Enterprise and Industry Innovation Union Scoreboard 2014 More information on the European union is available on the Internet (http://europa. eu) Cataloguing data can be found at the end of this publication.
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International data 4 Innovation Union Scoreboard 2014 Malta (MT), Poland (PL), Portugal (PT), Slovakia (SK) and Spain (ES) is below that of the EU average.
and at a higher rate than the EU. Methodological note The Innovation Union Scoreboard (IUS) 2014 uses the most recent available data from Eurostat and other internationally recognised sources with data referring to 2012 for 11
It monitors innovation trends across the EU Member States including Croatia, from this edition as the 28th Member State,
Data sources and data availability The Innovation Union Scoreboard uses the most recent statistics from Eurostat
The data relates to actual performance in 2009 (1 indicator 2010 (9 indicators), 2011 (4 indicators) and 2012 (11 indicators)( these are the most recent years for
which data are highlighted available as by the underlined years in the last column in Table 1). Data availability is good for 19 Member States with data being available for all 25 indicators.
For 7 Member States (Croatia, Cyprus, Estonia, Latvia, Lithuania, Malta, Slovakia and the UK) data is missing for one indicator
and for 1 Member State (Slovenia) data is missing for 2 indicators. For Venture capital investment data is available for 20 Member States.
Changes to the IUS 2013 Although the general methodology of the IUS 2014 remained unchanged there have been three modifications as compared to the IUS 2013.
At the request of the European council to benchmark national innovation policies and monitor the EU's performance against its main trading partners, the European commission has developed a new indicator on innovation output
By adding data on Employment in fastgrowing firms of innovative sectors there are positive rank changes for Estonia, Ireland and Spain and negative rank changes for Austria, Cyprus and Portugal (cf.
Data source: Numerator Data source: Denominator Years covered ENABLERS Human resources 1. 1. 1 New doctorate graduates (ISCED 6) per 1000 population aged 25-34 Eurostat Eurostat 2004
2011 1. 1. 2 Percentage population aged 30-34 having completed tertiary education Eurostat Eurostat 2005 2012 1. 1. 3 Percentage youth
Average performance is measured using a composite indicator building on data for 25 indicators going from a lowest possible performance of 0 to a maximum possible performance of 1. Average performance reflects performance in 2011/2012
due to a lag in data availability. 2 For non-EU countries the indicator measures the share of non-domestic doctoral students. 3 Section 6. 1 gives a brief explanation of the calculation methodology.
innovation performance per dimension 5 The variance of a data set is the arithmetic average of the squared differences between the values
Estonia's strong performance has to be interpreted with care as the score for this dimension is based on one indicator only (R&d expenditures in the public sector) as data on venture capital investments are not available.
and during the crisis. The eightyear period corresponds with data availability from the Community Innovation Survey starting with the CIS 2004.6 Performance changes over time will be discussed separately for each of the innovation performance groups.
Average performance is measured using a composite indicator building on data for 12 indicators ranging from a lowest possible performance of 0 to a maximum possible performance of 1. Average performance reflects performance in 2010/2011
due to a lag in data availability. Note: Average annual growth rates of the innovation index have been calculated over an eight-year period (2006-2013.
and the EU the growth rate for the EU in this figure is not comparable to the one discussed before. 30 Innovation Union Scoreboard 2014 Methodology For all countries data availability is limited more than for the European countries (e g. comparable innovation survey
data are not available for many of these countries). Furthermore, the economic and/or population size of these countries outweighs those of many of the individual Member States
patents) and there are no indicators using innovation survey data as such data are not available for most of the global competitors
or are not directly comparable with the European community Innovation Survey (CIS) data. The indicator measuring the Share of the population aged 30 to 34 having completed tertiary education has been replaced by the same indicator but for a larger age group,
namely 25 to 64 as data for the age group 30 to 34 is not available for most countries.
