TableauBordInnovationRegionalUE 2014 En

8/12/2019 TableauBordInnovationRegionalUE 2014 En

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Enterpriseand Industry

RegionalInnovationScoreboard

2014


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

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European Union, 2014Reproduction is authorised provided the source is acknowledged.

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The views expressed in this report, as well as the inormation included in it, do not necessarily reflect theopinion or position o the European Commission and in no way commit the institution.

This report was prepared by:

Hugo Hollanders & Nordine Es-Sadki,Maastricht University (Maastricht Economic and Social Research Instituteon Innovation and Technology MERIT).

Bianca Buligescu,Maastricht University (Maastricht Economic and Social Research Institute on

Innovation and Technology MERIT) (Annex 5)

Lorena Rivera Leon, Elina Griniece & Laura Roman,Technopolis Group (Chapter 4 and corresponding Annex)

Coordinated and guided by:

Boniacio Garcia Porras, Head o Unit, and Tomasz Jerzyniak

Directorate-General or Enterprise and IndustryDirectorate B sustainable Growth and EU 2020

Unit B3 Innovation Policy or Growth

Acknowledgements:

The authors are grateul to all Member States which have made available regional data rom their Community Innovation

Survey. Without these data, the construction o a Regional Innovation Scoreboard would not have been possible. We also

thank our colleague Ren Wintjes (MERIT) or his ideas or improving the RIS measurement methodology. All maps in this

report have been created by MERIT with Region Map Generator (http://www.cciyy.com/).

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Regional InnovationScoreboard2014


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TABLE OF CONTENTS

4 EXECUTIVE SUMMARY

6 1. INTRODUCTION

8 2. RIS INDICATORS, REGIONS AND DATA AVAILABILITY

8 2.1 Indicators

10 2.2 Regional coverage

12 2.3 Regional data availability

14 3. REGIONAL INNOVATION PERFORMANCE

14 3.1 Regional perormance groups

18 3.2 Perormance changes over time

21 3.3 Barriers and drivers to regional innovation

24 4. REGIONAL RESEARCH AND INNOVATION POTENTIAL THROUGH EU FUNDING

24 4.1 Introduction

24 4.2 EU unding instruments or increasing regional research and innovation capacity27 4.3 Indicators and data availability

29 4.4 Regional absorption and leverage o EU unding

36 4.5 Conclusions

37 5. RIS METHODOLOGY

37 5.1 Missing data: imputations

40 5.2 Composite indicators

41 5.3 Group membership

42 ANNEX 1: RIS indicators46 ANNEX 2: Regional innovation perormance groups

52 ANNEX 3: Perormance maps per indicator

63 ANNEX 4: RIS normalised database

72 ANNEX 5: Use/absorption o EU unding and regional innovation perormance

74 ANNEX 6: Regional Systems o Innovation


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Regional InnovationScoreboard 20144

have been published in 2002, 2003, 2006, 2009 and

2012. The RIS 2014 provides both an update o theRIS 2012 but also introduces some changes in themeasurement methodology.

Regional perormance groups

Similar as in the IUS where countries are classified into4 different innovation perormance groups, Europesregions have also been classified into Regional Innovationleaders (34 regions), Regional Innovation ollowers(57 regions), Regional Moderate innovators (68 regions)and Regional Modest innovators (31 regions).

This 6thedition o the Regional Innovation Scoreboard

(RIS) provides a comparative assessment o innovationperormance across 190 regions o the European Union,Norway and Switzerland. The RIS accompanies theInnovation Union Scoreboard (IUS) which benchmarksinnovation perormance at the level o Member States.

Where the IUS provides an annual benchmark oMember States innovation perormance, regionalinnovation benchmarks are less requent and lessdetailed due to a general lack o innovation data atthe regional level. The Regional Innovation Scoreboardaddresses this gap and provides statistical acts on

regions innovation perormance. Previous RIS reports

Executive summary

Map created with Region Map Generator


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5Regional InnovationScoreboard 2014

The most innovative regions are typically

in the most innovative countries

Despite the act that there is variation in regionalperormance within countries, regional perormancegroups do match the corresponding IUS countryperormance groups quite well. Most o the regionalinnovation leaders and innovation ollowers are locatedin the IUS Innovation leaders and ollowers and mosto the regional moderate and modest innovators arelocated in the IUS Moderate and Modest innovators.

However, 14 countries have regions in two perormance

groups and our Member states, France, Portugal,Slovakia and Spain, have regions in 3 different regionalperormance groups, which indicate more pronouncedinnovation perormance differences within countries.Only Austria, Belgium, Bulgaria, Czech Republic,Greece and Switzerland show a relatively homogenousinnovation perormance as all regions in those countriesare in the same perormance group.

All the EU regional innovation leaders (27 regions) arelocated in only eight EU Member States: Denmark,Germany, Finland, France, Ireland, Netherlands, Swedenand United Kingdom. This indicates that innovation

excellence is concentrated in relatively ew areas inEurope.

For most regions innovation has improved

over time

An analysis over the seven-year period 2004-2010shows that innovation perormance has improved ormost regions (155 out o 190). For more than hal othe regions (106) innovation has grown even more thanthe average o the EU. At the same time innovationperormance worsened or 35 regions scattered across

15 countries. For 4 regions perormance even declinedat a very sharp rate o more than -10% on averageper year.

Drivers o regional innovation

Additional analyses have explored the impact opotential drivers o regional innovation. Regions wherepeople have a more positive attitude to new thingsand ideas (European Social Survey) have avourableconditions or both entrepreneurship and innovation.Regions with a well-developed system o public

financial support or innovation with high shares oinnovating companies receiving some orm o public

financial support are also more innovative than regions

where ewer firms benefit rom such support. With alack o finance being one o the most important barriersto innovation this result shows in regions with a lack oprivate unding policies providing public unding can besuccessul in promoting innovation.

Regional research and innovation potential

through EU unding

The analysis o the use o EU unding or researchand innovation in the last programming period 2007-2013 distinguishes among 5 typologies o regions:

Framework Programme leading absorbers (15.85%);Structural Funds (SFs) leading users targeting researchand technological activities (3.66%); StructuralFunds leading users prioritising services or businessinnovation and commercialisation (6.10%); Users o SFor both types o RTDI priorities with similar medium-to-high amounts o SF committed to projects targetingboth o the above fields (3.66%); and regions with lowuse o Structural Funds, which make up the majority oregions included in the analysis (71%).

To understand the extent to which the EU unding isreflected in the innovation perormance o the recipient

regions, a cross-analysis o the regions absorptiono EU unding and their results in the ramework othe RIS 2014 was perormed. The analysis showsthat, while there are several regions that can beclassified as pockets o excellence in terms o theirFP participation and regional innovation capacity,only a ew o the regions that are using EU unds orbusiness innovation more intensely are above averageinnovation perormers. The greatest majority o the EUregions in the analysed sample are low absorbers oFP unding and SFs and exhibit moderate to modestlevels o innovation. These findings point to the act

that the regional innovation paradox continues to be adominant eature o the European regional innovationlandscape that calls or more policy attention in theuture programming period.

RIS methodology

The RIS 2014 replicates the IUS methodology usedat national level to measure perormance o the EUregional systems o innovation distinguishing betweenEnablers, Firm activities and Outputs. The RIS 2014uses data or 11 o the 25 indicators used in the IUS

or 190 regions across Europe (22 EU member statestogether with Norway and Switzerland).


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1. IntroductionInnovation Index which summarizes the perormance

o a range o different indicators. IUS distinguishesbetween 3 main types o indicators Enablers, Firmactivities and Outputs and 8 innovation dimensions,capturing in total 25 indicators. The measurementramework is presented in Figure 1.

The Regional Innovation Scoreboard is a regional

extension o the Innovation Union Scoreboard. TheInnovation Union Scoreboard (IUS) gives a comparativeassessment o the innovation perormance at thecountry level o the EU Member States and otherEuropean countries. Innovation perormance ismeasured using a composite indicator the Summary

Figure 1: Measurement ramework o the Innovation Union Scoreboard


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Innovation also plays an important role at the regional

level as regions are important engines o economicdevelopment. Economic literature has identified threestylized acts: 1) innovation is not uniormly distributedacross regions, 2) innovation tends to be spatiallyconcentrated over time and 3) even regions withsimilar innovation capacity have different economicgrowth patterns. Regional Systems o Innovation (RSI)have become the ocus o many academic studies andpolicy reports.1Attempts to monitor RSIs and regionsinnovation perormance are severely hindered by a lacko regional innovation data.

