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JRC85353.pdf

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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:

Over this time, it developed a tool based on a database of original ICT activity indicators,

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,

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...

56 5. 1. 12 Scientific Publications in Computer science...58 5. 1. 13 Outward ICT R&d Internationalisation...

129 7. 1 Normalization and Rescaling of Data...129 7. 2 European ICT Poles of Excellence Composite Indicators...

Data Sources...131 8. 1 QS WORLD UNIVERSITY RANKINGS by QS...131 8. 2 ICT FP7 by EC DG Connect...

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.

By using the data collected in the project and organized along three types of ICT activities (see Figure 2),

and chapter 8 Annex 3) describes the data sources used in the EIPE study. Methodological note:

Strong clustering of ICT activity Larger areas of intensive ICT activities, sometimes including a 1st tier region,

A deeper case-study level of analysis of the data shows that EIPES are characterised by several commonalities

innovation and business and have gained an enviable hub position in a usually very complex web of network connections.

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

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

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

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

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

centres Agrd 11 4 Scientific publications in Computer science Agrd 12 13 Internationalisation Outward ICT R&d internationalisation Intrd 1 4 Inward ICT

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

R&d centres Agrd 11 11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward

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

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

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

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

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

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

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

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

. 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

Port Talbot 32 272 Indicator description Indicator ID Agrd 12 Name of indicator Scientific publications in Computer science What does it measure?

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

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

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

definition of ICT patents following IPC taxonomy (OECD 2008b) Unit of observation NUTS 3 Source Patent data:

Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 79 Figure 43:

Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:

dimension Based on NACE Rev. 2 Unit of observation NUTS 3 Source European Investment Monitor by Ernst & young (Section 8. 5) Reference year (s

They are presented together with a first indication of the data sources used and their time coverage.

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.

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

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

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

Scientific publications in Computer science What does it measure? It measures the total number of ICT R&d FP7 projects to which organisations,

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

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

Web of Science b Thomson Reuters (Section 8. 3) Reference year (s) considered 2007-2011 2012 2000-2012 122 6. 1. 2

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

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.

To the extent allowed by the availability of indicators and data, a mix of measures capturing the input

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

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?

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

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.

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.

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.

In addition to the extent allowed by the availability of indicators and data, a mix of measures capturing the input

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

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:

R&d, innovation and business. 7. 1 Normalization and Rescaling of Data Before aggregating the information,

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

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,

Like any ranking at the global level, it is constrained by the availability of data from every part of its scope.

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.

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

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

The analysis of the Framework Programme 7 programmes and participants is based on the database provided by the DG Connect in November 2011,

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,

In addition, literature which shows the greatest impact in a field covered by Web of Science,

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

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,

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

Since 1997, data is collected for all European countries and is published on a quarterly basis. Up to 2011,

Projects included in the database have to comply with several criteria to be considered as international investments.

The basic description of each investment project described by the EIM data includes the name of the firm

The data collected by the EIM enables to: Review developments and movements in the inward investment marketplace, identify emerging sectors, industries and clusters,

Undertake in depth, wide-ranging data analysis; for example: Which is Europe's most popular location for headquarters 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),

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.

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.

when interpreting the data. The methodology developed to identify regions on the basis of addresses of the patents inventor (s)

data (assets, capital stock, number of employees, etc.)and R&d expenditures. This information was included combines information in the following sources:

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:

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,

Geographic coverage: EU 27; The ICT industry was defined according to the NACE Rev 2 definition of the ICT sector (OECD 2007;

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.'

'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.'

'How Well do Venture capital Databases Reflect Actual Investments?''In SSRN (Ed.).Nepelski, D. & De Prato, G. 2013a.'

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:

'Science, Technology and Industry Outlook.''Paris: OECD. European commission EUR 26579 Joint Research Centre Institute for Prospective Technological Studies Title:

JRC85356.pdf

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/.JRC85356 EUR 26264 EN ISBN 978-92-79-34484-8 (pdf) ISSN 1831-9424 (online) doi:

10.2791/35893 Luxembourg: Publications Office of the European union, 2013 European union, 2013 Reproduction is authorised provided the source is acknowledged.