Indicators used in the international comparison Main type/innovation dimension/indicator Data source: Numerator Data source:
Denominator Most recent year Date not available for ENABLERS Human resources 1. 1. 1 New doctorate graduates (ISCED 6) per 1000 population aged 25-34 OECD, Eurostat OECD,
and Exports of knowledge-intensive services data are not available. Innovation Union Scoreboard 2014 33 graduates and Knowledge-intensive services exports the US has managed to improve its performance lead.
For international scientific co-publications and most-cited publications data are not available. Innovation Union Scoreboard 2014 37 performance leads Canada has on R&d expenditures in the public sector
For two indicators International scientific co-publications and Most-cited publications data are not available. 38 Innovation Union Scoreboard 2014 outperforming the EU only on two indicators:
For the indicator New doctorate graduates data are not available. 42 Innovation Union Scoreboard 2014 5. Country profiles This section provides more detailed individual profiles for all European countries.
Non-R&d innovation expenditures and for the Contribution of Medium and High tech exports to the trade balance.
No data for Venture capital investments. Innovation Union Scoreboard 2014 49 Ireland is an Innovation follower.
No data for Venture capital investments. 54 Innovation Union Scoreboard 2014 Italy is a Moderate innovator.
No data for Venture capital investments. 56 Innovation Union Scoreboard 2014 Latvia is a Modest innovator.
No data for Venture capital investments. Innovation Union Scoreboard 2014 57 Lithuania is a Moderate innovator.
No data for Venture capital investments. 58 Innovation Union Scoreboard 2014 Luxembourg is an Innovation follower.
No data for Venture capital investments. Innovation Union Scoreboard 2014 61 The netherlands is an Innovation follower.
No data for Venture capital investments. Innovation Union Scoreboard 2014 67 Slovakia is a Moderate innovator.
No data for Venture capital investments. 68 Innovation Union Scoreboard 2014 Finland is an Innovation leader
No data for Non-R&d innovation expenditures and SMES innovating in-house. Innovation Union Scoreboard 2014 71 Iceland is an Innovation follower.
No data for Venture capital investments, Non-R&d innovation expenditures and SMES innovating in-house. 10 Over the whole 2006-2013 period Community trademarks grew strongly as shown in the graph showing the growth rates per indicator.
No data for SMES with marketing or organisational innovations. 74 Innovation Union Scoreboard 2014 The Former Yugoslav Republic of Macedonia is a Modest innovator.
No data for Venture capital investments, PCT patent applications in societal challenges and Employment in fast-growing firms of innovative sectors.
No data for International scientific co-publications, Most cited scientific publications, Venture capital investments, PCT patent applications,
No data for Venture capital investments. Innovation Union Scoreboard 2014 77 6. Innovation Union Scoreboard methodology Step 1:
Setting reference years For each indicator a reference year is identified based on data availability for all countries for
which data availability is at least 75%.%For most indicators this reference year will be lagging 1 or 2 years behind the year to
Imputing for missing values Reference year data are used then for 2013, etc. If data for a year-in-between is not available we substitute with the value for the previous year.
If data are not available at the beginning of the time series, we replace missing values with the latest available year.
The following examples clarify this step and show how‘missing'data are imputed. If data are missing for all years,
no data will be imputed (the indicator will be left empty). 6. 1 How to calculate composite indicators The overall innovation performance of each country has been summarized in a composite indicator (the Summary Innovation Index.
The methodology used for calculating this composite innovation indicator will now be explained in detail. Example 1 (LATEST year MISSING) 2013 2012 2011 2010 2009 Available relative to EU score N/A 150 120 110 105 Use most recent
year 150 150 120 110 105 Example 2 (year-IN-BETWEEN MISSING) 2013 2012 2011 2010 2009 Available relative to EU score
Transforming data if data are skewed highly Most of the indicators are fractional indicators with values between 0%and 100%.
and can have skewed data distributions (where most countries show low performance levels and a few countries show exceptionally high performance levels).
and data have been transformed using a square root transformation: Venture capital investments, Publicprivate co-publications, PCT patent applications, PCT patent applications in societal challenges and License and patent revenues from abroad.
for two countries, Germany and The netherlands, data for Non-EU doctorate students have become available increasing the number of indicators for these two countries used for calculating the innovation index as compared to last year.
and a negative effect of using more recent data. 11 A geometric mean is an average of a set of data that is different from the arithmetic average.
