Report EUR 26579 EN 2014 Authors: Giuditta de Prato, Daniel Nepelski Editors: Marc Bogdanowicz, Zoe Kay Mapping the European ICT Poles of Excellence: The Atlas of ICT Activity in Europe JRC-IPTS/DG CONNECT Joint Project nr. 31786-2010-06 Third Main Title Line Third Line European commission Joint Research Centre Institute for Prospective Technological Studies Contact information Address: Edificio Expo. c/Inca Garcilaso, 3. E-41092 Seville (Spain) E-mail: jrc-ipts-secretariat@ec. europa. eu Tel.:++34 954488318 Fax:++34 954488300 http://ipts. jrc. ec. europa. eu http://www. jrc. ec. europa. eu Legal Notice Neither the European commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European union Freephone number(*:*00 800 6 7 8 9 10 11(*)Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. A great deal of additional information on the European union is available on the Internet. It can be accessed through the Europa server http://europa. eu/.JRC85353 EUR 26579 EN ISBN 978-92-79-36782-3 (pdf) ISBN 978-92-79 -36783-0 (print) ISSN 1831-9424 (online) ISSN 1018-5593 (print) doi: 10.2791/72405 Luxembourg: Publications Office of the European union, 2014 European union, 2014 Reproduction is authorised provided the source is acknowledged. Printed in Spain 1 Acknowledgments This analysis was produced by the Information Society Unit at the European commission's Joint Research Centre-Institute for Prospective Technological Studies for DG Communications networks, Content and Technology (Project Nr 31786-2010-06. It is part of the project: European ICT Poles of Excellence (EIPE) jointly funded by DG CONNECT and JRC-IPTS. The authors wish to thank and acknowledge the following experts and colleagues for their valuable input and comments: Zoe Kay (DG CONNECT), Jean-paul Simon (JPS Consulting), Andrea De Panizza (OECD), Marc Bogdanowicz (JRC-IPTS), Paul Desruelle (JRC-IPTS) and all the participants at the EIPE Expert Workshop, Seville, 11-12 november 2010. Finally, thorough checking and editing of the text by Patricia Farrer is acknowledged gratefully. How to cite this publication Please cite this publication as: De Prato, G. and Nepelski, D. 2014)' Mapping the European ICT Poles of Excellence. The Atlas of ICT Activity in Europe',JRC Scientific and Policy Reports EUR 26579 EN. Seville: JRC-IPTS. 2 Preface The European ICT Poles of Excellence (EIPE) research project is a joint project of DG CNECT and the JRC Institute for Prospective Technological Studies (Project Nr 31786-2010-06. It investigated the issues of growth, jobs and innovation, which have become the main priorities of the European union's growth strategy programme‘Europe 2020'.'The overall objectives of the EIPE project are to set the general conceptual and methodological conditions for defining, identifying, analysing and monitoring the existence and progress of current and future EIPE, in order to develop a clear capacity to distinguish these among the many European ICT clusters, observe their dynamics and offer an analysis of their characteristics. The EIPE project spanned the period between 2010 and 2013. Over this time, it developed a tool based on a database of original ICT activity indicators, which was enriched with geographical information to allow localisation and aggregation at NUTS 3. The tool helps to answer such questions as: How is ICT R&d, innovation and economic activity distributed in Europe? Which locations are attracting new investments in the ICT sector? What is the position of individual European locations in the global network of ICT activity? The EIPE project had four main steps (see Figure 1). First, European ICT Poles of Excellence were defined. Second, a statistical methodology to identify EIPE was elaborated. Third, the empirical mapping of EIPE was performed and fourth, an in depth analysis of five NUTS 3 regions was undertaken. This work was documented in a series of EIPE reports: Defining European ICT Poles of Excellence. A Literature Review, Identifying European ICT Poles of Excellence. The Methodology, Mapping the European ICT Poles of Excellence. The Atlas of ICT Activity in Europe. Analysing the European ICT Poles of Excellence. Case studies of Inner London East, Paris, Kreisfreie Stadt Darmstadt, Dublin and Byen Kobenhavn. Key Findings and Implications of the European ICT Poles of Excellence project. Figure 1: Overview of the EIPE project More information on the European ICT Poles of Excellence (EIPE) project can be found under: http://is. jrc. ec. europa. eu/pages/ISG/EIPE. html 3 Table of contents Acknowledgments...1 Preface...2 1. Introduction...7 2. European ICT Poles of Excellence...9 3. The 1st Tier EIPES...13 3. 1 Munchen Kreisfreie Stadt...15 3. 2 Inner London East...17 3. 3 Paris...19 3. 4 European ICT Poles of Excellence and their Neighbourhoods...21 3. 4. 1 Munchen Kreisfreie Stadt...21 3. 4. 2 Inner London East...22 3. 4. 3 Paris...23 4. The ICT Activity Sub-indicators...24 4. 1 The ICT R&d Composite Sub-indicator...25 4. 2 The ICT Innovation Composite Sub-indicator...28 4. 3 The ICT Business activity composite sub-indicator...31 5. Individual EIPE Indicators...34 5. 1 ICT R&d...36 5. 1. 1 Universities ranked in QS University ranking...36 5. 1. 2 Academic ranking of a Computer science Faculty...38 5. 1. 3 Employer Ranking of a Computer science Faculty...40 5. 1. 4 Citations Ranking of a Computer science Faculty...42 5. 1. 5 R&d Expenditures by ICT Firms...44 5. 1. 6 ICT FP7 Funding to Private Organisations...46 5. 1. 7 ICT FP7 Participations...48 5. 1. 8 ICT FP7 Funding to SMES...50 5. 1. 9 ICT FP7 Participations by SMES...52 5. 1. 10 Location of ICT R&d Centres...54 5. 1. 11 Ownership of ICT R&d Centres...56 5. 1. 12 Scientific Publications in Computer science...58 5. 1. 13 Outward ICT R&d Internationalisation...60 5. 1. 14 Inward ICT R&d Internationalisation...62 5. 1. 15 Degree in ICT R&d Network...64 5. 1. 16 Closeness Centrality in ICT R&d Network...66 4 5. 1. 17 Betweenness Centrality in ICT R&d Network...68 5. 1. 18 Eigenvector Centrality in ICT R&d Network...70 5. 2 ICT Innovation...72 5. 2. 1 Investment in Intangibles by ICT Firms...72 5. 2. 2 Venture capital Financing of ICT Firms...74 5. 2. 3 ICT Patents...76 5. 2. 4 International Co-inventions...78 5. 2. 5 Degree in ICT Innovation Network...80 5. 2. 6 Closeness Centrality in ICT Innovation Network...82 5. 2. 7 Betweenness Centrality in ICT Innovation Network...84 5. 2. 8 Eigenvector Centrality in ICT Innovation Network...86 5. 3 ICT Business...88 5. 3. 1 Location of ICT Scoreboard Headquarters...88 5. 3. 2 Ownership of ICT Scoreboard Affiliates...90 5. 3. 3 Location of ICT Scoreboard Affiliates...92 5. 3. 4 Location of ICT Firms...94 5. 3. 5 ICT Employment...96 5. 3. 6 Growth in ICT Employment...98 5. 3. 7 Turnover by ICT Firms...100 5. 3. 8 Growth in Turnover by ICT Firms...102 5. 3. 9 Number of New Investments in the ICT Sector...104 5. 3. 10 Outward ICT Business Internationalisation...106 5. 3. 11 Inward ICT Business Internationalisation...108 5. 3. 12 In-degree in ICT Business Network...110 5. 3. 13 Out-degree in ICT Business Network...112 5. 3. 14 Closeness Centrality in ICT Business Network...114 5. 3. 15 Betweenness Centrality in ICT Business Network...116 5. 3. 16 Eigenvector Centrality in ICT Business Network...118 6. Annex I: EIPE Indicators...120 6. 1 ICT R&d Activities Indicators...120 6. 1. 1 ICT R&d Agglomeration Indicators (Agrd...120 6. 1. 2 ICT R&d Internationalisation Indicators (Intrd...122 6. 1. 3 ICT R&d Networking (Netrd...122 6. 2 ICT Innovation Activities Indicators...123 6. 2. 1 Agglomeration of Innovation (Agin...123 6. 2. 2 Internationalisation of ICT Innovation (Intin...123 6. 2. 3 Networking in ICT Innovation (Netin...124 5 6. 3 ICT Business activities Indicators...125 6. 3. 1 Agglomeration of Business activities (Agbuss...125 6. 3. 2 Internationalisation of ICT Business activities (Intbuss...127 6. 3. 3 Networking in ICT Business activities (Netbuss...127 7. Annex 2: Composite Indicators...129 7. 1 Normalization and Rescaling of Data...129 7. 2 European ICT Poles of Excellence Composite Indicators...130 8. 1 Annex 3: Data Sources...131 8. 1 QS WORLD UNIVERSITY RANKINGS by QS...131 8. 2 ICT FP7 by EC DG Connect...132 8. 3 Bibliometrics: Web of Science by Thomson Reuters...132 8. 4 R&d Centre Location by IHS isuppli...132 8. 5 European Investment Monitor by Ernst & young...133 8. 6 Patent Data: REGPAT by OECD...134 8. 7 Company-level Information: ORBIS by Bureau Van dijk...134 8. 8 Venture capital: Venturesource by Dow jones...135 References...136 7 1. Introduction This is the third EIPE Report. It presents the results of an empirical mapping of ICT activity in Europe and the ranking of the top European NUTS 3 regions based on their performance in the EIPE Composite Indicator (EIPE CI. It also ranks regions by each of the 42 indicators which contributed to the building of the EIPE composite indicator. This report offers a snapshot of the performance of regions that are identified as the main locations of ICT activity in Europe. It is meant to provide a comprehensive picture of how ICT activity is distributed across Europe and where its main locations are. This information is expected to give a better overview of the European ICT landscape. In order to provide dynamic access to the information gathered within the EIPE project, this report is accompanied by an online visualisation tool. 1 This report builds on the previous two EIPE reports, which have led to the definition of EIPE (Nepelski et al. 2013) and the elaboration of the methodology for an empirical identification of EIPE (De Prato and Nepelski 2013a. EIPE are defined as follows: European ICT Poles of Excellence (EIPE) are geographical agglomerations of best performing Information and Communication Technologies production, R&d and innovation activities, located in the European union, that exert a central role in global international networks. Following this definition, an empirical framework has been elaborated, which is presented graphically in Figure 2. Figure 2: Empirical framework to identify ICT Poles of Excellence This report implements the method to identify EIPE that was developed in the second EIPE report (De Prato and Nepelski 2013a. By using the data collected in the project and organized along three types of ICT activities (see Figure 2), it presents the results of ranking all of the 1, 303 European NUTS3 level regions according to the following criteria: EIPE Composite Indicator (EIPE CI), which is composed of o an ICT R&d sub-indicator, o an ICT Innovation sub-indicator, o an ICT Business sub-indicator, 1 Available at: http://is. jrc. ec. europa. eu/pages/ISG/EIPE. html 8 42 individual indicators that were defined in the course of the EIPE study and that served to construct the above mentioned sub-indicators and the final EIPE Composite Indicator (See Chapter 6). The EIPE study distinguishes three main types of regions according to the intensity of ICT activity: 1st tier region, i e. scoring between 81 and 100 on the EIPE CI, 2nd tier region, i e. scoring between 61 and 80 on the EIPE CI), and 3rd tier region, i e. scoring between 41 and 60 on the EIPE CI). In the following, Chapter 2 presents the EIPE final Composite Indicator (CI) Ranking. Chapter 3 gives more details on the performance of the three regions that have been identified as 1st tier locations of ICT activity in Europe. This is followed by the presentation of the rankings based on the three composite sub-indicators (SI) namely ICT R&d, ICT Innovation and ICT Business activity (Chapter 4). Chapter 5 shows the ranks for each of the individual indicators, grouped along the above mentioned ICT activities (Sections 5. 1, 5. 2 and 5. 3). Finally, Chapter 6 presents the full list of indicators with their main characteristics. Chapter 7 provides the main details on the methodology used for the construction of the composite indicators and chapter 8 Annex 3) describes the data sources used in the EIPE study. Methodological note: The EIPE Ranking The EIPE ranking is based on the EIPE Composite Indicator (CI), an indicator that is formed by compiling individual indicators into a single index, on the basis of an underlying model of the multidimensional concept that was introduced in EIPE Report 2 (De Prato and Nepelski 2013a). The EIPE CI is computed on the basis of the composite sub-indicators created for each of the activities: ICT R&d, Innovation and Business, by aggregating the values of the three sub-indicators and thus synthesising all information in one final EIPE CI. Sub-indicator values are weighted equally. In order to present EIPE CI on a scale from 0 to 100, the values are standardized with the Minimax procedure. The EIPE ranking, as well as all the ranking for each of the presented indicators, is determined by applying the following criterion: to a region a RANK is attributed by associating it with a number which is one plus the number of distinct regions that come before the region in question. If two or more regions tie for a rank, to each of the tied region is attributed the same rank. For example, if two regions have the same value of 100 in the EIPE CI, they will both rank the same, i e. 1. The region that follows, i e. the next one to score a lower value in the EIPE CI, e g. 99 (thus the one with the next highest EIPE CI), will be ranked 3, because in the row above, there are two regions, rather than one. The rank is increased every time the values upon which the list is ordered change. As a consequence of the application of this criterion, the rankings do not always show consecutive integers as markers of the ranking position. The integer number qualifying the position of the region X in the ranking corresponds to the number of distinct ranks that come before region X, plus one. This method of ranking is not a dense ranking, as a dense ranking method would have returned no gaps in the ranking (the rank of each row would have been one plus the number of distinct ranks coming before the row in question). ) 9 2. European ICT Poles of Excellence Three high performance regions first tier ICT Poles of Excellence Three European NUTS 3 regions were identified that are considered as 1st tier regions, i e. EIPE CI between 81 and 100 (see Table 1). These regions are: 1. Munchen Kreisfreie Stadt (DE212), Germany (EIPE CI=100), 2. Inner London East (UK12), UK (EIPE CI=97), 3. Paris (FR101), France (EIPE CI=95. According to Table 1, there are eight 2nd tier regions, i e. EIPE CI between 61 and 80, and thirty three 3rd tier regions, i e. EIPE CI between 41 and 60. High geographical concentration of ICT activity in Europe Only a very small number of EU regions demonstrate intensive ICT activity, and a large share of the total EU ICT activity is concentrated in them (see Figure 3). The distribution of the values of the EIPE Indicator is visible in Figure 4. It shows that ICT excellence is concentrated in a relatively small number of regions. About 86%of European regions score less than 20 in the EIPE indicator. This concentration process is observable in all the indicators. Strong clustering of ICT activity Larger areas of intensive ICT activities, sometimes including a 1st tier region, are made up of several regions belonging to the same neighbourhood (see Figure 3). These agglomerated regions include half the top 34. The other half of the top 34 regions appear isolated (in geographical terms: mainly capital cities, several important locations of ICT R&d and a few remaining regions. Excellence builds on high performance across all activities Excellence builds on high and balanced performance in all activities, i e. ICT R&d, Innovation and Business, and in all three characteristics: Agglomeration, Internationalisation and Networking (see Figure 5). 10 Table 1: Top performing regions according to the EIPE Composite Indicator (EIPE CI>41) Level EIPE Rank NUTS3 Code Region name EIPE CI 1st tier 1 DE212 Munchen, Kreisfreie Stadt 100 2 UKI12 Inner London-East 97 3 FR101 Paris 95 2nd tier 4 DE122 Karlsruhe, Stadtkreis 80 5 UKH12 Cambridgeshire CC 78 6 SE110 Stockholms lan 77 7 DE711 Darmstadt, Kreisfreie Stadt 73 8 FI181 Uusimaa 70 9 NL414 Zuidoost-Noord-Brabant 70 10 NL326 Groot-Amsterdam 64 11 BE242 Arr. Leuven 61 3rd tier 12 DEA22 Bonn, Kreisfreie Stadt 59 13 FR105 Hauts-de-Seine 59 14 ITC45 Milano 59 15 DE300 Berlin 58 16 IE021 Dublin 57 17 DEA21 Aachen Kreisfreie Stadt 55 18 NL333 Delft en Westland 55 19 UKJ14 Oxfordshire 51 20 UKM25 Edinburgh, City of 51 21 DE111 Stuttgart, Stadtkreis 50 22 DE125 Heidelberg, Stadtkreis 49 23 DE21H Munchen, Landkreis 49 24 BE100 Arr. de Bruxelles-Capitale 48 25 DK011 Byen Kobenhavn 48 26 UKJ11 Berkshire 48 27 AT130 Wien 47 28 ES300 Madrid 46 29 UKJ23 Surrey 45 30 DE712 Frankfurt am Main, Kreisfreie Stadt 44 31 UKJ33 Hampshire CC 43 32 DE252 Erlangen, Kreisfreie Stadt 42 33 FR103 Yvelines 42 34 DED21 Dresden, Kreisfreie Stadt 41 Note: The table includes the ranking of 34 best scoring out of 1303 European NUTS 3 regions, i e. scoring above 41 points on the EIPE Composite Indicator. 1st Tier regions score between 81 and 100, 2nd tier regions between 61 and 80 and 3rd tier regions between 41 and 60 on the EIPE CI. The scale of the EIPE Composite Indicator represents a normalized scale with minimum 0 and maximum 100. The EIPE raw indicator is a z-scores indicator computed over equally weighted 42 indicators. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 11 Figure 3: ICT activity in Europe according to the EIPE Composite Indicator 12 Figure 4: Frequency of EIPE Composite Indicator values Table 2: Descriptive statistics of the EIPE Composite Indicator Number of observations Mean value Standard deviation Variance 1303 12.05 11.08 122.88 13 3. The 1st Tier EIPES Excellence builds on high performance across all activities Excellence builds on a high and balanced performance in all activities, i e. ICT R&d, Innovation and Business, and in all three characteristics: Agglomeration, Internationalisation and Networking. This is illustrated by the top three EIPES and their performance across the sub-indicators. According to Figure 5, the performance of the individual regions across the three dimensions is balanced quite. For example, München Kreisfreie Stadt, number 1 in the overall EIPE comparison, ranks 1st in the ICT R&d, 3rd in the ICT innovation and 4th in the ICT business ranking. Similarly, Inner London East holds 5th, 9th and 1st place in the individual sub-indicators. Figure 5: Performance of the top three EIPES across ICT activities Diversity dominates However, the regions are also highly diverse, as regards their size (e g. population, area), their status (e g. global cities, capital cities, regional capital cities, etc.),their institutions and their general or dedicated policies (e g. at national, regional and local level. The local industrial composition varies, favouring the development of ICT activity in close relation to specific vertical sectors. This in turn contributes to the diversity in specialisation, each region having one or several specific strengths. The internationalisation of each activity follows a different pattern, some regions have a more local orientation (within the EU), while others have far reaching connections (the US and Asia). Each region has developed a different portfolio of partners, resulting in different network structures emerging for activities, locations, etc. Not all regions share a neighbourhood with one or several similarly ranked regions. Proximity is distributed unevenly and some regions are isolated more than others. A deeper case-study level of analysis of the data shows that EIPES are characterised by several commonalities but are also very diverse (Nepelski and De Prato 2013b). Among the commonalities, the concentration-as-a-rule observed from a geographical perspective is also observable in the activities of the public and private organisations, their activities and their financing. All regions have global reach, with intense cross-border activities in ICT R&d, innovation and business and have gained an enviable hub position in a usually very complex web of network connections. Also, the current assets of each region appear to be rooted deep in time, with their current activities and 14 profile resulting from a history several decades as regards their industrial structure, policy decisions, institutional settings, migration and education outcomes, etc. All of the above aspects impact the region and result in very differently balanced EIPE profiles of EIPES (see Figure 6). These small differences in performance of individual locations across the sub-indicators give some hints regarding the composition and details of the European ICT landscape. In particular, it shows how different and unique each location is and that all of them have their strengths and weaknesses. Figure 6: Comparison of the performance of the top three EIPES across three ICT activities 15 3. 