Over this time, it developed a tool based on a database of original ICT activity indicators,

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,

29 4. 1 Normalization and rescaling of data...29 4. 2 European ICT Poles of Excellence Composite Indicator (EIPE CI...

30 4. 3 Sensitivity analysis...30 5 Data Sources...32 5. 1 QS WORLD UNIVERSITY RANKINGS by QS...32 5. 2 FP7 database by EC DG Connect...

33 5. 3 Bibliometrics: Web of Science by Thomson Reuters...33 5. 4 ICT R&d centre location:

Design Activity Tool by IHS isuppli...34 5. 5 European Investment Monitor by Ernst & young...34 5. 6 Patent data:

REGPAT by OECD...35 5. 7 Company-level information: ORBIS by Bureau Van dijk...36 5. 8 Venture capital:

Venturesource by Dow jones...37 6 Annex: Technical issues...39 6. 1 Definition and characteristics of a network structure...

39 6. 2 Patent data and patent-based internationalisation measures...42 References...47 5 1 Introduction ICT-related innovation is considered at the core of economic recovery, growth and productivity.

On the one hand, technological progress in ICT-producing sectors is an important driver of growth, as evidenced for example by its role in the productivity acceleration observed in the late 90s in the US.

On the other hand, ICT-enabled innovation in ICT using sectors has provided the base for permanent and widespread growth-enhancing effects of ICT adoption throughout the economy.

An additional challenge of the EIPE project was that this identification process had to be based only on the analysis of quantitative data,

The present report documents the methodologies and data sources used for this purpose. 1 Available at:

and also international reach and centrality in global networks. This view encompasses a certain affiliation with the concept of industrial clusters,

by determining the best available data sources, indicators and measurements that will help us to identify

Existing data sources also allow us to derive science and technology indicators such as the technology balance, bibliometrics or the technology intensity of the products or industries concerned (OECD, 2002.

Subject to data sources availability and their compliance with the EIPE project needs (see Section 2. 3),

In particular, the EIPE project builds up a measurement of ICT R&d activity by observing the actual presence of ICT technology producers (universities, companies, R&d facilities), their R&d expenditures and bibliometric data.

and acquisition of machinery, equipment and software. As a result, among the few available indicators of technology output, patent-based indicators are probably the most frequently used (Griliches, 1990;

Other ways of measuring innovation that make use of the existing data sources rely on the fact that high economic dynamics are a key source and channel of technological and non-technological innovation.

Subject to data sources availability and their compliance with the EIPE project needs (see Section 2. 3),

More disaggregated data on business activity show business indicators such as the number of firms, employment, capital, turnover, value added, profits,

The second is data on small businesses and their owners produced by a wide range of nonofficial organizations that aim to capture the dynamism in the economy.

Subject to data sources availability and their compliance with the EIPE project needs (see Section 2. 3),

the EIPE project builds this measurement by observing the actual presence and development of ICT firms (headquarters and affiliates, employment data, turnover and investments.

Agglomeration characteristics Spatial proximity of similar and related firms and industries and the general tendency of people and economic activity to locate in large cities and economic core regions lead to agglomeration.

and therefore is the most adequate for the EIPE project (see also 2. 2). Subject to data sources availability

the EIPE project builds up this measurement by observing the level of agglomeration of technology producers (universities, companies, R&d facilities), R&d expenditures and bibliometric data.

Subject to data sources availability and their compliance with the EIPE project needs (see Section 2. 3),

and their role, e g. broker-gatekeeper. 8 Subject to data sources availability and their compliance with the EIPE project needs (see Section 2. 3) the EIPE project takes into account the above mentioned approaches to identify measure

of computers and peripheral equipment 263 Manufacture of communication equipment 264 Manufacture of consumer electronics 268 Manufacture of magnetic and optical media ICT services 4651 Wholesale of computers

, computer peripheral equipment and software 4652 Wholesale of electronic and telecommunications equipment and parts 5820 Software publishing 61 Telecommunications 62 Computer programming,

consultancy and related activities 631 Data processing, hosting and related activities; web portals 951 Repair of computers and communication equipment With respect to the technology, examples of the characterization used include:

o Computer science and engineering with respect to university faculties, o Computer science with respect to scientific publications, o ICT hardware and software with respect to R&d activity performed in R&d centres,

o ICT technological fields defined by the International Patent Classification (IPC) system with respect to patents. Throughout the EIPE project, several approaches to specifying

the selection criteria varying between data sources. Choosing the spatial unit of observation One of the central problems in the quantitative analysis of the geography of economic activity is the lack of data at regional level with a satisfactory level of granularity (Koschatzky & Lo

2007). ) A region is defined as a tract of land with more or less definitely marked boundaries, which often serves as an administrative unit below the level of the nation state.