IUS 2013 Due to Data updates More data DE, NL New indicator Total EU27---BE 0 0 0 0 BG 0 0 0
0 0 0 0 UK 0 0 0 0 The table on the right provides a breakdown of the change in performance rank due to 1) data updates,
2) improved data availability for Germany and The netherlands and 3) adding the new indicator on Fast-growing firms in innovative sectors.
The table shows that data updates are the main driver of rank changes causing a rank change for 12 countries.
Having additional data for Germany and The netherlands has no effect on the ranking of countries.
CII*=100*CII/CIEU Note that the results for country i depend on the data from the other countries as the smallest and largest scores used in the normalisation procedure are calculated over all countries. 82 Innovation Union
or English speaking countries given the coverage of Scopus'publication data. Countries like France and Germany
The 2010 Methodology report provides detailed instructions how to calculate this indicator (http://www. proinno-europe. eu/sites/default/files/page/11/12/IUS 2010 METHODOLOGY REPORT. pdf). 88 Innovation Union
graphic symbols and typographic typefaces but excluding computer programs. It also includes products that are composed of multiple components,
Knowledgeintensive activities are defined, based on EU Labour force Survey data, as all NACE Rev. 2 industries at 2-digit level where at least 33%of employment has a higher education degree (ISCED5
such as telecommunications, and provide inputs to the innovative activities of other firms in all sectors of the economy.
International data European commission Innovation Union Scoreboard 2014 2014 94 pp 210 x 297 mm ISSN 1977-8244 ISBN 978-92
You can obtain their contact details on the Internet (http://ec. europa. eu) or by sending a fax to+352 2929-42758.
Free publications: via EU Bookshop (http://bookshop. europa. eu). Priced subscriptions (e g. annual series of the Official Journal of the European union and reports of cases before the Court of Justice of the European union:
Data source: Eurostat Comparison with EIS 2009: The comparable EIS 2009 indicator focuses on doctorate graduates in science and engineering (S&e) and social sciences and humanities (SSH) following the recommendations received from Member States and experts during the revision of the EIS in 2008
Data source: Eurostat Comparison with EIS 2009: The comparable EIS 2009 indicator is defined more broadly as it takes the share of population aged 25-64 with tertiary education.
) 2010 MAIN TYPE/Innovation dimension/indicator COMMENT Data source Reference year (s) latest year used for IUS 2010 ENABLERS ENABLERS Human resources Human resources
(IUS) 2010 MAIN TYPE/Innovation dimension/indicator COMMENT Data source Reference year s) latest year used for IUS 2010 FIRM ACTIVITIES FIRM
TYPE/Innovation dimension/indicator COMMENT Data source Reference year (s) latest year used for IUS 2010 OUTPUTS OUTPUTS Innovators Innovators 3. 1
Data source: Eurostat 1. 2. 1 International scientific co-publications as%of total scientific publications of the country Numerator:
Data availability for this indicator is limited to the EU27 Member States. Note: This indicator was introduced to better capture research performance.
Data source: Science Metrix/Scopus 1. 2. 2 Scientific publications among the top-10%most cited publications worldwide as%of total scientific publications of the country Numerator:
or English speaking countries given the coverage of Scopus'publication data. Countries like France and Germany, where researchers publish relatively more in their own language,
Data source: Science Metrix/Scopus 1. 2. 3 Non-EU doctorate holders as%of total doctorate holders of the country Numerator:
Data source: Eurostat 1. 3. 1 Public R&d expenditures(%of GDP) Numerator: All R&d expenditures in the government sector (GOVERD) and the higher education sector (HERD.
Data source: Eurostat 1. 3. 2 Venture capital(%of GDP) Numerator: Venture capital investment is defined as private equity being raised for investment in companies.
Data are broken down into two investment stages: Early stage (seed+start-up) and Expansion and replacement (expansion and replacement capital.