1 Munchen Kreisfreie Stadt Table 3: Munchen Kreisfreie Stadt EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 32 Academic ranking of a Computer science faculty Agrd 2 10 Employer ranking of a Computer science faculty Agrd 3 11 Citations ranking of a Computer science faculty Agrd 4 29 R&d expenditures by ICT firms Agrd 5 5 ICT FP7 funding Agrd 6 4 ICT FP7 participations Agrd 7 4 ICT FP7 funding to SMES Agrd 8 4 ICT FP7 participations by SMES Agrd 9 4 Location of ICT R&d centres Agrd 10 32 Ownership of ICT R&d centres Agrd 11 7 Scientific publications in Computer science Agrd 12 23 Internationalisation Outward ICT R&d internationalisation Intrd 1 5 Inward ICT R&d internationalisation Intrd 2 30 Networking Degree in ICT R&d network Netrd 1 1 Closeness centrality in ICT R&d network Netrd 2 1 Betweenness centrality in ICT R&d network Netrd 3 1 Eigenvector centrality in ICT R&d network Netrd 4 1 Innovation Agglomeration Investment in intangibles by ICT firms Agin 1 48 Venture capital financing to ICT firms Agin 2 14 ICT patents Agin 3 9 Internationalisation International co-inventions Intin 1 45 Networking Degree in ICT innovation network Netin 1 1 Closeness centrality ICT innovation network Netin 2 1 Betweenness centrality ICT innovation network Netin 3 1 Eigenvector centrality ICT innovation network Netin 4 182 Business Agglomeration Location of ICT Scoreboard Headquarters Agbuss 1 33 Ownership of ICT Scoreboard affiliates Agbuss 2 24 Location of ICT Scoreboard affiliates Agbuss 3 11 Location of ICT firms Agbuss 4 7 ICT employment Agbuss 5 13 Growth in ICT employment Agbuss 6 1265 Turnover by ICT firms Agbuss 7 19 Growth in turnover by ICT firms Agbuss 8 1264 New business investments in the ICT sector Agbuss 9 10 Internationalisation Outward ICT business internationalisation Intbuss 1 34 Inward ICT business internationalisation Intbuss 2 18 Networking In-degree in ICT business network Netbuss 1 4 Out-degree in ICT business network Netbuss 2 2 Closeness centrality in ICT business network Netbuss 3 1 Betweenness centrality in ICT business network Netbuss 4 10 Eigenvector centrality in ICT business network Netbuss 5 8 Note: The table reports the performance of Munchen Kreisfreie Stadt (DE212) in each out of the 42 indicators used in the EIPE ranking and grouped around three dimensions, i e. ICT R&d, ICT Innovation and ICT Business. The scale represents the rank in the comparison with the remaining 1, 302 European Nuts 3 regions. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 16 Figure 7: Performance of Munchen Kreisfreie Stadt across 42 EIPE indicators 17 3. 2 Inner London East Table 4: Inner London East EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 18 Academic ranking of a Computer science faculty Agrd 2 7 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 6 R&d expenditures by ICT firms Agrd 5 7 ICT FP7 funding Agrd 6 18 ICT FP7 participations Agrd 7 17 ICT FP7 funding to SMES Agrd 8 18 ICT FP7 participations by SMES Agrd 9 17 Location of ICT R&d centres Agrd 10 314 Ownership of ICT R&d centres Agrd 11 16 Scientific publications in Computer science Agrd 12 4 Internationalisation Outward ICT R&d internationalisation Intrd 1 16 Inward ICT R&d internationalisation Intrd 2 260 Networking Degree in ICT R&d network Netrd 1 4 Closeness centrality in ICT R&d network Netrd 2 4 Betweenness centrality in ICT R&d network Netrd 3 7 Eigenvector centrality in ICT R&d network Netrd 4 5 Innovation Agglomeration Investment in intangibles by ICT firms Agin 1 15 Venture capital financing to ICT firms Agin 2 1 ICT patents Agin 3 372 Internationalisation International co-inventions Intin 1 561 Networking Degree in ICT innovation network Netin 1 50 Closeness centrality ICT innovation network Netin 2 30 Betweenness centrality ICT innovation network Netin 3 76 Eigenvector centrality ICT innovation network Netin 4 11 Business Agglomeration Location of ICT Scoreboard Headquarters Agbuss 1 20 Ownership of ICT Scoreboard affiliates Agbuss 2 6 Location of ICT Scoreboard affiliates Agbuss 3 1 Location of ICT firms Agbuss 4 1 ICT employment Agbuss 5 5 Growth in ICT employment Agbuss 6 82 Turnover by ICT firms Agbuss 7 5 Growth in turnover by ICT firms Agbuss 8 1264 New business investments in the ICT sector Agbuss 9 2 Internationalisation Outward ICT business internationalisation Intbuss 1 27 Inward ICT business internationalisation Intbuss 2 2 Networking In-degree in ICT business network Netbuss 1 1 Out-degree in ICT business network Netbuss 2 5 Closeness centrality in ICT business network Netbuss 3 2 Betweenness centrality in ICT business network Netbuss 4 4 Eigenvector centrality in ICT business network Netbuss 5 1 Note: The table reports the performance of Inner London East (UKI12) in each out of the 42 indicators used in the EIPE ranking and grouped around three dimensions, i e. ICT R&d, ICT Innovation and ICT Business. The scale represents the rank in the comparison with the remaining 1, 302 European Nuts 3 regions. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 18 Figure 8: Performance of Inner East London across 42 EIPE indicators 19 3. 3 Paris Table 5: Paris EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 37 Academic ranking of a Computer science faculty Agrd 2 8 Employer ranking of a Computer science faculty Agrd 3 8 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms Agrd 5 3 ICT FP7 funding Agrd 6 7 ICT FP7 participations Agrd 7 7 ICT FP7 funding to SMES Agrd 8 7 ICT FP7 participations by SMES Agrd 9 7 Location of ICT R&d centres Agrd 10 78 Ownership of ICT R&d centres Agrd 11 4 Scientific publications in Computer science Agrd 12 13 Internationalisation Outward ICT R&d internationalisation Intrd 1 4 Inward ICT R&d internationalisation Intrd 2 86 Networking Degree in ICT R&d network Netrd 1 2 Closeness centrality in ICT R&d network Netrd 2 2 Betweenness centrality in ICT R&d network Netrd 3 2 Eigenvector centrality in ICT R&d network Netrd 4 2 Innovation Agglomeration Investment in intangibles by ICT firms Agin 1 3 Venture capital financing to ICT firms Agin 2 2 ICT patents Agin 3 49 Internationalisation International co-inventions Intin 1 121 Networking Degree in ICT innovation network Netin 1 5 Closeness centrality ICT innovation network Netin 2 5 Betweenness centrality ICT innovation network Netin 3 6 Eigenvector centrality ICT innovation network Netin 4 19 Business Agglomeration Location of ICT Scoreboard Headquarters Agbuss 1 26 Ownership of ICT Scoreboard affiliates Agbuss 2 30 Location of ICT Scoreboard affiliates Agbuss 3 60 Location of ICT firms Agbuss 4 8 ICT employment Agbuss 5 2 Growth in ICT employment Agbuss 6 82 Turnover by ICT firms Agbuss 7 2 Growth in turnover by ICT firms Agbuss 8 90 New business investments in the ICT sector Agbuss 9 3 Internationalisation Outward ICT business internationalisation Intbuss 1 20 Inward ICT business internationalisation Intbuss 2 47 Networking In-degree in ICT business network Netbuss 1 14 Out-degree in ICT business network Netbuss 2 3 Closeness centrality in ICT business network Netbuss 3 5 Betweenness centrality in ICT business network Netbuss 4 3 Eigenvector centrality in ICT business network Netbuss 5 4 Note: The table reports the performance of Paris (FR101) in each out of the 42 indicators used in the EIPE ranking and grouped around three dimensions, i e. ICT R&d, ICT Innovation and ICT Business. The scale represents the rank in the comparison with the remaining 1, 302 European Nuts 3 regions. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 20 Figure 9: Performance of Paris across 42 EIPE indicators 21 3. 4 European ICT Poles of Excellence and their neighbourhoods 3. 4. 1 Munchen Kreisfreie Stadt Figure 10: Geographical position of EIPE in Germany Figure 11: München Kreisfreie Stadt within NUTS2 region Oberbayern (NUTS1: Bayern) 22 3. 4. 2 Inner London East Figure 12: Geographical position of EIPE in the UK Figure 13: Inner London East within NUTS2 region Inner London (NUTS1: London) 23 3. 4. 3 Paris Figure 14: Geographical position of EIPE in France Figure 15: Paris within NUTS2 region Ile-de-france (NUTS1: Ile-de-france) 24 4. The ICT Activity Sub-indicators The methodological framework proposed and described in the second EIPE Report allows us to identify a set of 42 indicators, attributed to a matrix of ICT activities and their characterisation (De Prato and Nepelski 2013a), as shown in Figure 2. A composite sub-indicator (CSI) has been developed for each of the ICT activities in order to account for the performance and endowment of regions. The definitions adopted for the ICT activities are: R&d activities: Research and development comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge and the use of this stock of knowledge to devise new applications (OECD 2002. R&d is often scientific or for the development of particular technologies and is carried frequently out as corporate or governmental activity (OECD 2008a). Innovation activities: Innovations comprise implemented technologically-new products and processes and significant technological improvements in products and processes (OECD 2005. Business activities: These activities relate to the production of tangible and intangible goods and services that are produced and meet the needs of consumers in the market and encompass the aggregate economic activities of the commercial and manufacturing sectors of an economy. In this section, the composite EIPE Sub-Indicators (CSI), computed on the basis of the indicators corresponding to each activity, are presented. For each CSI, the ranking of 30 regions with the highest result in terms of the analysed indicator is presented. Then, the geographic distribution of each ICT activity is given on the map of Europe at NUTS 3 level. Finally some descriptive statistics and a frequency graph are provided. 25 4. 1 The ICT R&d Composite Sub-indicator Table 6: Top performing regions according to the ICT R&d CSI Rank NUTS3 Code Region name ICT R&d SI EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 DE122 Karlsruhe, Stadtkreis 96 4 3 FR101 Paris 94 3 4 NL333 Delft en Westland 83 17 5 UKI12 Inner London-East 78 2 6 BE242 Arr. Leuven 73 11 7 DEB32 Kaiserslautern, Kreisfreie Stadt 67 36 8 FI181 Uusimaa 62 9 9 DEA21 Aachen, Kreisfreie Stadt 60 18 10 DE125 Heidelberg, Stadtkreis 57 23 11 UKM25 Edinburgh, City of 55 20 12 NL414 Zuidoost-Noord-Brabant 54 8 13 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 52 25 14 DE711 Darmstadt Kreisfreie Stadt 51 7 15 UKH12 Cambridgeshire CC 51 5 16 ITC45 Milano 51 14 17 NL326 Groot-Amsterdam 50 10 18 ITE43 Roma 48 40 19 GR300 Attiki 48 49 20 AT130 Wien 47 27 21 IE021 Dublin 46 16 22 SE110 Stockholms lan 46 6 23 ES511 Barcelona 44 42 24 UKJ14 Oxfordshire 43 19 25 DE111 Stuttgart , Stadtkreis 41 21 26 DEA22 Bonn, Kreisfreie Stadt 40 12 27 ES300 Madrid 39 28 28 UKE21 York 39 63 29 DE300 Berlin 38 15 30 PL127 Miasto Warszawa 38 50 Note: The table includes the ranking of 30 best scoring out of 1303 European NUTS 3 regions. The regions are ranked based on their performance measured by the ICT R&d Sub-Indicator. The scale represents a normalized scale with minimum 0 and maximum 100. The ICT R&d Sub-Indicator is weighted based on equally 18 indicators. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 26 Figure 16: Distribution of the ICT R&d activity in Europe according to the ICT R&d CSI 27 Figure 17: Frequency of the ICT R&d CSI values 676 357 115 48 33 27 12 9 4 5 6 2 2 1 1 1 1 1 2 0 200 400 600 800 Frequency 0 20 40 60 80 100 R&d Sub-Indicator Table 7: Descriptive statistics of the ICT R&d CSI Number of observations Mean value Standard deviation Variance 1303.00 6. 69 10.61 112.56 28 4. 2 The ICT Innovation Composite Sub-indicator Table 8: Top performing regions according to the ICT Innovation CSI Rank NUTS3 Code Region name ICT Innovation SI EIPE Rank 1 UKH12 Cambridgeshire CC 100 5 2 SE110 Stockholms lan 91 6 3 DE212 Munchen, Kreisfreie Stadt 91 1 4 FR101 Paris 88 3 5 NL414 Zuidoost-Noord-Brabant 80 8 6 DE21H Munchen, Landkreis 76 22 7 DE300 Berlin 75 15 8 UKJ33 Hampshire CC 72 31 9 UKI12 Inner London-East 69 2 10 UKJ11 Berkshire 67 26 11 UKJ14 Oxfordshire 66 19 12 FR105 Hauts-de-Seine 66 13 13 DEA21 Aachen, Kreisfreie Stadt 65 18 14 FI181 Uusimaa 65 9 15 UKJ23 Surrey 65 29 16 DED21 Dresden Kreisfreie Stadt 63 34 17 DE252 Erlangen, Kreisfreie Stadt 63 32 18 ITC45 Milano 63 14 19 UKI11 Inner London -West 61 65 20 FR714 Isere 60 35 21 DEA22 Bonn, Kreisfreie Stadt 58 12 22 UKM25 Edinburgh, City of 56 20 23 DE711 Darmstadt, Kreisfreie Stadt 55 7 24 DE139 Lorrach 54 94 25 DE232 Regensburg, Kreisfreie Stadt 53 64 26 DK011 Byen Kobenhavn 53 24 27 UKI23 Outer London-West and North West 52 68 28 FR103 Yvelines 52 33 28 SE224 Skane lan 52 37 30 DEG03 Jena, Kreisfreie Stadt 51 39 Note: The table includes the ranking of 30 best scoring out of 1, 303 European NUTS 3 regions. The regions are ranked based on their performance measured by the ICT Innovation Sub-indicator. The scale represents a normalized scale with minimum 0 and maximum 100. The ICT Innovation Sub-indicator is weighted based on equally 8 indicators. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 29 Figure 18: Distribution of the ICT Innovation activity in Europe according to the ICT Innovation CSI 30 Figure 19: Frequency of the ICT Innovation CSI values 118 52 207 357 231 124 91 46 25 18 11 3 5 7 1 2 1 1 2 1 0 100 200 300 400 Frequency 0 20 40 60 80 100 Innovation Sub-Indicator Table 9: Descriptive statistics of the ICT Innovation CSI Number of observations Mean value Standard deviation Variance 1303 20.04 12.45 155.03 31 4. 3 The ICT Business activity composite sub -indicator Table 10: Top performing regions according to the ICT Business CSI Rank NUTS3 Code Region name ICT Innovation SI EIPE Rank 1 UKI12 Inner London -East 100 2 2 DE711 Darmstadt, Kreisfreie Stadt 79 7 3 NL326 Groot-Amsterdam 74 10 4 DE212 Munchen, Kreisfreie Stadt 62 1 5 DE122 Karlsruhe, Stadtkreis 62 4 6 FR101 Paris 59 3 7 SE110 Stockholms lan 59 6 8 FR105 Hauts-de-Seine 54 13 9 DEA22 Bonn, Kreisfreie Stadt 53 12 10 FI181 Uusimaa 51 9 11 UKH12 Cambridgeshire CC 49 5 12 IE021 Dublin 49 16 13 DE25C Weissenburg-Gunzenhausen 47 118 14 DE718 Hochtaunuskreis 46 51 15 NL414 Zuidoost-Noord-Brabant 46 8 16 DEB11 Koblenz Kreisfreie Stadt 44 83 17 ES300 Madrid 40 28 18 DK012 Kobenhavns omegn 40 41 19 DE712 Frankfurt am Main , Kreisfreie Stadt 39 30 20 DE111 Stuttgart, Stadtkreis 38 21 21 DK011 Byen Kobenhavn 38 24 22 ITC45 Milano 38 14 23 UKJ31 Portsmouth 37 91 24 DE300 Berlin 36 15 25 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel -Hoofdstad 36 25 26 DEA11 Dusseldorf, Kreisfreie Stadt 36 38 27 DE21B Freising 36 57 28 AT130 Wien 34 27 29 BE242 Arr. Leuven 33 11 30 DE21H Munchen, Landkreis 32 22 Note: The table includes the ranking of 30 best scoring out of 1, 303 European NUTS 3 regions. The regions are ranked based on their performance measured by the ICT Business Sub-indicator. The scale represents a normalized scale with minimum 0 and maximum 100. The ICT Business Sub-indicator is weighted based on equally 16 indicators. For further methodological details please refer to Annexes of the current report and to the methodological report documenting the methodology behind the EIPE ranking (De Prato and Nepelski 2013a). 32 Figure 20: Distribution of the ICT Business activity in Europe according to the ICT Business CSI 33 Figure 21: Frequency of the ICT Business CSI values 620 382 128 81 40 16 9 9 3 5 3 2 2 1 1 1 0 200 400 600 Frequency 0 20 40 60 80 100 Business activity Sub-Indicator Table 11: Descriptive statistics of the ICT Business CSI Number of observations Mean value Standard deviation Variance 1303.00 7. 95 8. 47 71.80 34 5. Individual EIPE Indicators A list of indicators for the EIPE project has been selected carefully on the basis of the EIPE methodological framework of activities and their characteristics and the discussion on their empirical measurements. The full list of these indicators meeting the characteristics specified by the definition, framework and criteria, can be found in Table 12. This list constitutes the EIPE ID card, which provides a schematic presentation of the organisation of the EIPE indicators around the three activities and their three characteristics. A full list of indicators and their description can be found in Section 6. 1. 35 Table 12: Overview of the EIPE indicators: the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms Agrd 5 5 ICT FP7 funding to private organisations Agrd 6 6 ICT FP7 participations Agrd 7 7 ICT FP7 funding to SMES Agrd 8 8 ICT FP7 participations by SMES Agrd 9 9 Location of ICT R&d centres Agrd 10 10 Ownership of ICT R&d centres Agrd 11 11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward ICT R&d internationalisation Intrd 2 14 Networking Degree in ICT R&d network Netrd 1 15 Closeness centrality in ICT R&d network Netrd 2 16 Betweenness centrality in ICT R&d network Netrd 3 17 Eigenvector centrality in ICT R&d network Netrd 4 18 Innovation Agglomeration Investment in intangibles by ICT firms Agin 1 19 Venture capital financing to ICT firms Agin 2 20 ICT patents Agin 3 21 Internationalisation International co-inventions Intin 1 22 Networking Degree in ICT innovation network Netin 1 23 Closeness centrality ICT innovation network Netin 2 24 Betweenness centrality ICT innovation network Netin 3 25 Eigenvector centrality ICT innovation network Netin 4 26 Business Agglomeration Location of ICT Scoreboard Headquarters Agbuss 1 27 Ownership of ICT Scoreboard affiliates Agbuss 2 28 Location of ICT Scoreboard affiliates Agbuss 3 29 Location of ICT firms Agbuss 4 30 ICT employment Agbuss 5 31 Growth in ICT employment Agbuss 6 32 Turnover by ICT firms Agbuss 7 33 Growth in turnover by ICT firms Agbuss 8 34 New business investments in the ICT sector Agbuss 9 35 Internationalisation Outward ICT business internationalisation Intbuss 1 36 Inward ICT business internationalisation Intbuss 2 37 Networking In-degree in ICT business network Netbuss 1 38 Out-degree in ICT business network Netbuss 2 39 Closeness centrality in ICT business network Netbuss 3 40 Betweenness centrality in ICT business network Netbuss 4 41 Eigenvector centrality in ICT business network Netbuss 5 42 36 5. 1 ICT R&d 5. 1. 1 Universities ranked in QS University ranking Table 13: Top ranking regions according to the Universities ranked in QS University ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12 Gwynedd 100 266 2 DE711 Darmstadt, Kreisfreie Stadt 83 7 3 DE125 Heidelberg, Stadtkreis 82 23 4 DE423 Potsdam, Kreisfreie Stadt 77 82 5 UKE21 York 60 63 6 NL333 Delft en Westland 55 17 7 DE142 Tubingen, Landkreis 54 55 8 UKJ32 Southampton 50 130 9 DEA21 Aachen, Kreisfreie Stadt 46 18 10 DE122 Karlsruhe, Stadtkreis 40 4 11 UKF14 Nottingham 39 139 12 UKG33 Coventry 38 169 13 DEA22 Bonn Kreisfreie Stadt 36 12 13 UKJ14 Oxfordshire 36 19 13 SE121 Uppsala lan 36 47 16 DK011 Byen Kobenhavn 35 24 17 BE310 Arr. Nivelles 32 151 18 UKI12 Inner London-East 30 2 18 NL331 Agglomeratie Leiden en Bollenstreek 30 70 20 UKK11 Bristol, City of 27 48 21 UKL22 Cardiff and Vale of Glamorgan 26 107 22 BE242 Arr. Leuven 25 11 22 UKM25 Edinburgh, City of 25 20 24 UKG31 Birmingham 23 83 25 BE234 Arr. Gent 22 94 26 UKE32 Sheffield 21 270 27 DE111 Stuttgart, Stadtkreis 20 21 27 IE021 Dublin 20 16 27 UKH12 Cambridgeshire CC 20 5 Indicator description Indicator ID Agrd 1 Name of indicator Universities ranked in the QS University ranking What does it measure? Measures the number of universities in QS university ranking based in a region Unit of measurement Region's share in the total number of EU ranked universities to a region's share in the EU population Definition of ICT dimension none Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 37 Figure 22: Frequency of the Universities ranked in QS University ranking indicator values 1248 9 8 8 7 4 3 6 1 1 2 1 1 1 2 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Number of universities ranked in QS Table 14: Descriptive statistics of the Universities ranked in QS University ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 16 7. 01 49.17 38 5. 1. 