For practical reasons connected with data availability and regional policy implementation, the NUTS classification is based

NUTS 3 150 000 800 000 1303 The standard level of regional data availability provided by, for example,

For some statistics and some countries only NUTS level 1 data are available. For the purposes of this study, the NUTS 3 level was chosen as the unit of analysis,

and compare data in a harmonised and standardized way across the entire European union. This unit of analysis gives us the (theoretical) opportunity to observe over 1300 spatially standardised areas across the EU,

However, because different data providers use different data formats in reporting the names of organisations, the categorisation of data, the location and geographic information (e g. city, ZIP CODE,

which aims to map ICT-related activities and the lack therefore of a number of data at the general level,

Selecting and processing data sources The choice of the spatial unit of observation, i e. NUTS 3,

and the policy-driven focus on ICT creates a double constraint on data. It implies that in most cases,

there is no official data available to 15 illustrate the activities and characteristics as defined for the purpose of the EIPE project.

Hence, a number of data selected for the EIPE project come from nonofficial data sources, e g. private databases.

a range of the most reliable and recognized data providers were tested carefully and selected, such as Thomson Reuters for bibliometrics, Bureau Van dijk for company-level information, Dow jones for venture capital data, etc.

The eight primary data sources used in EIPE are the following: FP7 data on FP participation from EC DG Connect, REGPAT by OECD, QS WORLD UNIVERSITY RANKINGS by QS, Web of Science by Thomson Reuters, Design Activity Tool by IHS isuppli, European

Investment Monitor by Ernst & young, ORBIS by Bureau Van dijk, and Venturesource by Dow jones. 9 More details about these data sources can be found in Chapter 5. Selecting indicators A list of indicators for the EIPE project was selected carefully on the basis of the above-described framework of activities and their characteristics and the discussion on their empirical measurements.

In this selection process the following additional criteria were applied: Validity: an indicator must be able to capture a relevant dimension of the issues at stake.

In order to ensure this, indicators whose use can be traced and validated by previous research literature were selected (see Section 2. 2). Measurability:

or surveys. 9 Some secondary data sources were used such as the (ICT) industrial scoreboard (JRC-IPTS).

They are not listed here as they were used as secondary tools to support the processing and extraction of data from the primary ones. 16 3 EIPE indicators Table 2 offers a first schematic presentation of the organisation of the nine

However, due to the limited availability of data and potential indicators meeting the requirements of this study,

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

11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward ICT R&d internationalisation Intrd

and described in Table 4. They are presented together with a first indication of the data sources used and their time coverage.

ICT firms in the EU to a region's share in the EU population Region's share in the total EU FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE Rev

ORBIS by Bureau Van dijk (see Section 5. 7) FP7 database by EC DG Connect (see Section 5. 2) Reference year (s) considered 2011 2005-2011 2007-2011 10

of indicator FP7 participations FP7 funding to SMES FP7 participations by SMES Location of ICT R&d centres Ownership of ICT R&d centres Scientific publications in Computer science

in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of FP7 participations to a region's share in the EU population Region's share in the total EU FP7 funding

'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

. 4) Computer science as defined by Web of Science classification of Research Areas Unit of observation NUTS 3 Source FP7 database by EC DG Connect (see Section 5. 2) ICT

Web of Science by Thomson Reuters (see Section 5. 3) Reference year (s) considered 2007-2011 2012 2000-2012 Data on the agglomeration of ICT R&d is extracted from information

The performance of universities and computer science faculties across the world, as reported by the QS University ranking.

