to harvest the potential of Big data? To meet these challenges, innovation is no longer a simple strategic option,
and artificial intelligence, will open new business models and opportunities for growth. Future convergence will be defined as convergence of products (Eg. electric car
for backup or peak power. The power is controlled through data communication processes between the vehicle, an EV charging stand and the home energy management (HEM) system.
This demographic data implies a shift in healthcare spending and policy reform the two main areas that will impacted he hugely by this trend.
Social Innovation to answer Society's Challenges 2014 Frost & Sullivan 12 www. frost. com Access to healthcare, insurance coverage, pension reforms,
Smart technology will create an opportunity for our growing cities to operate far more efficiently than they do today using interconnected sensors and data analytics;
and data sharing to deliver joint service. Another is‘Nemid'a unique digital identity developed for Danish citizens to access government services and private services such as internet banking and postal services. 4. Live Example:
and business) o There is an opportunity for technology convergence (such as digital intelligence, internet of everything and data analytics in buildings, homes, grids, water networks, hospitals, cities, factories and transport
RESEARCH METHODOLOGY Frost & Sullivan has employed a multifaceted methodology leveraging its unparalleled global database covering more industry ver ticals across more metrics of measurement in more geographies than any other firm globally.
and enriched with secondary research across a host of both external and internal data sources,
data-driven approach to market quantification and forecasting through triangulated data inputs to derive its initial quantification of the global market potential represented by Social Innovation.
Social Innovation to answer Society's Challenges 2014 Frost & Sullivan 23 www. frost. com This Whitepaper has been developed by Frost & Sullivan in conjunction with Hitachi, Ltd.
This information, data and reports are needed more promptly and must be produced more speedily. This should include progress updates on projects from implementing bodies as well as quantitative data;
-The levels of bureaucracy and administrative burden on beneficiaries within the RDP need to be reduced further
Big data is a goldmine for companies...p. 6 Boosting e-skills in European higher education requires political will at national level...
while helping Europe to reap the benefits of the booming digital economy, Microsoft's senior director for EU institutional Affairs said.
At the same time, digitisation created six million jobs globally in 2011, despite the economic downturn, as ICT is adopted widely in all corners of society.
Experts believe a new wave of big data and smartphone applications has the highest potential in terms of job creation.
there is one EU data commissioner but the legislation in countries is still different. When we go to Germany,
Big data is a goldmine for companies Computer algorithms are better at diagnosing severe cancer than humans,
and big data can predict crimes before they are committed and earn businesses money. Kenneth Cukier is data editor at The Economist
and co-author with Viktor Mayer-Schönberger of Big data: A Revolution That Will Transform How We Live Work
and Think. Translated into 20 languages, the book was a New york times Bestseller. He spoke to Euractiv's James Crisp about what big data can teach us.
What is big data? Well there's no single definition, which is probably a good thing, because to define it is to constrain it.
Broadly speaking, though mankind has more information now than ever, and these huge amounts of data can teach us things that are extremely interesting,
in fact things we would never have been able to find out with smaller amounts. That's done by placing different algorithms onto these large amounts of data.
Let me give you an example. Google handles more than a billion searches in the United states every day and stores them all.
and compared them against historical influenza data from the Centers for Disease Control and Prevention.
After running half a billion calculations against their data Google identified 45 terms that strongly coincided with CDC's data on flu outbreaks.
The Google trends method has been criticised, because its been wrong in some instances. However that is not the whole story.
The autopilot system on their airplanes collects data. Some of the data it collects has improved actually the accuracy of German weather forecasting by 7%
which is a considerable improvement. Lufthansa now sells that data to a meteorological company, which is a great example of how big data can be commodified.
So big data can be sold? Absolutely. In fact big data is a potential gold mine. There are a few forward-thinking companies who have realised they can sell the data they collect as they go about their everyday work.
It will be a revenue generator. In the future I expect to see companies employing data or chief information officers,
who will be responsible for this. It's not just companies. In the future, each of us will be able to sell our data.
People will upload data to online data exchanges, neutral platforms which can bring the data to the marketplace for a fair price.
