitalregion, is the most innovative region in Hungaryand all other regions are modest innovators.

In the Netherlands we observe 3 moderate innovators,4 innovation ollowers and 4 innovation leaders.

Oststerreich (Vienna) (AT1) is an innovation leaderwithin Austria.

Poland is a moderate innovator with 15 regionsbeing a modest innovator and Mazowieckie

(Warsaw) (PL12) being a moderate innovator. Lisboa (PT17) is an innovation leader and the most

innovative Portuguese region.

Bucuresti Ilov (RO32), a moderate innovator, ismuch more innovative than any other Romanianregion.

In Slovakia (a moderate innovator) Bratislavskkraj (SK01) is the most innovative region being amoderate innovator. The other regions are modestinnovators.

Finland is an innovation leader, but 2 Finnish regionslag behind in their innovation perormance, in

particular land (FI2) which is a moderate innovator.

RIS 2012 region groups IUS 2011 country groups


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In Sweden we nd 5 innovation leaders, 2

innovation ollowers and 1 moderate innovator. East o England (UKH) and South East (UKJ) are

innovation leaders within the UK. Northern Ireland(UKN) lags behind being a moderate innovator andall other regions are innovation ollowers.

Almost all Swiss regions are innovation leaders.Only Ostschweiz (CH05) is an innovationollower.

For Norway 5 regions are an innovation ollower,

The perormance results appear relatively stableover time (as can be seen rom a visual inspectiono Figure 2). But between 2007 and 2011 we dond changes in overall group membership acrossEurope in as many as 14 European countries with42 changes in regional group membership (c.Annex 1). Most o these are positive changes with 9innovation ollowers becoming an innovation leader,13 moderate innovators becoming an innovation

ollower and 13 modest innovators becoming a

1 region is a moderate innovator and 1 region is a

modest innovator. In Croatia (a moderate innovator), Sjeverozapadna

Hvratska (Zagreb) (HR01) is an innovation ollower.

These ndings conrm that capital regions are moreinnovative than non-capital regions. This is alsoconrmed in Figure 4 below which shows the dierencein perormance between capital and non-capitalregions in each o the countries with at least 3 regions.

moderate innovator. But we also observe 7 negativechanges, with 2 innovation leaders slipping downto becoming an innovation ollower, 2 innovationollowers becoming a moderate innovator and 3moderate innovators becoming a modest innovator(c. Annex 2 showing group membership or eachregion or 2007, 2009 and 2011).

Figure 4: A comparison o capital regions with non-capital regions


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3.2 A urther renement o the cluster groups

The identiied perormance groups correlate wellwith the IUS perormance groups but, with 190regions covered, provide insuicient detail toobserve dierences in regional perormance. Thesame clustering technique (Hierarchical clustering,Wards method) has thereore been applied to

each o the 4 perormance groups and withineach group 3 urther subgroups could be deined.For reasons o simplicity, we label these as high,medium and low innovating regions. In total wethus have 12 perormance groups as summarizedin Table 5.

Within each perormance group we ind relativelyequal shares o high, medium and low innovators.We also observe more variation across the years,with e.g. the number o high leading innovatorsincreasing rom 10 in 2007 to 13 in 2009. Thesemore detailed groups are shown in regional mapsin Figure 5. A comparison o the maps shows amuch higher degree o variation in innovation

perormance over time at the regional level than atthe country level where perormance groups haveproven to be stable over time (c. IUS 2011 report).A small number o 8 regions show a continuousimprovement over time as shown in Table 6. BassinParisien (FR2), Calabria (ITF6) and Mazowieckie(PL12) show this continuous improvement withintheir broader perormance group.

Table 5: 12 regional perormance groups

2007 Leader Follower Moderate Modest Total number o regions

High 10 24 18 21 73

Medium 9 13 10 21 53

Low 15 17 12 20 64

Total number o regions 34 54 40 62 190


High 11 18 14 16 59

Medium 12 20 16 24 72

Low 15 15 12 17 59



High 13 27 18 16 74

Medium 17 14 9 17 57Low 11 17 12 19 59


Table 6: Continuous improvement in regional innovation perormance

2007 2009 2011

DE9 Niedersachsen Follower - high Leader - low Leader - medium

FR2 Bassin Parisien Moderate - low Moderate- medium Moderate- high

FR5 Ouest Moderate - medium Moderate- high Follower - low

ITF6 Calabria Modest - low Modest - medium Modest - high

ITG2 Sardegna Modest - medium Modest - high Moderate low

PL12 Mazowieckie Moderate - low Moderate- medium Moderate- high

PT17 Lisboa Follower - medium Follower - high Leader - low

CH07 Ticino Follower - high Leader - low Leader - medium


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Figure 5: RIS detailed perormance group maps

The EU Member States Cyprus, Estonia, Latvia, Lithuania, Luxembourg and Malta are not included in the RIS analysis. In the IUS 2011 Cyprus, Estoniaand Luxembourg are innovation ollowers, Malta is a moderate innovator and Latvia and Lithuania are modest innovators. Map created with RegionMap Generator.

2011

2007 2009


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In this section we compare the Regional Innovation Indexand the Regional Competitiveness Index (RCI) (Annoni andKozovska, 2010)6. First we briey discuss the denition oregional competitiveness and the construction o the RCI.