Data source: Eurostat (EVCA (European Venture capital Association) is the primary data source for VC expenditure data) 9 2. 1. 1 Business R&d expenditures(%of GDP) Numerator:
All R&d expenditures in the business sector (BERD), according to the Frascati-manual definitions, in national currency and current prices.
Data source: Eurostat 2. 1. 2 Non-R&d innovation expenditures(%of total turnover) Numerator: Sum of total innovation expenditure for enterprises, in national currency and current prices excluding intramural and extramural R&d expenditures.
Data source: Eurostat (Community Innovation Survey) 2. 2. 1 SMES innovating in-house(%of all SMES) Numerator:
Data are taken from CIS 2008 questions 2. 2 and 3. 2, i e. those SMES which are either:
Data source: Eurostat (Community Innovation Survey)( cf. Box 1) 10 Box 1: Calculation of the indicator on SMES innovating in-house Data on product
and/or process innovators innovating in-house are not directly available from Eurostat. The indicator has been estimated as follows.
From Eurostat data are extracted online from inn cis6 prod-Product and process innovation for size categories between 10 and 49 and between 50 and 249 (i e.
From Eurostat data are extracted online from inn cis6 type-Enterprises by type of innovation activity for SMES on:(
product and process innovators Data on (9) Total enterprises are used for the denominator. Step 5:
because almost all large firms are involved in innovation co-operation. 11 Data source: Eurostat (Community Innovation Survey) 2. 2. 3 Public-private co-publications per million population Numerator:
‘research articles',‘research reviews',notes'and‘letters')published in the Web of Science database. These co-publications have been allocated to one
Data are two-year averages. Data source: CWTS/Thomson Reuters database. All data manipulations have been done by CWTS (Leiden University, http://www. cwts. nl.
2. 3. 1 PCT patent applications per billion GDP (in PPP€) Numerator: Number of patents applications filed under the PCT,
at internationational pase, designating the European Patent office (EPO). Patent counts are based on the priority date, the inventor's country of residence and fractional counts.
Denominator: Gross domestic product in Purchasing Power Parity Euros. Rationale: The capacity of firms to develop new products will determine their competitive advantage.
Data source: OECD/Eurostat Comparison with EIS 2009: This indicator replaces the EIS 2009 indicator on number of EPO patent applications per million population.
Data source: OECD/Eurostat 2. 3. 3 Community trademarks per billion GDP (in PPP€) Numerator:
Data source: OHIM (Office of Harmonization for the Internal Market)/ Eurostat Comparison with EIS 2009:
graphic symbols and typographic typefaces but excluding computer programs. It also includes products that are composed of multiple components,
Data source: OHIM (Office of Harmonization for the Internal Market)/ Eurostat Comparison with EIS 2009:
Data are taken from CIS 2008 questions 2. 1 and 3. 1, i e. those SMES which have introduced either:
for purchasing, accounting, or computing. Denominator: Total number of SMES. Rationale: Technological innovation as measured by the introduction of new products (goods or services) and processes is key to innovation in manufacturing activities.
Data source: Eurostat (Community Innovation Survey) 3. 1. 2 SMES introducing marketing or organisational innovations as%of SMES Numerator:
and/or organisational innovation to one of their markets Data are taken from CIS 2008 questions 8. 1 and 9. 1,
Data source: Eurostat (Community Innovation Survey) 14 3. 1. 1 High-growth innovative firms Numerator:
The report of the High Level Panel on the Measurement of Innovation3 has provided ample support for the use of an indicator on fastgrowing innovative firms.
Data source: Not yet available 3. 2. 1 Employment in knowledge-intensive activities as%of total employment Numerator:
Knowledge-intensive activities are defined, based on EU Labour force Survey data, as all NACE Rev. 2 industries at 2-digit level where at least 25%of employment has a higher education degree (ISCED5A or ISCED6).
