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.
from monitoring applications such as closed-circuit television (CCTV) security systems to more advanced applications integrating live data and feedback from a variety of information sources (e g. parking guidance, weather information).
Various forms of wireless communication for both short-range and long-range data exchange (UHF, VHF, Wimax, GSM, etc.;
and more costly microprocessors, allowing for more sophisticated applications such as model-based process control and artificial intelligence;
Sensing technology employing sensors to feed control systems with both vehicle-based data (from devices such as radar, RFID readers, infrared-and visible-band cameras) and infrastructure-based data
and travel data; continuity of traffic and freight management ITS services in European transport corridors and conurbations;
and road/traffic data, including local roadworks. The in-vehicle technologies needed 3g telecommunications for the accuracy and speed of delivery to make services usable and useful
and that only really became possible a decade on.''A strong impetus to progress was given by establishment of the esafety Forum early in 2003, following consultation between the Commission, ERTICO ITS Europe, industry and public-sector stakeholders.
/2001 31/05/2004 Website http://www. transport-research. info/web/projects/Otherwise collectively described as Advanced Driver Assistance Systems (ADAS), these typically employ onboard sensors, together with digital maps and other computerised data,
Speed alert using satellite navigation data to signal that a vehicle is travelling too quickly when approaching a limited-speed road section.
'Improved human-machine interface It became apparent from an early stage that, given the large and growing range of available data sources and types,
Centralised processing of data on the natural and infrastructure conditions of a road network makes it possible to generate alerts,
and bridges can be combined with data from moving vehicles to provide operators, maintenance authorities and road users with rapid warning of emerging problems.
and transmits real-time data to a central server, where it can be analysed by sophisticated prediction and decision-making models.
‘Closing the loop by using the vehicles themselves to send data back to traffic control centres will bring great improvements in the efficiency of management and the safety of road users,
while also allowing much fuller coverage of the road systems than is possible today, 'says Vincent Blervaque, Director of Development and Deployment at ERTICO ITS Europe.‘
For interurban networks and secondary roads, greater reliance on in-car systems to provide‘floating car data,
'in conjunction with smaller amounts of roadside hardware, would allow coverage to be extended at much lower cost in terms of installation and maintenance.
so that they can exchange data with roadside infrastructure, display information to the drivers (or passengers on public transport) and communicate wirelessly with other vehicles and the infrastructure.
and the coordinated manner in which the data can be managed, will greatly increase the quality and reliability of personalised information available to drivers about their immediate environment and impending situations.
The same data can also be used to extend the functionality of in-vehicle safety systems for example,
Using real-time and context-specific data, trusted travel assistants will be able to plan each journey
based on data provided via RTTI services. For passenger transport, the envisaged systems embrace all types of mobility available to users buses, taxis, train, metro, walking, cycling, etc.
With increasing demand, especially in urban areas, it becomes more and more crucial to have ready access to accurate realtime data for pre-trip planning and on-the spot response to changing needs or conditions.
With the aid of cooperative systems, journey planners could ultimately provide real-time schedule data for individual bus stops or rail stations,
/2008 30/11/2010 Website www. access-to-all. eu WISETRIP Wide scale network of e-systems for multimodal journey planning and delivery of trip intelligent personalised data.
and are restricted in scale of coverage. Consequently, they do not respond to the need for multimodal travel.
including digital mapping, the monitoring of dangerous goods and live animals, and interoperability of electronic fee collection for trucks.
which will facilitate data sharing and exchange from different sources and provide data processing and management to support a variety of services.
New generations of traffic management systems will integrate data from vehicles, to provide dynamic, predictive and adaptive control of traffic flows.
data collection and information exchange via mushrooming social networking websites. C H A p T E R 8 Conclusions and the way forward I N t E L L
and transport data from various sources, with an emphasis on quality, standardisation and cost-efficiency;
This is also reflected by the participation data of the 7th Framework Programme: among the new member states the second highest number of successful applications was submitted from Hungary,
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Preliminary data R&d expenditure in Hungary
National Innovation Office, CORDA database R&d expenditure per company in Hungary between 2001 and 2011, by size classes (large enterprises and the average of all enterprises, HUF million
%and knowledge-intensive services exports (12%)according to additional data from the Innovation Scoreboard. Indicators in the fields of finance
Nevertheless, according to the estimates of the European commission (based on data of the European Venture capital Association) a significant change has occurred in the field of venture capital investments due to JEREMIE funds:
Data of the Innovation Union Scoreboard, 2011 Hungary Figure 10 Medium and high-tech product exports Non-EU doctorate students Venture capital as a per cent of GDP 2pct patent applications
Leading researchers use international sources, infrastructure and databases. Cutting-edge researchers capable of qualified and outstanding scientific performance;
IT-based national innovation service system with regard to the international best practices. 2) The operation of a central RDI information evaluation and service database
and copyright protection (e g. designs, database protection. 5) The implementation of the intellectual property protection strategy of the Hungarian Intellectual Property Office and its inclusion into the RDI strategy. 1) The support for establishing
According to 2011-2012 data almost 70%of research and development expenditure was financed by the business sector and 15-15%was financed from abroad
and interministerial co-ordination, monitoring the implementation of the strategy (the professional data collection and data analysis background
The compilation of strategic-level indicators and the collection of related statistical data is a task of the RDI Observatory.