2 Academic ranking of a Computer science Faculty Table 15: Top ranking regions according to the Academic Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12 Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 87 19 3 UKI22 Outer London-South 73 114 4 UKM25 Edinburgh, City of 58 20 5 UKD31 Greater manchester South 49 88 6 NL333 Delft en Westland 46 17 7 UKI12 Inner London-East 44 2 8 FR101 Paris 42 3 9 DE300 Berlin 39 15 10 DE212 Munchen, Kreisfreie Stadt 38 1 10 BE242 Arr. Leuven 38 11 12 NL326 Groot-Amsterdam 38 10 13 UKM34 Glasgow City 37 78 14 DEA21 Aachen Kreisfreie Stadt 36 18 15 NL414 Zuidoost-Noord-Brabant 35 8 16 UKJ32 Southampton 35 130 17 DE122 Karlsruhe, Stadtkreis 35 4 18 NL310 Utrecht 34 46 19 FI181 Uusimaa 32 9 20 IE021 Dublin 32 16 21 ITC45 Milano 32 14 22 DK042 Ostjylland 32 129 23 AT130 Wien 31 27 24 ITE43 Roma 31 40 25 SE110 Stockholms lan 31 6 26 UKK11 Bristol, City of 28 48 27 FR714 Isere 28 35 28 UKG33 Coventry 27 169 29 UKE21 York 27 63 29 ITD55 Bologna 27 76 Indicator description Indicator ID Agrd 2 Name of indicator Academic ranking of a Computer science faculty What does it measure? Measures the performance of the Computer science faculty according to the academic ranking of QS Unit of measurement The highest rank of a Computer science faculty in the academic ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 39 Figure 23: Frequency of the Academic Computer science faculty QS Ranking indicator values 1244 1 5 5 12 11 8 9 2 2 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Academic ranking of a Computer science faculty Table 16: Descriptive statistics of the Academic Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 38 7. 25 52.59 40 5 . 1. 3 Employer Ranking of a Computer science Faculty Table 17: Top ranking regions according to the Employer Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12 Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 95 19 3 UKI12 Inner London-East 68 2 4 UKI22 Outer London -South 57 114 5 UKM25 Edinburgh, City of 53 20 6 ITC45 Milano 49 14 6 UKK11 Bristol, City of 49 48 8 FR101 Paris 48 3 9 UKD31 Greater manchester South 48 88 10 UKG33 Coventry 47 169 11 DE212 Munchen, Kreisfreie Stadt 45 1 12 IE021 Dublin 38 16 12 FI181 Uusimaa 38 9 14 NL335 Groot-Rijnmond 38 72 15 ITD55 Bologna 36 76 16 UKM34 Glasgow City 36 78 17 DE122 Karlsruhe Stadtkreis 35 4 18 DK011 Byen Kobenhavn 34 24 19 ES511 Barcelona 33 42 20 UKE42 Leeds 33 284 21 DEA21 Aachen, Kreisfreie Stadt 33 18 22 SE110 Stockholms lan 33 6 23 SE224 Skane lan 33 37 24 NL333 Delft en Westland 32 17 25 UKK12 Bath and North East Somerset, North Somerset and South Gloucestershire 32 69 26 UKG31 Birmingham 32 83 27 NL326 Groot-Amsterdam 31 10 28 UKF22 Leicestershire CC and Rutland 30 208 29 DE300 Berlin 29 15 30 GR300 Attiki 28 49 Indicator description Indicator ID Agrd 3 Name of indicator Employer ranking of a Computer science faculty What does it measure? Measures the performance of the Computer science faculty according to the employer ranking of QS Unit of measurement The highest rank of a Computer science faculty in the employer ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 41 Figure 24: Frequency of the Employer ranking of a Computer science faculty indicator values 1244 1 3 3 12 12 11 6 6 1 1 1 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 Employer ranking of a Computer science faculty Table 18: Descriptive statistics of Employer Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 47 7. 63 58.27 42 5 . 1. 4 Citations Ranking of a Computer science Faculty Table 19: Top ranking regions according to the Citations Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12 Gwynedd 100 266 2 PL127 Miasto Warszawa 91 50 3 NL335 Groot-Rijnmond 77 72 4 FR101 Paris 75 3 5 IE021 Dublin 73 16 6 UKI12 Inner London-East 72 2 7 NL331 Agglomeratie Leiden en Bollenstreek 64 70 8 UKJ14 Oxfordshire 61 19 9 NL326 Groot-Amsterdam 61 10 10 UKI11 Inner London-West 58 65 11 UKH12 Cambridgeshire CC 55 5 12 DEA22 Bonn, Kreisfreie Stadt 51 12 13 DE423 Potsdam Kreisfreie Stadt 50 82 14 UKI22 Outer London-South 50 114 15 DK011 Byen Kobenhavn 49 24 15 UKD31 Greater manchester South 49 88 17 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 49 25 18 BE211 Arr. Antwerpen 47 54 19 GR122 Thessaloniki 46 171 20 FI181 Uusimaa 45 9 20 UKJ32 Southampton 45 130 22 DE142 Tubingen, Landkreis 44 55 23 SE121 Uppsala lan 43 47 24 SE224 Skane lan 42 37 25 BE242 Arr. Leuven 41 11 26 UKE32 Sheffield 41 270 27 ES511 Barcelona 40 42 28 DK042 Ostjylland 37 129 29 DE212 Munchen Kreisfreie Stadt 37 1 30 BE234 Arr. Gent 37 94 Indicator description Indicator ID Agrd 4 Name of indicator Citations ranking of a Computer science faculty What does it measure? Measures the performance of the Computer science faculty according to the citations ranking of QS Unit of measurement The highest rank of a Computer science faculty in the citations ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 43 Figure 25: Frequency of the Citations Computer science faculty QS Ranking indicator values 1243 3 11 10 9 6 7 3 2 3 2 2 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Citations ranking of a Computer science faculty Table 20: Descriptive statistics of Citations Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 94 9. 57 91.58 44 5 . 1. 5 R&d Expenditures by ICT Firms Table 21: Top ranking regions according to R&d expenditures by ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE122 Karlsruhe , Stadtkreis 100 4 2 FI181 Uusimaa 63 9 3 FR101 Paris 36 3 4 SE110 Stockholms lan 31 6 5 DE212 Munchen, Kreisfreie Stadt 13 1 6 DEA22 Bonn, Kreisfreie Stadt 10 12 7 UKI12 Inner London-East 10 2 8 DE711 Darmstadt, Kreisfreie Stadt 9 7 9 NL414 Zuidoost-Noord-Brabant 9 8 10 UKH12 Cambridgeshire CC 8 5 11 DEA47 Paderborn 6 74 12 BE254 Arr. Kortrijk 4 162 13 BE253 Arr. Ieper 4 194 14 FR103 Yvelines 4 33 15 DE21B Freising 4 57 16 NL230 Flevoland 4 280 17 UKI21 Outer London-East and North East 3 151 18 UKC22 Tyneside 3 175 19 DK032 Sydjylland 3 300 20 NL326 Groot -Amsterdam 3 10 21 DE235 Cham 3 284 22 UKE41 Bradford 3 298 23 ITC45 Milano 2 14 24 ES300 Madrid 2 28 25 DEA25 Aachen, Kreis 2 110 26 AT221 Graz 1 52 27 NL336 Zuidoost-Zuid-Holland 1 301 28 DK041 Vestjylland 1 279 29 AT223 Ostliche Obersteiermark 1 522 30 UKJ23 Surrey 1 29 Indicator description Indicator ID Agrd 5 Name of indicator R&d expenditures by ICT firms What does it measure? Measures the average annual amount spent on R&d in the ICT sector Unit of measurement Region's share in the R&d expenditures by ICT firms in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) Reference year (s) considered 2005-2011 45 Figure 26: Frequency of the R&d expenditures by ICT firms indicator values 1292 4 3 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 R&d expenditures by ICT firms Table 22: Descriptive statistics of R&d expenditures by ICT firms indicator Number of observations Mean value Standard deviation Variance 1303 0. 27 3. 60 12.99 46 5. 1 . 6 ICT FP7 Funding to Private Organisations Table 23: Top ranking regions according to ICT FP7 funding to private organisations indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE122 Karlsruhe, Stadtkreis 100 4 2 DEB32 Kaiserslautern, Kreisfreie Stadt 82 36 3 NL333 Delft en Westland 68 17 4 DE212 Munchen, Kreisfreie Stadt 55 1 5 BE242 Arr. Leuven 46 11 6 DEA21 Aachen, Kreisfreie Stadt 33 18 7 FR101 Paris 29 3 8 DE125 Heidelberg, Stadtkreis 27 23 9 AT221 Graz 26 52 10 NL414 Zuidoost-Noord-Brabant 22 8 11 DE222 Passau, Kreisfreie Stadt 22 123 12 DE711 Darmstadt, Kreisfreie Stadt 22 7 13 UKG13 Warwickshire 21 85 14 UKD53 Sefton 21 223 15 ITE17 Pisa 20 165 15 GR431 Irakleio 20 251 17 DEB35 Mainz Kreisfreie Stadt 19 44 18 UKI12 Inner London-East 19 2 19 DE111 Stuttgart, Stadtkreis 19 21 20 ITD20 Trento 19 198 21 DEF0F Stormarn 18 190 22 DED16 Freiberg 17 103 23 FI181 Uusimaa 17 9 24 DEG03 Jena , Kreisfreie Stadt 17 39 25 UKM25 Edinburgh, City of 17 20 26 DE943 Oldenburg (Oldenburg), Kreisfreie Stadt 16 234 27 DEG0F Ilm-Kreis 16 126 28 DED21 Dresden, Kreisfreie Stadt 16 34 29 DE232 Regensburg, Kreisfreie Stadt 14 64 30 AT332 Innsbruck 14 276 Indicator description Indicator ID Agrd 6 Name of indicator ICT FP7 funding What does it measure? Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total EU ICT FP7 funding to a region's share in the EU population Definition of ICT dimension ICT areas of the ICT FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 47 Figure 27: Frequency of the ICT FP7 funding to private organisations indicator values 1197 57 21 12 7 3 1 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 FP7 funding to private organisations Table 24: Descriptive statistics of ICT FP7 funding to private organisations indicator Number of observations Mean value Standard deviation Variance 1303 1. 39 5. 58 31.18 48 5 . 1. 7 ICT FP7 Participations Table 25: Top ranking regions according to ICT FP7 participations indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEB32 Kaiserslautern, Kreisfreie Stadt 100 36 2 DE122 Karlsruhe, Stadtkreis 98 4 3 NL333 Delft en Westland 85 17 4 DE212 Munchen, Kreisfreie Stadt 67 1 5 BE242 Arr. Leuven 61 11 6 DEA21 Aachen, Kreisfreie Stadt 45 18 7 DE125 Heidelberg, Stadtkreis 44 23 7 FR101 Paris 44 3 9 DE222 Passau, Kreisfreie Stadt 38 123 10 AT221 Graz 37 52 10 UKD53 Sefton 37 223 12 DE711 Darmstadt, Kreisfreie Stadt 36 7 13 GR431 Irakleio 34 251 14 NL414 Zuidoost-Noord-Brabant 33 8 15 DEG03 Jena Kreisfreie Stadt 32 39 15 ITE17 Pisa 32 165 17 UKI12 Inner London-East 31 2 18 DEF0F Stormarn 30 190 18 ITD20 Trento 30 198 20 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 29 25 21 UKG13 Warwickshire 29 85 22 DE111 Stuttgart, Stadtkreis 28 21 23 SI021 Osrednjeslovenska 26 185 24 FI181 Uusimaa 25 9 25 DED16 Freiberg 25 103 26 DEG0F Ilm-Kreis 23 126 26 UKL17 Bridgend and Neath Port Talbot 23 272 28 DED21 Dresden, Kreisfreie Stadt 22 34 29 GR434 Chania 22 445 30 AT130 Wien 21 27 Indicator description Indicator ID Agrd 7 Name of indicator ICT FP7 participations What does it measure? It measures the total number of ICT R&d ICT FP7 projects to which organisations, located in the observed region, have participated to Unit of measurement Region's share in the total number of ICT FP7 participations to a region's share in the EU population Definition of ICT dimension ICT areas of the ICT FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 49 Figure 28: Frequency of the ICT FP7 participations indicator values 1139 72 38 19 10 6 7 4 2 1 1 1 1 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 Number FP7 participations Table 26: Descriptive statistics of ICT FP7 participations indicator Number of observations Mean value Standard deviation Variance 1303 2. 36 7. 37 54.39 50 5. 1. 8 ICT FP7 Funding to SMES Table 27: Top ranking regions according to ICT FP7 funding to SMES indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE122 Karlsruhe , Stadtkreis 100 4 2 DEB32 Kaiserslautern, Kreisfreie Stadt 82 36 3 NL333 Delft en Westland 68 17 4 DE212 Munchen , Kreisfreie Stadt 55 1 5 BE242 Arr. Leuven 46 11 6 DEA21 Aachen, Kreisfreie Stadt 33 18 7 FR101 Paris 29 3 8 DE125 Heidelberg, Stadtkreis 27 23 9 AT221 Graz 26 52 10 NL414 Zuidoost-Noord-Brabant 22 8 11 DE222 Passau, Kreisfreie Stadt 22 123 12 DE711 Darmstadt, Kreisfreie Stadt 22 7 13 UKG13 Warwickshire 21 85 14 UKD53 Sefton 21 223 15 ITE17 Pisa 20 165 15 GR431 Irakleio 20 251 17 DEB35 Mainz Kreisfreie Stadt 19 44 18 UKI12 Inner London-East 19 2 19 DE111 Stuttgart, Stadtkreis 19 21 20 ITD20 Trento 19 198 21 DEF0F Stormarn 18 190 22 DED16 Freiberg 17 103 23 FI181 Uusimaa 17 9 24 DEG03 Jena , Kreisfreie Stadt 17 39 25 UKM25 Edinburgh, City of 17 20 26 DE943 Oldenburg (Oldenburg), Kreisfreie Stadt 16 234 27 DEG0F Ilm-Kreis 16 126 28 DED21 Dresden, Kreisfreie Stadt 16 34 29 DE232 Regensburg, Kreisfreie Stadt 14 64 30 AT332 Innsbruck 14 276 Indicator description Indicator ID Agrd 8 Name of indicator ICT FP7 funding to SMES What does it measure? It measures the total amount of ICT R&d ICT FP7 funding given to SMES located in the observed region Unit of measurement Region's share in the total EU ICT FP7 funding to SMES to a region's share in the EU population Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8 . 2) Reference year (s) considered 2007-2011 51 Figure 29: Frequency of the ICT FP7 funding to SMES indicator values 1197 57 21 12 7 3 1 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 FP7 funding to SMES Table 28: Descriptive statistics of ICT FP7 funding to SMES indicator Number of observations Mean value Standard deviation Variance 1303 1. 39 5. 58 31.18 52 5. 1 . 9 ICT FP7 Participations by SMES Table 29: Top ranking regions according to ICT FP7 participations by SMES indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEB32 Kaiserslautern , Kreisfreie Stadt 100 36 2 DE122 Karlsruhe, Stadtkreis 98 4 3 NL333 Delft en Westland 85 17 4 DE212 Munchen , Kreisfreie Stadt 67 1 5 BE242 Arr. Leuven 61 11 6 DEA21 Aachen, Kreisfreie Stadt 45 18 7 DE125 Heidelberg, Stadtkreis 44 23 7 FR101 Paris 44 3 9 DE222 Passau, Kreisfreie Stadt 38 123 10 AT221 Graz 37 52 10 UKD53 Sefton 37 223 12 DE711 Darmstadt, Kreisfreie Stadt 36 7 13 GR431 Irakleio 34 251 14 NL414 Zuidoost-Noord-Brabant 33 8 15 DEG03 Jena Kreisfreie Stadt 32 39 15 ITE17 Pisa 32 165 17 UKI12 Inner London-East 31 2 18 DEF0F Stormarn 30 190 18 ITD20 Trento 30 198 20 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 29 25 21 UKG13 Warwickshire 29 85 22 DE111 Stuttgart, Stadtkreis 28 21 23 SI021 Osrednjeslovenska 26 185 24 FI181 Uusimaa 25 9 25 DED16 Freiberg 25 103 26 DEG0F Ilm-Kreis 23 126 26 UKL17 Bridgend and Neath Port Talbot 23 272 28 DED21 Dresden, Kreisfreie Stadt 22 34 29 GR434 Chania 22 445 30 AT130 Wien 21 27 Indicator description Indicator ID Agrd 9 Name of indicator ICT FP7 participations by SMES What does it measure? It measures the total number of ICT R&d FP7 projects to which SMES, located in the observed region, have participated to Unit of measurement Region's share in the total number of ICT FP7 SMES participations to a region's share in the EU population Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 53 Figure 30: Frequency of the ICT FP7 participations by SMES indicator values 1139 72 38 19 10 6 7 4 2 1 1 1 1 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 Number of FP7 participations by SMES Table 30: Descriptive statistics of ICT FP7 participations by SMES indicator Number of observations Mean value Standard deviation Variance 1303 2. 35 7. 37 54.39 54 5. 1 . 10 Location of ICT R&d Centres Table 31: Top ranking regions according to Location of ICT R&d centres indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE261 Aschaffenburg , Kreisfreie Stadt 100 144 2 DE252 Erlangen, Kreisfreie Stadt 98 32 3 DE117 Heilbronn, Stadtkreis 84 163 4 DE243 Coburg , Kreisfreie Stadt 83 178 5 DE147 Bodenseekreis 82 128 6 DE223 Straubing, Kreisfreie Stadt 76 365 7 DK012 Kobenhavns omegn 74 41 8 DE222 Passau, Kreisfreie Stadt 67 123 9 DE262 Schweinfurt, Kreisfreie Stadt 63 604 10 DEF02 Kiel, Kreisfreie Stadt 57 106 10 DE713 Offenbach am Main, Kreisfreie Stadt 57 356 12 DE913 Wolfsburg Kreisfreie Stadt 57 186 13 DE211 Ingolstadt, Kreisfreie Stadt 55 275 14 DE112 Boblingen 55 67 15 DE712 Frankfurt am Main, Kreisfreie Stadt 51 30 15 DE232 Regensburg, Kreisfreie Stadt 51 64 17 UKJ31 Portsmouth 51 91 18 UKM25 Edinburgh , City of 50 20 19 DE217 Dachau 50 156 20 DE241 Bamberg, Kreisfreie Stadt 49 377 21 UKJ11 Berkshire 48 26 22 IE013 West 48 122 23 DEA11 Dusseldorf, Kreisfreie Stadt 47 38 24 DE115 Ludwigsburg 46 45 25 DE24B Kulmbach 45 674 26 DEB3D Donnersbergkreis 44 120 26 DEF04 Neumunster, Kreisfreie Stadt 44 361 28 DE21H Munchen Landkreis 43 22 28 DE919 Osterode am Harz 43 567 30 SE123 Ostergotlands lan 40 66 Indicator description Indicator ID Agrd 10 Name of indicator Location of ICT R&d centres What does it measure? It measures the total number of ICT R&d centres located in the observed region Unit of measurement Region's share in the total number of R&d centres located in the EU to a region's share in the EU population Definition of ICT dimension Based on HIS isuppli classification of the major"semiconductors influencers"Unit of observation NUTS3 Source R&d Centre location by IHS isuppli (Section 8. 4) Reference year (s) considered 2012 55 Figure 31: Frequency of the Location of ICT R&d centres indicator values 1111 98 38 26 5 8 4 4 3 3 1 1 1 0 500 1000 Frequency 0 20 40 60 80 100 Location of ICT R&d centres Table 32: Descriptive statistics of Location of ICT R&d centres indicator Number of observations Mean value Standard deviation Variance 1303 2. 46 6. 71 45.08 56 5. 1 . 11 Ownership of ICT R&d Centres Table 33: Top ranking regions according to Ownership of ICT R&d centres indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE243 Coburg , Kreisfreie Stadt 100 178 2 DE115 Ludwigsburg 55 45 3 NL331 Agglomeratie Leiden en Bollenstreek 28 70 4 FR101 Paris 26 3 5 DE125 Heidelberg, Stadtkreis 20 23 6 DE929 Region Hannover 19 60 7 DE212 Munchen, Kreisfreie Stadt 16 1 8 DE147 Bodenseekreis 16 128 9 NL326 Groot-Amsterdam 14 10 10 FI181 Uusimaa 13 9 11 SE110 Stockholms lan 12 6 12 FR105 Hauts-de-Seine 12 13 13 NL421 Noord-Limburg 12 219 14 DEA11 Dusseldorf Kreisfreie Stadt 10 38 15 DEA5B Soest 9 258 16 UKI12 Inner London-East 7 2 17 DEA47 Paderborn 6 74 18 UKE41 Bradford 5 298 19 ITE43 Roma 4 40 20 ITC45 Milano 1 14 Indicator description Indicator ID Agrd 11 Name of indicator Ownership of ICT R&d centres What does it measure? It measures the total number of ICT R&d centres owned worldwide by companies located in the observed region Unit of measurement Region's share in the total number of R&d centres owned by EU firms to a region's share in the EU population Definition of ICT dimension Based on HIS isuppli classification of the major"semiconductors influencers"Unit of observation NUTS3 Source R&d Centre location by IHS isuppli (Section 8. 4) Reference year (s) considered 2012 57 Figure 32: Frequency of the Ownership of ICT R&d centres indicator values 1247 27 15 5 2 4 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Ownership of ICT R&d centres Table 34: Descriptive statistics of Ownership of ICT R&d centres indicator Number of observations Mean value Standard deviation Variance 1303 0. 77 4. 12 13.93 58 5. 1 . 12 Scientific Publications in Computer science Table 35: Top ranking regions according to scientific publications in Computer science indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL333 Delft en Westland 100 17 2 DE138 Konstanz 93 53 3 DE711 Darmstadt, Kreisfreie Stadt 89 7 4 UKI12 Inner London-East 88 2 5 DED16 Freiberg 79 103 6 BE242 Arr. Leuven 75 11 7 DEB3D Donnersbergkreis 72 120 8 DEB32 Kaiserslautern, Kreisfreie Stadt 70 36 9 UKE21 York 67 63 10 DEA21 Aachen, Kreisfreie Stadt 64 18 11 DE122 Karlsruhe, Stadtkreis 61 4 12 GR411 Lesvos 57 1189 13 FR101 Paris 49 3 14 GR232 Achaia 47 234 15 UKM25 Edinburgh City of 45 20 16 DE125 Heidelberg, Stadtkreis 45 23 17 UKH31 Southend-on-sea 44 257 18 UKH12 Cambridgeshire CC 42 5 19 ITE17 Pisa 41 165 20 BE234 Arr. Gent 37 94 21 DE279 Neu-Ulm 37 92 22 UKG13 Warwickshire 34 85 23 DE212 Munchen, Kreisfreie Stadt 34 1 24 UKF14 Nottingham 34 139 25 NL414 Zuidoost-Noord-Brabant 34 8 26 UKJ14 Oxfordshire 33 19 27 DE142 Tubingen, Landkreis 33 55 28 NL326 Groot-Amsterdam 33 10 29 UKM34 Glasgow City 32 78 30 UKL17 Bridgend and Neath Port Talbot 32 272 Indicator description Indicator ID Agrd 12 Name of indicator Scientific publications in Computer science What does it measure? It measures the total number of scientific publications, in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of publications in Computer science to a region's share in the EU population Definition of ICT dimension Computer science as defined by Web of Science classification of Research Areas Unit of observation NUTS 3 Source Bibliometrics: Web of Science by Thomson Reuters (Section 8. 3) Reference year (s) considered 2000-2012 59 Figure 33: Frequency of the scientific publications in Computer science indicator values 1172 38 18 21 13 8 12 2 3 4 1 2 1 2 2 2 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Scientific publications Table 36: Descriptive statistics of scientific publications in Computer science indicator Number of observations Mean value Standard deviation Variance 1303 2. 32 9. 45 89.45 60 5. 1. 