Dening regional competitiveness

Many authors, with Krugman (1996)7 and Porter(Porter and Ketels, 2003)8 among others, agree on thedenition o competitiveness as productivity, which ismeasured by the value o goods and services producedby a nation per unit o human, capital and naturalresources. They see as the main goal o a nation the

production o high and raising standard o living or itscitizens which depends essentially on the productivitywith which a nations resources are employed.However, regional competitiveness cannot be regardedas a macroeconomic concept. A region is neither a simpleaggregation o rms nor a scaled version o nations(Gardiner et al., 2004)9. Hence, regional competitivenessis not simply resulting rom a stable macroeconomicramework or entrepreneurship on the micro-level. Newpatterns o competition are recognizable, especiallyat the regional level: or example, geographicalconcentrations o linked industries, like clusters, are oincreasing importance and the availability o knowledge

and technology based tools show high variability withincountries (Annoni and Kozovska, RCI 2010 report).An interesting broad denition o regional competitivenessis the one reported by Meyer-Stamer (2008, p. 7)10:

We can defne (systemic) competitiveness o a

territory as the ability o a locality or region to generate

high and rising incomes and improve livelihoods o the

people living there.

This denition, on which the RCI index is build upon, ocuseson the close link between regional competitiveness and

regional prosperity, characterizing competitive regionsnot only by output-related terms such as productivity butalso by overall economic perormance such as sustainedor improved level o comparative prosperity (Bristow,2005)11. Huggins (2003)12 underlines, in act, that truelocal and regional competitiveness occurs only whensustainable growth is achieved at labour rates thatenhance overall standards o living.

Construction o the RCIThe main goal o the European Regional Competi-tiveness Index is to map economic perormance andcompetitiveness at the NUTS 2 regional level or all EUMember States. On the basis o existing competitive-ness studies discussed in the RCI 2010 report (Annoniand Kozovska, 2010), an ideal ramework or RCI isproposed which includes eleven major pillars. The re-erence or these eleven pillars is the well-establishedGlobal Competitiveness Index (GCI), published yearly bythe World Economic Forum (WEF). The pillars includedin the RCI ramework are13:

1. Institutions2. Macroeconomic Stability3. Inrastructure4. Health5. Quality o Primary and Secondary Education6. Higher Education/Training and Lielong Learning7. Labour Market Eciency8. Market Size9. Technological Readiness10. Business Sophistication11. Innovation

The RCI is set up based upon values computed orthese eleven dierent pillars. For a detailed discussionon the computation o these pillar values and on whichindicators they are based we reer to the RCI Report2010 (Annoni and Kozovska, 2010 pp. 59-205).

The RCI urthermore controls or the degree oheterogeneity on the development stage o Europeanregions. This approach is based on a similar methodthe WEF adopts or the GCI (Schwab and Porter, 2007;Schwab, 2009). In the RCI case, regional economiesare divided into medium, transition and high

stage o development. The development stage o theregions is computed on the basis o the regional GDPat current market prices (year 2007) measured as PPPper inhabitants and expressed as percentage o theEU average GDP%. EU regions are then classiedinto three groups o medium, transition or high stageaccording to a GDP% respectively lower than 75%,between 75% and 100% and above 100%.

6 Annoni , P. and K. Kozovska (2010), EU Regional Competitiveness Index 2010, EUR 24346 EN 2010.7 Krugman, P. (1996), Making sense o the competitiveness debate, Oxord Review o Economic Policy 12(3): 17-25.8

Porter, M.E. and Ketels, C.H.M. (2003), UK Competitiveness: moving to the next stage. Institute o strategy and competitiveness, Harvard Business School: DTIEconomics paper n. 3.

9 Gardiner, B., Martin, R., Tyler, P. (2004), Competitiveness, Productivity and Economic Growth across the European Regions, Regional Studies 38: 1045-1067.10 Meyer-Stamer, J. (2008), Systematic Competitiveness and Local Economic Development. In Shamin Bodhanya (ed.), Large Scale Systemic Change: Theories,

Modelling and Practices.11 Bristow, G. (2005), Everyones a winner: problematising the discourse o regional competitiveness, Journal o Economic Geography 5: 285-304.12 Huggins, R. (2003), Creating a UK Competitiveness Index: regional and local benchmarking, Regional Studies 37(1): 89-96.13 The GCI also includes Goods market eiciency and Financial market as pillars, but they have been excluded in the RCI. Furthermore GCI combines Health and

Primary education in one pillar, RCI separates the two. For a discussion on this see the RCI 2010 report (Annoni and Kozovska, 2010 pp. 28-29)

3.3 Comparison with the Regional Competitiveness Index


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The eleven pillars are subdivided in three groups opillars, mostly coinciding with the WEF groups. The rstgroup o pillars includes Institutions, MacroeconomicStability, Inrastructure, Health, and Quality o Primaryand Secondary Education (see Table 8). These areconsidered as actors which are strictly necessary

or the basic unctioning o any economy. The simpleaverage o these pillars gives the rst competitivenesssub-index. Except or the pillar Macroeconomic Stabilitythe expectation is that this rst group does not have astrong correlation with the RIS.The second group o pillars includes Higher Education/Training and Lielong Learning, Labour Market Eciencyand Market Size. They describe an economy which ismore sophisticated, with a higher potential skilledlabour orce and a structured labour market. Thesepillars are used or the computation (simple average)o the second pillar group. We expect this pillar groupto be somewhat related to one o the main type o

RIS indicators enablers and more specically itsdimension, Human Resources.The last group o pillars comprises all the high tech

and innovation related pillars: Technological Readiness,Business Sophistication and Innovation. A region withhigh scores in these sectors is expected to have the mostcompetitive economy. The RIS is expected to correlatestrong and signicantly with this last pillar group.Given the pillar classication, EU regions are assigned

dierent weights according to their developmentstage. The set o weights assigned or the RCIcomputation stems rom the WEF approach with somemodications to accommodate or the act that EUregions do not show the same level o heterogeneity,in terms o stages o development, as the countriescovered by WEF.The regions classied into the medium stage areassigned the weights that WEF assigns to the eciency-driven economy (corresponding to the WEF intermediategroup), while the weights o the high stage are thosewhich WEF uses or the innovative-driven economy. Theweights o the transition stage o development have

been chosen as the middle point between the weightso the rst and third stages. Table 8 displays the pillar-groups and the development stage weights.