(21) Manufacture of computer, electronic and optical products (26) Air transport (51) Publishing activities (58) Motion picture, video and television programme production, sound recording and music publishing activities (59) Programming
and broadcasting activities (60) Telecommunications (61) Computer programming, consultancy and related activities (62) Information service activities (63) Financial service activities,
except insurance and pension funding (64) Insurance, reinsurance and pension funding, except compulsory social security (65) Activities auxiliary to financial services and insurance activities (66) Legal and accounting activities (69) Activities of head offices;
report of the High Level Panel on the Measurement of Innovation established by Ms Máire Geoghegan-Quinn, European Commissioner for Research and Innovation.
Knowledge-intensive activities provide services directly to consumers, such as telecommunications, and provide inputs to the innovative activities of other firms in all sectors of the economy.
Data source: Eurostat Comparison with EIS 2009: The indicator on knowledge-intensive activities replaces EIS 2009 indicators 3. 2. 1 on employment in medium-high
Data source: UN Comtrade/Eurostat 3. 2. 3 Knowledge-intensive services exports as%of total services exports Numerator:
Data source: Eurostat (Balance of payments statistics)/ UN Service Trade 16 3. 2. 4 Sales of new to-market and new to-firm innovations as%of turnover Numerator:
Data source: Eurostat (Community Innovation Survey) Comparison with EIS 2009: This indicator combines EIS 2009 indicators 3. 2. 5 on sales of new to-market products and 3. 2. 6 on sales of new to-firm products. 3
Data source: Eurostat Note:.This is a highly skewed indicator and a square root transformation has been used to reduce the volatility and skewed distribution of this indicator.
Data availability The Innovation Union Scoreboard uses the most recent statistics from Eurostat and other internationally recognised sources as available at the time of analysis. International sources have been used wherever possible
Note that the most recent data for the indicators are available at different years (cf.
though the data relate to actual performance in 2007 (4 indicators), 2008 (10 indicators) and 2009 (10 indicators).
The availability of data country by country at each year is given in Table 2 showing that non-EU27 countries have lower availability.
The indicator Venture capital has the lowest data availability in the database (69%across all Countries.
Country by country data availability (in percentage) 2010 2009 2008 2007 2006 EU27 100 100 100 100 100 BE 100 100 100 100
Transforming data that have skewed highly distributions across Countries Most of the indicators are fractional indicators with values between 0%and 100%.
In the IUS 2010 report data are transformed using a square root transformation after outliers have been removed (cf.
Imputation of missing values If data for the latest year are missing, they are imputed with the data of the latest available year.
If data for a year-in-between are missing, they are imputed with the value of the previous year.
If data are not available at the beginning of the time series, they are imputed with the oldest available year (see Table 4). Table 4:
Examples of imputation Example 1 (latest year missing) 2010 2009 2008 2007 Available relative to EU27 score Missing 150 120 110 Use most recent year 150
130 120 Missing Substitute with oldest available year 150 130 120 120 In case the data for an indicator are not available for a given country at any time point
Transforming data highly skewed data Most of the indicators are fractional indicators with values between 0%and 100%.
and can have skewed data distributions (where most countries show low performance levels and a few countries show exceptionally high performance levels).
and data have been transformed using a square root transformation: Non-EU doctorate students, Venture capital, PCT patents in societal challenges and License and patent revenues from abroad.
(i e. 1/24 if data for all 24 indicators are available), contrary to option 1 above.
strategies to measure country progress over time, Joint Research Centre, mimeo. 9 A geometric mean is an average of a set of data that is different from the arithmetic average.
Data availability for this indicator is limited to the EU27 Member States. Belgium, Denmark, Finland, Netherlands and Sweden have more than 1000 copublications per million population.
or English speaking countries given the coverage of Scopus'publication data. Countries like France and Germany, where researchers publish relatively more in their own language,
For several countries data are not available as the domestic Venture capital markets are too small to collect such data.
graphic symbols and typographic typefaces but excluding computer programs. It also includes products that are composed of multiple components
http://www. proinno-europe. eu/EIS2008/website/docs/EIS 2008 METHODOLOGY REPORT. pdf) OECD-JRC (2008), Handbook on Constructing Composite Indicators:
but it inevitably requires retraining of users in the next upgrade, and can increase scepticism. Risk of errors and inefficiencies increases when organisations are forced to run paper and computer systems in parallel. 8,
9 Workarounds abound, the potential streamlining of work processes is hard to realise, and staff put great effort into maintaining multiple systems.