The Regional Innovation Scoreboard (RIS) addressesthis gap and provides statistical acts on regionsinnovation perormance. Following the revision othe European Innovation Scoreboard (EIS) into theInnovation Union Scoreboard in 2010, the RIS 2012provided both an update o earlier RIS reports and itresembled the revised IUS measurement rameworkat the regional level. Regions were ranked in ourgroups o regions showing different levels o regionalinnovation perormance. The RIS 2014 provides bothan update o the RIS 2012 but also introduces somechanges in the measurement methodology. First, theimputation techniques or estimating missing data

have been modified with the aim to standardizethe imputation techniques and make them moretransparent. Secondly, group membership is not, asin the RIS 2012, determined by a statistical clusteranalysis, but by applying the same method as usedin the IUS by grouping regions based on their relativeperormance to the EU.

Section 2 discusses the availability o regional data, the

indicators that are used or and the regions which areincluded in the RIS 2014. Section 3 presents results orthe Regional Innovation Index and group membershipin our distinct regional innovation perormancegroups. Section 3 also discusses perormance trendsover time. Section 4 provides a separate analysison the relationship between the use o two main EUunding instruments and innovation perormance:the 7th Framework Programme or Research andTechnological Development (FP7) and the StructuralFunds (SF). The results show that the regionalinnovation paradox continues, i.e. that the majority

o regions receiving large amounts o FP and SFunds are less innovative. Section 5 discusses the ullmethodology or calculating the Regional InnovationIndex and or imputing missing data.

The years used in the titles o the previous RIS reportsreer to the years in which the individual editions werepublished, i.e. RIS 2012, RIS 2009 and RIS 2006. Thesedates do not reer to the reerence years or datacollection as the timeliness o regional data is laggingseveral years behind the date o publication o the RISreport. For the RIS 2014 most recent data are reerringto 2012 or 1 indicator, 2011 or 1 indicator, 2010 or

8 indicators and 2008 or 1 indicator. A reerence tothe most recent perormance year in this report shouldthus be interpreted as reerring to the year 2010. Theseven-year period used in the growth analyses reersto 2004-2010.

1 Annex 6 provides a more detailed discussion of Regional Systems of Innovation.


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Innovation Union Scoreboard Regional Innovation Scoreboard

ENABLERS

Human resources

New doctorate graduates (ISCED 6) per 1000 population aged 25-34 Regional data not available

Percentage population aged 30-34 having completed tertiary education Percentage population aged 25-64 having completed tertiary education

Percentage youth aged 20-24 having attained at least upper secondary leveleducation

Regional data not available

Open, excellent and attractive research systems

International scientific co-publications per million population Regional data not available

Scientific publications among the top 10% most cited publicationsworldwide as % o total scientific publications o the country


Non-EU doctorate students as a % o all doctorate students Regional data not available

Finance and support

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

Venture capital (early stage, expansion and replacement) as % o GDP Regional data not available

FIRM ACTIVITIES

Firm investments

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

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

Linkages & entrepreneurship

SMEs innovating in-house as % o SMEs Identical

Innovative SMEs collaborating with others as % o SMEs Identical

Public-private co-publications per million population Regional data not available

Intellectual assets

PCT patent applications per billion GDP (in PPS) EPO patent applications per billion regional GDP (PPS)

PCT patent applications in societal challenges per billion GDP (in PPS) Regional data not available

Community trademarks per billion GDP (in PPS) Regional data not available

Community designs per billion GDP (in PPS) Regional data not available

OUTPUTS

Innovators

SMEs introducing product or process innovations as % o SMEs Identical

SMEs introducing marketing or organisational innovations as % o SMEs Identical

Employment in ast-growing firms o innovative sectors Regional data not available

Economic effects

Employment in knowledge-intensive activities (manuacturing andservices) as % o total employment

Employment in medium-high/high-tech manuacturing andknowledge-intensive services as % o total workorce

Contribution o medium-high and high-tech product exports tothe trade balance


Knowledge-intensive services exports as % total service exports Regional data not avai lable

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

License and patent revenues rom abroad as % o GDP Regional data not available

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


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Depending on differences in regional data availabilitythe RIS covers 55 NUTS 1 level regions and 135NUTS 2 level regions (Table 2). The EU MemberStates Cyprus, Estonia, Latvia, Lithuania, Luxembourgand Malta have not been included as the regionaladministrative level as such does not exist in thesecountries (NUTS 1 and NUTS 2 levels are identicalwith the country territory).

2.2 Regional coverage

The RIS covers 190 regions or 22 EU Member States aswell as Norway and Switzerland at different NUTS levels.The NUTS classification (Nomenclature o territorialunits or statistics) is a hierarchical system or dividingup the economic territory o the EU and it distinguishesbetween 3 different levels: NUTS 1 captures major socio-economic regions, NUTS 2 captures basic regions or theapplication o regional policies and NUTS 3 capturessmall regions or specific diagnoses.3

3 The current NUTS 2010 classification is valid from 1 January 2012 until 31 December 2014 and lists 97 regions at NUTS 1, 270 regions at NUTS 2 and 1294

regions at NUTS 3 level.


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The last 2 indicators are not included in the RIS or differentreasons. The indicator measuring reduced labour costs waspart o an indicator on resource efficiency used in the RIS2009 but the indicator was no longer used in the RIS 2012as the indicator on resource efficiency was removed romthe list o indicators used in IUS. The resource efficiencyindicator combined two indicators, the indicator on reducedlabour costs and an indicator on reduced use o materialsand energy. The latter was not included anymore in the CIS2010 and was replaced by the indicator measuring publicfinancial support. For this indicator no regional data rom

earlier CIS surveys are available and the indicator hasthereore not been included in the current RIS.

Timeliness of regional data

The timeliness o regional data is lagging several yearsbehind the date o publication o the RIS report. For theRIS 2014 most recent data are reerring to 2012 or1 indicator (tertiary education), 2011 or 1 indicator(employment in medium-high/high-tech manuacturingand knowledge-intensive services), 2010 or 8 indicators(all 6 indicators using CIS data and both indicators publicand private R&D expenditures) and 2008 or 1 indicator(EPO patents). Following the availability o regional data

or 4 waves o the CIS (CIS 2004, CIS 2006, CIS 2008 andCIS 2010), the RIS will present regional innovation resultsor 4 reerence years: 2004, 2006, 2008 and 2010.

Data availability by indicator and country

The RIS database contains 8,360 data cells (190 regions,11 indicators and 4 years) o which, due to missingdata, at first 2,439 data cells (29.2%) are missing. Dataavailability particularly depends on the availability oregional CIS data. As shown in Table 3, data availabilitywas below average or all indicators using CIS data.But also or R&D expenditures regional level data are

not available or at least 1 out o 4 regions. Only or2 indicators data availability is above 90%.

2.3 Regional data availability

Regional innovation data or 5 indicators are directlyavailable rom Eurostat. For the share o populationaged 25-64 having completed tertiary education, R&Dexpenditures in the public and business sector, EPOpatent applications and employment in medium-high/high-tech manuacturing and knowledge-intensiveservices regional data can be extracted rom Eurostatsonline regional database.4 For the 6 indicators usingCommunity Innovation Survey (CIS) data howeverregional data are not directly available rom Eurostatand a special data request had to be made to obtain

regional CIS data.

Regional CIS data request

To collect regional CIS data, in 2012 data requestswere made by Eurostat to most Member Statesexcluding those countries or which NUTS 1 andNUTS 2 levels are identical with the country territoryor countries or which national CIS samples are toosmall to allow them to deliver reliable regional leveldata (e.g. Germany). In august 2013, Eurostat sharedregional CIS 2010 data with the project team or 17countries (Austria, Belgium, Bulgaria, Croatia, CzechRepublic, Finland, France, Hungary, Italy, Norway,

Poland, Portugal, Romania, Slovakia, Slovenia, Spainand Sweden) or the ollowing indicators:

Non-R&D innovation expenditure

SMEs innovating in-house

Innovative SMEs collaborating with others

Product or process innovators

Marketing or organisational innovators

Sales o new-to-market and new-to-firm innovations

Reduced labour costs being o high importance ordeveloping product or process innovations

Any public financial support or innovation activities

rom either local government, national governmentor the European Union.

4 http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/regional_statistics/data/database


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There are also huge differences or regional dataavailability between countries. Data availability is verygood at 95% or more or 7 countries (Belgium, Bulgaria,Czech Republic, Poland Romania, Slovakia and Slovenia)(Table 4), good (below 95% but above average) or

8 countries (Austria, Finland, France, Hungary, Norway,Portugal, Spain and Sweden), below average or5 countries (Germany, Ireland, Italy, Netherlands andthe UK) and ar below average or 4 countries (Croatia,

Denmark, Greece and Switzerland). To improve dataavailability several imputation techniques have beenused to provide estimates or all missing data. Dataavailability afer imputation improves to 98.9% and isat least 99% or almost all countries except Finland

(96%) and the UK (91%). Chapter 5 provides moredetails on the imputation techniques and Annex 4shows the database or all regions and indicators aferimputation.