Table 7: Thresholds (% GDP) or the denition o stages o development

Table 8: The 11 pillars o RCI classied into three groups and weighting scheme or each development stage

Stage o development % o GDP (PPP/inhabitants

Medium < 75

Transition 75 and < 100

High 100

Weights assigned according to the region stage

MEDIUM STAGE TRANSITION STAGE HIGH STAGE

First pillar-group (Basic)

- Institutions

0.4 0.3 0.2

- Macroeconomic stability

- Inrastructure

- Health

- Quality o primary and secondary education

Second pillar-group (Eciency)

- Higher education and training

0.5 0.5 0.5- Labour market eciency

- Market size

Third pillar-group (Innovation)

- Technological readiness

0.1 0.2 0.3- Business sophistication

- Innovation


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Figure 6: Scatter plot o RII 2011 and RCI 2010

Figure 7: Scatter plot o RII 2011 and RCI 2010Innovation pillar

Figure 8: Scatter plot o RII Firm activities and RCI 2010Innovation pillar

It can be seen that or all development stages the

highest weight is assigned to the second pillar group. Theimportance o the rst group o pillar decreases goingrom medium to high stage o development, while thelast pillar group is correspondingly gaining importance.

Correlation o the RIS and RCI

As can be seen in Figure 6, the RIS and RCI are strong andpositively related. The partial correlation, controlling orregional levels o GDP, is 0.655. The relationship between

The positive and signicant correlation o the RIS andthe RCI stems mostly rom the third pillar group o theRCI. This third pillar group has strong links with the RIS(c. Figure 7).The partial correlation o the RIS and the third pillar is0.706. This is mainly due to the act that the underlying

these two indexes can be seen as respectively cause and

eect rather than a one way direction. The competitiveperormance o a region and its innovative perormancestrongly rely on its knowledge intensive employment. Hugginsand Davies (2006)14 have characterized this two-oldrelationship as ollows: i) highly educated population is a keyingredient or business perormances; ii) regions which arecompetitive in terms o creativity, economic perormanceand accessibility also tend to host high value-added andknowledge intensive employment (Huggins and Davies, 2006).

indicators o the third pillar group are similar to thethree main RIS indicators. For instance the third pillaris very strongly and positively correlated with RIS rmactivities (partial correlation o 0.702) (c. Figure 8).This is due to similar indicators used or the innovationpillar (patent applications and scientic publications).

14 Huggins, R., Davies, W. (2006) European Competitiveness Index 2006-07. University o Wales Institute, Cardi UWIC: Robert Huggins Associates Ltd.

http://www.cforic.org/downloads.php


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The third pillar has the weakest positive relationship withRIS Outputs with a partial correlation o 0.381 (Figure 10).However, these indices do both use a similar indicatoron an important determinant o the positive relationshipbetween the RIS and RCI, namely; Employment intechnology and knowledge-intensive sectors.

3.4 Relative perormance analysisThis section identiies regions with similarpatterns o innovation perormance. The sum operormance across the composite indexes orEnablers, Firm activities and Outputs has beenadjusted to equal the same value o 3 across allregions in order to exclude absolute dierencesin perormance between regions.

The third pillar group is also positively related to RIS

Enablers (partial correlation o 0.510) as a result o

As can be seen in Table 8, irm activities, as oneo the three main indicators o the RIS, has thestrongest links with individual pillar groups and theRCI.

Based on their relative perormance we can identiy3 groups o regions using hierarchical cluster analysis(Wards method). The rst group includes 266 regionswith a balanced perormance structure (c. Figures 11and 12). The second group includes 171 regions havinga signicant strength in Enablers. The third groupincludes 133 regions having a signicant strength inOutputs (and a signicant weakness in Enablers).

similar indicators on higher educated population and

public R&D expenditures.

Figure 9: Scatter plot o RII Enablers and RCI 2010Innovation pillar

Figure 10: Scatter plot o RII Outputs and RCI 2010Innovation pillar

Table 8: Partial correlations RIS and RCI

RCI 1st pillar

Basic

RCI 2nd pillar

Efciency

RCI 3rd pillar

InnovationRCI weighted

RIS Enablers .336 .358 .510 .440

RIS Firm activities .682 .530 .702 .696

RIS Outputs .280 .227 .381 .323

RIS RII .596 .498 .706 .655

Note: All correlations are signicant at 1%. 260 observations, control variable is per capita GDP.


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A comparison o the regional innovation perormancegroups and the relative perormance groups shows thatthe majority o innovation leaders and high perorminginnovation ollowers are characterised by a balancedperormance structure. The majority o the moderate

innovators have a relative strength in outputs andthe majority o the modest innovators have a relativestrength in enablers. Regions wishing to improve theirinnovation perormance should thus pursue a morebalanced perormance structure15.

Figure 11: Relative strengths and weaknesses

15 A similar result at the country level was reported in Arundel, A. and H. Hollanders, "Innovation Strengths and Weaknesses", European Trend Chart on

Innovation Technical Paper, Brussels: European Commission, DG Enterprise and Industry, December 2005.

Table 9: Matching absolute and relative perormance groups

Balanced perormers Enablers strength Outputs strength Total number o regions

INNOVATION LEADERS


High 25 2 7 34

Medium 23 6 9 38

Low 25 10 6 41

INNOVATION FOLLOWERS


High 42 15 12 69

Medium 24 12 11 47

Low 24 15 10 49

MODERATE INNOVATORS


High 15 15 20 50

Medium 13 12 10 35

Low 12 11 13 36

MODEST INNOVATORS


High 21 21 11 53

Medium 16 30 16 62

Low 26 22 8 56


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Figure 12: Maps relative perormance

The EU Member States Cyprus, Estonia, Latvia, Lithuania, Luxembourg and Malta are not included in the RIS analysis.