For a detailed description of the data source, see Section 5. 1. Information about the funding

For a detailed description of the data source, see Section 5. 2. The location and ownership of over 2, 800 ICT R&d centres belonging to more than 170 multinational ICT companies across the world

For a detailed description of the data source, see Section 5. 4. The scientific output

measured in terms of the number of publications in the computer science research area, of the research institutions in Europe for the period 2000-2012 from the Web of Science by Thomson Reuters. For a detailed description of the data source, see Section 5. 3. 20 Company-level

information on R&d expenditures in the ICT sector for the period 2005-2011 in Europe stemming from the ORBIS database by Bureau Van dijk. For a detailed description of the data source,

see Section 5. 7. 3. 1. 2 Internationalisation of ICT R&d (Intrd) The 2 indicators characterising the internationalisation of ICT R&d activity are listed

and described in Table 5. They are presented together with a first indication of the data sources used and their time coverage.

Another way of addressing the issue of ICT R&d internationalisation would be to look at the FP7 data.

However, due to its focus, this type of data would not allow us to take into account the global dimension of ICT R&d activity.

Thus, the information contained in FP data is used to construct other indicators e g. R&d agglomeration or ICT R&d networking.

Design Activity Tool by IHS isuppli (see Section 5. 4) Reference year (s) considered 2012 Data on the internationalisation of ICT R&d is extracted from information available about the location

For a detailed description of the data source, see Section 5. 4. 3. 1. 3 Networking in ICT R&d (Netrd) Networking measures addressing the ICT R&d activity rely on the network analysis of the locations of FP7

and described in Table 6. They are presented together with a first indication of the data sources used and their time coverage. 21 Table 6:

Source FP7 database by EC DG Connect (see Section 5. 2) Reference year (s) considered 2007-2011 Data on the networking of ICT R&d is extracted from information available about the funding

For a detailed description of the data source, see Section 5. 2. 3. 2 ICT innovation activities indicators 3. 2. 1 Agglomeration of ICT innovation (Agin

and described in Table 7. It offers a first indication of the data sources used and their time coverage.

To the extent allowed by the availability of indicators and data, the proposed indicators capture the input (investment in intangibles,

With venture capital data we aim to capture indirectly the dynamics of emerging new innovative companies:

at the time of publication of this report, there was no serious European-wide collection of data on these dynamics.

Similarly, patent counting and analysis has become one of the main acknowledged sources of information on innovation output across the world, particularly since the creation and divulgation of the EPO's PATSTAT database.

Venturesource by Dow jones (Section 5. 8) Patent data: REGPAT by OECD (Section 5. 6) Reference year (s) considered 2005-2012 2000-2012 2000-2009 Data on the agglomeration of ICT innovation is extracted from information available about:

Company-level information on investments in intangibles by over 1, 200 ICT firms located Europe wide in the period between 2005 and 2012 provided by ORBIS by Bureau Van dijk. For a detailed description of the data source,

see Section 5. 7. Over 26,000 venture capital deals executed in Europe in the ICT sector between 2000 and 2012,

data collected by Dow jones. For a detailed description of the data source, see Section 5. 8. Patenting activities of for over 5, 000 regions in the period between 2000 and 2009.

For a detailed description of the data source REGPAT by OECD see Section 5. 6. 3. 2. 2 Internationalisation of ICT innovation (Intin) The indicator characterising the internationalisation of ICT innovation activities is described in Table 8

. This table offers a first indication of the data sources used and their time coverage.

Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (Section 5. 6) Reference year (s) considered 2000-2009 Data on the internationalization of ICT Innovation is extracted from the information available about patenting activities for over 5, 000 regions

for the period between 2000 and 2009. For a detailed description of the data source, REGPAT by OECD, see Section 5. 6. 3. 2. 3 Networking in ICT innovation (Netin) The 4 indicators characterising the networking of ICT Innovation

activity are listed and described in Table 9. They are presented together with a first indication of the data sources used and their time coverage.