Table 3: Data availability by indicator

DATA AVAILABILITY

Population having completed tertiary education 94.9%

Employment in medium-high/high-tech manuacturing and knowledge-intensive services 91.8%

EPO patent applications 87.6%

R&D expenditure in the business sector 75.1%

R&D expenditure in the public sector 71.8%

All indicators 70.8%

Product or process innovators (CIS) 64.5%

Innovative SMEs collaborating with others (CIS) 64.2%

Marketing or organisational innovators (CIS) 63.3%SMEs innovating in-house (CIS) 60.9%

Non-R&D innovation expenditure (CIS) 55.3%

Sales o new-to-market and new-to-firm innovations (CIS) 49.6%

Table 4: Data availability by country

COUNTRYNUMBER OF

REGIONSDATA

AVAILABILITYCOUNTRY

NUMBER OFREGIONS

DATAAVAILABILITY

BG Bulgaria 2 100.0% FR France 9 72.5%

CZ Czech Republic 8 100.0% SE Sweden 8 72.7%

SK Slovakia 4 100.0% NO Norway 7 72.4%

RO Romania 8 99.1% IT Italy 21 64.9%

SI Slovenia 2 97.7% UK United Kingdom 12 56.8%

PL Poland 16 95.7% IE Ireland 2 45.5%

BE Belgium 3 95.5% NL Netherlands 12 44.9%

PT Portugal 7 92.5% DE Germany 16 44.6%

ES Spain 19 91.9% EL Greece 4 38.6%

HU Hungary 7 86.4% DK Denmark 5 27.3%

AT Austria 3 81.8% HR Croatia 3 28.8%

FI Finland 5 74.5% CH Switzerland 7 18.2%


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3. Regional innovation performance

Most regions are either an Innovation ollower orModerate innovator (Table 5) with about 2 out o3 regions belonging to one o these groups (groupmembership or each region is shown in Annex 2). Thenumber o regions included in the group o Innovationollowers has increased since 2004, mostly by regionsmoving up rom the group o Moderate innovators. Thegroup o Innovation leaders is quite stable including34 regions.

The Regional Moderate innovators perorm belowthe EU average on all indicators. Relative strengthsare in Non-R&D innovation expenditure and Saleso new-to-market and new-to-firm innovations. TheModerate innovators perorm below average onseveral indicators related to business perormance, inparticular to R&D expenditures in the business sectorand EPO patent applications where perormance isabout hal that o the EU average. Low businessR&D expenditures and high Non-R&D innovation

expenditures indicate that companies in theseregions innovate more by adopting technologiesand innovation already developed elsewhere andless so by developing really new product or processinnovations themselves.

The Regional Modest innovators perorm below theEU average on all indicators and in particular on theindicators related to business perormance. Theseregions are relatively well equipped with a well-educated population (72% o the EU average) butace weaknesses in most other domains o their

regional innovation system.

3.1 Regional perormance groups

Europes regions are grouped into different and distinctinnovation perormance groups based on their relativeperormance on the Regional Innovation Index comparedto that o the EU. The thresholds in relative perormanceare the same as those used in the Innovation UnionScoreboard. Regional Innovation leaders are those regionswhich perorm 20% or more above the EU average.Regional Innovation ollowers are regions perormingbetween 90% and 120% o the EU average. RegionalModerate innovators are regions perorming between50% and 90% o the EU average and regional modest

innovators perorm below 50% o the EU average.

The Regional Innovation leaders have the highestperormance in all indicators except the share oinnovative SMEs collaborating with others (Table 6). Inparticular in R&D expenditures in the business sector,SMEs innovating in-house, EPO patent applications andProduct or process innovators the Innovation leadersperorm very well with average perormance levels o30% or more above the EU average. The Innovationleaders perorm relatively weak on Non-R&D innovationexpenditures and the share o SMEs with marketing or

organisational innovations. There results confirm theresult obtained in the IUS that business activity andhigher education are key strengths o Innovation leaders.

The Regional Innovation ollowers perorm close toaverage on most indicators except or Innovative SMEscollaborating with others and SMEs innovating in-house,where average perormance is 35% resp. 18% abovethat o the EU average. The Innovation ollowers perormless well on indicators related to the perormance otheir business sector: perormance on R&D expendituresin the business sector, Non-R&D expenditures and EPO

patent applications is below 90% that o the EU.

Table 5: Distribution o regional perormance groups

REGIONAL INNOVATIONLEADERS

REGIONAL INNOVATIONFOLLOWERS

REGIONAL MODERATEINNOVATORS

REGIONAL MODESTINNOVATORS

2004 34 50 79 27

2006 33 51 78 28

2008 31 55 76 28

2010 34 57 68 31


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Table 6: Perormance characteristics o the regional perormance groups

REGIONALINNOVATION

LEADERS

REGIONALINNOVATIONFOLLOWERS

REGIONALMODERATE

INNOVATORS

REGIONALMODEST

INNOVATORS

Population having completed tertiary education 120 109 81 72

R&D expenditure in the public sector 120 100 69 40

R&D expenditure in the business sector 133 83 52 23

Non-R&D innovation expenditure 102 86 93 69

SMEs innovating in-house 131 118 70 24

Innovative SMEs collaborating with others 126 135 59 33EPO patent applications 135 84 43 20

Product or process innovators 138 101 67 26

Marketing or organisational innovators 103 98 80 31

Employment in medium-high/high-tech manuacturingand knowledge-intensive services

121 94 86 62

Sales o new-to-market and new-to-firm innovations 115 94 91 45

Average scores or each perormance group relative to the EU average (=100)


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A geographical map o the regional perormance groups

is shown in Figure 2. The map reveals that there is aninnovation divide between Northern and Western Europeancountries and those in the East and South. This innovationdivide is similar to that observed in the IUS at country level.

Within countries there is variation in regional perormance(Table 7). In 4 countries (France, Portugal, Slovakia andSpain) there are 3 different regional perormance groupsand in 14 countries are 2 different regional perormancegroups. Only in Austria, Belgium, Bulgaria, CzechRepublic, Greece and Switzerland all regions are in thesame perormance group as the country at large.

Despite the variation in regional perormance within

countries, regional perormance groups do match thecorresponding IUS country perormance groups quitewell. Most o the Regional Innovation leaders areound in countries identified as Innovation leaders inthe IUS, i.e. Denmark, Finland, Germany, Sweden andSwitzerland. Some Regional Innovation leaders areound in IUS Innovation ollowers: Utrecht and Noord-Brabant in the Netherlands, East o England and SouthEast in the UK, Southern and Eastern in Ireland andle de France in France. All the EU Regional Innovationleaders (27 regions) are located in only eight EUMember States.

Figure 2: Regional perormance groups RIS 2014



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stable over time see Table 5 . Between 2004 and2010 in total 77 changes in group membership havetaken place o which 40 to a higher perormance groupand 37 to a lower perormance group (c. Figure 3 andthe regional group memberships over time in Annex 2).

3.2 Perormance changes over time

3.2.1 Divergence in regional innovation performance

There are changes in the composition o the regionalperormance groups over time as the number oregional Innovation leaders, Innovation ollowers,Moderate innovators and modest innovators is not

Figure 3: Regional perormance groups over time

2004

2008

2006

2010



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Most changes in perormance groups took place ina limited number o regions. Five regions changedperormance group 3 times5and 17 regions changedperormance group twice6. None o these regionsmanaged to consistently improve their perormance.Regions either moved down to a lower perormancegroup and then moved up again or they moved up to ahigher perormance group and then moved down again.

Average perormance or the Innovation leaders,Innovation ollowers and Moderate innovators has

been improving over time (Table 9) with the Innovationollowers growing astest with an average annualgrowth rate o 3.9%. For the Modest innovatorsperormance has declined between 2004 and 2010 Atthe level o regional perormance groups the Innovation

A comparison o the initial perormance levels in 2004and the change in perormance between 2004 and2010 or all 190 regions confirms that there is no

There is no relation between the relative number ochanges in group membership and the innovationperormance o the country (Table 8). Most changes inperormance groups are observed in Slovakia, Belgiumand Hungary. For Bulgaria, Greece, Slovenia andSwitzerland no region moved between groups.

leaders and Innovation ollowers, on average, aregrowing aster than both the Moderate innovators and

Modest innovators indicating that at regional levelthere is no convergence o innovation perormance:perormance differences between regions seem tobecome larger not smaller.

process o catching-up with less innovative regionsgrowing at a higher rate than more innovative regions.