2011

2007 2009


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4. Methodology

4.1 Imputation o missing dataFor many regions data are not available or all indicators.For a representative comparison o perormance acrossregions using composite indicators we should have100% data availability whereas average regionaldata availability or RIS regions is 70%. Beore theimputation there are 2058 out o a total o 6840

values missing, meaning that 30% o the cells areempty. The imputation procedure is implementedentirely in Excel using linear regression and anotherhierarchical procedure. Full details are provided in theRIS 2009 Methodology report.

Not only regional values are missing but also values atnational level, whilst all values or the EU27 aggregateare available. The imputation is based on the ollowingprocedure:

Consider a missing value or indicator Y in region Ror a given year, e.g. Y-2009.

IF a value is available or Y-2011 in region R, THENapply linear regression between Y-2009 andY-2011 ELSE{nd the indicator Z with the highest correlationwith Y (Z can span both years).IF correlation between Y and Z is > 0.6 AND avalue is available or Z in R THENapply linear regression between Y and Z.}

Afer regression, not all o the missing values couldbe imputed. Regression was not successul as manyregions have missing values or the pairs o indicatorsthat are employed in the regression.

The remaining values are imputed using a hierarchicalprocedure, which rst imputes missing values atnational level using values at EU27 level and, in a

second phase, imputes missing values at regionallevel using values at national level. The procedure isillustrated hereafer.

The procedure calculates or each indicator Y, wherepossible, the ratios between the values o Y or country

C and or EU27. Then, the median16

ratio across theindicators is calculated. The missing value or indicatorZ in country C is imputed by assuming that or Z themedian ratio just computed applies between C andEU27. Given that all values or EU27 are available, allmissing values at national level can be imputed.

The procedure calculates or each indicator Y, wherepossible, the ratios between the values o Y or regionR and or country C. Then, the median ratio across theindicators is calculated. The missing value or indicatorZ in country R is imputed by assuming that or Z themedian ratio just computed applies between R and C.

Given that all national values all available, all missingvalues at regional level can be imputed.

4.2 Composite indicatorsThe regional innovation indexes have been calculated asa weighted average o the 12 indicators. The approachresembles a mix o the methodology used in the RIS2009 and the IUS 2011. In the RIS 2009 a weightingschedule was used which reected the overall weightso Enablers, Firm activities and Outputs and the overallweights o the CIS indicators in the EIS 2009. Applying

a similar weighting scheme to the RIS 2011 would givethe indicator weights as shown in Table 10.

16 It was decided to consider the median values instead o the mean value, as the distribution o the ratios contained, in some instances, some outliers.

The methodology used for the Regional Innovation Scoreboard is fully described in

an accompanying methodology report which is available as a thematic paper at theEuropean Commission website (http://ec.europa.eu/enterprise/policies/innovation/policy/

regional-innovation/index_en.htm ).


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Table 10: Indicator weights using RIS 2009 methodology

Weight in

Enablers

Weight o

Enablers in IUS

Weight o

indicator in RIS

1.1.2 Percentage population aged 25-64having completed tertiary education

1/2 8/24 16.67%

1.3.1 R&D expenditure in the public sectoras % o regional GDP

1/2 8/24 16.67%

Weight o

non-CIS

indicators in

Firm activities

Weight o

indicator

in non-CIS

indicators

Weight in

Firm activities

Weight o

Firm activities

in IUS

Weight o

indicator in RIS

2.1.1 R&D expenditure in the businesssector as % o regional GDP

2/3 1/3 2/9 9/24 8.33%

2.2.3 Public-private co-publications permillion population 2/3 1/3 2/9 9/24 8.33%

2.3.1 EPO patents applications per billionregional GDP (in PPS)

2/3 1/3 2/9 9/24 8.33%

Weight o CIS

indicators in

Firm activities

Weight o

indicator in

CIS indicators

2.1.2 Non-R&D innovation expenditures as% o turnover

1/3 1/3 1/9 9/24 4.17%

2.2.1 SMEs innovating in-house as % oSMEs

1/3 1/3 1/9 9/24 4.17%

2.2.2 Innovative SMEs collaborating withothers as % o SMEs

1/3 1/3 1/9 9/24 4.17%

Weight onon-CIS

indicators in

Outputs

Weight oindicator

in non-CIS

indicators

Weight in

Outputs

Weight o

Outputs in IUS

Weight o

indicator in RIS

3.2.1 Employment in knowledge-intensiveservices + Employment in medium-high/high-tech manuacturing as %o total workorce

4/7 100% 4/7 7/24 16.67%

Weight o

CIS indicators

in Outputs

Weight o

indicator in

CIS indicators

3.1.1 SMEs introducing product or processinnovations as % o SMEs

3/7 33.33% 1/7 7/24 4.17%

3.1.2 SMEs introducing marketing ororganisational innovations as % oSMEs

3/7 33.33% 1/7 7/24 4.17%

3.2.4 Sales o new to market and new torm innovations as % o turnover

3/7 33.33% 1/7 7/24 4.17%

The combined weight o the CIS indicators would be 25%,identical to the weight o these indicators in the IUS. Butthe table also shows that some indicators have a weight 4times that o the CIS indicators and this overemphasized therelative importance o these indicators. We have thereoredecided to combine the weights shown in Table 9 with a

scheme o equal weights where each o the 12 indicatorswould receive a weight o 8.33%. The combination o

weights results in the percentage share o each o theindicators in the RIS composite index as shown in Table 11.

All data have been normalized using the sameprocedure as in the IUS, where the normalized value isequal to the dierence between the real value and the

lowest value across all regions divided by the dierencebetween the highest and lowest value across all regions.