Networking measures addressing ICT R&d activity rely on network analysis of the locations of coinventors who are based in different locations

) Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:

REGPAT by OECD (Section 5. 6). Reference year (s) considered 2000-2009 Data on the ICT Innovation networking is extracted from the information available about global patenting activities for over 5, 000 regions

For a detailed description of the data source, REGPAT by OECD see Section 5. 6. 3. 3 ICT business activities indicators 3. 3. 1 Agglomeration of business activities (Agbuss

It offers a first indication of the data sources used and their time coverage. 13 To the extent allowed by the availability of indicators and data,

a mix of measures capturing business activities is proposed that, in addition, acknowledges the importance given to the business activities deployed by ICT multinationals

ORBIS by Bureau Van dijk see Section 5. 7) European Investment Monitor by Ernst & young (Section 5. 5) Reference year (s) considered 2005-2011 2005-2011

2005-2011 2000-2011 26 Data on the agglomeration of ICT business activities is extracted from information available about:

Company level information on investments in intangibles by over 1, 200 ICT firms located Europe wide for the period between 2005 and 2012 provided by ORBIS by Bureau Van dijk. For a detailed description of the data source,

data collected by Ernst& Young. For a detailed description of the data source, see Section 5. 5. 3. 3. 2 Internationalisation of ICT business activities (Intbuss) The 2 indicators characterising the internationalisation of ICT business activities are listed

and described in Table 11, which offers a first indication of the data sources used and their time coverage.

The measurement of the internationalization of business activity is proxied in EIPE by the information on the location of business affiliates owned by companies belonging to the (ICT) Industrial Scoreboard and the location of their respective Headquarters.

ORBIS by Bureau Van dijk (see Section 5. 7) Reference year (s) considered 2008 Data on the internationalisation of ICT business activity is extracted from company level information provided by ORBIS by Bureau

Van dijk. For a detailed description of the data source, see Section 5. 7. 3. 3. 3 Networking in ICT business activities (Netbuss) The 4 indicators characterising the networking of ICT business activity are listed

They are presented together with a first indication of the data sources used and their time coverage.

ORBIS by Bureau Van dijk (see Section 5. 7) Reference year (s) considered 2008 Data on the networking of ICT business activity is extracted from company-level information provided by ORBIS by Bureau Van dijk

. For a detailed description of the data source, see Section 5. 7. 14 We focus our attention on bilateral relationships between regions

and rescaling of data Most indicators are incommensurate with others, and have different measurement units.

Normalization process In order to normalise the data used in this study, a standardization method, i e. z-scores, is used.

in order to present EIPE CI on a scale from 0 to 100, the values are standardized with the Minimax procedure. 4. 3 Sensitivity analysis An important issue related to the construction of composite indicators is weighting.

a sensitivity analysis is applied. Sensitivity analysis is the study of how the uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input (Saltelli, Tarantola,

& Campolongo, 2000). 31 The weightage allocated to each sub-indicator is varied by between the three sub-indices in the following way:

and its results showed not to affect the final ranking in any significant way. 32 5 Data Sources The following eight databases have been the primary data sources used to elaborate the indicators and measurements of EIPE:

1. QS WORLD UNIVERSITY RANKINGS by QS, 2. FP7 database by EC DG Connect, 3. Bibliometrics: Web of Science by Thomson Reuters, 4. ICT R&d centres locations:

Design Activity Tool by IHS isuppli, 5. European Investment Monitor by Ernst & young, 6. Patent data:

REGPAT by OECD, 7. Company level information: ORBIS by Bureau Van dijk, 8. Venture capital: Venture Source by Dow jones. In the following sections,

each of the data source is described. 5. 1 QS WORLD UNIVERSITY RANKINGS by QS The rankings of Universities

and Computer science and Electronic Faculties originate from the QS WORLD UNIVERSITY RANKINGS. It was formed in 2008 to meet the increasing public interest in comparative data on universities and organisations,

and the growing demand for institutions to develop deeper insight into their competitive environment. 16 The QS WORLD UNIVERSITY RANKINGS currently considers over 2,

of which is Computer science, additional faculty-level information is extracted for the purpose of the EIPE study.

the EIPE study used QS proprietary datasets to investigate its subject area at three levels, namely academic and employer reputation surveys and the Scopus data for the Citations per Faculty indicator.

The data for citations originate from Scopus by Elsevier E. V. 17 Papers in Scopus are tagged with an ASJC (All Science Journal Classification) code

The main reason why this data source was selected for EIPE is that in addition to the university ranking, it also offers the rankings described above by teaching subject,

including Computer science. This information allows us to observe the location of research and education in ICT activities at world-level.

This data source, though carefully selected from a range of data sources pursuing similar purposes, shows some limitations.