13 Outward ICT R&d Internationalisation Table 37: Top ranking regions according to Outward ICT R&d internationalisation indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE243 Coburg , Kreisfreie Stadt 100 178 2 DE115 Ludwigsburg 70 45 3 NL331 Agglomeratie Leiden en Bollenstreek 51 70 4 FR101 Paris 32 3 5 DE212 Munchen, Kreisfreie Stadt 26 1 6 NL326 Groot-Amsterdam 24 10 7 DE929 Region Hannover 24 60 8 FI181 Uusimaa 21 9 9 SE110 Stockholms lan 20 6 10 FR105 Hauts-de-Seine 19 13 11 NL421 Noord-Limburg 19 219 12 DEA5B Soest 15 258 13 DEA11 Dusseldorf, Kreisfreie Stadt 15 38 14 DE125 Heidelberg Stadtkreis 14 23 15 DE147 Bodenseekreis 13 128 16 UKI12 Inner London-East 11 2 17 UKE41 Bradford 9 298 18 DEA47 Paderborn 9 74 19 ITE43 Roma 4 40 20 ITC45 Milano 1 14 Indicator description Indicator ID Intrd 2 Name of indicator Outward ICT R&d internationalisation What does it measure? It measures the number of ICT R&d centres located abroad (outside the country) that are owned by companies'headquarters located in a region Unit of measurement Region's share in the total number of R&d centres located abroad that are owned by companies'headquarters located in the EU to a region's share in the EU population Definition of ICT dimension Based on HIS isuppli classification of the major"semiconductors influencers"Unit of observation NUTS3 Source R&d Centre location by IHS isuppli (Section 8. 4) Reference year (s) considered 2012 61 Figure 34: Frequency of the Outward ICT R&d internationalisation values 1285 2 3 4 4 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Outward ICT R&d internationalisation Table 38: Descriptive statistics of Outward ICT R&d internationalisation indicator Number of observations Mean value Standard deviation Variance 1303 0. 38 4. 19 17.54 62 5. 1 . 14 Inward ICT R&d Internationalisation Table 39: Top ranking regions according to Inward ICT R&d internationalisation indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE261 Aschaffenburg , Kreisfreie Stadt 100 144 2 DE223 Straubing, Kreisfreie Stadt 76 365 3 DK012 Kobenhavns omegn 74 41 4 DE252 Erlangen , Kreisfreie Stadt 65 32 5 DE713 Offenbach am Main, Kreisfreie Stadt 57 356 6 DE913 Wolfsburg, Kreisfreie Stadt 57 186 7 UKM25 Edinburgh, City of 50 20 8 DE217 Dachau 50 156 9 UKJ11 Berkshire 48 26 10 IE013 West 48 122 11 DE112 Boblingen 46 67 12 DEB3D Donnersbergkreis 44 120 12 DEF04 Neumunster Kreisfreie Stadt 44 361 14 DE919 Osterode am Harz 43 567 15 DE22C Dingolfing-Landau 37 580 16 DEB32 Kaiserslautern , Kreisfreie Stadt 35 36 17 UKH12 Cambridgeshire CC 34 5 17 UKJ31 Portsmouth 34 91 19 DE136 Schwarzwald-Baar-Kreis 32 96 19 DE147 Bodenseekreis 32 128 21 DE21H Munchen, Landkreis 32 22 22 DEG06 Eichsfeld 31 242 23 SI024 Obalno-kraska 31 639 24 DE712 Frankfurt am Main, Kreisfreie Stadt 30 30 25 UKJ33 Hampshire CC 29 31 25 DEA11 Dusseldorf, Kreisfreie Stadt 29 38 25 DE21J Pfaffenhofen a d. Ilm 29 409 28 DE80E Nordwestmecklenburg 29 769 29 DEF02 Kiel Kreisfreie Stadt 28 106 30 BE242 Arr. Leuven 28 11 Indicator description Indicator ID Intrd 2 Name of indicator Inward ICT R&d internationalisation What does it measure? It measures the number of ICT R&d centres located in a region that are owned by foreign companies Unit of measurement Region's share in the total number of R&d centres owned by foreign companies in the EU to a region's share in the EU population Definition of ICT dimension Based on HIS isuppli classification of the major"semiconductors influencers"Unit of observation NUTS3 Source R&d Centre location by IHS isuppli (Section 8. 4 ) Reference year (s) considered 2012 63 Figure 35: Frequency of the Inward ICT R&d internationalisation indicator values 1079 63 58 33 22 24 8 2 3 3 2 2 1 1 1 1 0 200 400 600 800 1000 Frequency 0 20 40 60 80 100 Inward ICT R&d internationalisation Table 40: Descriptive statistics of Inward ICT R&d internationalisation indicator Number of observations Mean value Standard deviation Variance 1303 3. 06 8. 59 73.77 64 5. 1 . 15 Degree in ICT R&d Network Table 41: Top ranking regions according to Degree in ICT R&d network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen , Kreisfreie Stadt 100 1 2 FR101 Paris 97 3 3 ES300 Madrid 86 28 4 UKI12 Inner London-East 85 2 4 GR300 Attiki 85 49 6 ITC45 Milano 85 14 7 ITE43 Roma 85 40 8 ES511 Barcelona 80 42 9 FI181 Uusimaa 78 9 10 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 74 25 11 AT130 Wien 72 27 12 DE122 Karlsruhe, Stadtkreis 70 4 13 BE242 Arr. Leuven 69 11 14 DE300 Berlin 69 15 15 SE110 Stockholms lan 67 6 16 ITC11 Torino 67 56 17 NL414 Zuidoost-Noord-Brabant 62 8 18 NL333 Delft en Westland 62 17 19 HU101 Budapest 61 73 20 FR105 Hauts-de-Seine 60 13 21 NL326 Groot-Amsterdam 59 10 22 FR103 Yvelines 57 33 23 PT171 Grande Lisboa 57 93 24 DE111 Stuttgart, Stadtkreis 55 21 25 UKG13 Warwickshire 55 85 26 AT221 Graz 54 52 26 GR122 Thessaloniki 54 171 28 IE021 Dublin 54 16 28 ES523 Valencia/Valencia 54 213 30 ITD20 Trento 53 198 Indicator description Indicator ID Netrd 1 Name of indicator Degree in ICT R&d network What does it measure? It measures the total number of connections a region maintains with other regions through organizations participating in common ICT FP7 projects Unit of measurement Rank between 0 and 1 Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 65 Figure 36: Frequency of the Degree in ICT R&d network indicator values 672 254 135 69 55 44 16 15 16 10 7 3 2 1 1 1 1 1 0 200 400 600 800 Frequency 0 20 40 60 80 100 FP7 network degree Table 42: Descriptive statistics of degree in ICT R&d network indicator Number of observations Mean value Standard deviation Variance 1303 8. 42 11.63 135.40 66 5. 1. 16 Closeness Centrality in ICT R&d Network Table 43: Top ranking regions according to Closeness centrality in ICT R&d network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 FR101 Paris 98 3 3 ES300 Madrid 93 28 4 UKI12 Inner London-East 92 2 4 GR300 Attiki 92 49 6 ITC45 Milano 92 14 7 ITE43 Roma 92 40 8 ES511 Barcelona 90 42 9 FI181 Uusimaa 89 9 10 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 87 25 11 AT130 Wien 86 27 12 DE122 Karlsruhe, Stadtkreis 85 4 13 BE242 Arr. Leuven 84 11 14 DE300 Berlin 84 15 15 SE110 Stockholms lan 83 6 16 ITC11 Torino 83 56 17 NL414 Zuidoost-Noord-Brabant 81 8 18 NL333 Delft en Westland 81 17 19 HU101 Budapest 80 73 20 FR105 Hauts-de-Seine 80 13 21 NL326 Groot-Amsterdam 79 10 22 FR103 Yvelines 78 33 23 PT171 Grande Lisboa 78 93 24 DE111 Stuttgart, Stadtkreis 77 21 25 UKG13 Warwickshire 77 85 26 AT221 Graz 77 52 26 GR122 Thessaloniki 77 171 28 IE021 Dublin 77 16 28 ES523 Valencia/Valencia 77 213 30 ITD20 Trento 76 198 Indicator description Indicator ID Netrd 2 Name of indicator Closeness centrality in ICT R&d network What does it measure? It measures the average distance that each node is from all other nodes in the network Unit of measurement Rank between 0 and 1 Definition of ICT dimension ICT areas of the ICT FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 67 Figure 37: Frequency of the Closeness centrality in ICT R&d network indicator values 549 1 4 3 8 12 35 60 79 122 160 117 62 36 17 18 8 4 6 2 0 200 400 600 Frequency 0 20 40 60 80 100 FP7 network closeness Table 44: Descriptive statistics of Closeness centrality in ICT R&d network indicator Number of observations Mean value Standard deviation Variance 1303 29.64 27.11 735.27 68 5. 1. 17 Betweenness Centrality in ICT R&d Network Table 45: Top ranking regions according to Betweenness centrality in ICT R&d network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 FR101 Paris 82 3 3 ITE43 Roma 59 40 4 ES300 Madrid 55 28 5 ITC45 Milano 54 14 6 GR300 Attiki 53 49 7 UKI12 Inner London-East 52 2 8 ES511 Barcelona 45 42 9 FI181 Uusimaa 42 9 10 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 37 25 11 AT130 Wien 34 27 12 SE110 Stockholms lan 28 6 13 BE242 Arr. Leuven 26 11 14 DE300 Berlin 25 15 15 DE122 Karlsruhe Stadtkreis 24 4 16 ITC11 Torino 22 56 17 AT221 Graz 17 52 18 NL333 Delft en Westland 17 17 19 HU101 Budapest 15 73 20 DE111 Stuttgart, Stadtkreis 15 21 21 NL326 Groot-Amsterdam 15 10 22 NL414 Zuidoost -Noord-Brabant 15 8 23 RO321 Bucuresti 14 215 24 PT171 Grande Lisboa 13 93 25 FR105 Hauts-de-Seine 12 13 26 IE021 Dublin 12 16 27 ES213 Vizcaya 12 240 28 SI021 Osrednjeslovenska 12 185 29 UKG13 Warwickshire 11 85 30 ES523 Valencia/Valencia 11 213 Indicator description Indicator ID Netrd 3 Name of indicator Betweenness centrality in ICT R&d network What does it measure? It measures the number of shortest paths in a network that traverse through that node Unit of measurement Rank between 0 and 1 Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 69 Figure 38: Frequency of the Betweenness centrality in ICT R&d network indicator values 1197 51 20 11 11 3 3 1 1 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 FP7 network betweenness centrality Table 46: Descriptive statistics of Betweenness centrality in ICT R&d network indicator Number of observations Mean value Standard deviation Variance 1303 1. 53 5. 97 35.65 70 5 . 1. 18 Eigenvector Centrality in ICT R&d Network Table 47: Top ranking regions according to Eigenvector centrality in ICT R&d network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 FR101 Paris 99 3 3 ES300 Madrid 78 28 4 GR300 Attiki 75 49 5 UKI12 Inner London-East 72 2 6 ITC45 Milano 68 14 7 ITE43 Roma 64 40 8 ES511 Barcelona 52 42 9 FI181 Uusimaa 45 9 10 AT130 Wien 41 27 11 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 40 25 12 DE300 Berlin 40 15 13 BE242 Arr. Leuven 37 11 14 SE110 Stockholms lan 36 6 15 DE122 Karlsruhe Stadtkreis 36 4 16 FR105 Hauts-de-Seine 34 13 17 NL414 Zuidoost-Noord-Brabant 32 8 18 ITC11 Torino 32 56 19 FR103 Yvelines 32 33 20 HU101 Budapest 27 73 21 NL333 Delft en Westland 26 17 22 GR122 Thessaloniki 25 171 23 DE111 Stuttgart, Stadtkreis 24 21 24 PT171 Grande Lisboa 24 93 25 NL326 Groot-Amsterdam 22 10 26 ITD20 Trento 22 198 27 IE021 Dublin 22 16 28 UKG13 Warwickshire 20 85 29 PL127 Miasto Warszawa 19 50 30 ES523 Valencia/Valencia 19 213 Indicator description Indicator ID Netrd 4 Name of indicator Eigenvector centrality in ICT R&d network What does it measure? It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1 Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year (s) considered 2007-2011 71 Figure 39: Frequency of the Eigenvector centrality in ICT R&d network indicator values 1193 49 22 11 6 3 4 3 3 1 1 1 1 1 2 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 FP7 network eigenvector centrality Table 48: Descriptive statistics of Eigenvector centrality in ICT R&d network indicator Number of observations Mean value Standard deviation Variance 1303 1. 81 7. 52 56.59 72 5 . 2 ICT Innovation 5. 2. 1 Investment in Intangibles by ICT Firms Table 49: Top ranking regions according to Investment in intangibles by ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEA22 Bonn , Kreisfreie Stadt 100 12 2 UKI21 Outer London-East and North East 30 151 3 FR101 Paris 23 3 4 DE122 Karlsruhe, Stadtkreis 10 4 5 ITC45 Milano 7 14 6 NL332 Agglomeratie's-Gravenhage 7 80 7 SE110 Stockholms lan 4 6 7 LU000 Luxembourg (Grand-Duche) 4 71 9 DK011 Byen Kobenhavn 4 24 10 ES300 Madrid 3 28 11 FI181 Uusimaa 2 9 12 BE212 Arr. Mechelen 2 150 13 DE711 Darmstadt Kreisfreie Stadt 2 7 14 DEF0B Rendsburg-Eckernforde 2 138 15 UKI12 Inner London-East 2 2 16 UKC22 Tyneside 1 175 17 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 1 25 18 UKJ11 Berkshire 1 26 19 DEB1B Westerwaldkreis 1 264 20 DEB11 Koblenz, Kreisfreie Stadt 1 83 21 AT130 Wien 1 27 22 FR105 Hauts-de -Seine 1 13 23 PT171 Grande Lisboa 1 93 24 FR108 Val-d'Oise 0 249 25 UKH12 Cambridgeshire CC 0 5 26 NL326 Groot-Amsterdam 0 10 27 DK032 Sydjylland 0 300 28 DEA47 Paderborn 0 74 29 FR103 Yvelines 0 33 30 PL325 Rzeszowski 0 376 Indicator description Indicator ID Agin 1 Name of indicator Investment in intangibles by ICT firms What does it measure? Measures the average annual amount spent on intangibles in the ICT sector Unit of measurement Region's share in the total investments in intangibles by ICT firms in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 73 Figure 40: Frequency of the Investment in intangibles by ICT firms indicator values 1297 2 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Investment in intangibles by ICT Table 50: Descriptive statistics of Investment in intangibles by ICT firms indicator Number of observations Mean value Standard deviation Variance 1303 0. 16 2. 99 8. 97 74 5 . 2. 2 Venture capital Financing of ICT Firms Table 51: Top ranking regions according to Venture capital financing of ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKI12 Inner London-East 100 2 2 FR101 Paris 80 3 3 UKH12 Cambridgeshire CC 79 5 4 DK011 Byen Kobenhavn 70 24 5 IE021 Dublin 67 16 6 SE110 Stockholms lan 65 6 7 UKJ11 Berkshire 54 26 8 FI181 Uusimaa 52 9 9 DE232 Regensburg, Kreisfreie Stadt 50 64 10 UKN01 Belfast 49 338 11 FR105 Hauts-de-Seine 49 13 12 UKM25 Edinburgh, City of 48 20 13 FI200 Aland 40 687 14 DE212 Munchen Kreisfreie Stadt 39 1 15 UKK11 Bristol, City of 38 48 16 BE253 Arr. Ieper 35 194 17 BE242 Arr. Leuven 30 11 18 DE926 Holzminden 29 558 19 FI1A2 Pohjois-Pohjanmaa 29 58 20 SE123 Ostergotlands lan 28 66 20 DED16 Freiberg 28 103 22 UKG33 Coventry 27 169 23 UKM28 West Lothian 27 219 24 DE711 Darmstadt, Kreisfreie Stadt 26 7 25 DE122 Karlsruhe, Stadtkreis 25 4 26 UKM34 Glasgow City 25 78 27 DE252 Erlangen, Kreisfreie Stadt 24 32 28 BE212 Arr. Mechelen 23 150 29 DE423 Potsdam, Kreisfreie Stadt 22 82 30 DE111 Stuttgart Stadtkreis 22 21 Indicator description Indicator ID Agin 2 Name of indicator Venture capital financing to ICT firms What does it measure? Measures the amount of venture capital invested in the ICT sector Unit of measurement Region's share in the total VC funding in to ICT firms in the EU to a region's share in the EU population Definition of ICT dimension Based on the Dow jones classification of industry segments Unit of observation NUTS 3 Source Venture capital: Venturesource by Dow jones (Section 8. 8) Reference year (s) considered 2000-2012 75 Figure 41: Frequency of the Venture capital financing of ICT firms indicator values 1130 73 47 20 7 9 1 3 1 3 3 2 1 1 1 1 0 500 1000 Frequency 0 20 40 60 80 100 Venture capital financing of ICT firms Table 52: Descriptive statistics of Venture capital financing of ICT firms indicator Number of observations Mean value Standard deviation Variance 1303 2. 43 7. 73 59.71 76 5. 2 . 3 ICT Patents Table 53: Top ranking regions according to ICT patents indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL414 Zuidoost-Noord-Brabant 100 8 2 DE252 Erlangen, Kreisfreie Stadt 51 32 3 DEA21 Aachen, Kreisfreie Stadt 38 18 4 DE21H Munchen, Landkreis 34 22 5 DE257 Erlangen-Hochstadt 26 170 6 DE136 Schwarzwald-Baar-Kreis 23 96 7 DE21L Starnberg 23 97 8 DE218 Ebersberg 22 149 9 DE212 Munchen, Kreisfreie Stadt 21 1 10 DE232 Regensburg, Kreisfreie Stadt 21 64 11 FI197 Pirkanmaa 21 117 12 DE238 Regensburg, Landkreis 18 268 13 DE111 Stuttgart, Stadtkreis 18 21 14 UKH12 Cambridgeshire CC 17 5 15 DE115 Ludwigsburg 17 45 16 DE144 Ulm Stadtkreis 16 369 17 DE711 Darmstadt, Kreisfreie Stadt 16 7 18 FR714 Isere 15 35 19 FI181 Uusimaa 14 9 20 DE112 Boblingen 14 67 21 DE21C Furstenfeldbruck 14 137 22 FI1A2 Pohjois-Pohjanmaa 14 58 23 DE125 Heidelberg, Stadtkreis 13 23 24 DE21F Miesbach 13 342 25 DE248 Forchheim 13 387 26 DE925 Hildesheim 13 195 27 DEG03 Jena , Kreisfreie Stadt 13 39 28 DE133 Emmendingen 12 148 29 DE122 Karlsruhe, Stadtkreis 11 4 30 DE258 Furth, Landkreis 11 403 Indicator description Indicator ID Agin 3 Name of indicator ICT patents What does it measure? It measures the amount of ICT patent applications with inventors residing in the region Unit of measurement Region's share in the total number of ICT patents in the EU to a region's share in the EU population Definition of ICT dimension Based on the OECD definition of ICT patents following IPC taxonomy (OECD 2008b) Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 77 Figure 42: Frequency of the ICT patents indicator values 1200 60 25 7 6 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 ICT patents Table 54: Descriptive statistics of ICT patents indicator Number of observations Mean value Standard deviation Variance 1303 1. 43 4. 49 19.80 78 5. 2. 4 International Co-inventions Table 55: Top ranking regions according to International co-inventions indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE139 Lorrach 100 94 2 DEB3C Bad Durkheim 42 154 3 DE13A Waldshut 40 321 4 DEA21 Aachen, Kreisfreie Stadt 38 18 5 DEB36 Neustadt an der Weinstrasse, Kreisfreie Stadt 37 389 6 DE125 Heidelberg, Stadtkreis 36 23 7 DEB3I Rhein-Pfalz-Kreis 35 109 8 DE71A Main-Taunus-Kreis 32 218 9 AT342 Rheintal-Bodenseegebiet 31 125 10 UKH12 Cambridgeshire CC 30 5 11 DEB34 Ludwigshafen am Rhein, Kreisfreie Stadt 30 287 12 DE711 Darmstadt, Kreisfreie Stadt 28 7 13 BE336 Bezirk Verviers -Deutschsprachige Gemeinschaft 27 676 14 DEB38 Speyer Kreisfreie Stadt 27 304 15 FR422 Haut-Rhin 27 188 16 DE21H Munchen, Landkreis 27 22 17 DE252 Erlangen, Kreisfreie Stadt 27 32 18 DE138 Konstanz 26 53 19 DE131 Freiburg im Breisgau, Stadtkreis 25 238 20 DEA24 Leverkusen, Kreisfreie Stadt 25 145 21 DE146 Biberach 25 390 22 BE341 Arr. Arlon 25 867 23 DE21L Starnberg 23 97 24 AT331 Ausserfern 22 887 25 DEB31 Frankenthal (Pfalz), Kreisfreie Stadt 21 337 26 BE345 Arr. Virton 21 1045 27 DE11C Heidenheim 20 455 28 DE21C Furstenfeldbruck 20 137 28 DE126 Mannheim, Stadtkreis 20 277 30 BE242 Arr. Leuven 20 11 Indicator description Indicator ID Intin 1 Name of indicator International co-inventions What does it measure? It measures the number of international ICT patents, i e. patents with at least two inventors residing in different countries, and attributes to the observed region the (fractional) count) of those patents for which at least one inventor is residing in the region. Unit of measurement Region's share in the total number of international ICT patents in the EU to a region's share in the EU population Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b. Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 79 Figure 43: Frequency of the International co-inventions indicator values 1056141 50 19 15 11 4 4 2 1 0 200 400 600 800 1000 Frequency 0 20 40 60 80 100 International innovation collaborations Table 56: Descriptive statistics of International co-inventions indicator Number of observations Mean value Standard deviation Variance 1303 2. 94 5. 94 35.26 80 5. 2. 5 Degree in ICT Innovation Network Table 57: Top ranking regions according to Degree in ICT innovation network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen , Kreisfreie Stadt 100 1 2 DE300 Berlin 88 15 3 DE21H Munchen, Landkreis 80 22 4 UKH12 Cambridgeshire CC 70 5 5 FR101 Paris 69 3 6 UKJ33 Hampshire CC 63 31 7 SE110 Stockholms lan 60 6 8 DE128 Rhein-Neckar-Kreis 59 99 9 FR105 Hauts-de-Seine 56 13 10 DE111 Stuttgart, Stadtkreis 55 21 11 ITC45 Milano 54 14 12 DE115 Ludwigsburg 53 45 13 UKJ14 Oxfordshire 52 19 14 DED21 Dresden Kreisfreie Stadt 52 34 15 DE112 Boblingen 51 67 16 FR103 Yvelines 50 33 17 UKI11 Inner London-West 50 65 18 DE716 Darmstadt-Dieburg 49 115 19 DE712 Frankfurt am Main, Kreisfreie Stadt 49 30 20 UKJ23 Surrey 49 29 21 DE929 Region Hannover 49 60 22 FR714 Isere 48 35 23 DE600 Hamburg 48 87 24 DE252 Erlangen , Kreisfreie Stadt 48 32 25 DEA23 Koln, Kreisfreie Stadt 47 43 26 UKI23 Outer London-West and North West 46 68 27 DE125 Heidelberg, Stadtkreis 45 23 28 DE257 Erlangen-Hochstadt 44 170 29 UKJ11 Berkshire 44 26 30 AT130 Wien 44 27 Indicator description Indicator ID Netin 1 Name of indicator Degree in ICT innovation network What does it measure? It measures the total number of connections a region maintains with other regions through joint inventions Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b. Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 81 Figure 44: Frequency of the Degree in ICT innovation network indicator values 672 254 135 69 55 44 16 15 16 10 7 3 2 1 1 1 1 1 0 200 400 600 800 Frequency 0 20 40 60 80 100 Innovation network degree Table 58: Descriptive statistics of Degree in ICT innovation network indicator Number of observations Mean value Standard deviation Variance 1303 8. 42 11.63 135.40 82 5. 2. 