panel data evidence for the OECD countries. OECD Economic Studies No. 33,2001/II. http://www. oecd. org/economy/productivityandlongtermgrowth/18450995. pdf Baumol, William (2005:
Empirical Evidence from Firm-level Panel Data. Institute of technology and Regional Policy-Joanneum Research. http://www. tip. ac. at/publications/schibany0304 rd%20financing. pdf Sveikauskas, Leo (2007:
o recognition of RDI activities by awarding RDI prizes, o ensuring media coverage of the corporate social responsibility activities (CSR) of innovative companies.
the indicator is certified based on data (e g. it is measured by an independent and trustworthy organisation or it is carried out administratively with a minimum chance for errors etc.).
KSH data until 2010 1, 4 1, 2 1, 0 0, 8 0, 6 0, 4 0, 2 0, 0 2
KSH data until 2010 1, 4 1, 2 1, 0 0, 8 0, 6 0, 4 0, 2 0, 0 2
KSH data until 2010 70 000 60 000 50 000 40 000 30 000 20 000 10 0000 Target value:
56,000 researchers and developers employed Figure 31 Since the values in the databases are updated not (the Register includes data on headcount from 2008 and 2009)
and sometimes they are not suitable for measurement (due to the ambiguous responses of data providers) the Register will be updated by the RDI Observatory in 2013
The number of the research institutions operating research infrastructures whose research personnel exceed the given number of researchers (according to 2008-2009 data) at least as many researchers are employed at the research organisation 10 15 20
The disciplinary classifications can be filtered from the database of the NEKIFUT project; however, these categories cannot always be brought into line with the disciplines in the Strategy a solution for the problem will be sought
The baselines of the larger research and technological development groups are determined according to the data of the NEKIFUT Register
and the databases of the Strategic Research Infrastructures (hereinafter referred to as SRIS) in the first instance (subsequently,
and nonliving material, data banks, information systems and services that are essential for scientific research activities and the dissemination of results.
Community Innovation Survey data The STI programme of measures was fulfilled by reviewing and evaluating 32 measures;
and Forfás and the most recent data was released by the CSO on 19 february 2013. This survey examines R&d activities performed across the business sector in 2011.
Forfás survey data is represented in the following charts by a perforated line. If you require further information about this survey please contact:
and below the OECD average of 1. 58 per cent. 1 Where data for 2011 was unavailable the next closest year was used 0. 16%0. 16%0. 17%0. 23
Comparing 2011 data from Figure 12 and 13 shows the occupations spending most of their time on R&d (as determined by FTES divided by headcount) are Phd researchers (87 per cent), other researchers (84 per cent),
or likely to recruit at Diploma level (down from 38 per cent in 2009) FORFÁS BERD 2011/2012 ANALYSIS 23 3. Number of R&d-performing firms In this section data gathered on the number of R&d-active companies
the right to be forgotten legal provision can significantly raise the cost of doing business for a wide range of data providers,
including cloud computing, Internet of things, data analytics and big data, IT-powered robotics, intelligent agents, mobile commerce, improved self-serve kiosks, 3d printing, location awareness, and machine learning.
THE IMPACT OF ICT ON EUROPEAN PRODUCTIVITY A principal reason the EU has had lower productivity growth than the United states
OECD data show that from 1985 to 2010 ICT capital contributed 0. 53 percentage points to the average annual GDP growth rate in the United states and 0. 56 percentage points in the United kingdom,
and standardized data exchange with trading partners contributed to significant increases in labor productivity. 60 Similarly,
ICT assets as percentage gross fixed capital formation, 201196 ICT investment shows up in survey data on ICT use as well.
while the latter is likely to delay progress in the emerging area of big data analytics. Regulations don't just increase costs poorly-designed
Recently several European countries have proposed data mining and data collection taxes, directed specifically at large internet companies such as Google
and Facebook. 125 Higher taxes on ICT-producing companies may raise the price of ICT goods and services for everyone else.
The french tax on data collection would tax companies based on the number of users they collect data on, apparently with no regard to the actual market value of the data.
many European countries have focused recently on building their own domestic data centers, rather than ensuring that European ICT users have access to the cheapest and highest quality cloud data providers.