The main constraint is that it offers only a limited number of universities, which does not allow us to cover the entire population of the European higher education institutions. 5. 2 FP7 database by EC DG Connect The Framework Programmes for Research and Technological Development,

also called Framework Programmes or abbreviated to FP1, through to FP7, are funding programmes created by the European union

The analysis of the Framework Programme 7 programmes and participants is based on the database provided by DG Connect in November 2011

The main reasons why this data source was selected for EIPE is that it offers a proxy for public R&d expenditures in ICT

This data source, though carefully selected, shows some limitations. The main constraint is that it offers only a limited snapshot of EU-level publicly-financed ICT R&d in Europe.

Web of Science by Thomson Reuters The Web of Science is an online academic citation index provided by Thomson Reuters. It is designed to provide access to multiple databases, cross-disciplinary research,

In addition, literature which shows the greatest impact in a field covered by the Web of Science,

The Web of Science has indexing coverage from 1900 to the present. 17 More information at:

the Web of Science encompasses over 11,000 journals selected on the basis of impact evaluations.

Coverage includes the sciences, social sciences, arts, and humanities, and it is also cross disciplinary. For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered.

The main reason why this data source was selected for EIPE is that it offers a comprehensive overview of scientific output throughout the world divided into individual research areas

which permits the inclusion of EIPE-relevant fields such as Computer science. This information allows us to observe the location of ICT R&d activity.

This data source, though carefully selected from a range of data sources pursuing similar purposes,

has some limitations. The main constraint is that it offers only limited possibilities with respect to the extraction of information at the level of, for example, authors.

Instead, only aggregation of information at the institutional level is possible. 5. 4 ICT R&d centre location:

Design Activity Tool by IHS isuppli The data used for the purpose of identification of ICT R&d centre locations originates from the 2011 IHS isuppli database,

The data on R&d locations is collected by IHS isuppli, an industry consultancy, 18 to map R&d locations

The main reason why this data source was selected for EIPE is that it offers relatively detailed unique information on the location and ownership of ICT R&d centres worldwide.

This data source, though carefully selected from a range of data sources pursuing similar purposes, shows some limitations.

For example, the characteristics of the dataset do not allow the building of time series. Also, the information available from this data source concentrates on the number of R&d centres, their ownership and location,

as detailed information on employment or R&d expenditures in those centres is not available at this level of granularity. 5. 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, but excludes investments in their home countries.

Since 1997, data has been collected from all European countries and is published on a quarterly basis. As of 2011,

it included over 40,000 observations. The EIM is recognized as a comprehensive industry standard, tracking investment projects across Europe.

The basic description of each investment project described by the EIM data includes the name of the firm, the parent company name

The data collected by the EIM enables to: Review developments and movements in the inward investment marketplace, identify emerging sectors, industries and clusters,

Undertake in depth, wide-ranging data analysis; for example: Which is Europe's most popular location for headquarters investments?

The main reason why this data source was selected for EIPE is that it offers relatively detailed unique information on new investments in Europe and,

This data source, though carefully selected from a range of data sources pursuing similar purposes,

has some limitations. For example, as the EIM relies on data collection from the media, the main advantage of this source of information,

i e. being up-to-data and the speed of the information provision, can also be a disadvantage.

This is related to the fact that not all investments are reported by the media and, hence, they will not be available from this source to the EIM. 5. 6 Patent data:

REGPAT by OECD The OECD REGPAT database stores patent data, based on patent applications to the EPO and PCT filings, linked to more than 5 500 regions using the inventors/applicants addresses.

This information has 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. The selection of ICT patents follows the definition by OECD (OECD, 2008b.

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.

utility facilities including telecommunications networks, airports, ports or other, fixed infrastructure investments, extraction activities (ores, minerals or fuels),

when interpreting the data. The methodology developed to identify regions on the basis of the addresses of patent inventors consists of an iterative procedure that matches postal codes and/or town names

The main reason why this data source was selected for EIPE is that it offers unique information on patenting activity at regional level across a number of countries,

This data source, though carefully selected, shows some limitations, which, if not taken into account, can affect the results of the EIPE project or their interpretation.

ORBIS by Bureau Van dijk Company-level information is taken from the ORBIS database by Bureau Van dijk. It contains comprehensive information on companies worldwide.