6 Closeness Centrality in ICT Innovation Network Table 59: Top ranking regions according to Closeness centrality in ICT innovation network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 92 1 2 DE300 Berlin 91 15 3 DE21H Munchen, Landkreis 90 22 4 UKH12 Cambridgeshire CC 90 5 5 FR101 Paris 90 3 6 UKJ33 Hampshire CC 89 31 7 SE110 Stockholms lan 89 6 8 FR105 Hauts-de-Seine 88 13 9 UKI11 Inner London-West 87 65 10 UKJ14 Oxfordshire 87 19 11 UKJ23 Surrey 87 29 12 ITC45 Milano 87 14 13 FR103 Yvelines 87 33 14 FR714 Isere 87 35 15 UKJ11 Berkshire 87 26 16 UKI23 Outer London-West and North West 86 68 17 UKH23 Hertfordshire 86 90 18 DE128 Rhein-Neckar-Kreis 86 99 19 DE111 Stuttgart, Stadtkreis 86 21 20 FI181 Uusimaa 86 9 21 DE112 Boblingen 86 67 22 DEA23 Koln, Kreisfreie Stadt 86 43 23 DE115 Ludwigsburg 85 45 24 DED21 Dresden, Kreisfreie Stadt 85 34 25 FR104 Essonne 85 62 26 FR823 Alpes-Maritimes 85 77 27 DE125 Heidelberg, Stadtkreis 85 23 28 NL414 Zuidoost-Noord-Brabant 85 8 29 UKH33 Essex CC 85 111 30 UKI12 Inner London-East 85 2 Indicator description Indicator ID Netin 2 Name of indicator Closeness centrality in ICT innovation network What does it measure? It measures the average distance that each node is from all other nodes in the network Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b. Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 83 Figure 45: Frequency of the Closeness centrality in ICT innovation network indicator values 115 1 2 3 5 7 28 21 41 70 120 198 209 197 160 90 31 5 0 50 100 150 200 Frequency 0 20 40 60 80 100 Innovation network closeness centrality Table 60: Descriptive statistics of Closeness centrality in ICT innovation network indicator Number of observations Mean value Standard deviation Variance 1303 59.94 21.87 478.53 84 5. 2. 7 Betweenness Centrality in ICT Innovation Network Table 61: Top ranking regions according to Betweenness centrality in ICT innovation network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 DE300 Berlin 84 15 3 SE110 Stockholms lan 60 6 4 ITC45 Milano 55 14 5 DE21H Munchen, Landkreis 52 22 6 FR101 Paris 46 3 7 UKH12 Cambridgeshire CC 43 5 8 UKJ33 Hampshire CC 32 31 9 FR105 Hauts-de-Seine 31 13 10 FR714 Isere 31 35 11 DE128 Rhein-Neckar -Kreis 27 99 12 BE242 Arr. Leuven 26 11 13 FI181 Uusimaa 25 9 14 DED21 Dresden Kreisfreie Stadt 24 34 15 FR103 Yvelines 24 33 16 UKJ23 Surrey 23 29 17 BG411 Sofia (stolitsa) 23 263 18 AT130 Wien 23 27 19 ES300 Madrid 23 28 19 DE600 Hamburg 23 87 21 RO321 Bucuresti 22 215 22 UKJ14 Oxfordshire 22 19 23 UKI11 Inner London-West 21 65 24 DE111 Stuttgart, Stadtkreis 20 21 25 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 17 25 26 ITE43 Roma 17 40 26 DE929 Region Hannover 17 60 28 UKH14 Suffolk 16 210 29 FR104 Essonne 16 62 30 DE115 Ludwigsburg 16 45 Indicator description Indicator ID Netin 3 Name of indicator Betweenness centrality in ICT innovation network What does it measure? It measures the number of shortest paths in a network that traverse through that node Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b. Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 85 Figure 46: Frequency of the Betweenness centrality in ICT innovation network indicator values 1197 51 20 11 11 3 3 1 1 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Innovation network betweenness centrality Table 62: Descriptive statistics of Betweenness centrality in ICT innovation network indicator Number of observations Mean value Standard deviation Variance 1303 1. 53 5. 97 35.64 86 5 . 2. 8 Eigenvector Centrality in ICT Innovation Network Table 63: Top ranking regions according to Eigenvector centrality in ICT innovation network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12 Cambridgeshire CC 100 5 2 UKJ33 Hampshire CC 98 31 3 SE110 Stockholms lan 94 6 4 UKJ14 Oxfordshire 85 19 5 UKJ23 Surrey 83 29 6 DED21 Dresden, Kreisfreie Stadt 82 34 7 UKI11 Inner London-West 81 65 8 NL414 Zuidoost-Noord-Brabant 79 8 9 UKI23 Outer London-West and North West 72 68 10 UKJ11 Berkshire 71 26 11 UKI12 Inner London-East 66 2 12 UKM25 Edinburgh, City of 65 20 13 DEG03 Jena Kreisfreie Stadt 63 39 14 ITC45 Milano 58 14 15 DEB3I Rhein-Pfalz-Kreis 58 109 16 DEB35 Mainz, Kreisfreie Stadt 58 44 17 UKH23 Hertfordshire 57 90 18 FR714 Isere 56 35 19 FR101 Paris 56 3 20 UKK12 Bath and North East Somerset, North Somerset and South Gloucestershire 55 69 21 DEB3J Mainz-Bingen 55 254 22 SE224 Skane lan 54 37 23 UKK15 Wiltshire CC 51 203 24 SE232 Vastra Gotalands lan 50 59 25 UKL22 Cardiff and Vale of Glamorgan 50 107 26 DEB3C Bad Durkheim 49 154 27 UKJ32 Southampton 49 130 28 DEA52 Dortmund Kreisfreie Stadt 48 89 29 DEA23 Koln, Kreisfreie Stadt 48 43 29 UKJ13 Buckinghamshire CC 48 116 Indicator description Indicator ID Netin 4 Name of indicator Eigenvector centrality in ICT innovation network What does it measure? It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b). Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 87 Figure 47: Frequency of the Eigenvector centrality in ICT innovation network indicator values 637 239 133 81 65 40 36 19 15 13 4 8 1 2 2 1 3 1 1 2 0 200 400 600 Frequency 0 20 40 60 80 100 Innovation network eigenvector centrality Table 64: Descriptive statistics of Eigenvector centrality in ICT innovation network indicator Number of observations Mean value Standard deviation Variance 1303 9. 62 13.40 179.57 88 5. 3 ICT Business 5. 3. 1 Location of ICT Scoreboard Headquarters Table 65: Top ranking regions according to Location of ICT Scoreboard Headquarters indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE25C Weissenburg -Gunzenhausen 100 118 2 UKH12 Cambridgeshire CC 92 5 2 UKJ31 Portsmouth 92 91 4 BE253 Arr. Ieper 88 194 5 DEB11 Koblenz, Kreisfreie Stadt 87 83 6 FI181 Uusimaa 86 9 7 DE718 Hochtaunuskreis 82 51 8 BE242 Arr. Leuven 77 11 9 DK012 Kobenhavns omegn 73 41 10 DE235 Cham 71 284 11 DEG0B Schmalkalden-Meiningen 70 202 12 DE138 Konstanz 67 53 13 DE711 Darmstadt, Kreisfreie Stadt 65 7 14 DE122 Karlsruhe Stadtkreis 64 4 15 SE110 Stockholms lan 61 6 16 DE133 Emmendingen 58 148 17 DE21B Freising 57 57 18 DK011 Byen Kobenhavn 56 24 19 DE264 Aschaffenburg, Landkreis 54 127 20 UKI12 Inner London-East 53 2 21 DE735 Schwalm-Eder-Kreis 50 237 22 DE145 Alb-Donau-Kreis 49 309 23 FI1A2 Pohjois-Pohjanmaa 48 58 24 UKE21 York 47 63 24 DE12B Enzkreis 47 212 26 FR101 Paris 45 3 26 DEG01 Erfurt, Kreisfreie Stadt 45 143 28 DE136 Schwarzwald-Baar-Kreis 44 96 29 UKJ14 Oxfordshire 43 19 29 NL333 Delft en Westland 43 17 Indicator description Indicator ID Agbus 1 Name of indicator Location of ICT Scoreboard Headquarters What does it measure? It measures the number of ICT Scoreboard Headquarters located in the observed region Unit of measurement Region's share in the total number of ICT Scoreboard Headquarters located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 89 Figure 48: Frequency of the Location of ICT Scoreboard Headquarters indicator values 1206 12 11 14 13 4 6 2 8 6 3 3 2 2 3 1 1 3 2 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 ICT Scoreboard Headquarters Table 66: Descriptive statistics of Location of ICT Scoreboard Headquarters indicator Number of observations Mean value Standard deviation Variance 1303 2. 50 10.95 119.94 90 5. 3. 2 Ownership of ICT Scoreboard Affiliates Table 67: Top ranking regions according to Ownership of ICT Scoreboard affiliates indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE711 Darmstadt , Kreisfreie Stadt 100 7 2 DEB15 Birkenfeld 47 245 3 DE25C Weissenburg-Gunzenhausen 40 118 4 DE718 Hochtaunuskreis 29 51 5 DEB11 Koblenz, Kreisfreie Stadt 28 83 6 UKI12 Inner London-East 26 2 7 DE122 Karlsruhe, Stadtkreis 25 4 8 DEA22 Bonn, Kreisfreie Stadt 22 12 9 NL326 Groot-Amsterdam 20 10 10 DE137 Tuttlingen 18 157 11 DEG03 Jena, Kreisfreie Stadt 16 39 12 NL414 Zuidoost-Noord-Brabant 15 8 13 DEB35 Mainz Kreisfreie Stadt 15 44 14 DE80G Parchim 15 333 15 NL332 Agglomeratie's-Gravenhage 14 80 16 DE735 Schwalm-Eder-Kreis 14 237 17 UKJ31 Portsmouth 14 91 18 SE231 Hallands lan 14 166 19 DE111 Stuttgart, Stadtkreis 14 21 20 UKH12 Cambridgeshire CC 13 5 21 AT342 Rheintal-Bodenseegebiet 13 125 22 DE138 Konstanz 12 53 23 IE021 Dublin 11 16 24 DE212 Munchen, Kreisfreie Stadt 11 1 25 DEA11 Dusseldorf, Kreisfreie Stadt 10 38 26 UKH21 Luton 10 265 27 DE276 Augsburg, Landkreis 10 177 28 SE110 Stockholms lan 10 6 29 DEA47 Paderborn 10 74 30 FR101 Paris 9 3 Indicator description Indicator ID Agbus 2 Name of indicator Ownership of ICT Scoreboard affiliates What does it measure? It measures the number of ICT Scoreboard affiliates owned worldwide by ICT Scoreboard Headquarters located in the observed region Unit of measurement Region's share in the total number of ICT Scoreboard affiliates owned by EU ICT Scoreboard Headquarters to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 91 Figure 49: Frequency of the Ownership of ICT Scoreboard affiliates indicator values 1247 27 15 5 2 4 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Ownership of ICT Scoreboard affiliates Table 68: Descriptive statistics of Ownership of ICT Scoreboard affiliates indicator Number of observations Mean value Standard deviation Variance 1303 0. 77 4. 12 17.02 92 5. 3 . 3 Location of ICT Scoreboard Affiliates Table 69: Top ranking regions according to Location of ICT Scoreboard affiliates indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKI12 Inner London-East 100 2 2 DE711 Darmstadt, Kreisfreie Stadt 77 7 3 IE021 Dublin 52 16 4 DE122 Karlsruhe, Stadtkreis 49 4 5 UKJ31 Portsmouth 46 91 6 DEB11 Koblenz, Kreisfreie Stadt 45 83 7 DEG03 Jena, Kreisfreie Stadt 44 39 8 DE718 Hochtaunuskreis 42 51 9 NL326 Groot-Amsterdam 41 10 10 DEB3D Donnersbergkreis 38 120 11 DE212 Munchen , Kreisfreie Stadt 37 1 12 DE21B Freising 35 57 13 DEA22 Bonn, Kreisfreie Stadt 35 12 14 UKH12 Cambridgeshire CC 34 5 15 DE21H Munchen Landkreis 33 22 16 DEA11 Dusseldorf, Kreisfreie Stadt 33 38 17 DE712 Frankfurt am Main, Kreisfreie Stadt 30 30 18 DE111 Stuttgart, Stadtkreis 27 21 19 DE25C Weissenburg-Gunzenhausen 27 118 20 DE137 Tuttlingen 27 157 21 DEB35 Mainz, Kreisfreie Stadt 27 44 22 UKH21 Luton 26 265 23 DK011 Byen Kobenhavn 26 24 24 DE23A Tirschenreuth 25 478 25 DE279 Neu-Ulm 25 92 26 FR105 Hauts-de-Seine 24 13 27 DE261 Aschaffenburg, Kreisfreie Stadt 24 144 28 SE110 Stockholms lan 24 6 29 DE264 Aschaffenburg, Landkreis 23 127 30 NL327 Het Gooi en Vechtstreek 20 133 Indicator description Indicator ID Agbus 3 Name of indicator Location of ICT Scoreboard affiliates What does it measure? It measures the total number of ICT Scoreboard affiliates located in the observed region Unit of measurement Region's share in the total number of ICT Scoreboard affiliates located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 93 Figure 50: Frequency of the Location of ICT Scoreboard affiliates indicator values 1111 98 38 26 5 8 4 4 3 3 1 1 1 0 500 1000 Frequency 0 20 40 60 80 100 Location of ICT Scoreboard affiliates Table 70: Descriptive statistics of Location of ICT Scoreboard affiliates indicator Number of observations Mean value Standard deviation Variance 1303 2. 46 6. 71 45.08 94 5. 3 . 4 Location of ICT Firms Table 71: Top ranking regions according to Location of ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKI12 Inner London -East 100 2 2 BE253 Arr. Ieper 49 194 3 SE110 Stockholms lan 46 6 4 UKH12 Cambridgeshire CC 45 5 5 UKJ12 Milton Keynes 39 98 6 DE711 Darmstadt, Kreisfreie Stadt 36 7 7 DE212 Munchen, Kreisfreie Stadt 35 1 8 FR101 Paris 32 3 9 FR105 Hauts-de-Seine 31 13 10 DE266 Rhon-Grabfeld 31 563 11 UKE11 Kingston upon Hull, City of 30 351 12 UKE21 York 26 63 13 NL326 Groot-Amsterdam 26 10 14 DE222 Passau Kreisfreie Stadt 25 123 15 UKG33 Coventry 25 169 16 UKJ23 Surrey 24 29 17 UKJ14 Oxfordshire 24 19 17 DEB11 Koblenz, Kreisfreie Stadt 24 83 19 FI181 Uusimaa 24 9 20 DE121 Baden-Baden, Stadtkreis 24 570 21 IE021 Dublin 23 16 22 DE94H Wittmund 22 1000 23 DE122 Karlsruhe, Stadtkreis 22 4 24 UKF14 Nottingham 21 139 25 NL327 Het Gooi en Vechtstreek 21 133 26 DE271 Augsburg, Kreisfreie Stadt 19 181 27 UKG13 Warwickshire 19 85 28 UKH23 Hertfordshire 19 90 29 UKL11 Isle of Anglesey 19 721 30 UKJ11 Berkshire 18 26 Indicator description Indicator ID Agbus 4 Name of indicator Location of ICT firms What does it measure? It measures the number of ICT firms located in the observed region Unit of measurement Region's share in the total number of ICT firms located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 95 Figure 51: Frequency of the Location of ICT firms indicator values 1135 93 30 20 10 4 4 3 3 1 0 500 1000 Frequency 0 20 40 60 80 100 Number of ICT firms located in the region Table 72: Descriptive statistics of Location of ICT firms indicator Number of observations Mean value Standard deviation Variance 1303 1. 84 5. 75 33.06 96 5. 3. 5 ICT Employment Table 73: Top ranking regions according to ICT employment indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEA22 Bonn, Kreisfreie Stadt 100 12 2 FR101 Paris 33 3 3 DE122 Karlsruhe, Stadtkreis 21 4 4 FI181 Uusimaa 12 9 5 UKI12 Inner London-East 9 2 6 SE110 Stockholms lan 8 6 7 UKI21 Outer London-East and North East 6 151 8 FR108 Val-d'Oise 5 249 9 ES300 Madrid 5 28 10 UKJ11 Berkshire 5 26 11 NL332 Agglomeratie 's-Gravenhage 5 80 12 AT223 Ostliche Obersteiermark 4 522 13 DE212 Munchen Kreisfreie Stadt 4 1 14 FR105 Hauts-de-Seine 4 13 15 DK032 Sydjylland 4 300 16 DE279 Neu-Ulm 4 92 17 NL230 Flevoland 4 280 18 DE711 Darmstadt, Kreisfreie Stadt 3 7 19 DK011 Byen Kobenhavn 3 24 20 DEB1B Westerwaldkreis 3 264 21 ITC45 Milano 2 14 22 FI1A2 Pohjois-Pohjanmaa 2 58 22 DEB11 Koblenz, Kreisfreie Stadt 2 83 24 LU000 Luxembourg (Grand-Duche) 2 71 25 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 2 25 26 FR103 Yvelines 2 33 27 PL127 Miasto Warszawa 2 50 28 DEA46 Minden-Lubbecke 2 617 29 DEF0B Rendsburg-Eckernforde 2 138 30 DE21B Freising 2 57 Indicator description Indicator ID Agbus 5 Name of indicator ICT employment What does it measure It measures the total employment in ICT firms in the observed region Unit of measurement Region's share in the total employment by ICT firms located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 97 Figure 52: Frequency of the ICT employment indicator values 1292 7 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 ICT employment Table 74: Descriptive statistics of ICT employment indicator Number of observations Mean value Standard deviation Variance 1303 0. 21 3. 05 9. 28 98 5. 3. 6 Growth in ICT Employment Table 75: Top ranking regions according to Growth in ICT employment indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 PT171 Grande Lisboa 100 93 2 PL325 Rzeszowski 76 376 3 NL113 Overig Groningen 69 246 3 UKH31 Southend-on-sea 69 257 3 DEA1B Kleve 69 317 6 NL327 Het Gooi en Vechtstreek 61 133 6 SE213 Kalmar lan 61 491 8 FI1A2 Pohjois -Pohjanmaa 53 58 8 UKK12 Bath and North East Somerset, North Somerset and South Gloucestershire 53 69 8 DEB11 Koblenz, Kreisfreie Stadt 53 83 8 DK013 Nordsjaelland 53 102 8 DE222 Passau Kreisfreie Stadt 53 123 8 AT312 Linz-Wels 53 142 8 FR718 Haute-Savoie 53 153 8 SK010 Bratislavsky kraj 53 260 8 ITC47 Brescia 53 383 8 UKL23 Flintshire and Wrexham 53 464 18 UKM25 Edinburgh, City of 46 20 18 DE125 Heidelberg, Stadtkreis 46 23 18 FR714 Isere 46 35 18 DEA23 Koln, Kreisfreie Stadt 46 43 18 SE121 Uppsala lan 46 47 18 DE929 Region Hannover 46 60 18 DE501 Bremen, Kreisfreie Stadt 46 112 18 DEA12 Duisburg, Kreisfreie Stadt 46 184 18 BE332 Arr. Liege 46 251 18 UKE42 Leeds 46 284 18 UKL18 Swansea 46 297 18 UKC23 Sunderland 46 363 18 DEE05 Anhalt-Bitterfeld 46 425 Indicator description Indicator ID Agbus 6 Name of indicator Growth in ICT employment What does it measure? It measures employment growth in ICT firms in the observed region Unit of measurement Growth rate in%Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 99 Figure 53: Frequency of the Growth in ICT employment indicator values 2 7 7 23 1183 51 13 10 2 3 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Growth in ICT employment Table 76: Descriptive statistics of Growth in ICT employment indicator Number of observations Mean value Standard deviation Variance 1303 30.50 5. 05 25.54 100 5. 3. 7 Turnover by ICT Firms Table 77: Top ranking regions according to Turnover by ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEA22 Bonn, Kreisfreie Stadt 100 12 2 FR101 Paris 25 3 3 DE122 Karlsruhe, Stadtkreis 19 4 4 FI181 Uusimaa 17 9 5 UKI12 Inner London-East 15 2 6 UKI21 Outer London-East and North East 15 151 7 SE110 Stockholms lan 9 6 8 NL332 Agglomeratie's-Gravenhage 8 80 9 LU000 Luxembourg (Grand-Duche) 5 71 10 DEF0B Rendsburg-Eckernforde 5 138 11 DE279 Neu-Ulm 4 92 12 ES300 Madrid 4 28 13 ITC45 Milano 4 14 14 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 4 25 15 DK011 Byen Kobenhavn 4 24 16 DEB1B Westerwaldkreis 4 264 17 DEA47 Paderborn 3 74 18 NL414 Zuidoost-Noord-Brabant 2 8 19 DE212 Munchen, Kreisfreie Stadt 2 1 20 DE711 Darmstadt, Kreisfreie Stadt 2 7 21 DEE05 Anhalt-Bitterfeld 2 425 22 DK032 Sydjylland 2 300 23 FR108 Val-d 'Oise 2 249 24 UKJ11 Berkshire 2 26 25 DE24A Kronach 2 582 26 UKE31 Barnsley, Doncaster and Rotherham 2 392 27 FR105 Hauts-de-Seine 1 13 28 BE212 Arr. Mechelen 1 150 29 AT130 Wien 1 27 30 PT171 Grande Lisboa 1 93 Indicator description Indicator ID Agbus 7 Name of indicator Turnover by ICT firms What does it measure? It measures the average annual turnover by ICT firms in the observed region Unit of measurement Region's share in the total turnover by ICT firms located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 101 Figure 54: Frequency of the Growth in ICT employment indicator values 1293 4 4 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Turnover by ICT firms Table 78: Descriptive statistics of Growth in ICT employment indicator Number of observations Mean value Standard deviation Variance 1303 0. 21 3. 04 9. 23 102 5. 3 . 8 Growth in Turnover by ICT Firms Table 79: Top ranking regions according to Growth in turnover by ICT firms indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DEA1B Kleve 100 317 1 UKL23 Flintshire and Wrexham 100 464 3 FR718 Haute-Savoie 90 153 3 SK010 Bratislavsky kraj 90 260 3 PL325 Rzeszowski 90 376 6 DE21H Munchen, Landkreis 81 22 6 DEA25 Aachen, Kreis 81 110 6 DE501 Bremen , Kreisfreie Stadt 81 112 6 UKH31 Southend-on-sea 81 257 10 DE300 Berlin 72 15 10 DE711 Darmstadt, Kreisfreie Stadt 72 7 10 UKJ33 Hampshire CC 72 31 10 DE712 Frankfurt am Main Kreisfreie Stadt 72 30 10 DEG03 Jena, Kreisfreie Stadt 72 39 10 DEA23 Koln, Kreisfreie Stadt 72 43 10 ITC11 Torino 72 56 10 DE929 Region Hannover 72 60 10 NL331 Agglomeratie Leiden en Bollenstreek 72 70 10 UKM34 Glasgow City 72 78 10 DEB11 Koblenz, Kreisfreie Stadt 72 83 10 UKJ12 Milton Keynes 72 98 10 DK013 Nordsjaelland 72 102 10 DEF02 Kiel, Kreisfreie Stadt 72 106 10 DEA41 Bielefeld, Kreisfreie Stadt 72 172 10 DEA12 Duisburg, Kreisfreie Stadt 72 184 10 DEB1B Westerwaldkreis 72 264 10 UKL18 Swansea 72 297 10 ITD42 Udine 72 358 10 UKC23 Sunderland 72 363 10 ITC47 Brescia 72 383 Indicator description Indicator ID Agbus 8 Name of indicator Growth in turnover by ICT firms What does it measure? It measures turnover growth in ICT firms in the observed region Unit of measurement Growth rate in%Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 103 Figure 55: Frequency of the Growth in turnover by ICT firms indicator values 1 4 8 9 18 1174 58 22 4 3 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 Growth in turnover by ICT firms Table 80: Descriptive statistics of Growth in turnover by ICT firms indicator Number of observations Mean value Standard deviation Variance 1303 54.37 5. 61 31.47 104 5. 3. 9 Number of New Investments in the ICT Sector Table 81: Top ranking regions according to Number of new investments in the ICT sector indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE711 Darmstadt, Kreisfreie Stadt 100 7 2 UKI12 Inner London-East 44 2 3 FR101 Paris 29 3 4 UKN01 Belfast 24 338 5 UKE21 York 22 63 6 DK011 Byen Kobenhavn 21 24 7 UKJ11 Berkshire 20 26 8 NL326 Groot-Amsterdam 13 10 9 FR823 Alpes-Maritimes 12 77 10 DE212 Munchen, Kreisfreie Stadt 12 1 11 IE021 Dublin 12 16 12 UKH12 Cambridgeshire CC 10 5 13 UKK14 Swindon 10 293 14 FR716 Rhone 10 61 15 SE110 Stockholms lan 10 6 16 IE025 Southwest (IRL) 10 121 17 IE012 Midland 9 611 18 UKM28 West Lothian 9 219 19 IE013 West 9 122 20 UKK11 Bristol, City of 8 48 21 DEA11 Dusseldorf, Kreisfreie Stadt 8 38 22 DE111 Stuttgart, Stadtkreis 8 21 23 UKM34 Glasgow City 7 78 24 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel -Hoofdstad 7 25 24 UKG32 Solihull 7 663 26 HU101 Budapest 7 73 27 DEE01 Dessau-Rosslau, Kreisfreie Stadt 7 920 28 UKJ23 Surrey 7 29 29 UKJ12 Milton Keynes 6 98 30 IE023 Midwest 6 205 Indicator description Indicator ID Agbus 9 Name of indicator New business investments in the ICT sector What does it measure? It measures the number of new investments in the ICT sector in the observed region Unit of measurement Region's share in the total number of new investments in the ICT sector to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source European Investment Monitor by Ernst & young (Section 8. 