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These values are rst transormed using a power root

transormation i the data are not normally distributed.

Most o the indicators are ractional indicators with valuesbetween 0% and 100%. Some indicators are unboundindicators, where values are not limited to an upper threshold.These indicators can have skewed data distributions (wheremost regions show low perormance levels and a ewregions show exceptionally high perormance levels). Forall indicators data will be transormed using a square root

The data have then been normalized using the min-maxprocedure where the transormed score is rst subtracted withthe minimum score over all regions in 2006, 2008 and 2010

and then divided by the dierence between the maximum andminimum scores over all regions in 2006, 2008 and 2010:

transormation with power Ni the degree o skewness o

the raw data exceeds 0.5 such that the skewness o thetransormed data is below 0.5 (none o the imputed dataare included in this process):

Table 11 summarizes the degree o skewness beoreand afer the transormation and the power N used inthe transormation.

The maximum normalised score is thus equal to 1 and the

minimum normalised score is equal to 0. These normalisedscores are then used to calculate the composite indicators.

Table 11: Percentage contribution indicators to RII, degree of skewness and transformation for each of the RIS indicators

RIS 2009

weights

Equal

weights

RIS 2011

weights

Degreeo skew-

ness beoretransormation

Power used in

transormation

Degree oskewness

aer trans-ormation

ENABLERS

1.1.2 Percentage population aged 25-64having completed tertiary education

16.67% 8.33% 12.5% 0.150 1 0.150

1.3.1 R&D expenditure in the public sectoras % o regional GDP

16.67% 8.33% 12.5% 0.853 2/3 0.215

FIRM ACTIVITIES

2.1.1 R&D expenditure in the businesssector as % o regional GDP

8.33% 8.33% 8.33% 1.715 1/3 0.259

2.1.2 Non-R&D innovation expenditures as% o turnover

4.17% 8.33% 6.25% 1.158 1/2 0.193

2.2.1 SMEs innovating in-house as % oSMEs

4.17% 8.33% 6.25% -0.015 1 -0.015

2.2.2 Innovative SMEs collaborating withothers as % o SMEs

4.17% 8.33% 6.25% 0.275 1 0.275

2.2.3 Public-private co-publications permillion population

8.33% 8.33% 8.33% 3.343 1/3 0.358

2.3.1 PCT patents applications per billionregional GDP (in PPS)

8.33% 8.33% 8.33% 2.197 1/3 0.229

OUTPUTS

3.1.1 SMEs introducing product or processinnovations as % o SMEs

4.17% 8.33% 6.25% 0.113 1 0.113

3.1.2 SMEs introducing marketing or

organisational innovations as % oSMEs 4.17% 8.33%6.25% 0.667 2/3 0.368

3.2.1 Employment in knowledge-intensiveservices + Employment in medium-high/high-tech manuacturing as %o total workorce

4.17% 8.33% 12.5% 0.003 1 0.003

3.2.4 Sales o new to market and new torm innovations as % o turnover

16.67% 8.33% 6.25% 0.225 1 0.225


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5. Regional research and innovationpotential through EU unding17,18

5.1 Introduction

This special chapter o the Regional Innovation Scoreboard(RIS 2012) aims to understand the relationship o the use o

two main EU unding instruments and innovation perormance:

the Framework Programmes or Research and Technological

Development (FP6 and FP7), and the Structural Funds (SFs).

Firstly, the chapter proposes a typological classication o

EU regions according to their use o EU unds, providing

a landscape o the EU regions use o Structural Funds

or business innovation and the regional participation

in FP unded research, technological development and

demonstration projects. The chapter ocuses on the case o

regional SF support or business innovation, and investigates

whether the regions capacity to invest in business innovationimproved over the past two programming periods, and i

this improvement is linked with an increased participation in

the Framework Programme competitive unding.

Secondly, it addresses the link between the use o EU

unds and regional innovation perormance by making

use o the results o the RIS 2012. Does the regions

absorption capacity and leverage power o EU unding

match their level o innovativeness? Or are the most

innovative regions mobilising more local resources in

support o innovation and particularly rom the private

sector? More particularly, the chapter aims to contribute to

the debate o the so called regional innovation paradox-

or the contradiction between the comparatively greater

need to spend on innovation in lagging regions and their

relatively lower capacity to absorb public unds earmarked

or the promotion o innovation and to invest in innovation

related activities due to their low innovation perormance.

The study will contribute to the debate on the role o EU

unding instruments in a multilevel governance system

and help to understand to what extent these unds

complement and reinorce national and regional innovation

policies. It also contributes in understanding the challengeso improving coordination and seeking synergies and

impacts o various EU interventions at regional level.

Section 5.2 gives a brie overview o the broad use o

SF and FP unds across all regions in the periods 2000-

2006 and 2007-2013, showing a general landscape o

the absorption o EU unds. Sections 5.3 and 5.4 describe

the indicators, data sources and methodology used or

the analysis. Section 5.5 presents the dierent typological

groups o regions according to their use o EU unds and

innovation perormance. Section 5.6 concludes.

5.2 The use o EU unding at regional levelThe Structural Funds are an instrument o the EUs cohesionpolicy through which the EU invests in job creation,

competitiveness, economic growth, improved quality o lie

and sustainable development, in line with the Europe 2020

strategy19. They are an important source o investment in

research and innovation in regions, with 19.5 billion o

expenditure in this eld in 2000-2006 and around 69 billion

allocated to business innovation in 2007-201320. Relative

to the total value o Structural Funds available or each

period, the unds or business innovation represented 11%

o the total SF expenditures in 2000-2006, and 20% o all

allocations o available unds in the period 2007-2013.