5 BE2, HU33, NL12, PL32, PT36 DK02, ES43, ES53, ES7, FR2, HU23, HU31, NL13, NL31, AT2, PL22, RO22, SK02, SK04, FI2, UKN, HR02

Table 8: Changes in regional perormance groups by country

Slovakia 41.7% Austria 22.2% France 11.1% Germany 4.2%

Belgium 33.3% Croatia 22.2% United Kingdom 11.1% Sweden 4.2%

Hungary 33.3% Netherlands 22.2% Romania 8.3% Bulgaria 0%

Denmark 26.7% Finland 20.0% Italy 6.3% Greece 0%

Portugal 23.8% Ireland 16.7% Norway 4.8% Slovenia 0%

Poland 22.9% Spain 14.0% Czech Republic 4.2% Switzerland 0%

Regional Innovation Index scores

Table 9: Perormance changes regional perormance groups

REGIONAL INNOVATIONLEADERS

REGIONAL INNOVATIONFOLLOWERS

REGIONAL MODERATEINNOVATORS

REGIONAL MODESTINNOVATORS

2004 0.541 0.420 0.316 0.213

2006 0.539 0.439 0.331 0.232

2008 0.552 0.450 0.339 0.221

2010 0.562 0.475 0.333 0.199

Average annualgrowth rate 2004-2010

1.3% 3.9% 1.8% -2.2%


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Member States, Norway and Switzerland have improved

their perormance over time, at the regional levelresults are different. Where innovation has improvedor the majority o European regions (or 155 regionsperormance improved between 2004 and 2010)perormance worsened or 35 regions (Figure 4).

3.2.2 Individual performance changes

Similar to the variation in regional innovationperormance levels within countries, also growthperormance or individual regions can be quite differentrom that o other regions in the same country or thecountry at large. Where the IUS 2014 shows that all

Figure 4: Regional innovation growth perormance



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In 14 countries or at least one region innovation has

become worse. Average annual growth has beenstrongly negative (below -2.5%) or 14 regions owhich 7 Polish regions, 4 Spanish regions and 1 regionin Croatia, Italy and Romania (see Figure 5). Growthhas been below -10% in Ciudad Autnoma de Ceuta(ES), Ciudad Autnoma de Melilla (ES), Podlaskie (PL)and Kujawsko-Pomorskie (PL). Less negative growthbetween -2.5 and 0% is observed or 21 regions owhich 3 regions in Poland, 2 regions in Czech Republic,Denmark, Norway, Sweden and the UK and 1 regionin Belgium, France, Greece, Hungary, Italy, Portugal,Romania and Spain.

Positive growth between 0% and 2.5%, the average or

the EU, is observed or 49 regions o which 9 regionsin the UK, 6 in Germany, 4 in Czech Republic, Italy andSweden and 3 in Finland, France, Poland, Spain andSweden.

The majority o regions, 106 in total, have grown at ahigher rate than the EU average. At least one region inevery country has grown at a higher rate than the EUaverage and all regions in Austria, Ireland, Netherlandsand Switzerland have grown at a higher rate than theEU average.

3.3 Barriers and drivers to regional innovation

This section makes a comparison between regionalindicators either measuring ramework conditions orregional innovation or the impact o innovation oneconomic perormance and the Regional InnovationIndex. This comparison is ruitul as more indicatorsbecome available at the regional level that might havean influence on the innovation perormance o specific

regions.

Educational attainment, ICT inrastructure, the avail-ability o finance, an environment conducive to newinnovative activities and strong clusters are some othe potential drivers o business innovation. First a briediscussion o these indicators and the rationale orconsidering them is provided. The ull definitions anddata availability o these indicators can be ound in theRIS 2014 Methodology Report. Secondly a correlationanalysis is carried out to find empirical evidence orthe existence o a possible relationship between these

indicators and regional innovation perormance.

Indicators used in the analysis

Educational attainment is already partly covered inthe RIS but the indicator on tertiary education onlycaptures ormal training but not the training peoplereceived afer completing their ormal education. Theindicator Participation in life-long learning per100 population aged 25-64 captures this aspect oeducational attainment. The rationale or including this

indicator is that a central characteristic o a knowledgeeconomy is continual technical development andinnovation. Individuals need to continually learn newideas and skills or to participate in lie-long learning. Alltypes o learning are valuable, since it prepares peopleor learning to learn. The ability to learn can then beapplied to new tasks with social and economic benefits.

Broadband accessis a proxy or the existence o awell-developed ICT inrastructure. Although in many EUregions broadband access is widely spread variation inthe levels across regions is still high. Thereore realisingEurope's ull e-potential depends on creating theconditions or electronic commerce and the Internet toflourish across all EU regions. This indicator capturesthe relative use o this e-potential by the number ohouseholds that have access to broadband.

It is important to improve the ramework conditions or

innovation. The 2006 Aho Group Report on "Creatingan Innovative Europe recommended the need orEurope to provide an innovation riendly market or itsbusinesses.7 Rather than stressing innovation inputssuch as R&D, the report stresses innovation demandand the myriad o socio-cultural actors that encourageinnovation. Social attitudes towards innovation can bedefined as consumers receptiveness to try and adoptinnovative products and services.8Attitudes towardsinnovation captures positive attitudes to peoples

7 http://ec.europa.eu/invest-in-research/action/2006_ahogroup_en.htm8 Buligescu, B., Hollanders, H. and Saebi, T. (2012), Social attitudes to innovation and entrepreneurship. PRO INNO Europe: INNO Grips II report, Brussels: European

Commission, DG Enterprise and Industry (http://ec.europa.eu/enterprise/policies/innovation/files/proinno/innovation-intelligence-study-4_en.pdf).


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the company received any public financial support13

or innovation activities rom either local or regionalauthorities, central government or the European Union.As data are available or only 82 regions, additionaldata have been estimated using the CIS imputationtechnique also used or estimating missing CIS data inthe RIS.

Linkages between possible drivers to innovation

and innovation performance

Correlation analysis is used to analyse the link betweenthese indicators and the RIS regional perormanceindexes. The correlation analysis is conducted by

constructing variables that combine data or ourperiods in time, using, or each indicator, the most recentdata available and data which are 2, 4 and 6 yearsless recent. With 190 regions included or every timeperiod, a maximum o 760 observations are possible tocalculate correlations. This maximum is only obtainedin the correlation analysis or Participation in lie-longlearning as or the other indicators data is missing andor the Share o Share o innovators receiving any typeo public unding data are available or one period only.

Results rom the correlation analysis are shown in Table10. The Regional Innovation Index is positively and

significantly correlated with the indicator Participationin lie-long learning. This implies that regions witha higher share o population that participates incontinuous training and learning activities are moreinnovative. I the population in a specific region has ahigh share o people investing in their human capitalby continuously learning and developing technicalskills then this will eventually lead to new applications,spillovers, attracting investments and setting examplesor uture generations. All these actors are influentialor the business environment and the innovativeperormance o a region. The results thus show that

it is important to continuously upgrade skills afer thecompletion o ormal education.

receptiveness to new innovations. The indicator

measures the share o people who either think it is veryimportant to think new ideas and be creative or to trynew and different things.9One can or instance arguethat a region with a population that finds it importantto be creative and to start up business is a avourableenvironment or knowledge creation. This avourablecondition should then positively influence the regionalinnovation perormance.

Companies innovate in collaboration with otherprivate and public partners. The proximity o strongcollaboration partners can benefit companies

innovation perormance. Proximity and interaction opartners is captured by clusters. A cluster can be definedat the geographic concentration o interconnectedbusinesses, suppliers and associated institutions.The relative presence o clusters is measured by anindicator on Employment in strong clusters, whichis measured by looking at employment in 2-star and3-star clusters as defined by the European ClusterObservatory.10The 2-star and 3-star cluster regions aremore specialised in a specific industry than the overalleconomy across all regions. According to the ClusterObservatory, this is likely to be an indication that thisregion attracts economic activity leading to (stronger)

spill-over effects and linkages.11

Companies ace a range o diverse actors preventingthem to innovate or hindering their innovation activities.Results rom the CIS 2010 show that or 22% o allcompanies12the lack o finance rom sources outsidethe company was a highly important actor hamperinginnovation activities. Finance rom outside the companycan include finance rom private and public sources.The availability o public financial support could thushelp companies to innovate and it is measured bythe Share of innovators receiving any type of

public funding. For constructing the indicator regionalCIS 2010 data is used on the share o innovatingcompanies responding positively to the question i

9 Data are taken from the European Social Survey. The RIS 2014 Methodology report provides more details.10 The European Cluster Observatory assigns 0, 1, 2 or 3 stars depending 1) if employment reaches a sufficient share of total European employment, 2) if a region

is more specialised in a specific cluster category than the overall economy across all regions, 3) if a cluster accounts for a larger share of a region's overallemployment. Full details about the methodology used by the European Cluster Observatory are available at http://www.clusterobservatory.eu/index.html

11 The Regional Competitiveness Report 2013 uses a similar indicator on the share of employees in strong clusters among high-tech clusters to measure regions

innovation performance (http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/6th_report/rci_2013_report_final.pdf ).12 Both innovating companies (23%) and non-innovating companies (21%) equally report that the lack of external sources of finance is hampering their innovation activities.13 Financial support can include tax credits or deductions, grants, subsidised loans, and loan guarantees. Research and other innovation activities conducted entirely

for the public sector under contract are excluded.