5) Reference year (s ) considered 2000-2011 105 Figure 56: Frequency of the Number of new investments in the ICT sector indicator values 1258 29 9 4 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Number of new investments in the ICT sector Table 82: Descriptive statistics of Number of new investments in the ICT sector indicator Number of observations Mean value Standard deviation Variance 1303 0. 68 3. 63 13.16 106 5 . 3. 10 Outward ICT Business Internationalisation Table 83: Top ranking regions according to Outward ICT business internationalisation indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE25C Weissenburg -Gunzenhausen 100 118 2 NL326 Groot-Amsterdam 90 10 3 NL414 Zuidoost-Noord-Brabant 77 8 4 DE122 Karlsruhe, Stadtkreis 72 4 5 SE231 Hallands lan 69 166 6 AT342 Rheintal-Bodenseegebiet 56 125 7 DE718 Hochtaunuskreis 55 51 8 DEB11 Koblenz, Kreisfreie Stadt 50 83 9 DEB35 Mainz, Kreisfreie Stadt 48 44 10 NL324 Agglomeratie Haarlem 44 183 11 DK012 Kobenhavns omegn 39 41 12 DE735 Schwalm-Eder-Kreis 37 237 13 UKH12 Cambridgeshire CC 36 5 14 BE211 Arr. Antwerpen 34 54 14 LU000 Luxembourg (Grand-Duche) 34 71 16 NL333 Delft en Westland 33 17 17 DE711 Darmstadt Kreisfreie Stadt 33 7 18 DEA22 Bonn, Kreisfreie Stadt 33 12 19 SE110 Stockholms lan 32 6 20 FR101 Paris 31 3 21 FI181 Uusimaa 30 9 22 BE242 Arr. Leuven 30 11 23 BE254 Arr. Kortrijk 30 162 24 IE021 Dublin 29 16 25 FR105 Hauts-de-Seine 29 13 26 DEG03 Jena, Kreisfreie Stadt 29 39 27 UKI12 Inner London-East 28 2 28 DK013 Nordsjaelland 27 102 29 DE138 Konstanz 23 53 30 DE21L Starnberg 23 97 Indicator description Indicator ID Intbus 1 Name of indicator Outward ICT business internationalisation What does it measure? It measures the number of affiliates located abroad (outside the country) that are owned by ICT Scoreboard Headquarters located in a region Unit of measurement Region's share in the total number of affiliates located abroad that are owned by European ICT Scoreboard Headquarters to a region 's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 107 Figure 57: Frequency of the Outward ICT business internationalisation indicator values 1136 76 33 15 15 6 6 8 1 1 3 1 1 1 0 500 1000 Frequency 0 20 40 60 80 100 Outward ICT business internationalisation Table 84: Descriptive statistics of Outward ICT business internationalisation indicator Number of observations Mean value Standard deviation Variance 1303 2. 39 7. 49 56.14 108 5. 3 . 11 Inward ICT Business Internationalisation Table 85: Top ranking regions according to Inward ICT business internationalisation indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 IE021 Dublin 100 16 2 UKI12 Inner London-East 94 2 3 NL326 Groot-Amsterdam 72 10 4 DK011 Byen Kobenhavn 58 24 5 DE711 Darmstadt, Kreisfreie Stadt 58 7 6 UKJ31 Portsmouth 55 91 7 UKJ42 Kent CC 50 75 8 DE21B Freising 45 57 9 DE21H Munchen, Landkreis 39 22 10 AT130 Wien 39 27 10 DE137 Tuttlingen 39 157 12 LU000 Luxembourg (Grand-Duche) 38 71 13 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 37 25 14 UKJ12 Milton Keynes 36 98 15 DE122 Karlsruhe Stadtkreis 36 4 16 CZ010 Hlavni mesto Praha 35 101 17 DK012 Kobenhavns omegn 34 41 18 DE212 Munchen, Kreisfreie Stadt 33 1 19 FR105 Hauts-de-Seine 33 13 20 SE110 Stockholms lan 32 6 21 DEG06 Eichsfeld 31 242 22 DEA11 Dusseldorf, Kreisfreie Stadt 30 38 23 DE712 Frankfurt am Main, Kreisfreie Stadt 29 30 23 BE241 Arr. Halle-Vilvoorde 29 135 25 UKK11 Bristol, City of 28 48 26 BE212 Arr. Mechelen 26 150 27 FI181 Uusimaa 26 9 28 PL127 Miasto Warszawa 26 50 29 UKH12 Cambridgeshire CC 24 5 30 NL310 Utrecht 24 46 Indicator description Indicator ID Intbus 2 Name of indicator Inward ICT business internationalisation What does it measure? It measures the number of affiliates located in a region that are owned by ICT Scoreboard Headquarters located abroad Unit of measurement Region's share in the total number of affiliates owned by foreign ICT Scoreboard Headquarters in the EU to a region 's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 109 Figure 58: Frequency of the Inward ICT business internationalisation indicator values 1224 26 11 10 4 5 10 3 1 1 1 2 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Inward ICT business internationalisation Table 86: Descriptive statistics of Inward ICT business internationalisation indicator Number of observations Mean value Standard deviation Variance 1303 1. 50 7. 34 53.96 110 5. 3 . 12 In-degree in ICT Business Network Table 87: Top ranking regions according to In-degree in ICT business network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKI12 Inner London-East 100 2 2 ES300 Madrid 88 28 3 ITC45 Milano 80 14 4 DE212 Munchen, Kreisfreie Stadt 77 1 5 NL326 Groot-Amsterdam 71 10 6 AT130 Wien 69 27 7 IE021 Dublin 61 16 8 FR105 Hauts -de-Seine 60 13 9 DE600 Hamburg 55 87 10 SE110 Stockholms lan 55 6 11 PL127 Miasto Warszawa 52 50 12 DE300 Berlin 49 15 13 CZ010 Hlavni mesto Praha 46 101 14 FR101 Paris 44 3 15 DE712 Frankfurt am Main Kreisfreie Stadt 42 30 16 DEA11 Dusseldorf, Kreisfreie Stadt 42 38 17 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel -Hoofdstad 40 25 18 ES511 Barcelona 37 42 19 DK011 Byen Kobenhavn 36 24 20 FI181 Uusimaa 34 9 21 NL310 Utrecht 34 46 22 DE21H Munchen, Landkreis 28 22 23 DE111 Stuttgart, Stadtkreis 26 21 23 DEA1C Mettmann 26 86 25 DE122 Karlsruhe, Stadtkreis 26 4 26 DEA23 Koln, Kreisfreie Stadt 25 43 27 FR104 Essonne 24 62 28 DE711 Darmstadt, Kreisfreie Stadt 24 7 28 DK012 Kobenhavns omegn 24 41 30 FR103 Yvelines 22 33 Indicator description Indicator ID Netbus 1 Name of indicator In-degree in ICT business network What does it measure? It measures the total number of connections a region maintains with other regions whenever an ICT Scoreboard Headquarters located in that region owns an affiliate located in other regions Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 111 Figure 59: Frequency of the In-degree in ICT business network indicator values 1147 80 28 12 10 5 2 2 4 2 1 2 2 1 1 1 1 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Business network in-degree Table 88: Descriptive statistics of In-degree in ICT business network indicator Number of observations Mean value Standard deviation Variance 1303 2. 50 8. 08 65.25 112 5. 3 . 13 Out-degree in ICT Business Network Table 89: Top ranking regions according to Out-degree in ICT business network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL326 Groot-Amsterdam 100 10 2 DE212 Munchen, Kreisfreie Stadt 85 1 3 FR101 Paris 76 3 4 DE711 Darmstadt, Kreisfreie Stadt 75 7 5 UKI12 Inner London-East 67 2 6 SE110 Stockholms lan 66 6 7 DE300 Berlin 65 15 8 FR105 Hauts-de-Seine 64 13 9 NL414 Zuidoost-Noord-Brabant 62 8 10 DE122 Karlsruhe, Stadtkreis 59 4 11 DE111 Stuttgart, Stadtkreis 56 21 12 FI181 Uusimaa 52 9 13 DE718 Hochtaunuskreis 51 51 14 ES300 Madrid 43 28 15 DE712 Frankfurt am Main Kreisfreie Stadt 40 30 16 DEB15 Birkenfeld 37 245 17 DEA22 Bonn, Kreisfreie Stadt 36 12 18 IE021 Dublin 35 16 18 BE211 Arr. Antwerpen 35 54 20 UKH12 Cambridgeshire CC 35 5 21 FR103 Yvelines 34 33 22 NL310 Utrecht 33 46 23 DE25C Weissenburg-Gunzenhausen 31 118 24 ITC45 Milano 31 14 24 NL332 Agglomeratie's-Gravenhage 31 80 24 NL423 Zuid-Limburg 31 81 27 DK012 Kobenhavns omegn 29 41 28 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 29 25 28 DEA11 Dusseldorf, Kreisfreie Stadt 29 38 30 DE138 Konstanz 27 53 Indicator description Indicator ID Netbus 2 Name of indicator Out-degree in ICT business network What does it measure? It measures the total number of connections a region maintains with other regions by hosting affiliates owned by ICT Scoreboard Headquarters located in other regions Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 113 Figure 60: Frequency of the Out-degree in ICT business network indicator values 1183 44 20 17 5 8 6 5 2 2 2 2 3 2 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Business network out-degree Table 90: Descriptive statistics of Out-degree in ICT business network indicator Number of observations Mean value Standard deviation Variance 1303 2. 06 8. 33 69.43 114 5 . 3. 14 Closeness Centrality in ICT Business Network Table 91: Top ranking regions according to Closeness centrality in ICT business network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL326 Groot-Amsterdam 100 10 2 SE110 Stockholms lan 97 6 3 FR101 Paris 97 3 4 UKI12 Inner London-East 97 2 5 NL414 Zuidoost-Noord-Brabant 96 8 6 FR105 Hauts-de-Seine 96 13 7 DE122 Karlsruhe, Stadtkreis 96 4 8 DE718 Hochtaunuskreis 95 51 9 FI181 Uusimaa 95 9 10 DE212 Munchen, Kreisfreie Stadt 94 1 11 UKH12 Cambridgeshire CC 88 5 12 DE111 Stuttgart, Stadtkreis 88 21 13 DK012 Kobenhavns omegn 88 41 14 DE712 Frankfurt am Main Kreisfreie Stadt 88 30 15 UKJ23 Surrey 87 29 16 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 87 25 17 ITC45 Milano 87 14 18 DEA22 Bonn, Kreisfreie Stadt 87 12 19 IE021 Dublin 86 16 20 FR103 Yvelines 86 33 20 SE231 Hallands lan 86 166 22 DE711 Darmstadt, Kreisfreie Stadt 85 7 23 BE242 Arr. Leuven 84 11 24 SE224 Skane lan 84 37 25 ES300 Madrid 84 28 26 BE211 Arr. Antwerpen 84 54 27 DE300 Berlin 83 15 28 LU000 Luxembourg (Grand-Duche) 83 71 29 DE138 Konstanz 83 53 30 DK013 Nordsjaelland 82 102 Indicator description Indicator ID Netbus 3 Name of indicator Closeness centrality in ICT business network What does it measure? It measures the average distance that each node is from all other nodes in the network Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 115 Figure 61: Frequency of the Closeness centrality in ICT business network indicator values 999 23 6 23 32 36 59 47 41 15 12 1 9 0 200 400 600 800 1000 Frequency 0 20 40 60 80 100 Business network closeness centrality Table 92: Descriptive statistics of Closeness centrality in ICT business network indicator Number of observations Mean value Standard deviation Variance 1303 15.53 28.82 830.72 116 5. 3. 15 Betweenness Centrality in ICT Business Network Table 93: Top ranking regions according to Betweenness centrality in ICT business network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 DE212 Munchen, Kreisfreie Stadt 100 1 2 UKI12 Inner London-East 99 2 3 NL326 Groot-Amsterdam 75 10 4 ES300 Madrid 47 28 5 FR101 Paris 47 3 6 FR105 Hauts-de-Seine 44 13 7 DE712 Frankfurt am Main , Kreisfreie Stadt 39 30 8 DE300 Berlin 37 15 9 DE122 Karlsruhe, Stadtkreis 35 4 10 ITC45 Milano 35 14 11 SE110 Stockholms lan 34 6 12 DE111 Stuttgart, Stadtkreis 31 21 13 DE711 Darmstadt Kreisfreie Stadt 30 7 14 DE718 Hochtaunuskreis 26 51 14 DE600 Hamburg 26 87 16 AT130 Wien 23 27 17 IE021 Dublin 23 16 18 NL414 Zuidoost-Noord-Brabant 19 8 19 DEA11 Dusseldorf, Kreisfreie Stadt 18 38 20 FI181 Uusimaa 18 9 21 UKH12 Cambridgeshire CC 12 5 22 NL310 Utrecht 12 46 23 BE100 Arr. de Bruxelles-Capitale /Arr. van Brussel-Hoofdstad 11 25 24 UKJ23 Surrey 10 29 25 DK011 Byen Kobenhavn 10 24 25 PL127 Miasto Warszawa 10 50 27 DK012 Kobenhavns omegn 9 41 28 DE21H Munchen, Landkreis 9 22 28 DE25C Weissenburg-Gunzenhausen 9 118 30 DEA22 Bonn Kreisfreie Stadt 8 12 Indicator description Indicator ID Netbus 4 Name of indicator Betweenness centrality in ICT business network What does it measure? It measures the number of shortest paths in a network that traverse through that node Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 117 Figure 62: Frequency of the Betweenness centrality in ICT business network indicator values 1257 20 6 3 2 2 3 4 1 2 1 2 0 500 1000 1500 Frequency 0 20 40 60 80 100 Business network betweenness centrality Table 94: Descriptive statistics of Betweenness centrality in ICT business network indicator Number of observations Mean value Standard deviation Variance 1303 0. 88 5. 84 34.19 118 5 . 3. 16 Eigenvector Centrality in ICT Business Network Table 95: Top ranking regions according to Eigenvector centrality in ICT business network indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKI12 Inner London-East 100 2 2 NL326 Groot-Amsterdam 61 10 3 IE021 Dublin 31 16 4 FR101 Paris 31 3 5 DEA11 Dusseldorf, Kreisfreie Stadt 30 38 6 FR105 Hauts-de-Seine 29 13 7 ITC45 Milano 26 14 8 DE212 Munchen, Kreisfreie Stadt 25 1 9 SE110 Stockholms lan 25 6 10 FI181 Uusimaa 20 9 11 BE100 Arr. de Bruxelles-Capitale/Arr. van Brussel-Hoofdstad 15 25 12 ES300 Madrid 14 28 13 NL310 Utrecht 14 46 14 ES511 Barcelona 13 42 15 UKG31 Birmingham 12 83 16 DK012 Kobenhavns omegn 12 41 17 PL127 Miasto Warszawa 11 50 18 HU101 Budapest 11 73 19 UKJ23 Surrey 11 29 19 UKJ12 Milton Keynes 11 98 21 FR104 Essonne 10 62 22 DEA1C Mettmann 10 86 23 FR103 Yvelines 9 33 24 NL414 Zuidoost-Noord-Brabant 9 8 25 FR107 Val-de-Marne 8 124 26 DE300 Berlin 8 15 27 DE712 Frankfurt am Main, Kreisfreie Stadt 8 30 28 BE242 Arr. Leuven 8 11 28 CZ010 Hlavni mesto Praha 8 101 30 DE122 Karlsruhe, Stadtkreis 7 4 Indicator description Indicator ID Netbus 5 Name of indicator Eigenvector centrality in ICT business network What does it measure? It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) 8. 7 Company-level Information: ORBIS by Bureau Van dijk 8. 7 Company-level Information: ORBIS by Bureau Van dijk (see Section 0) Reference year (s) considered 2005-2011 119 Figure 63: Frequency of the Eigenvector centrality in ICT business network indicator values 1258 23 11 1 1 4 3 1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Business network eigenvactor centrality Table 96: Descriptive statistics of Eigenvector centrality in ICT business network indicator Number of observations Mean value Standard deviation Variance 1303 0. 64 4. 15 17.19 120 6 . Annex I: EIPE Indicators This section gives a complete overview of all the 42 indicators used in the EIPE rankings. They are presented together with a first indication of the data sources used and their time coverage. The indicators and their characteristics are described further in the next chapter of this report. A detailed description of specific methodologies applied to elaborate each indicator, as well as the data sources used, is given in detail in the Annexes. For methodological details, please refer to the second EIPE Report (De Prato and Nepelski 2013a. 6. 1 ICT R&d Activities Indicators 6. 1. 1 ICT R&d Agglomeration Indicators (Agrd) The indicators concerning the agglomeration of ICT R&d activity are listed and described in Table 97. With 13 different measurements, they cover a broad range of aspects related to inputs and outputs in R&d, and to the presence of major knowledge production public and private organisations. Table 97: ICT R&d Agglomeration indicators (Agrd) Indicator ID Agrd 1 Agrd 2 Agrd 3 Agrd 4 Agrd 5 Agrd 6 Name of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms ICT FP7 funding What does it measure? Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science faculty in citations ranking Region's share in the R&d expenditures by ICT firms in the EU to a region's share in the EU population Region's share in the total EU ICT FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE Rev. 2 ICT areas of the FP7 programme Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (Section 8. 1) Company level information: Orbis by Bureau Van dijk (Section 8. 7) ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year 2011 2005-2011 2007-2011 121 (continued: ICT R&d Agglomeration indicators (Agrd) Indicator ID Agrd 7 Agrd 8 Agrd 9 Agrd 10 Agrd 11 Agrd 12 Name of indicator ICT FP7 participations ICT FP7 funding to SMES ICT FP7 participations by SMES Location of ICT R&d centres Ownership of ICT R&d centres Scientific publications in Computer science What does it measure? It measures the total number of ICT R&d FP7 projects to which organisations, located in the observed region, have participated to It measures the total amount of ICT R&d FP7 funding given to SMES located in the observed region It measures the total number of ICT R&d FP7 projects to which SMES, located in the observed region, have participated to It measures the total number of ICT R&d centres located in the observed region It measures the total number of ICT R&d centres owned worldwide by companies located in the observed region It measures the total number of scientific publications, in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of ICT FP7 participations to a region's share in the EU population Region's share in the total EU ICT FP7 funding to SMES to a region's share in the EU population Region's share in the total number of ICT FP7 SMES participations to a region's share in the EU population Region's share in the total number of R&d centres located in the EU to a region's share in the EU population Region's share in the total number of R&d centres owned by EU firms to a region's share in the EU population Region's share in the total number of publications in Computer science to a region's share in the EU population Definition of ICT dimension ICT areas of the FP7 programme Based on HIS isuppli classification of the major"semiconductors influencers"Computer science as defined by Web of Science classification of Research Areas Unit of observation NUTS 3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) R&d Centre location by IHS isuppli (Section 8 . 4) Bibliometrics: Web of Science b Thomson Reuters (Section 8. 3) Reference year (s) considered 2007-2011 2012 2000-2012 122 6. 1. 2 ICT R&d Internationalisation Indicators (Intrd) To address the issue of internationalisation of ICT-related R&d activity in NUTS 3 level spatial units across the EU, a distinction between in-and outward internationalization of R&d activities based in a location is made. Table 98: ICT R&d Internationalisation indicators (Intrd) Indicator ID Intrd 1 Intrd 2 Name of indicator Outward ICT R&d internationalisation Inward ICT R&d internationalisation What does it measure? It measures the number of ICT R&d centres located abroad (outside the country) that are owned by companies'headquarters located in a region It measures the number of ICT R&d centres located in a region that are owned by foreign companies Unit of measurement Region 's share in the total number of R&d centres located abroad that are owned by companies'headquarters located in the EU to a region's share in the EU population Region's share in the total number of R&d centres owned by foreign companies in the EU to a region's share in the EU population Definition of ICT dimension Based on HIS isuppli classification of the major"semiconductors influencers"Unit of observation NUTS 3 Source R&d Centre location by IHS isuppli (Section 8. 4) Reference year 2012 6. 1. 3 ICT R&d Networking (Netrd) A set of networking measures addressing R&d activity has been constructed, which relies on the network analysis of the locations of ICT FP7 programme participants. Below, the key elements of the network are described. ICT R&d networking indicators are listed in Table 99. For a full description of the methodology of network analysis and the indicators applied, see the second EIPE Report (De Prato and Nepelski 2013a. The data source on ICT FP7 programmes is described in Section 8. 2. Network design: A straightforward way of representing the locations of ICT FP7 programme participants as a network is through drawing a line connecting two different regions whenever two organizations from these regions participate in the same ICT FP7 programme (Cassi et al. 2008). Thus, knowing the location of each participant, we can build a directed network. In a formal way, we identify our set of nodes, V, as the regions where ICT FP7 programmes partners are located, and the set of arcs, A as the bilateral relationships that exist whenever an organization from one region participates in a ICT FP7 programme together with an organization from a different region. 2 Actors: NUTS 3 regions located in the EU 27. Relationships: A link between two regions exists whenever an organization from one region participates in an ICT FP7 programme together with an organization from a different region. Data source: The analysis is conducted using the data on ICT FP7 programmes by DG Connect and is described in Section 8. 2. Network measures: According to the above defined methodology, based on the number of connections between regions and a subsequent analysis of these connections, indicators are constructed. These are listed and described in Table 99.2 In the following, we focus our attention on bilateral relationships between regions and do not take into account loops, i e. when a company's R&d centre and headquarter are located in the same region. 