Figure 12 shows a comparison o the distribution o

average structural unds expenditures/allocations by type

o regions per year/per capita in both periods analysed. The

highest annual Structural Funds investments per capita

were targeted towards supporting services or business

innovation across all three types o regions21. Objective 1

regions spent the highest amounts o unds on support

or services in the rst period (7.46/year/capita), ollowed

by Objective 3 regions (3.5/year/capita). Furthermore,

17

This chapter was prepared by Lorena Rivera Lon and Laura Roman rom Technopolis Group.18 The analysis in this chapter is at NUTS 2 level as this is the level o detail or which data on Structural Funds and Framework Programmes or Research and

Technological Development (FP6 and FP7) are available.19 See DG REGIO, What is regional policy?http://ec.europa.eu/regional_policy/what/index_en.cfm20 See section 3 or the deinition o the indicators or structural unds or business innovation used in this chapter.21 The unds were targeted towards three types o regions in 2000-2006, according to the previous programmings period development objectives: Objective

1 unds targeted regions in need o structural adjustment, with a GDP per capita o less than 75% o the EU average; Objective 2 regions were the ones

undergoing economic and social conversion (industrial, rural, urban and isheries-dependent zones); Objective 3 unds supported improved training and

employment policies in regions.

Figure 12: Average annual Structural Funds expenditure/allocations per capita by type of region, 2000-2006 and 2007-2013

Source:

Data warehouse Directorate

General Regional Policy

European Commission,

Regional estimates by Unit

C3 DG REGIO; data analysis

by Technopolis Group.


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Figure 13: Overview o FP6 (2002-2006) and FP7 (2007-2013) average participation by type o regions, ( per capita)

the investments in ramework conditions or business

innovation (including R&D investments) were the second

highest expenditure in all regions, with 4.5/year/capitaspent in Objective 1 regions.For the current programming period, Figure 12 shows

that the Structural Funds annual allocations per capita

supporting ramework conditions or business innovation

(19/year/capita) are on average almost equal to the

annual average support or services or business innovation

(19.8/year/capita) in Convergence regions22. The regions

belonging to the Competitiveness and Employment

objective allocated on average more unds to services or

business innovation (6/year/capita) than to enhancing

ramework conditions (3.8/year/capita). It is also visible

that the bulk o the unds were allocated to Convergence

Since the individual regions participation in the Framework

Programme is conditioned by the location o research

inrastructure within their boundaries, an overview o the

average FP unds attracted by the regions needs to beconsidered with care. As shown in Figure 13, Objective 3

regions were the ones attracting the highest amount o FP6

unds, worth on average around 92.3 million per region,

or 73 per capita. Objective 2 regions were not very ar

behind, as their average participation in FP6 amounted to

79.4 million. However, the latter only attracted an average

o 35 in per capita terms. Comparatively, objective 1

regions attracted 21.4 million o FP6 unds, or 14.4

per capita on average. The low absorbers in the current

FP7 are Convergence regions, which attracted 13.4 per

capita on average (or an average o 22.7 million each)

(up to February 2012), while the Competitiveness regionsreached an amount our times higher o 55.4 per capita

regions, with 71.8% o the absolute volume o Structural

Funds reported as allocated or business innovation, while

the Competitiveness (RCE) regions have a smaller amounto unds allocated (28.1% o the total Structural Funds or

business innovation).

Investments in ICT and digital inrastructure, and

environmental technologies or eco-innovation are low

across most regions in both periods23. Objective 1 regions

spent 1.5/year/capita on ICT stimulating measures in

2000-2006, while the Convergence regions allocated on

average 3.8/year/capita or ICT in the current period.

Structural Fund investments o Objective 2 and Objective

3 regions in 2000-2006 as well as the reported allocations

o the Competitiveness regions in 2007-2013 were close

to zero in the eld o ICT and environmental technologies.

(or a total o 116.3 million) on average per region.

The leverage o the unds (dierence between the total cost

o the projects and the total subsidies received) is generally

lower in FP7 or Competitiveness and Convergence regionsthan in FP6 or the three types o regions respectively. It

is interesting to note that or 55.4 per capita absorbed

in Competitiveness regions in FP7 so ar, the contribution

o the region to the project cost amounted on average to

17.7 per capita. In contrast, the leverage or the average

FP6 participation in Objective 2 and 3 regions amounted

to around hal o the average total subsidies received in

nominal terms and per capita terms. For a total o 92.2

million absorbed rom FP6 unds in Objective 3 regions on

average, the leverage amounted to 52.4 million per region,

compared to 79.3 absorbed on average in Objective 2

regions, and only 6.6 per capita leveraged on average inObjective 1 regions.

Source: External Common Research Data Warehouse E-CORDA o the Directorate General Research and Innovation o the EuropeanCommission (cut-o date 16 February 2012). Data analysis by Technopolis Group.

Note: The indicator leverage shows the dierence between the total cost o research in all projects and the total amount o subsidies granted.

22 In the 2007-2013 period, the Structural Funds target primarily regions belonging to the Convergence Objective (with a GDP below 75% o the EU average)

and to the Regional Competitiveness and Employment Objective (with a GDP higher than 75% o the EU average).23 However, it is important to note that the ields o investment included in both indicators are dierent or the two periods, see Table 2 or more details. The

comparison between these indicators in the two periods needs to be treated with care.


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5.3 Indicators and data availability

5.3.1 Data sources

Two are the main data sources used in this analysis:

Structural Funds data was obtained rom the datawarehouse o the Directorate General or RegionalPolicy o the European Commission (regionalestimates by Unit C3 DG REGIO)

Framework Programme data was obtained romthe External Common Research Data WarehouseE-CORDA o the Directorate General Research andInnovation o the European Commission (cut-odate 16 February 2012)

In order to link the use o EU unding in regions withregional innovation perormance, the chapter makesuse o the results o the assessment o regionalinnovation perormance calculated in the main sectiono this report as part o the RIS 2012.