Geographic coverage: EU 27; The ICT industry was defined according to the NACE Rev 2 definition of the ICT sector (OECD, 2007;

Time coverage between 2005 and 2011, the last available date. Besides providing the company-level information that was used to count the number of firms or the employment,

and the location of their respective Headquarters originates from the Orbis database. The analysis presented in this report is based on company data from the 2009 EU industrial R&d Scoreboard 3 (henceforth the Scoreboard) in which R&d investment data,

and economic and financial data from the last four financial years are presented for the 1, 000 largest EU and 1, 000 largest non-EU R&d investors in 2008.

The Scoreboard covers about 80%of all company R&d investments worldwide. From the Scoreboard we have extracted the sub-set of ICT sector companies,

and then it was merged with the Bvd Orbis database. The R&d Scoreboard collects information on R&d investment

The merge with the database Orbis was done in order to collect the information on the individual shareholders that have relevant participations in group headquarters.

%As a result, in our database, the individual observation is a group, for which we have the R&d Scoreboard information together with information on up to a potential maximum of five shareholders, with their legal entity and details of the amount of shares.

%The main reason why this data source was selected for EIPE is that it offers unique and standardized information on company-level information for the ICT sector that can be presented regionalised

This data source, though carefully selected from a range of data sources pursuing similar purposes,

has some limitations. The most important limitation is the geographical coverage and the incompleteness of the data collected.

In addition, there are significant problems concerning the extraction of detailed information, e g. on a firm's ownership structure. 5. 8 Venture capital:

Venturesource by Dow jones Dow jones Venturesource provides comprehensive data on venture capital-backed and private equity-backed companies including their investors and executives in every region, industry sector and stage of development

2002), who provide a detailed overview of this database and compare it with Venture Economics (an alternative source of information),

the Venturesource data are generally more reliable, more complete, and less biased. This database contains information on venture capital transactions, the financed companies and the financing firms.

The data are reported largely self by venture capital firms, but the database conducted several plausibility checks.

The selection of ICT companies was based on Dow jones classifications and includes companies belonging to the following industry segments:

Communications & Networks, Electronics & Computers, Information Services, Semiconductors, Software and Other IT. This data source was selected for EIPE

because it offers unique and standardized information on venture capital deals with all the detailed information concerning the financed

and financing entities. In addition, it allows us to select deals that concern the ICT sector.

This data source, though carefully selected from a range of data sources pursuing similar purposes,

has some limitations. Venturesource relies on the voluntary information provision by Venture capital funds and companies.

Google's Pagerank is a variant of the Eigenvector centrality measure (Spizzirri, 2011. In practical terms, eigenvector centrality is a measure of the importance of a node in a network,

based on importance of its neighbours expressed by the quality of their connections. 42 6. 2 Patent data

App 1 and j N i ij App App 1 All computations for this case are shown in the middle part of Table 14.

7) i ij N j Inv Invapp 1 (7')j ij N i App Invapp 1 The bottom part of Table 14 indicates all computations

Computation of measures of internationalisation of three fictitious patents P p ij ijp Invinv Inv 1 j=US j=DE j=FR i N

Centrality in social networks conceptual clarification. Social networks, 1 (3), 215-239. Fujita, M, . & Thisse, J.-F. 1996).

Economics of Agglomeration: C. E. P. R. Discussion Papers. Fujita, M, . & Thisse, J.-F. 2002).

The internationalisation of technology analysed with patent data. Research Policy, 30 (8), 1253-1266. Hagedoorn, J,

How Well do Venture capital Databases Reflect Actual Investments? Kominers, S. 2013. Measuring agglomeration. Boston: Harvard university.

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:

Science, Technology and Industry Outlook. Paris: OECD. OECD. 2010. Measuring Innovation: A New Perspective. Paris:

A gravity model using patent data. Research Policy, 39 (8), 1070-1081. Puga, D. 2010.

Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society Series A, 168 (2), 307-323.

Sensitivity analysis as an Ingredient of Modeling. Statistical Science, 15 (4), 377-395. Spizzirri, L. 2011.

An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality.

Both of those identification processes are based on quantitative data, built on a set of relevant criteria leading to measurable indicators.

and data sources used in the study. z As the Commission's in-house science service,

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