123 Table 99: ICT R&d Networking indicators (Netrd) Indicator ID Netrd 1 Netrd 2 Netrd 3 Netrd 4 Name of indicator Degree in ICT R&d network Closeness centrality in ICT R&d network Betweenness centrality in ICT R&d network Eigenvector centrality in ICT R&d network What does it measure? It measures the total number of connections a region maintains with other regions through organizations participating in common ICT FP7 projects It measures the average distance that each node is from all other nodes in the network It measures the number of shortest paths in a network that traverse through that node It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1. Definition of ICT dimension ICT areas of the FP7 programme Unit of observation NUTS 3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year 2007-2012 6. 2 ICT Innovation Activities Indicators 6. 2. 1 Agglomeration of Innovation (Agin) As in the case of R&d activities, the set of indicators used to quantify and map innovation across the EU is composed of indicators dealing with agglomeration of innovation activity in NUTS 3 level spatial units. To the extent allowed by the availability of indicators and data, a mix of measures capturing the input and outputs of innovation activities is proposed. Table 100 lists and describes all the indicators. Table 100: ICT Innovation Agglomeration indicators (Agin) Indicator ID Agin 1 Agin 2 Agin 3 Name of indicator Investment in intangibles by ICT firms Venture capital financing of ICT firms ICT patents Unit of measurement Average annual amount in Euro per 1000 inhabitants Total amount in Euro per 1000 inhabitants Number of ICT patents per 1000 inhabitants Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) Venture capital: Venturesource by Dow jones (Section 8. 8) Patent data: REGPAT by OECD (Section 8. 6) Reference year 2005-2012 2000-2012 2000-2009 6. 2. 2 Internationalisation of ICT Innovation (Intin ) Regarding the internationalization of innovation, patent-based indicators are used. The analysis uses measures of internationalisation that are based on the presence of inventors residing in different regions of the world. An international patent application is defined in the analysis presented here as one that includes at least two inventors residing in different countries. Using this methodology, we use the concept of internationalisation of innovation measured by international 124 co-invention. This concept is used to construct a relative measure of international collaboration between inventors. The data on regional patents represents the input to innovation activities and the relevant data originates from the Regpat database (see Section 8. 6). Table 101: ICT Innovation Internationalisation indicators (Intin) Indicator ID Intin 1 Name of indicator International co-inventions What does it measure? It measures the number of international ICT patents, i e. patents with at least two inventors residing in different countries, and attributes to the observed region the (fractional) count) of those patents for which at least one inventor is residing in the region. Unit of measurement Region's share in the total number of international ICT patents in the EU to a region's share in the EU population Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b. Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (Section 8. 6) Reference year (s) considered 2000-2009 6. 2. 3 Networking in ICT Innovation (Netin) A set of networking measures addressing innovation activity has been constructed, which relies on the network analysis of the location of inventors based in different locations and jointly developing ICT inventions. Below, the key elements of the network are described. ICT Innovation Networking indicators are listed in Table 102. For a full description of the methodology of network analysis and indicators applied, see the second EIPE Report (De Prato and Nepelski 2013a. Network design: To construct a network depicting the concept of innovation networking, a network of technological collaborations between inventors based on patent data has been built. The methodology was proposed by Breschi, Cassi and Malerba (2007) and used by De Prato and Nepelski (2012. This approach uses the information that each patent application has: a list of inventors, i e. the people who developed a particular invention, and information about their place of residence. Actors: NUTS3 regions located in the EU27 and TL3 regions in the remaining OECD countries. Relationships: An intuitive way of representing the set of interregional or international coinventions by using patent data as a network is to draw a line connecting two regions that share a patent developed by their residents. By doing this for the entire pool of co-inventions, we are able to construct a network of technological collaborations. The relationship between different locations can be described as the total sum of co-inventions developed by inventors residing in different regions. According to (Guellec and Van Pottelsberghe de la Potterie 2001), the total number of patents co-invented by residents of region i in collaboration with researchers in other regions is i j i ij Coinn Coinn. 1) Data source: The analysis is conducted using the data on REGPAT by OECD (see section 8. 6). Network measures: In the above context, based on the number of connection of a region, we can define the measures of regions'centrality. All indicators listed in Table 102.125 Table 102: ICT Innovation Networking indicators (Netin) Indicator ID Netin 1 Netin 2 Netin 3 Netin 4 Name of indicator Degree in ICT innovation network Closeness centrality in ICT innovation network Betweenness centrality in ICT innovation network Eigenvector centrality in ICT innovation network What does it measure? It measures the total number of connections a region maintains with other regions through joint inventions It measures the average distance that each node is from all other nodes in the network It measures the number of shortest paths in a network that traverse through that node It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1 Rank between 0 and 1 Rank between 0 and 1 Rank between 0 and 1 Definition of ICT dimension Based on the OECD definition of ICT following IPC taxonomy (OECD 2008b). Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source REGPAT by OECD, see section 8. 6 Reference year (s) considered 2000-2009 6 . 3 ICT Business activities Indicators 6. 3. 1 Agglomeration of Business activities (Agbuss) As in the case of the R&d and innovation activities, the set of indicators used to quantify and map business across the EU is composed of indicators related to agglomeration of business activity in NUTS 3 spatial units. In addition to the extent allowed by the availability of indicators and data, a mix of measures capturing the input and outputs of business activities is proposed. Table 103 lists the relevant indicators. 126 Table 103: ICT Business Agglomeration indicators (Agbuss) Indicator ID Agbuss 1 Agbuss 2 Agbuss 3 Agbuss 4 Agbuss 5 Name of indicator Location of ICT Scoreboard Headquarters Ownership of ICT Scoreboard affiliates Location of ICT Scoreboard affiliates Location of ICT firms ICT employment What does it measure? It measures the number of ICT Scoreboard Headquarters located in the observed region It measures the number of ICT Scoreboard affiliates owned worldwide by ICT Scoreboard Headquarters located in the observed region It measures the total number of ICT Scoreboard affiliates located in the observed region It measures the number of ICT firms located in the observed region It measures the total employment in ICT firms in the observed region Unit of measurement Region's share in the total number of ICT Scoreboard Headquarters located in the EU to a region's share in the EU population Region's share in the total number of ICT Scoreboard affiliates owned by EU ICT Scoreboard Headquarters to a region's share in the EU population Region's share in the total number of ICT Scoreboard affiliates located in the EU to a region's share in the EU population Region's share in the total number of ICT firms located in the EU to a region's share in the EU population Region's share in the total employment by ICT firms located in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) Reference year (s) considered 2008 2008 2008 2008 2005-2011 Indicator ID Agbuss 6 Agbuss 7 Agbuss 8 Agbuss 9 Name of indicator Growth in ICT employment Turnover by ICT firms Growth in turnover by ICT firms New business investments in the ICT sector What does it measure? It measures employment growth in ICT firms in the observed region It measures the average annual turnover by ICT firms in the observed region It measures turnover growth in ICT firms in the observed region It measures the number of new investments in the ICT sector in the observed region Unit of measurement Growth rate in%Region's share in the total turnover by ICT firms located in the EU to a region's share in the EU population Growth rate in%Region's share in the total number of new investments in the ICT sector to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) European Investment Monitor by Ernst & young (Section 8. 5) Reference year (s) considered 2005-2011 2005-2011 2005-2011 2000-2011 127 6. 3. 2 Internationalisation of ICT Business activities (Intbuss) The internationalization of business activity is proxied by information on the location of business affiliates owned by companies belonging to the ICT Scoreboard, which themselves are based abroad. The details of the indicator measuring the level of internationalisation of business activity in a region are given in Table 104. Table 104: ICT Business Internationalisation indicators (Intbuss) Indicator ID Intbuss 1 Intbuss 2 Name of indicator Outward ICT business internationalisation Inward ICT business internationalisation What does it measure? It measures the number of affiliates located abroad (outside the country) that are owned by ICT Scoreboard Headquarters located in a region It measures the number of affiliates located in a region that are owned by ICT Scoreboard Headquarters located abroad Unit of measurement Region's share in the total number of affiliates located abroad that are owned by European ICT Scoreboard Headquarters to a region's share in the EU population Region's share in the total number of affiliates owned by foreign ICT Scoreboard Headquarters in the EU to a region's share in the EU population Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) Reference year (s) considered 2008 6. 3. 3 Networking in ICT Business activities (Netbuss) A set of networking measures addressing the business activity has been constructed, which relies on the network analysis of the location of companies belonging to the ICT Scoreboard and their affiliates. Below, the key elements of the network are described. ICT Innovation Networking indicators are listed in Table 105. For a full description of the methodology of network analysis and indicators applied, see the second EIPE Report (De Prato and Nepelski 2013a. Network design: In order to address the issue of networking in the context of business activity, a network of international affiliates is created. A natural way of constructing a network of foreign affiliates is through the ownership and location relationship. A line between each pair of regions is drawn whenever a firm from one region owns an affiliate in another region, or vice versa. Thus we illustrate the destination of expansion of multinational enterprises (MNES) and the location of business activities. This allows us to track the existence of business relationships between regions. By doing this for all the regions owning and hosting MNE subsidiaries, we are able to create a unique map of ownership and location of business affiliates. 3 Actors: NUTS 3 regions located in the EU27 and TL3 regions in the remaining OECD countries. Relationships: A link between two regions exists whenever a company from one region invests in a new business activity in a different region. The direction of a link goes from a region where the investing company is located to the region in which investment is made. Data source: The analysis is conducted using the EIM data on foreign investments (see Section 8. 5). 3 In the following, we focus our attention on bilateral relationships between regions and do not take into account loops, i e. when a company's new investment and headquarter is located in the same region. 128 Network measures: In the above context, based on the number of incoming and outgoing connection to and from a region, the measures of regions'centrality are listed in Table 105. Table 105: ICT Business Networking indicators (Netbuss) Indicator ID Net Bus 1 Net Bus 2 Net Bus 3 Net Bus 4 Net Bus 5 Name of indicator In-degree in ICT business network Out-degree in ICT business network Closeness centrality in ICT business network Betweenness centrality in ICT business network Eigenvector centrality in ICT business network What does it measure? It measures the total number of connections a region maintains with other regions whenever an ICT Scoreboard Headquarters located in that region owns an affiliate located in other regions It measures the total number of connections a region maintains with other regions by hosting affiliates owned by ICT Scoreboard Headquarters located in other regions It measures the average distance that each node is from all other nodes in the network It measures the number of shortest paths in a network that traverse through that node It measures the importance of a node in a network, based on the importance of its direct neighbours Unit of measurement Rank between 0 and 1 Definition of ICT dimension Based on NACE Rev. 2 Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Company level information: Orbis by Bureau Van dijk (Section 8. 7) Reference year (s) considered 2008 129 7. Annex 2: Composite Indicators The selected indicators, their measurement and the resulting multiple rankings represent an abundance and diversity of information that is impossible to analyse at first glance. In order to provide synthetic comparable results for further analysis and interpretation, the information contained in individual indicators needs to be aggregated. This is done by constructing, step by step, a final composite EIPE indicator and sub-indicators reflecting three dimensions of ICT activity, i e. R&d, innovation and business. 7. 1 Normalization and Rescaling of Data Before aggregating the information, one needs to deal with the problem that most indicators can be incommensurate with others, and have different measurement units. For example, the number of patent applications is expressed per capita, while the share of ICT R&d centres owned by companies from a region and located there is expressed as a percentage of the total number of R&d centres owned by companies from a region. To deal with this problem, indicators are made comparable by converting them to the same measurement scale, by transforming them in pure, dimensionless, numbers (OECD-JRC 2008). This is a normalization process. After this, composite indicators are constructed. Below both methodologies applied in this study are described in detail. In order to normalise the data used in this study, a standardization method, i e. z-scores, is used. This method is the most commonly used because it converts all indicators to a common scale with an average of zero and standard deviation of one (EC-JRC 2005). The average of zero means that it avoids introducing aggregation distortions stemming from differences in indicator means. The scaling factor is the standard deviation of the indicator across the units of observations, i e. in the context of the current study NUTS 3 regions. In a more formal way, the normalized score of a raw score x is x z. 2) where is the mean of observations across the regions and d is the standard deviation across the regions. The quantity z represents the distance between the raw score and the population mean in units of the standard deviation. The advantage of z-scores over other normalisation methods is that an indicator with extreme values will have an intrinsically greater effect on the composite indicator This behaviour is desirable in the current study, as there is an intention to reward exceptional performance, that is, if an extremely good result on few indicators is thought to be better than a lot of average scores. In the next steps, the normalized scores are rescaled further in order to avoid the negative scores and to assure the incorporation of the indicators variability in the results. This is done through the minmax rescaling procedure, whose formula is: 100, max, min, min j j rj j rj x x x x Nx. 3) where Nxrj is the normalised and rescaled value of indicator j in the territorial unit r, xrj is the normalised raw value of indicator j in the territorial unit r, j, min x and j, max x are the minimum and maximum values of indicator j. This method has found its way into a number of policy-oriented projects. For example, z-scores are used for the two composite indicators of the knowledge-based economy, published by the European commission in Key Figures 2003-2004, for the environmental sustainability index developed at Yale university, and for the internal market index 2002 (EC-JRC 2005). 130 7. 2 European ICT Poles of Excellence Composite Indicators When it comes to constructing the Composite Indicator to aggregate all measurements for the elaboration of a final ranking of EIPE, there are two steps. First, composite sub-indicators are created, one for each of the activities: R&d, Innovation and Business. Second, an EIPE composite indicator is constructed, aggregating the values of the three earlier sub-indicators into a final one. An important issue related to the construction of composite indicators is the one of weighting. Unfortunately, no agreed methodology exists to weight individual indicators (EC-JRC 2005. In particular the context of the current study does not make the choice of a weighting scheme easy, as there is no theoretical framework that could say which indicator would be more influential than others. Considering this, it is proposed that equal weighting will be used in the process of constructing composite indicators. Three intermediate sub-indicators are organized along the three activities defined in the second EIPE Report (De Prato and Nepelski 2013a), i e.:R&d sub-indicator comprises of all relevant indicators included in Section 6. 1 normalized and equally weighted. Innovation sub-indicator comprises of all relevant indicators included in Section 6. 2 normalized and equally weighted. Business sub-indicator comprises of all relevant indicators included in Section 6. 3 normalized and equally weighted. In the second step, all information is synthesised into one composite indicator by aggregating the values of the three earlier sub-indicators. Sub-indicator values are weighted equally. The values of the final index are standardized with the Minimax procedure. 131 8. 1 Annex 3: Data Sources 8. 1 QS WORLD UNIVERSITY RANKINGS by QS The Computer science and Electronic Faculties rankings originate from the QS WORLD UNIVERSITY RANKINGS, which was formed in 2008 to meet the increasing public interest for comparative data on universities and organisations, and the growing demand for institutions to develop deeper insight into their competitive environment. 