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4. Regional research and innovationpotential through EU funding

matched by regions innovation perormance. In otherwords, are regions with high public investments in RTDImore likely to be innovation leaders? Or are regionswith low capacity to leverage unds or innovation alsolagging behind in terms o innovation perormance?

Similar to the analysis perormed in the RIS 2012,this chapter aims to contribute to investigating thevariety o orms that the regional innovation paradoxtakes in Europe, or the idea that lagging regions withgreater needs or support are prone to low absorption

o European unds and lack prioritisation o availableresources towards support or innovation.

Section 4.2 presents an overview o the instrumentsprovided at European level in support o regionalresearch and innovation activities. Section 4.3 givesan overview o the data used and presents the clusteranalysis and cross-analysis methodologies. Section 4.4describes the groups o EU regions based on their useo EU unds, and the results achieved when intersectingthe regions type o absorption o EU unds with theirinnovation perormance. Section 4.5 concludes.

4.1 Introduction

This chapter aims to provide evidence to contributeto a better understanding o the relationship betweenEU unding instruments such as the Structural Funds(SFs) and the Framework Programme or Researchand Technological Development (FP7) and regionsinnovation perormance.

Firstly, the chapter presents a categorisation oregions based on their extent o using and leveragingSFs to invest in the fields o research, technologicaldevelopment and innovation (RTDI) and o their

participation in FP7. This provides the landscape ohow European regions have been benefitting rom EUsupport in this specific domain. The chapter also givesan overview o the absorption capacity o regionsregarding the use o SFs with the most updatedavailable data on committed projects by the end o2012.

Secondly, this chapter analyses the extent to which theabsorption o EU unds is reflected in regions innovationperormance. The analysis will ocus on identiyingwhether regional investments in RTDI measures are

4.2.1 Structural Funds

Innovation is at the heart o Europe 2020 policyobjectives, yet there are significant differences inresearch and innovation capacity among the regions oEurope. The Structural Funds (SFs) are an instrumento the EUs cohesion policy that aim to counterbalancethese disparities by investing especially in thoseregions that lag behind in perormance. For this reason

the EU cohesion policy introduced two types o regionalunding objectives. The SF Convergence objective(CON) covers the regions that have GDP per capitabelow 75% o the EU average and aim to acceleratethe economic development in these regions. The

Regional Competitiveness and Employment objective(RCE) comprises all other regions above this thresholdand seek to reinorce competitiveness, employmentand attractiveness o these regions14.

In the period 2000-2006 the SF investment in researchand innovation reached 17.9 billion or 10% o thetotal SF budget. The committed SF unding15 under

RTDI priorities in the EU27 or the period 2007-2013amounted to 42.6 billion, constituting 16.3% o allavailable unds16. It is important to point out thatConvergence regions increased their share o researchand innovation in SF budgets on average by 12%

14 http://ec.europa.eu/regional_policy/15 Funding for selected projects (either already spent or earmarked for spending).16

Croatia is excluded in this calculation to enable better comparability between the periods.

4.2 EU unding instruments or increasing regional research and innovation capacity


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17 Regional Innovation Scoreboard 201218 The OPs are prepared by the EU Member States and negotiated with and ultimately validated by the European Commission. The implementation of OPs is done

by the Management Authorities of each Member State and their respective regions. The Commission is involved in the monitoring and quality control of fundsmanagement, alongside the country concerned.

19 In cases where there are particular pockets of less developed regions encompassed within more advanced NUTS 1 regions, several countries have opted for

tailored OPs to address specific challenges of such Cohesion regions. For instance, Germany has established a separate OP for the NUTS 2 region Lneburg (DE93)

focusing on improvement of infrastructure. Similar rationales have been applied to the Belgian region Hainaut (BE32) and two UK regions Cornwall and the Isles

of Scilly (UKK3) and Lowlands and Uplands of Scotland (UKM6).20 OPs of Greece do not follow a strict territorial logic. There are five OPs for these combinations of regions: 1) Attiki (EL3); 2) E Kriti, Nisia Aigaio (EL4); 3) Anatoliki

Makedonia, Thraki (EL11); 4) Thessalia, Sterea Ellada, Ipeiros (EL14+EL24+EL21); 5) Western Greece, Peloponnese, Ionian Islands (EL23+EL25+EL22)

Table 11: Territorial coverage o Operational Programmes in EU Member States

LEVEL COUNTRIES

NUTS 1 Belgium, Germany, Greece20, Netherlands, United Kingdom

NUTS 2 Austria, Spain, Finland, France, Hungary, Ireland, Italy, Poland, Portugal, Sweden

Country level (OPs organised by policypriorities not specific regions)

Bulgaria, Croatia, Czech Republic, Denmark, Romania, Slovakia

Country level (the countries are not split in regions) Estonia, Cyprus, Latvia, Lithuania, Malta, Slovenia

Source: Technopolis Group based on the DG REGIO Data Warehouse

compared to about 8% or RCE regions between bothperiods17. Taking into account the act that in absolutefigures the largest amount o unding has been allocatedto Convergence regions, SFs can be regarded as a majorfinancial input to narrow the innovation gap betweenadvanced and less developed regions.

While the SF is part o the EU budget, the spending othis unding is based on the system o shared respon-sibility between regions, national governments and

the European Commission. The unds are channelledthrough Operational Programmes (OPs) that coverthe policy priorities selected by respective countriesand/or regions.18 Depending on the countrys specificadministrative structure and the degree o centralisationo regional policy-mak ing, the OPs can be ormulat-ed at the level o NUTS 1 or NUTS 2 regions, or alsoat country level. Table 11 summarises the territorialcoverage o Operational Programmes 2007-2013 inall EU Member States.19


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is approximately 15%. Both EU15 and EU12 countries

have put policy importance on stimulating researchand technological activities allocating respectivelyaround 16% and 10% o the total SF budget. EU15countries have earmarked on average 11% o SFs orservices or business innovation and commercialisation,however EU12 countries allocated only some 5% o theavailable unding to this policy priority.

To provide an indication on countries prioritisation

o spending or RTDI priorities in their OPs, Figure 5presents a comparison o the shares o the EU SFsthat have been initially earmarked or supportingRTDI. In the period 2007-2013. EU15 countries haveallocated significantly larger shares o SFs to researchand innovation. On average the share or EU15countries is around 27%, while or EU12 countries it

Figure 5: Share o Structural Funds initially allocated under RTDI priorities, 2007-2013

Source: Technopolis Group based on the DG REGIO Data Warehouse

Blue: EU15 countries (dark: research and technological activities; light: services for business innovation and

commercialisation)

Orange: EU12 countries (dark: research and technological activities; light: services for business innovation and

commercialisation)


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4.2.2 Framework Programme for Research and

Technological DevelopmentThe Framework Programme or Research andTechnological Development (FP) is another EUintervention that provides significant unding orresearch and innovation, but differs in its nature. ISFs avour the emergence o the knowledge economyand aim to oster socio-economic cohesion, the FPis based on organisations bidding or competitiveunding based on criteria o excellence. For this reasonit is usually the case that innovation leaders are alsothe best perormers in attracting FP unds.

Since the individual regions participation in theFramework Programme is conditioned by the locationo research inrastructures within their boundaries,the data analysis o FP unds attracted by theregions needs to be considered with care. Centralised

research systems with wide networks o national

research institutes, or example, the French NationalCentre or Scientific Research (CNRS) or the SpanishNational Research Council (CSIC), will attribute theFP participation results to the region where the legalresidence o the public research institute networkin seated. This creates the so-called headquarterseffect, significantly boosting the FP perormance olarge capital regions. It must be taken into accountthat FP participation is also very much determined bytemporal path dependence. For example, it is knownthat FP6 projects have led to increased co-publicationactivity between project partners21. This implies that

strong visibility in FP6 could have led to more solidintegration o participants in excellent Europeanresearch networks ultimately improving also theirresults in FP7 competitive bids.

4.3 Indicators and data availability

4.3.1 Data availability and data sources

There are two main data sources used in this analysis:

1) Structural Funds data was obtained rom the datawarehouse o the Directorate General or RegionalPolicy o the European Commission. Different romthe data used in the RIS 2012, in this edition the SFdata was organised per Operational Programme.Another difference is that the data used concernscommitted unding, namely unds earmarkedor selected projects that are not yet backed-upwith invoices, but will most likely become actualexpenditures once the programming period isclosed.