and safety gains by using computers to automate existing manual processes. For example, computerised ordering systems largely substitute paper orders with electronic orders.
and computer systems that introduce new patient risks, staff frustration, and outcomes below expectation. The focus must shift from automation of clinical work to innovation;
requiring more elegant machines and software, according to the technophiles'arguments. Nor is it mostly a behavioural problem,
ZDNET Australia 15 dec 2008. http://www. zdnet. com. au/news/software/soa/E-Health-Australia-s-5bn-blackhole/0, 130061733,339293816
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Kell J. How hospitals confront new technology. Health Aff (Millwood) 2006; 25: 163-173.19 Christensen C, Bohmer R, Kenagy J. Will disruptive innovations cure health care?
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+34 954488318 Fax:++34 954488300 http://ipts. jrc. ec. europa. eu http://www. jrc. ec. europa. eu This publication is a Working Paper by the Joint Research Centre
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/.JRC79478 EUR 25964 EN ISBN 978-92-79-29828-8 (pdf) ISSN 1831-9424 (online) doi:
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since then a large database of original ICT innovation indicators, enriched with geographical information in order to allow localisation and aggregation at NUTS 3 and NUTS 2 level.
What is the position of individual locations in the global network of ICT activity? To date, the following additional publications have emerged from the research:
11 3. Data...12 4. Characteristics of the international R&d centres network...17 Connectivity...18 Centrality and clustering...
21 Countries'positions in the network...25 Core and periphery of the R&d network...28 5. The determinants of international R&d linkages...
30 6. Empirical results...35 7. Conclusions...39 Annex...42 References...43 5 1. Introduction It is recognized well that a number of corporations have begun slowly to seek new knowledge sources
whether R&d networks exhibit the properties of the core/periphery structure and identify the members of each group.
This type of analysis has been applied, for example, to patent data (Chao-Chih 2009, Han and Park 2006, Lai et al. 2011, Stefano and Francesco 2004, De Prato and Nepelski 2012) and bibliometric
data (Glänzel and Schubert 2005, Glänzel et al. 1999, Kretschmer 2004. In this context, our work delivers a valuable contribution by extending the analysis of knowledge networks by using a different type of information
Section 3 introduces the data used in the study and Section 4 analyses the characteristics of the R&d network and countries'positions in the network.
Further measure of a node's position in the network used in this study relates to the extent of clustering between nodes.
This property of a network structure can by captured by the clustering coefficient (Watts and Strogatz 1998
In directed networks, the clustering coefficient cc i C of node i is defined as:((1)) i icc i i k ke C (13) where ki is the degree of Vi
The clustering coefficient of a node is always a number between 0 and 1, where for a fully connected network CC=1. International R&d centres as a network A straightforward way of representing international R&d centres as a network is through drawing a line connecting two countries that share an R&d centre
i e. number of R&d sites. Hence, the line value function is W=wij, where ij is the link between two countries
for example, hardware and software research activities belong to different R&d types (for a full list of R&d types considered in this study see Table 4). This, together with the above point on the corporate control and location of R&d centres, leads
i. 3. Data The data used in this paper originates from the 2011 edition of an originally assembled company-level dataset dedicated to observe the internationalization of R&d.
The data on R&d locations was collected by isuppli, an industry consultancy. 1 In order to check how representative the sample is compared,
Table 2 displays the distribution of companies by their sector of main activity together with the number of R&d centres belonging to each sector.