5.3.2 Indicators

This chapter explores the use o Structural Fundsin business innovation according to a compositethematic categorisation o the elds o interventionor the periods o 2000-2006 and 2007-2013. Thecomparison o the indicators between the two periodsneeds to be considered with care, as the gures or

2000-06 are certied expenditures, while the 2007-2013 indicators reect the reported allocations ounds (i.e. not actual expenditures). Moreover, theamounts registered or each eld o investment aresel-reported by the regions, which might create someunobserved bias and thus diminish the validity o thedata analysis. In order to compare the use o structuralunds or business innovation or both periods and atthe regional level, the values o the unds are reportedat a per capita level or each region and annualised. Forthis, the data or the Member States that joined the EUin 2004 accounts or the act that they benetted rom

Structural Funds or only three years in 2000-2006.The relevant thematic categories o investment prioritiesestablished by DG REGIO or the Structural Funds weresummed into our main indicators that reect theamount o regional support or our core areas:

Framework conditions or business innova-

tion (including R&D): portrays the use o undsin support o improving the general conditions thatare in place in regions or research and innovationactivities, which have an impact on both the publicand private sectors perormance;

ICT and digital inrastructure: unds targeted

specically at improving the inrastructure orInormation and Communication Technology;

Environmental technologies or eco-innovation:

investments aimed to strengthen the take-upo sustainable and environmentally riendlytechnologies. It is included as a separate indicator inthe analysis based on the importance o the directlink that such support is considered to have as adriver or business innovation, particularly in the lastyears o increased support to the green economy asan EU policy priority;

Services or business innovation is an indicatorcomposed o the elds o investments that are

directly targeting the enhancement o innovationoutputs in enterprises (mainly advisory services,technology transer and training measures aimed atenterprises).

The Framework Programme unds were analysed basedon quantiying our major indicators or the participationo the regions in competitive research and technologydevelopment. In particular, the indicators shed light onthe strength o the private sectors participation in theprogramme by considering the ollowing dimensions:

The total amount o subsidies received bythe regional actors per year (per capita) indicates the

absorptive capacity o the region in attracting FP unds;

The leverage (per capita), or the dierencebetween the total cost o the projects and thetotal subsidies received in the region or the FPprojects undertaken, which shows the power othe regional research actors to raise additionalunds rom urther public or private sources tosupport competitive research;

The number o participations rom the

private sector (per thousand inhabitants) is linkedto the amount o private enterprises engaged in FPprojects in the region. It shows the strength o the

business sector as a research actor; Percentage o SME participation in private

sector shows the share o Small and MediumEnterprises in the total number o FP participationsrom the private sector. This indicator hints to thevibrancy o the business innovation environment inthe region.

Data is available or building all indicators or a total o271 NUTS2 regions o the 27 Member States. Table 12shows the categories o expenditures and allocationsthat are included in each indicator, based on DGREGIOs denitions or both periods. The titles o the

elds o investments were changed by DG REGIO romone period to the other.


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Table 12: Use o EU unds in regions, 2000-2006 and 2007-2013

Indicator Structural Funds 2000-2006 Structural Funds 2007-2013

Framework conditions

or business innovation

180. Research, technological development and innovation(RTDI)

181. Research projects based in universities and researchinstitutes

183. RTDI Inrastructure184. Training or researchers

01: R&TD activities in research centres02: R&TD inrastructure and centres o competence in a

specic technology04: Assistance to R&TD, particularly in SMEs (including

access to R&TD services in research centres)07: Investment in rms directly linked to research and innovation

ICT and digital

inrastructure

322. Inormation and Communication Technology (includingsecurity and sae transmission measures)

11: Inormation and communication technologies

15: Other measures or improving access to and

efcient use o ICT by SMEs

Environmental technologies

or eco-innovation

162. Environment-riendly technologies, clean and econom-ical energy technologies

06: Assistance to SMEs or the promotion o environmen-tally-riendly products and production processes

Services or

business

innovation

182. Innovation and technology transers, establishment onetworks and partnerships between businesses and/orresearch institutes

153. Business advisory services (including internation-alisation, exporting and environmental management,purchase o technology)

163. Business advisory services (inormation, business plan-ning, consultancy services, marketing, management,design, internationalisation, exporting, environmentalmanagement, purchase o technology)

164. Shared business services (business estates, incubatorunits, stimulation, promotional services, networking,conerences, trade airs)

324. Services and applications or SMEs (electronic commerceand transactions, education and training, networking)

03: Technology transer and improvement o cooperationnetworks

09: Other measures to stimulate research and innovationand entrepreneurship in SMEs

05: Advanced support services or rms and groups o rms62: Development o lie-long learning systems and strate-

gies in rms; training and services or employees ...63: Design and dissemination o innovative and more

productive ways o organising work14: Services and applications or SMEs (e-commerce, educa-

tion and training, networking, etc.)

FP6 AND FP7INDICATORS

Total amount o subsidies received (per capita)

Leverage (per capita)

Number o participations rom the private sector (per thousand inhabitants)

Percentage o SME participation in private sector

Source: Technopolis Group

5.4 MethodologyA cluster analysis was perormed to groupinormation on the use o EU unds in regions basedon their similarity on the dierent sub-indicators

presented in section 3. In order to perorm theanalysis and to avoid results being inluenced byscores o regions over-perorming, the datasethas been normalised or outliers scores with thenext best values24. Two periods are analysedand compared: 2000-2006, including the irstprogramming period (PP) o Structural Funds (SFs),and FP6 (2002-2006); and 2007-2013, accountingor the second PP o SFs and FP7.The method ok-means clustering has been used.This procedure attempts to identiy relativelyhomogenous groups o cases based on the

selected characteristics. It is useul when the aim

is to divide the sample in k clusters o greatestpossible distinction. Dierent k parameters weretested. Since the ultimate aim o the analysis was

to relate the clustering exercise o EU unds toinnovation perormance as per the results o theRIS 2012, the tested values or the k parameterstested ranged rom 2 to 5. The k-means algorithmsupplies k clusters, as distinct as possible, byanalysing the variance o each cluster. The aimo the algorithm is to minimise the variance oelements within the clusters, while maximisingthe variance o the elements outside the clusters.Cases were classiied using the method updatingcluster centres iteratively, with optimal solutionsor a k parameter value o 4; and 8 and 7 iterations

or both analysed periods respectively.