This focus on the ICT-producing sector appears to be misplaced. Rohman finds that the beneficial effects of the ICT sector for the broader European economy declined after the year 2000.149 Other recent evidence has shown that most of the productivity gains from ICT are due to ICT-using sectors.
but it still leads to efforts to get a cloud data center in rural France, instead of helping French EU firms have been less willing or able to reengineer business processes around the use of ICT.
and PAGE 28 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 whether promotion of the former through higher tariffs or other restrictions (like on cross-border data flows) will be detrimental to the latter.
including broadband telecommunications, Internet usage, and data. They should allow companies to more rapidly depreciate ICT investments for tax purposes,
PAGE 31 THE INFORMATION TECHNOLOGY & INNOVATION FOUNDATION JUNE 2014 due to emerging data nationalism the idea that data must be stored domestically
Data nationalism is a false promise because it is unlikely to deliver the expected benefits of privacy and security,
data nationalist policies are already a reality in some countries: both the Norwegian and Danish Data protection Authorities have issued rulings to prevent the use of cloud computing services by municipalities
183 There has been talk as well by European leaders of building a European network for communication so that data never physically crosses the Atlantic. 184 By definition,
The responsible use of data can lead to productivity gains and innovation. However overly stringent privacy rules limit the ability of enterprises to obtain these gains. 185 For example,
ENDNOTES 1. The Conference Board, Total Economy Database: January 2014 (total GDP EKS, labor productivity per hour worked EKS;
accessed April 2, 2014), http://www. conference-board. org/data/economydatabase/;/author calculations following Marcel P. Timmer et al.
The Conference Board, Total Economy Database. 3. Ibid. 4. Ibid. 5. Ibid. 6. Mary O'Mahony and Bart van Ark, eds.
The Conference Board, Total Economy Database: January 2014 (Table 5; accessed April 2, 2014), http://www. conference-board. org/data/economydatabase/.
/10. Ibid. 11. Ibid. 12. Ibid. Note that EU-28 productivity actually decreases due to the less-productive EU-13 increasing their share of GDP. 13.
Data unavailable for Croatia, Estonia, Latvia, and Slovenia. 16. Ibid. 17. Ibid. Data unavailable for Croatia, Estonia, Latvia and Slovenia;
Romania excluded because its extremely low initial productivity makes it an outlier. 18. Robert D. Atkinson, Competitiveness, Innovation and Productivity:
The Conference Board, Total Economy Database: January 2014 (total GDP EKS, labor productivity per hour worked EKS;
accessed April 2, 2014), http://www. conference-board. org/data/economydatabase/;/Timmer et al. Productivity and Economic growth in Europe.
The Conference Board, Total Economy Database. Assuming 1. 6 percent productivity growth. 21. Ibid. Assuming yearly productivity growth for EU-15 after 1995 was the actual rate for the United states,
Guidelines for Collecting and Interpreting Innovation Data (OECD, 2005). 29. Robert D. Atkinson, Competitiveness Innovation and Productivity:
A Survey of the Literature, OECD Digital economy Papers, no. 195 (2012), http://dx. doi. org/10.1787/5k9bh3jllgs7-en;
and Jon D. Samuels, A Prototype Industry-Level Production Account for the United states, 1947-2010 (presentation to the Final World Input-Output Database Conference, Groningen, The netherlands, April
2013), 24, http://www. worldklems. net/data/notes/jorgenson ho samuels. USPRODUCTIONACCOUNT. pdf. 42. Ibid. 30; David M. Byrne, Stephen D. Oliner,
New Evidence from Sector-Level Data on Developed and Developing Countries (working paper, Center for Research on Information technology and Organizations, 2001;
Evidence from Firm-Level Data, Electronic commerce Research 9, no. 3 (2009): 173-81.61. Philipp Koellinger, Impact of ICT on Corporate Performance, Productivity and Employment Dynamics (European commission Enterprise and Industry Directorate General, December 2006), http://ec. europa. eu
firm-level evidence using data envelopment analysis and econometric estimations, OECD Science, Technology and Industry Working papers, no. 2002/13 (September 2002), http://dx. doi. org
OECD, Country Statistical Profile 2012 (Investment Data and Shares of ICT Investment in Total Nonresidential GFCF;
The Conference Board, Total Economy Database: January 2014 (Table 5; accessed April 2, 2014), http://www. conference-board. org/data/economydatabase/.
/85. National Science Foundation, Science and Engineering Indicators 2014 (Figure 6-7, ICT business and consumer spending as a share of GDP;
OECD Statextracts, Productivity Database By Industry 2012. Growth of labour productivity, in per cent, Business Services Sector;
Protecting Privacy, Transmitting Data, Moving Commerce (European Centre for International Political economy/U s. Chamber of commerce, March 2013), https://www. uschamber. com/sites/default
Jacob Albert, France Wants to Tax Data mining, and It's Not a Bad Idea, Quartz, January 22, 2013,
Data for the EU Member States, Iceland and Norway (Luxembourg: European commission-eurostat, 2013). 130. Lorin M. Hitt, D. J. Wu,
Daniel Castro, The False Promise of Data Nationalism (Information technology and Innovation Foundation, December 2013), http://www2. itif. org/2013-false-promise-data-nationalism. pdf. 183.
David Jolly, European union Takes Steps Toward Protecting Data, New york times, March 12, 2014, http://www. nytimes. com/2014/03/13/business/international/european-union-takes-steps-toward-protecting-data. html. 187.
David Streitfeld European Court Lets Users Erase Records on Web, New york times, May 13, 2014, sec. A. 188.
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.
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
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.
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.
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
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
International data European commission Innovation Union Scoreboard 2014 2014 94 pp 210 x 297 mm ISSN 1977-8244 ISBN 978-92
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