2) Framework Programme data was obtained rom

the External Common Research Data WarehouseE-CORDA o the Directorate General Researchand Innovation o the European Commission. Thedatabase cut-off date is June 2013.

Based on the data availability only at OperationalProgramme level, the database or the analysis was

constructed o the OPs with data at NUTS 1 and 2levels, and does not include inormation on regions incountries where the OPs are managed at national levelonly. In total the analysis comprises 164 regions22.There are 58 regions under Convergence objective and104 RCE objective regions that represent respectively35% and 63% o all regions analysed. The structureo Greek Operational Programmes does not ollow astrict territorial rationale. Thereore, Greek regions aregrouped into NUTS 1 regions, one separate NUTS 2region and a mix o NUTS 2 regions23.

To link the expenditure o EU unding in regions withregional innovation perormance, the analysis makes useo the results o the assessment o regional innovationperormance presented in Section 3 o this report.

21 AVEDAS AG, NetPact (2009), Structuring Effects of Community Research The Impact of the Framework Programme on RTD on Network Formation22 Due to the recent administratively territorial reform carried out in Finland in 2013, the results do not disaggregate the region Helsinki-Uusimaa (FI1B) that has been

detached from the former region Etel-Suomi (FI18). In this analysis the results of Helsinki-Uusimaa (FI1B) are displayed within the new administrative region

Etel-Suomi (FI1C).23 Cf. footnote 21


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The Framework Programme unds were analysed based

on quantiying our major indicators or the participationo the regions in competitive research and technologydevelopment. The indicators were chosen to highlight inparticular the strength o the private sectors participationin the programme by considering the ollowing dimensions:

1) The total amount of subsidies received bythe regional actors per year (per capita) indicatesthe absorptive capacity o the region in attractingFP unds;

2) The leverage (per capita), or the differencebetween the total cost o the projects and the

total 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;

3) The number of participations from theprivate sector (per thousand inhabitants) islinked to the amount o private enterprises engagedin FP projects in the region. It shows the strength othe business sector as a research actor;

4) Percentage of SME participation in privatesector shows the share o SMEs in the totalnumber o FP participations rom the private sector.

This indicator gives a hint about the vibrancy o thebusiness innovation environment in the region.

Table 12 shows the categories o SF expenditures thatare included in each indicator, based on the definitionso DG REGIO and the selected FP indicators.

4.3.2 Indicators

As in the RIS 2012, the analysis is based on a compositethematic categorisation o the fields o SF interventionor the period 2007-2013. The figures under the specificexpenditure categories reflect unding committed toselected projects. The amounts registered or each fieldo investment are sel-reported by the regions, whichmight create some unobserved bias and thus diminishthe validity o the data analysis. In order to comparethe use o SF under RTDI priorities across regions in theEU, the values o the unds are reported at a per capitalevel or each region and annualised.

The relevant thematic categories o investment prioritiesestablished by DG REGIO or the Structural Funds weresummed into two main indicators that reflect the amounto regional support in two core areas:

1) Research and technological activities: por-trays the use o unds in support o improving theinrastructure, technological basis and RTDI capacityo the regional players which have an impact on boththe public and private sectors perormance;

2) Support services for business innovationand commercialisation: concerns the fieldso investments that are directly targeting the

enhancement o innovation outputs in enterprises(mainly advisory services, technology transerand training measures aimed at enterprises).This indicator includes also the field o assistanceto SMEs or the promotion o environmentallyriendly products and production processes.

Table 12: Categories o EU unds expenditure under RTDI priorities in the period 2007-2013

INDICATOR STRUCTURAL FUNDS 20072013

Research and technological activities

01: R&TD activities in research centres

02: R&TD inrastructure and centres o competence in a specific technology

04: Assistance to R&TD, particularly in SMEs (including access to R&TD services in research centres)

07: Investment in firms directly linked to research and innovation

Services or business innovation andcommercialisation

03: Technology transer and improvement o cooperation networks

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

05: Advanced support services or firms and groups o firms

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

14: Services and applications or SMEs (e-commerce, education and training, networking, etc.)

FP7 indicators

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


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4.3.3 Methodology

This years methodology differs rom that used in theRIS 2012. Given that the current programming periodo SFs is still running, it was not possible to providean analysis based on expenditures and instead data oncommitted unds to projects have been used. Becausethe current most updated data on SFs is structurallydifferent to data on expenditures or the programmingperiod 2000-2006, it is not possible to provide a cross-time comparison between both programming periods.24

A actor analysis was first perormed on all variablesin order to find actors among observed variables and

group variables with similar characteristics together.The actor analysis revealed that SFs data variablesand FP data variables are structurally different.

A cluster analysis was then perormed to group

inormation on the use o EU unds in regions basedon their similarity on the different sub-indicatorspresented in section 4.3.2.25 Hierarchical clusteringwas chosen as the method to cluster SFs data26and based on the characterisations o the differentclusters a total o 4 clusters or grouping SFs datawas obtained.

A similar clustering method was tested or obtainingtypologies related to the use and leverage o FP undingin regions. However, the results were not satisactory asa consequence, we have applied a similar methodology

o that used in chapter 3, identiying FP leadingabsorbers as those regions that perorm at least 120%o the sample average.

4.4 Regional absorption and leverage o EU unding

4.4.1 Regional absorption rate of SF funding

for RTDI

For the purpose o this chapter, we define the absorptionrate as the share o committed Structural Funds that

are allocated to specific projects under RTDI priorities.

The absorption rate has been linked to the capacityto use unds in support o RTDI, which is consideredcrucial or ensuring that the EU unding is making thegreatest effect on economic and social cohesion. It hasbeen recognised as an important concern in relation tothe implementation o cohesion policy. Many Member

States have experienced difficulties in the absorptiono SFs in the initial years afer the accession to theEU. The causes o these difficulties in taking up EUunding include shortage o resources to co-finance

projects, lack o long-term strategic vision rom thepolicy-makers, low administrative capacity to manageunds in terms o insufficient human resourcesand skills, weak inter-institutional cooperation andunderdeveloped public-private partnerships27. Whilethere are many interrelated actors that account orregions ability to absorb EU unding, a major part othem relates back to the quality o governance.

24 However, the RIS 2012 presents good evidence of the characteristics and funding performance of EU regions in the programming period 2000-2006.25 In order to perform the analysis and to avoid results being influenced by scores of regions over-performing, the dataset has been normalised for outliers scores

with the next best values.26 The chosen cluster method was between-group linkage. The interval measures are computed using Squared Euclidean distance.27 E.g. Zaman, G. and Georgescu, G. (2009) Structural Fund Absorption: A New Challenge For Romania?. Journal for Economic Forecasting, Institute for Economic

Forecasting, 6(1), 136-154


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not yet backed up by invoices and actually spent, this

data serves as a close proxy o unding absorptioncapacity by regions or research and innovation.

Figure 6 provides an overview o SF unding under RTDI

priorities that regions have committed to projects,illustrated as a share o the initially allocated undsunder RTDI priorities. While the committed unds are

Figure 6: The absorption o the allocated SF unding (under RTDI priorities) by regions, 2007-2013


rates are or almost all regions o Hungary, wherethe committed unding under RTDI priorities is very

marginal. The overall absorption o available undsseems to be weak also in a number o Greek, Spanish,Italian and Polish regions.

It is interesting to note that in the period 2007-2013regions in Northern Italy show a very high absorption

rate o SF unding. Good absorption rates are alsoound or a range o regions in Belgium, Swedenand the Netherlands. The lowest SF absorption


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in improving their research and technological

activities (on average 23.9 EUR per capita annually),and those leading userregions that prioritise sofermeasures targeting support services for businessinnovation and commercialisation (on average24.7 EUR per capita per year) (see Table 13 below). Itis interesting to note that the regions with the highestinvestments in research and technological activities areConvergence regions, 3 o them located in the Easternpart o Germany (Brandenburg, Sachsen, Sachsen-Anhalt) and 3 in Portugal (Norte, Centro and Alentejo).The regions with the highest investments in supportservices or business innovation and commercialisation

are scattered across the Mediterranean part o Europeand the Nordic countries, and split between theConvergence and Competitiveness Objective o theCohesion Policy: Anatoloki Makedonia, Thraki (EL11)in Greece, Sardegna (ITG2) and Puglia (ITF4) in Italy,Autonomous Region o Aores (PT20) in Portugal, twoOutermost regions in France (Martinique and CorseFR83), and the regions o Norra Mellansverige (SE31)and Mellersta Norrland (SE32) in Sweden.