24 Values representing the mean plus two standards deviations were normalised with the next best value considering that

68% o the values drawn rom a normal distribution are within one standard deviation > 0 away rom the mean ; about95% are within two standard deviations and about 99,7% lie within three standard deviations.


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Cluster analysis distinguishes our typologies oregions absorbing and leveraging EU unds over thetwo observation periods:

FP leading absorbers, or regions with low useo SFs or business innovation; and medium-to-high participation in FPs, leverage power, and FPparticipation rom the private sector;

SFs leading users, or regions with medium-to-high use o SFs or business innovation (includingR&D) and services (including ICTs and digitalinrastructure and environmental technologies); andlow participation in FPs and leverage power;

Full users/absorbers but at low levels,or regions with medium-to-high use o SFs or

The dierences in the characteristics o the use o EUunds are also observed or each o the typologiesacross both periods (c. Table 13). On average, FPleading absorbers received around 6 times moreo FP6 subsidies per capita (96) than the lowusers/absorbers (16) and had about 8 times moreleverage power in the period 2000-2006. Thegaps between both regions decreased in FP7, butincreased between FP leading absorbers and ullusers/absorbers. In contrast, SFs leading users spent7 times more o SFs to business innovation than

the low user regions in the period 2000-2006, and

business innovation and services, low use ounds or ICTs and digital inrastructure andenvironmental technologies; and low participationin FP and leverage power, but medium-highimportance o SMEs' participation in the privatesector;

Low users/absorbers, or regions with low use oSFs or business innovation; and low participation inFP and leverage power.

For these our groups we nd, over the two observationperiods (542 observations or 271 regions), a majorityo low users/absorbers (63%), ollowed by ull users/

absorbers (17%), FP leading absorbers (15%) and SFleading users (6%) (c. Figure 14).

the gap remained constant in their allocations orthe period 2007-2013. Moreover, the gap betweenSF leading users and ull/users absorbers doubledbetween the two periods. However, all regionsincreased considerably their per capita allocationsto business innovation in the period 2007-2013,compared to expenditures or 2000-2006.

Cluster membership is shown or each o the 271regions in the Annex to this chapter. When lookingat the countries that gather most o the regions

in each typology (c. Table 14), results show that

Figure 14: Maps o unding typology o regions

Maps created with Region Map Generator.

2000-2006 2007-2013

5.5 Regional absorption and leverage o EU unding


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most o the FP leading absorber regions are rom

Germany, the Netherlands, and the UK across bothperiods. German and UK regions also hold a largeshare o the low absorbers/users. The dichotomyo having large absorption o competitive undingthrough FPs in some regions, and low use o SFsor business innovation in others could relectthe dierences in regional capacities inside bothcountries in line with the results showed in the RIS2011, and the use o alternative unds in supporto business innovation (i.e. national sources nonSFs, and private sources).

Interesting changes occur between both periods inthe membership structure o SF leading users andull users/absorbers. Probably the most interestingcase is that o Greek regions, which were a largemajority in the typology o SF leading users in 2000-2006, to then being second most representatives oull users/absorbers in 2007-2013. This could showthree possible phenomena: a ull absorption o SFsin support o business innovation in the rst periodleading to other priorities in the allocation o undsor the second period; a lack o capacity to absorbSFs to business innovation in the second period(afer large investments in the rst period) leading

to changes in priorities; or a mix o both phenomenaacross regions.

In more detail, by comparing regional typologymembership with country group membership, weobserve the ollowing interesting acts:

Praha (CZ01) is a FP leading absorber region withinthe Czech Republic in both studied periods, whileall other Czech regions changed rom being lowabsorbers/users to SF leading users.

All Danish regions are low absorbers/users oEU unds in both periods, with the exception oHovedstaden (DK01), which became a FP leadingabsorber in FP7.

The large majority o German regions are lowabsorber/users o EU unding (64% in P1 and 69%in P2), ollowed by FP leading absorber regions(18% and 15% in both periods respectively), andull users/absorbers. The large majority o thelow users/absorbers and FP leading absorbers areObjective 2/RCE regions, whereas all ull users/

absorbers are Objective 1/Convergence regions.

None o the German regions are SF leading users.

Spain had a large majority o ull users/absorberregions in the period 2000-2006 (53%), and amajority o low users/absorber regions in theperiod 2007-2013.

In France, the large majority o regions are lowabsorbers/users (92% and 81% in each periodrespectively). Ile de France (FR10) is an FP leadingabsorber in both periods25, and the regions oCorse (FR83), Guadeloupe (FR91), Martinique(FR92) and Guyane (FR93), changed their typology

membership rom low users/absorbers to ullusers/absorbers between both periods.

Most o the Italian regions are low users/absorbers(81% and 62% in both periods). The region oSicilia (ITG1) was a SF leading user in 2000-2006,and Puglia (ITF4) was in 2007-2013. The regionso Liguria (ITC3), Provincia Autonoma Trento (ITD2),and Lazio (ITE4) are FP leading absorbers in bothperiods.

All Hungarian regions were low users/absorbersin the period 2000-2006, and most o them

became ull users/absorbers in 2007-2013, withthe exception o Hungarys capi

innovacion regional 2012 regions europa

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