4 The QS WORLD UNIVERSITY RANKINGS currently considers over 2, 000 and evaluates over 700 universities in the world, ranking the top 400. Like any ranking at the global level, it is constrained by the availability of data from every part of its scope. When attempting to exercise evaluations at a more granular level this becomes even more complex. There are however, some indicators that transcend the direct involvement of the institutions and can be stratified better by subject discipline. Based on natural groupings, response levels and expert advice, the ranking includes 52 subject disciplines among which there is the Computer science subject considered appropriate for the EIPE study. To construct measures of faculty performance, the QS uses its proprietary datasets that enable to drill down by subject area, namely academic and employer reputation surveys and the Scopus data for the Citations per Faculty indicator in the global rankings. These have been combined to produce the results. In detail each of the faculty ranking pieces can be described in the following way: Academic Reputation survey is the centrepiece of the QS WORLD UNIVERSITY RANKINGS since their inception in 2004. In 2010, it drew upon over 15,000 respondents to compile the results. In the survey, respondents are asked to identify the countries, regions and faculty areas that they have most familiarity with and up to two narrower subject disciplines in which they consider themselves expert. For EACH of the (up to five) faculty areas they identify, respondents are asked to list up to ten domestic and thirty international institutions that they consider excellent for research in the given area. They are not able to select their own institution. The threshold for academic respondents that any discipline must reach for publishing the results in that discipline has been set in year one at 150. The analysis places an emphasis on international reputation over domestic domestic responses are weighted individually at half the influence of an international response. This is a global exercise and will recognize institutions that have an international influence in these disciplines. Weightings are applied also to balance the representation by region. Employer reputation survey considers the students'employability as a key factor in the evaluation of international universities and in 2010 drew on over 5, 000 responses to compile the results for the overall rankings. The employer survey works on a similar basis to the academic one only without the channelling for different faculty areas. Employers are asked to identify up to ten domestic and thirty international institutions they consider excellent for the recruitment of graduates. They are asked also to identify from which disciplines they prefer to recruit. From examining where these two questions intersect, a measure of excellence in the given discipline is inferred. Employers seeking graduates from any discipline are weighted at 0. 1 and those from a parent category (i e. Social sciences) are weighted at 0. 25 relative to the weight of a direct response for the subject area. Also this analysis places an emphasis on international reputation over domestic, with domestic responses carrying half the individual weighting of international responses. Citations per Faculty takes into account the size of an institution while allowing observing its penetration the global research landscape. The data for citations originate from Scopus by Elsevier E. V. 5 Papers in Scopus are tagged with an ASJC (All Science Journal Classification) code which identifies the principal foci of the journal in which they were 4 More information under: http://www. topuniversities. com (last accessed 01.02.2012) 5 More information under http://www. scopus. com (last accessed 01.02.2012) 132 published. When aggregated together these totals per faculty and their associated citations provide an indicator of volume and quality of output in the given discipline. The scores in each category are aggregated through adaptive compilation. First of all, the publication of a given subject table is not dependent on all three indicators reaching their thresholds. In most cases, a minimum of two indicators in order to present a final list is required. Weightings are based on publications patterns and level of employer interest in the given subject area. Weightings are applied not evenly between indicators for different disciplines but are set relative to the pertinence of the indicator to the discipline and the depth of data available to evaluate it. Aggregation, similarly to the approach used in the overall QS WORLD UNIVERSITY RANKINGS a z-score is calculated for each indicator with the results scaled between 0 and 100 and then combined with the weightings as follows: Academic: 40%Employer: 30%Citations: 30%8. 2 ICT FP7 by EC DG Connect The Framework Programmes for Research and Technological Development, also called Framework Programmes or abbreviated FP1 through FP8, are funding programmes created by the European union in order to support and encourage research in the European Research Area (ERA). FP7 spans through the period between 2007 and 2013. The analysis of the Framework Programme 7 programmes and participants is based on the database provided by the DG Connect in November 2011, which is not available publically. In the current report, information on the FP7 is used and concerns only the Information and communication technologies (ICT) areas. The list of instruments through which projects were financed includes: CSA-ERA-PLUS, CSA-CA, CP-SICA-INFSO, CP-FP-INFSO-FET, CSA-SA, CP-IP, Noe, CP-CSA, CP -IP-INFSO-FET, CP-FP-INFSO, CP-FP, CSA-SA-INFSO-FET and CSA-CA-INFSO-FET. 8. 3 Bibliometrics: Web of Science by Thomson Reuters Web of Science is an online academic citation index provided by Thomson Reuters. It is designed for providing access to multiple databases, cross-disciplinary research, and in depth exploration of specialized subfields within an academic or scientific discipline. As a citation index, any cited paper will lead to any other literature (book, academic journal, proceedings, etc. which currently, or in the past, cites this work. In addition, literature which shows the greatest impact in a field covered by Web of Science, or more than one discipline, can be obtained selectively. For example, a paper's influence can be determined by linking to all the papers that have cited it. In this way, current trends, patterns, and emerging fields of research can be assessed. Web of Science has indexing coverage from the year 1900 to the present. Regarding the coverage, it encompasses over 11,000 journals selected on the basis of impact evaluations. This selection includes open-access journals and over 12 000 conferences each year (2009), spanning multiple academic disciplines. Coverage includes the sciences, social sciences, arts, and humanities, and across disciplines. For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered. 8. 4 R&d Centre Location by IHS isuppli The data used for the purpose of identification of R&d centre locations originates from the 2011 IHS isuppli database, a company-level dataset dedicated to observe the internationalization of R&d. It includes a list of R&d centres belonging to a number of high-tech companies together with their exact location and additional information on the type of R&d activity performed in these centres. 133 The data on R&d locations was collected by IHS isuppli, an industry consultancy, 6 with the aim of mapping R&d locations and activities of companies considered as the major semiconductor influencers, i e. the main users of semiconductors or, in other words the largest manufacturers of applied electronic and microelectronic products. In order to check how representative the sample is compared, we it to the R&d Scoreboard, a list of top 2000 R&d investors in Europe and the rest of the world, 7 and the list of companies filing their patents at the USPTO. The results of this checks revealed that the firms contained in the dataset represent nearly 30%of the 2008 R&d budget of all companies included in the R&d Scoreboard and more than 30%of all patent applications filed to the USPTO in 2009. This way we are assured that the sample is representative for the population of large high-tech multinational firms. Even if the characteristics of the dataset do not allow for building time series and, the dataset itself represents a unique collection of data for its coverage with a great level of details provided. 8. 5 European Investment Monitor by Ernst & young The European Investment Monitor (EIM) is a unique monitor of foreign investment in Europe by companies from all over the world, except for investments in the home country. Since 1997, data is collected for all European countries and is published on a quarterly basis. Up to 2011, it includes over 40,000 observations. The EIM identifies the project-based foreign inward investment announcements that are new, expanding, or co-located in an international context. 8 When the consulting group discovers a new project, they track it in order to determine the exact location at the city level. Projects included in the database have to comply with several criteria to be considered as international investments. There are no minimum investment size criteria, but the number of investments where less than 10 jobs are created. The basic description of each investment project described by the EIM data includes the name of the firm the parent company name, the name and the origin country of the parent company, the sector and both the country and the city of location. It also includes the function of each investment (unit of production and different service activities, such as headquarters, research & development centres, logistics, or sales & marketing offices). The EIM is recognized a as a comprehensive industry standard tracking investment projects across Europe. It is a business information tool used by both professionals involved in corporate location strategy and inward investment issues and academic researchers (De La Tour et al. 2011. It is a benchmark for government and private sector organizations wishing to identify trends in jobs and industries, business and investment. The data collected by the EIM enables to: Review developments and movements in the inward investment marketplace, identify emerging sectors, industries and clusters, Benchmark regions and develop location strategies, Undertake in depth, wide-ranging data analysis; for example: Which is Europe's most popular location for headquarters investments? What is the scale and nature of investment from South korea? Or what is Germany's market share of pharmaceutical investment? 6 More information under: http://www. isuppli. com (last accessed 01.02.2012) 7 More information under: http://iri. jrc. ec. europa. eu/research/scoreboard 2010. htm (last accessed 01.02.2012) 8 The EIM excludes mergers and acquisitions or joint ventures (unless these result in new facilities, new jobs created), licence agreements, retail and leisure facilities, hotels and real estate investments, utility facilities including telecommunications networks, airports, ports or other, fixed infrastructure investments, extraction activities (ores, minerals or fuels), portfolio investments (i e. pensions, insurance and financial funds), factory/production replacement investments (e g. a new machine replacing an old one, but not creating any new employment), not-for-profit organisations. 134 8. 6 Patent Data: REGPAT by OECD The OECD REGPAT database presents patent data that have been linked to NUTS3 regions according to the addresses of the applicants and inventors. The data have been regionalised at a very detailed level so that more than 2 000 regions are covered across OECD countries. When compiling or analysing indicators with regionalised patents, it is necessary to have some characteristics of patents and some rules in mind, so as to make the best use of the information and not misinterpret the indicators. The data from the REGPAT database, are constructed along the following principles: Inventor v. owner region: Patent data can be regionalised on the basis of the address of either the inventor or the holder. The inventors address usually indicates where the invention was made while the owners address indicates where the holder has its headquarters. These two concepts have obviously different economic interpretation, especially as many patents are filed by large companies having several establishments located in different regions and countries. Fractional v. whole counting: Patents usually have several inventors and can have several owners. When regionalising patents, a patent with, say, inventors in two regions can be attributed either wholly to the two regions, or shared (with a total of shares of 100%)between the two regions. As a significant proportion of patents have inventors from different regions it is important to specify what rule is used, and when one is better, to use it. For instance, when comparing the performance of regions it is recommended to use fractional accounting, which i) attributes to each region its actual contribution to the invention; ii) when summed over all regions gives a total of 100%.%On the other hand, when compiling an indicator like share of patents with co-inventors from another region, it is recommended to use whole counting both at the numerator and the denominator. Priority year: It is the year of first filing for a patent; it is the closest to the actual date of invention, and should therefore be used as the reference date when compiling patent indicators aimed at reflecting technological achievements. Other dates (national application, publication or grant) are dependent on administrative procedures and can be one to ten years after the invention and thus misleading when interpreting the data. The methodology developed to identify regions on the basis of addresses of the patents inventor (s) or applicant (s) consists of an iterative procedure that matches postal codes and/or town names, identified in the addresses, with regions using a set of lookup tables (such as a postal code-NUTS3 correspondence). 8. 7 Company-level Information: ORBIS by Bureau Van dijk Corporate Innovation, R&d and ownership structure information is retrieved by the JRC-IPTS Information Society Unit and contains comprehensive information on around 600 individual multinational firms. In general, concerning the company information, it includes among others such indicators as company name and sector of activity (NACE 4 digits), location of the company at detailed geographical level (city and/or NUTS2 region), structure of ownership, balance sheet data (assets, capital stock, number of employees, etc.)and R&d expenditures. This information was included combines information in the following sources: The 2011"EU Industrial R&d Investment Scoreboard, "which presents information on the top 1000 EU companies and 1000 non-EU companies investing in R&d in 2010. The Scoreboard includes data on R&d investment along with other economic and financial data from the last four financial years. 9 9 More information under: http://iri. jrc. ec. europa. eu/research/scoreboard 2010. htm (last accessed 01.02.2012). 135 ORBIS (Bureau Van dijk), which contains comprehensive information on companies worldwide. Regarding the selection of companies out of the ORBIS database and the construction of indicators on the number of employees, turnover, intangible and R&d expenditures at the NUTS 3 level, the following criteria were applied: Geographic coverage: EU 27; The ICT industry was defined according to the NACE Rev 2 definition of the ICT sector (OECD 2007; 10 Company status: Active companies; Type of entities: Industrial companies In order to avoid double-counting, separate searches were run using a filter on consolidation code. First, companies with consolidated accounts only and then companies with unconsolidated accounts only were selected. Time coverage between and 2011, the last available date. 8. 8 Venture capital: Venturesource by Dow jones Dow jones Venturesource provides comprehensive data on venture-backed and private equity-backed companies including their investors and executives in every region, industry sector and stage of development throughout the world. This database contains information on venture capital transactions, the financed companies and the financing firms. The data are reported largely self y VC firms, but several plausibility checks are conducted by the database providers. According to Kaplan et al. 20022002), who provide a detailed overview of this database and compare it with an alternative source of information which is Venture Economics, the Venturesource data are generally more reliable, more complete, and less biased than the Venture Economics data. 10 Primary codes only include: 261-Manufacture of electronic components and boards, 262-Manufacture of computers and peripheral equipment, 263-Manufacture of communication equipment, 264-Manufacture of consumer electronics, 268-Manufacture of magnetic and optical media, 4651-Wholesale of computers, computer peripheral equipment and software, 4652-Wholesale of electronic and telecommunications equipment and parts, 582-Software publishing, 611-Wired telecommunications activities, 612-Wireless telecommunications activities, 613-Satellite telecommunications activities, 619-Other telecommunications activities, 6201-Computer programming activities, 6202-Computer consultancy activities , 6209-Other information technology and computer service activities, 6311-Data processing, hosting and related activities, 6312-Web portals, 9511-Repair of computers and peripheral equipment, 9512-Repair of communication equipment. 136 References Cassi, L.,Corrocher, N.,Malerba, F. & Vonortas, N. 2008.''Research Networks As Infrastructure For Knowledge Diffusion In European Regions.''Economics of Innovation and New Technology, 17:7-8, 663-76. De La Tour, A.,Glachant, M. & Ménière, Y. 2011. Innovation and international technology transfer: The case of the Chinese photovoltaic industry. Paris: MINES Paristech. De Prato, G. & Nepelski, D. 2012.''Global technological collaboration network. Network analysis of international co-inventions.''Journal of Technology Transfer, 1-18. De Prato, G. & Nepelski, D. 2013a.''Identifying European ICT Poles of Excellence. The Methodology.''JRC Scientific and Policy Reports. Seville: JRC-IPTS. De Prato, G. & Nepelski, D. 2013b.''Mapping the European ICT Poles of Excellence. The Atlas of ICT Activity in Europe.''JRC Scientific and Policy Reports. Seville: JRC-IPTS. EC-JRC 2005.''Tools for Composite Indicators Building.''Ispra: EC-JRC. Guellec, D. & Van Pottelsberghe de la Potterie, B. 2001.''The internationalisation of technology analysed with patent data.''Research Policy, 30:8, 1253-66. Hidalgo, C a.,Klinger, B.,Barabási, A l. & Hausmann, R. 2007.''The Product Space Conditions the Development of Nations.''Science, 317: 5837,482-87. Kaplan, S n.,Strömberg, P. & Sensoy, B. A. 2002.''How Well do Venture capital Databases Reflect Actual Investments?''In SSRN (Ed.).Nepelski, D. & De Prato, G. 2013a.''Analysing the European ICT Poles of Excellence. Case studies of Inner London East, Paris, Kreisfreie Stadt Darmstadt, Dublin and Byen Kobenhavn.''JRC Scientific and Policy Reports. Seville: JRC-IPTS. Nepelski, D. & De Prato, G. 2013b.''Key Findings and Implications of the European ICT Poles of Excellence project.''JRC Scientific and Policy Reports. Seville: JRC-IPTS. Nepelski, D.,De Prato, G. & Bogdanowicz, M. 2013.''Defining European ICT Poles of Excellence. A Literature Review.''JRC Scientific and Policy Reports. Seville: JRC-IPTS. OECD-JRC 2008.''Handbook on Constructing Composite Indicators. Methodology and user Guide.''Paris: OECD-JRC. OECD 2002. Frascati Manual 2002. Proposed Standard Practice for Surveys on Research and Experimental Development. Paris: OECD Publishing. OECD 2005. Oslo Manual. Guidelines for Collecting and Interpreting Innovation Data. OECD Publishing. OECD 2007.''INFORMATION ECONOMY-SECTOR DEFINITIONS BASED ON THE INTERNATIONAL STANDARD INDUSTRY CLASSIFICATION (ISIC 4).'Paris: OECD. OECD 2008a.''OECD Factbook 2008: Economic, Environmental and Social Statistics.''Paris: OECD. OECD 2008b.''Science, Technology and Industry Outlook.''Paris: OECD. European commission EUR 26579 Joint Research Centre Institute for Prospective Technological Studies Title: Mapping the European ICT Poles of Excellence: The Atlas of ICT Activity in Europe Authors: Giuditta De Prato, Daniel Nepelski Luxembourg: Publications Office of the European union 2014-136 pp. 21.0 x 29.7 cm EUR Scientific and Technical Research series ISSN 1831-9424 (online), ISSN 1018-5593 (print) ISBN 978-92-79-36782-3 (pdf) ISBN 978-92-79-36783-0 (print) doi: 10.2791/72405 Abstract The EIPE Atlas presents the results of the empirical mapping of ICT activity in Europe and the ranking of the top European NUTS 3 regions based on their performance in EIPE Composite Indicator (EIPE CI), together with the ranks for the individual 42 indicators which contributed to the building of the EIPE composite indicators. The report offers a snapshot of the performance of regions that are identified as the main locations of ICT activity in Europe. It is meant to provide a comprehensive picture of how ICT activity is distributed across Europe and where its main locations are. This information is expected to give a better overview of the European ICT landscape activity and actors in each location and to reveal their strengths and weaknesses. z As the Commission's in-house science service, the Joint Research Centre's mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a crosscutting and multi-disciplinary approach. LF-NA-26579-EN-N doi: 10.2791/72405 ISBN 978-92-79-36782-3 (pdf

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