Over the period 2007-2013, the five groups o 164

regions include a majority o low users o StructuralFunds (116 regions or 70.7% o total). There were onlysix regions that were Users o SF or both types o RTDIpriorities (3.6% o regions), and 16 leading SF userso both types (i.e. high ocus o SFs on research andtechnological activities and support services or businessinnovation and commercialisation) (9.7% o total). Over90% o the 26 regions that are leading FP absorbers arelow users o Structural Funds (with an average annualcommitted expenditure o 3 EUR per capita). Only twoo the leading regions in FP participation also show ahigh use o Structural Funds, in particular targeting

services or business innovation and commercialisation(the Greek region o Attiki and the Swedish region vreNorrland). These two regions were also identified as FPleading absorbers in the RIS 2012.

The SF leading users have the highest shares o theseunds directed towards investments to research andbusiness innovation. They can be classified into two sub-categories: those SF leading usersthat invest most

Table 13: Number o regions and average characteristics o EU unds used / leveraged or the five typologies o regions

FP LEADINGABSORBERS

SF LEADINGUSERS FORRESEARCH

AND TECHNOL.ACTIVITIES

SF LEADINGUSERS FOR BUSINESS INNOVATION

AND COMMERCIALISATION

SF USERSFOR BOTH

RTDIPRIORITIES

SF LOWUSERS

Funding programme No. regions 26 6 10 6 116

SFs PP 2007-2013(unds committed toprojects selected):Euros/annual/percapita (Dec 2012)

Research andtechnological activities

2.4 23.9 7.1 15.1 2.9

Support services orbusiness innovation andcommercialisation

3.7 4.7 24.7 11.8 2.7

FP7 (June 2013)

Total amount o subsidiesreceived (per capita) 106.9 14.3 8.0 15.2 17.3

Leverage (per capita) 35.5 5.2 2.9 4.8 6.1

Number o participationsrom the private sector(per thousand inhabitants)

0.08 0.02 0.01 0.03 0.02

Percentage o SME partici-pation in private sector

62% 71% 47% 99% 69%



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The low users o SF show an interesting distribution operormance among regions, with 55% o the regionscategorised as moderate (40%) or modest (15%) innovators,and around 45% o the low SF user regions alling in theollower (35%) or leading innovator (9%) categories. There isa striking North-South and West-East division in the regionsthat are low absorbers o SF, signalling the act that the55% o low users o SF that show moderate and modest

innovation perormance are not prioritising measures toboost their innovation perormance, but may be ocusingtheir spending on other types o support, risking to continueto lag behind better perorming regions:

63% o the leading innovators but low SF users arerepresented by German regions, ollowed by British,Dutch and Finnish regions. Moreover, ollowerinnovators and low SF absorbers are 30% romFrance, 22.5% rom the UK and 15% rom Austria.

The modest innovators and low SF users are mostly

regions in Poland, Hungary and Spain, while themoderate innovators and low SF users are rom Italy(28%), Spain (26%), France (17%), Greece (13%),Poland (11%) and Hungary (9%).

The regions making high use o SFs or research andtechnological activities, as well as the Users o SF orboth types o RTDI priorities show a comparativelyeven distribution o their innovation perormancebetween ollowers and moderate innovators. The onlyleading region with regards to its use o SF or researchand technological activities exhibiting high innovationperormance is the German region Saxony (DED). In the

case o the leading SF users or services or businessinnovation and commercialisation, the majority othe regions are moderate innovators, located in theMediterranean regions, while there are a ew ollowerSwedish regions. A modest innovating region investinghigh amounts in business innovation is the PortugueseAutonomous Region o Madeira (PT3).

Different than in the RIS 2012, where the FP leadingabsorbers were rather evenly split between theleader and ollower innovator categories, thereare discrepancies in the distribution o innovation

perormance in the groups o FP leading absorberregions. 30.8% o the FP leading absorbers areinnovation leaders, whereas 50% o them are ollowersand 19.2% are moderate innovators.

4.4.3 Matching leverage and absorption capacity

to innovation performanceWhile the landscape o the regional absorption oEU unds or RTDI helps to identiy how the regionsare making use o EU support, this section aims tounderstand to what extent the absorption o EU undsis reflected in the regional innovation perormance othe regions. We perorm a cross analysis between the

different categories o regional use o EU unds and the

regions levels o innovation perormance as discussedin Chapter 3. We use the same classification oinnovation perormance as the RIS: leaders, ollowers,moderate and modest innovators. The cross analysisresults show 20 different groups o regions (seeTable 15 below and Annex 5 or the detailed overviewo the results).

Table 15: Use o EU unding and innovation perormance in 20 groups o regions

RIS INNOVATION PERFORMANCE GROUPS 2014 Leader Follower Moderate Modest

Typologies use oEU unding period2007-2013

FP leading absorber 8 13 5 0

SF leading user researchand technological activities

1 2 3 0

SF leading user business inno-vation and commercialisation

0 3 5 1

SF user or both types oRTDI priorities

0 2 2 0

SF low user 11 40 45 18


Note: the analysis does not contain five regions that were not classified or innovation perormance in the RIS: BE32 Hainaut, DE93

Lneburg, FI1D Pohjois- ja It-Suomi, UKK3 Cornwall and the Isles o Scilly and UKM6 Lowlands and Uplands o Scotland.


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4.4.4 Regional research and innovation potential

through EU funding: discussionThe analysis o the regions use o EU unding underRTDI priorities and the relationship with their innovationperormance shows striking eatures o the Europeanregional innovation landscape, confirming the trendsidentified in the RIS 2012 report.

Most FP leading absorbers show good to very goodperormance in regional innovation. In these regions,EU unds seem to have a complementary role in theregional innovation system. The use o EU unding ishigh and medium-to-high in a relatively low number o

FP leading absorber regions, which partly also exhibitinnovation potential that is higher than average.Nevertheless, a higher share o the regions investinglarger amounts in support or business innovationare moderate innovators. This might show a partialdisconnection between the regions innovation supportpolicies and the actual needs o innovators.

Regions absorbing low amounts o SF or businessinnovation make up the largest share o EU regions(71%). More than hal o the low SF users are moderateor modest innovators, located in Mediterranean regionsor in the Eastern European countries. This finding

points to the act that the lack o regions prioritisationo investments in innovation is reflected in their lowinnovation perormance.

Our results confirm that the regional innovationparadox28is at work in Europe, whereby less innovativeregions with greater needs or investments ininnovation and in solutions to structural problems havelower absorption capacity than perorming regions, andinvest lower amounts o resources into supporting RTDIactivities. According to recent research in the field29,these regions risk to remain locked in the middle-

income trap, in case the regions do not implementsolutions to restore their competitive advantage andto improve the quality o governance and the structuralproblems that they are acing.

There are several urther trends that need to be highlightedto better understand the context in which the regional

innovation paradox takes place in Europe. Beyond the

trends o globalisation, and diminishing competitivenesso EU regions, the complexity o the multi-level-governance schemes in place or implementing Europeanpolicies and particularly the unding programmes othe cohesion policy. While the Framework Programmeunds are awarded on a competitive basis to researchactors, the process o awarding o Structural Funds issubject to the Member States varieties o governancearrangements or implementing policies or (regional)development (ranging rom decentralised city, county- orregional-level planning to national level steering), whichultimately influences the success o the cohesion policy.

Recent scholar contributions suggest that there is a gapin implementing development policy thinking at nationallevel.30The placed-based approach to development hasbeen receiving wide recognition starting with the 2009World Bank report on the role o economic geography tolocal development31 and the 2009 Barca report or theEuropean Commission on reorming the cohesion policy.32Both reports emphasise the role o taking the interactionso economic geography and local and regional institutionsinto account, and o capitalising on the knowledge o localand external actors by engaging them in participatoryprocesses when delivering development policies. It is

argued that these approaches have been integratedinto the uture cohesion policy 2014-2020, but some othe national and regional implementation mechanismso development and innovation policies have remainedunchanged in EU Member States, based on ratherspatially-blind and top-down approaches.