2 Electronic equipment 19 11,11 336 10,35 3 Telecommunications Equipment 18 10,53 356 10,96 4 Automobiles & Parts 16 9, 36 425
2, 25 12 Computer Services 4 2, 34 109 3, 36 13 General Industrials 4 2, 34 172 5, 30
Technology Hardware & Equipment 3 1, 75 10 0, 31 19 Software 2 1 17 31 0, 95 20 Construction & Materials 1 0, 58 8 0, 25 21 Industrial Machinery 1 0, 58 15
Regarding R&d types, hardware, software and components are the most common ones performed in the R&d centres included in the dataset. 17 Table 4:
R&d centres'application and activity types R&d application Frequency%in total R&d type Frequency%in total 1 Automotive 422 17.69 1 Hardware 1092 36.88
2 Wireless 389 16.30 2 Software 833 28.13 3 Industrial 348 14.59 3 Components 530 17.90 4 Consumer 272 11.40
40 1. 35 7 Computer 173 7. 25 7 Quality assurance 16 0. 54 8 Military/Aerospace 150 6. 29 8
Data excluding loops. Source: Own calculations 4. Characteristics of the international R&d centres network Our analysis of the global network of R&d centres starts with its graphical illustration in Figure 1. The arcs represent the existence of a relationship
where a company from one country owns an R&d centres in another country. Arcs are weighted by the number of sites owned by country
i (a) and the number of sites located in country i (b). The size of nodes is a function of the number of R&d types a country's firms"receive"through R&d centres located abroad (a)
and the number of R&d types a country'sends'through foreign-owned R&d centres located on its territory (b). This representation aims at capturing the direction
The global network R&d a) Nodes weighted by the number a country's overseas R&d centres b) Nodes weighted by the number hosted R&d centres Source:
7 Average in-strength 141 Average out-strength 58 Closeness centralization 0, 107 Betweenness centralization 0, 038 Clustering centralization 0, 457 Source:
Centrality and clustering Turning to other measures of the network, we first consider the measure of closeness centrality.
and numerous countries that are connected only to the core countries. Again, because of these properties, the global R&d network shows strong similarities to the network of international trade
Such distribution of the betweenness centrality measure might be a sigh that the network exhibits a core/periphery structure, in
which few countries form the core of the network and the remaining ones are placed at the outskirts of the network.
Turning to the clustering centrality, it is worth noting that an analysis of this measure can be found, for example,
because networks with strong clustering properties are likely to reflect some strong geographical structure in which short-distance links count more than long-distance ones.
In the context of the R&d network, the value of clustering coefficient is 0. 46,
This type of clustering behaviour lets us conclude that'local'links tend to play an important role.
Furthermore, the negative skew of the clustering coefficient's distribution indicates that most of the countries tend to 0 100 200 300 Frequency 0. 02.04.06.08.1 Betweenness centrality Frequency kdensity betweennesscentrality 24 be members
Clustering coefficient distribution Note: The probability density function was estimated using the kdensity estimation procedure, i e. univariate kernel density estimation.
when we look at the relationship between the clustering coefficient and nodes'degree and strength (see Table 11).
Like in the case of the betweenness centrality, this property might hint that the global R&d network has a core/periphery structure.
where the Pearson 0 5 10 15 Frequency 0. 2. 4. 6. 8 1 Clustering coefficient Frequency kdensity clusteringcoefficient 25 correlation value is either close to or above 0
i e. closeness centrality and clustering coefficient, the problem of multicolinearity seems to be of lesser importance.
The betweenness centrality index, b i C, in Table 6 reflects the position of a country as a core or a hub in the network of international R&d centres.
Own calculations 28 Core and periphery of the R&d network The observations made in previous section concerning the betweenness
and closeness centrality indices delivered some hints indicating that the analysed R&d network might have core/periphery structure.
we want to empirically assess to what this network can be described as a core/periphery structure.
The notion of core/periphery is based on the fact that many real world networks can be divided into two distinct subgroups of actors that can be identified by the type and number of connections.
One subgroup is referred to as a core and another as a periphery (Alba and Moore 1978, Laumann and Pappi 1977.
The core of network is a dense, well-connected subgroup and, conversely, the periphery consists of nodes loosely connected to each other,
but connected to some members of the core. In the terminology of block modelling, the core is seen as a 1-block,
and the periphery is seen as a 0-block, where 1 represents the existence of a connection between two nodes and 0 the lack of it.
The core/periphery structure has been found in a number of studies on, for example, scientific citations network (Doreian 1985), international trade (Smith and White 1992) or corporate structures (Barsky 1999.
i e. where nodes belonging to the core are connected with other nodes from the core and periphery and nodes belonging to the periphery are connected only with some core nodes,
can be defined as: i j i j i j a,,, (14) otherwise if c CORE or c CORE i j i j 01,15) where
i j a, indicates the presence or absence of a connection in the observed data,
i c refers to the group (core or periphery) to which node i is assigned to,
and i, j a pattern matrix reflects the presence or absence of a connection in the ideal image (Borgatti and Everett 29 2000).
i e. the measure of core/periphery structure, achieves its maximum when and only when (the matrix of i j a,)
In other words, reports the results of Pearson correlation and it can be said that a network exhibits a core/periphery structure to the extent that the correlation between the ideal structure
and the data is large. In order to detect the core/periphery structure in our data, we use a genetic algorithm to find a partition such that correlation between the data
and the pattern matrix induced by the partition is maximized. 3 The results of the analysis are reported in Table 7. After 50 iterations,
we find that the final fitness measure is 0. 906 at the significance level p<0. 001.
this indicates that the underlying data exhibits very strong core/periphery structure. This is further confirmed by the reported density measures for individual partitions.
Whereas the core-core partition has a density level at 0. 886, the same value for the periphery-periphery partition is only slightly higher than zero.
In other words, the nodes belonging to the core are connected very well with each other and relatively well connected with peripheral nodes,
Core/periphery model statistics Goodness of fit*Starting fitness 0, 906 Final fitness 0, 906 Density matrix Core Periphery Core 0, 886
the majority of countries in the core are developed countries with a relatively 3 This algorithm is implemented in UCINET software Borgatti, S. P.,Everett, M. G. & Freeman, L. C. 2002.'
Software for Social network Analysis.'Harvard, MA: Analytic Technologies..30 high level of GDP and a sound R&d landscape.
However, the presence of such countries as China, India or Taiwan in the core indicates that these developing countries are slowly taking major and indispensable roles in the global R&d network.
and constitutes the core of the network, whereas the second one comprises of a large number of heterogeneous countries that build the periphery of the network.
Block membership by country Core Periphery Canada, China, Finland, France, Germany, India, Italy, Japan, South korea, Netherlands, Sweden, Switzerland, Taiwan, UK, USA Argentina
and common language variables is CEPII bilateral trade data Head, K.,Mayer, T. & Ries, J. 2010.'
http://www. cepii. fr/anglaisgraph/bdd/distances. htm 7 Data stems from the IMF. For more information please refer to:
http://www. imf. org/external/data. htm 34 R&d and hence are likely to have more links.
and is computed through fractional counting of inventors in each priority patent application submitted in 2007 to one of 59 patent offices around the world. 8 Our methodology of computing patent statistics for the purpose of this paper follows (De Rassenfosse
it may be a sign of a success from the creation of international R&d sites in the form of inventions developed with domestic inventors,
Turning our attention to the core of our analysis, i e. the impact of a country's position in the global R&d network on the likelihood of forming a link and its intensity,
we observe that it is highly relevant for both the number of R&d sites and R&d types.
Moreover, the closeness centrality coefficient has a very strong positive effect on the number of R&d sites as well as on the number of R&d types existing per link.
countries with a higher number of linkages with other countries tend to have less intensive relationships in terms of the number of R&d sites
This type of clustering reveals that there are strong'local'links, which however do not imply geographical or cultural proximity,
explains the strong core-periphery characteristics of the R&d network. In such a network, a number of countries are connected to only a few network'hubs,
First of all, the data used in the study cover only a selection of companies. Second, the richness of information on R&d activities of these companies is limited also.
2 Closeness centrality 0, 496*1 3 Clustering coefficient 0, 322*-0, 097 1 4 In-degree 0, 796*0, 465
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we apply social network analysis to study the locations of international R&d centres and the relationships between the countries owning
and identify its core members. Further, we include network indices in an empirical analysis of the R&d internationalisation determinants.
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