In addition, a similar statement can be made regardingthe policy thinking in the field o regional innovationsystems. The different ways o understandinginnovation in the Member States is reflected in the typeo governance mechanisms in the field o innovation

promotion: while some governments have transerredcompetencies to regions or local governments to betteroster a thriving environment or innovators, others havepreerred to maintain the top-down, linear approachto RTDI support.33This can be also recognised in theway Operational Programmes have been designed, asdiscussed in section 4.3. Some Member States have

28 See Oughton, C., Landabaso, M., Morgan, K. (2002): The Regional Innovation Paradox: Innovation Policy and Industrial Policy in Journal of Technology Transfer,

27, 97-100 pp.29 Reid, A., Muscio, A., R ivera-Leon, L. (forthcoming): An empirical test of the Regional Innovation Paradox: can smart specialisation overcome the paradox in the

central and eastern European countries?. Eichengreen, B., Park, D., Shin, K. (2013). Growth Slowdowns Redux: New Evidence on the Middle-Income Trap. NBER Working Paper No. 18673.30 See Barca, F., McCann,P, Rodriguez-Pose, A. (2012): The case for regional development intervention: place-based versus place-neutral approaches, in Journal of Regional

Science, vol. 52, no. 1, pp. 134-152.31 World Bank, (2009): World Development Report 2009: Reshaping Economic Geography, Washington DC: World Bank.32 Barca, F. (2009): An Agenda for a Reformed Cohesion Policy: A Place-Based Approach to Meeting European Union Challenges and Expectations, Independent Report, prepared

at the request of the European Commissioner for Regional Policy, European Commission, Brussels.33 Riche, M. (2010): Regional Innovation Governance, in Regional Focus, no. 2, 2010 DG Regional Policy, Brussels.


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most struck by the economic crisis. This shows that, inspite o the structural problems encountered and thediminishing competitiveness o EU regions, they havenot been prioritising innovation through EU unds as ameans to tackle these issues.

In order to understand to what extent EU unding isreflected in the innovation perormance o the recipientregions, a cross-analysis o the regions absorption oEU unding and their regional innovation perormance

was perormed. The analysis shows that, while thereare several regions that can be classified as pocketso excellence in terms o participation in the FPprogramme and in regional innovation, only ew o theregions that are using EU unds or business innovationmore intensely are ollower innovating regions, andonly one is a leading innovator. The majority o EUregions in the analysed sample are low absorbers oEU FP unds and SFs and exhibit moderate to modestlevels o innovation.

Taking into account their low use o SF in comparison

to other leading regions in the EU and the moderateto modest innovation potential, the analysis points tothe act that the regional innovation paradox continuesto be a eature o the European regional innovationlandscape, which needs to be tackled with morecare in the uture programming period. The differentapproaches to development policy thinking in termso place-based versus spatially-blind policies in theEU Member States, and the varieties o governancearrangements towards ostering (regional or national)innovation systems are urther challenges that need tobe taken into account as actors influencing the success

o European unding and ultimately the innovationperormance o regions.

4.5 Conclusions

The analysis o the use o EU unding in theprogramming period 2007-2013 shows that thereare 5 typologies o regions: Framework Programmeleading absorbers (15.85%); Structural Funds (SFs)leading users targeting research and technologicalactivities (3.66%); Structural Funds leading usersprioritising services or business innovation andcommercialisation (6.10%); Users o StructuralFunds or both types o RTDI priorities, with similarmedium-to-high amounts o SF committed to projects

targeting both types o priorities (3.66%); and regionswith low use o Structural Funds, which make up themajority o regions in our sample (71%).

This chapter also illustrated the low absorption capacityo several European regions by analysing the changesin initial allocations or business innovation supportwithin the SF Operational Programmes and the latestdata covering the amounts committed to projects inthe field o business innovation. It is striking that incountries such as Greece, Hungary, Italy, Spain andPoland, a large part o the regions resorted to awarding

less unding to innovation than initially oreseen,shifing priorities during the course o the economiccrisis away rom promoting innovation.

The absorption o FP unds shows that it is regions withlow use o SFs that are the most prominent participantsin the FP programme. This can be considered an evidenceo the complementarities o the two programmes inthese particular regions. However, there is a relativelylow share o regions that are making medium-to-high use o SFs or business innovation. The majorityo the regions that made use o low amounts o SFs

are generally Convergence regions, located in the NewMember States or in the Mediterranean countries

tailored the OPs based on the territorial boundaries oNUTS 2 or NUTS 1 regions, or also taking into accountthe socio-economic profile o distinct territories (e.g. theUK has a specific ERDF Operational Programme coveringthe NUTS 2 Convergence region Cornwall and Isles oScilly and a different one covering the NUTS 1 regionSouth West England where the NUTS 2 region belongs).On the other hand, many Member States, particularly in

Eastern Europe, have lef the design o the OperationalProgrammes or the national level (see Figure 7). Whilethere is no prescribed recipe or managing (regional)innovation systems, the variance in implementinginnovation policies and development policies in theEU, coupled with the quality o governance in severalMember States can be considered urther challengesto exiting the trap o the regional innovation paradox.


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5. RIS methodology5.1 Missing data: imputations

For 190 regions, 4 years (corresponding to havingregional data or 4 waves o the CIS) and 11 indicators,ull data availability would require data or 8,360 datacells. But data availability is not very good with 29.2%o data not being available. For several indicators, inparticular the indicators using CIS data, regional datais missing or a number o years or even or the entireperiod considered. Ideally, or calculating compositeindicators data should be available or all indicators,although some degree o missing data is acceptable(e.g. in the IUS or several countries data availability

is below 100%). To increase data availability, a CISregionalization technique has been used or theindicators using CIS data ollowed by a set o imputationtechniques or the remaining missing CIS data and theindicators not using CIS data.

5.1.1 CIS regionalization technique

I CIS data are missing or all regions but the aggregateor the country is available, a CIS regionalizationtechnique will be used using country and regional leveldata on employment and number o firms at the 2-digitindustry level assuming that industry intensities at thecountry level also hold at the regional level.

We explain the method or regionalizing the CIS databy using the share o firms with product innovations asan example:

Step 1:Calculate or each country Y the share ofirms with product innovations or each industry Iusing the CIS 2010 country level data: PI_Y_I

Step 2a:Identiy the employment share o industryI or region R: EMPL_R_I

Step 3a: Calculate the estimate or the shareo firms with product innovations by multiplying

EMPL_R_I with PI_Y_I: PI_EMPL_R_I Step 2b: Identiy the share o local units

(enterprises) o industry I or region R: ENTR_R_I

Step 3b: Calculate the estimate or the shareo firms with product innovations by multiplyingENTR_R_I with PI_Y_I: PI_ENTR_R_I

Step 4:Calculate the average o PI_EMPL_R_I andPI_ENTR_R_I as the estimate or the regional shareo product innovators: PI_R_I

The same method can be applied or all indicatorsusing CIS data. The RIS Methodology report includes

an example or an unnamed region or the share oproduct and process innovators using CIS 2010 data.

5.1.2 General imputation techniques

The ollowing techniques will be applied in the order asshown below.

At the country level, i data or both the previousand ollowing year are available first the average oboth years will be used , thenthat o the previous year and finally thato the ollowing year , where C denotesthe country, T the current year, T-1 the previous yearand T+1 the ollowing year. I data are not available

or the previous and ollowing year missing data willnot be imputed.

I regional data are available or the previousyear the ratio between the corresponding NUTSlevel and that at a higher aggregate level (NUTS1or NUTS2 regions, country level or NUTS1regions) or the previous year is multiplied withthe current value at the higher aggregate level:

, where R denotes theregion, C the country (as the higher aggregate level),T the current year and T-1 the previous year.

I regional data or the previous year is not available,the same procedure as in step 2 will be used butusing the ratio between the corresponding NUTSlevel and that at a higher aggregate level (NUTS1or NUTS2 regions, country level or NUTS1 regions)or the ollowing year: ,where R denotes the region, C the country (as thehigher aggregate level), T the current year and T+1the ollowing year.

I there are no regional data or both the previousand ollowing year, the higher level aggregate

((NUTS1 or NUTS2 regions, country level or NUTS1regions)), first that or the current year, and, i notavailable, that or the previous year otherwise thator the ollowing year: oror , where R denotes the region, C thecountry (as the higher aggregate level), t the currentyear, T-1 the previous year and T+1 the ollowing year.

I there are no regional and no country level dataavailable or the current, previous and ollowingyear, missing data will not be imputed.

The RIS Methodology report provides examples orsteps 3 and 4.


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particular innovative SMEs collaborating with others,

product and/or process innovators and marketingand/or organizational innovators. The estimatesare o lower quality or both indicators measuredas a share o turnover (with correlation coefficientsbelow 70%), i.e. non-R&D innovation expendituresand sales due to new-to-market and new-to-firmproducts.

Tables 17 and 18 provide details on the relative

share o imputations per country and indicator. Formost countries data availability afer imputation is100% except or Spain and France (99.0%), Finland(96.4%) and the UK (90.9%). For most indicatorsdata imputations have raised data availability to99% or more, except or EPO patent applications,Marketing or organisational innovators and Non-R&Dinnovation expenditure due to a lack o data at thecountry level.

5.1.3 Quality assessment of CIS estimates

The quality o regional estimates can be assessedby comparing the regional CIS 2010 estimates withreal regional CIS 2010 data or 87 to 127 regionsas made available by Member States and Norway.The regional estimates are o relatively good qualityor those indicators measured as

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