And there will be a market for this data as people realise the enormous potential of big data.
Will there be an impact on how people work? There will be a significant impact. This will be a revolution in the workplace.
Both white colour and blue collar jobs will be replaced by big data, but that destruction will also create jobs.
It's a demonstrable fact that a computer algorithm is better at diagnosing severe cancer than a human.
But in in a world where data shape decisions more and more, what purpose will remain for people,
or for intuition, or for going against the facts? Personally, I believe there will always remain a need for the human touch.
But it is hard to predict the impact of the big data revolution. What can policymakers do to ensure that the power of big data can be exploited?
The issue of data privacy and protection has been deservedly getting a lot of attention recently. What needs to happen is a change in law to reflect the reality of this type of statistical collection
and ensure it is aligned with our values. Current laws are broadly based on the idea of notice and consent.
Essentially, this means that if you want to use someone's data, you have to tell them what you are collecting and why.
That isn't really feasible with big data. Continued on Page 7 Euractiv ESKILLS FOR GROWTH SPECIAL REPORT 5-9 may 2014 7 Boosting e-skills in European higher education requires political will at national level With 25%of adults in the European union
lacking the necessary digital skills to effectively use information and communication technologies, according to a report by the Organisation for Economic Cooperation and Development (OECD) published in autumn 2013,
the European commission is facing various challenges in order to bridge the competitive gap with the rest of the world.
it is impossible to know what purpose the data will be used for. Small data is like a waltz.
There's a clear tempo with known steps. Big data is like a mosh pit or jazz-improv.
No one knows what's coming next. So regulators need to support this new reality, not least because of the huge potential of big data.
We need to move from a notice and consent to a system of consent which allows a person to give consent,
for that data to be used and reused and reused without knowing what the specific purpose is.
What are the dangers of big data? Of course there are risks, and there will be challenging questions for us to answer as we enter this new reality,
Big data could be used to predict which people are most likely to commit murder. That throws up interesting questions.
There is an argument to suggest that the 2008 Financial crisis was in a way a crisis of big data.
But despite that I am convinced big data will change the world for the better. Continued from Page 6 Continued on Page 8 8 5-9 may 2014 SPECIAL REPORT ESKILLS FOR GROWTH Euractiv between member states
in order to adjust it to the digital economy. Every year, approximately 100,000 new vacancies are created in an attempt to fill the gap between the‘e-skilled south'and‘e-demanding north'of Europe.
Athens signs National Coalition for Digital economy Greece is also signing the National Coalition for the Digital economy,
The National Coalition for Digital economy is our commitment. We mean it when we say that the digital literacy is on the top of our agenda,
Greek minister for Education Konstantinos Arvanitopoulos, told Euractiv Greece that the EU is prepared not efficiently for the challenges of the digital economy,
Need is the mother of innovation Chatzidakis noted that the revenues from big data are expected to amount to €16 billion on a global level,
The data mentioned is catalytic and shows us that this is the direction we need to move in,
and provides data, and other evidence that demonstrates how a business creates and delivers value to customers.
but today, riding on rails of application programming interfaces (APIS) and broadband fiber optics, we can‘‘mash up''digital services like Google's maps and Facebook's social newsfeed in no time and on a shoestring budget.
originally prepared for the White house Conference on Understanding the Digital economy: Data, Tools and Research, held at the U s. Department of commerce, Washington, D c.,25-26 may 1999.
It draws upon joint work with Edward Steinmueller, and with Gavin Wright, and has had the benefit of detailed editorial comments from Erik Brynolfsson.
Forthcoming in Understanding the Digital economy, eds. E. Brynolfsson and B. Kahin (eds. MIT Press. Please do not reproduce without author's expressed permission.
<paul. david@economics. ox. ac. uk>Understanding the Digital economy's Evolution and the Path of Measured Productivity Growth:
that mounting concerns about the absence of an evident link between progress in digital information technologies
"The precipitating event in the formation of this"problematic"view of the digital information technology was an offhand (yet nonetheless pithy) remark made in the summer of 1987 by Robert Solow, Institute Professor at MIT and Economics Nobel laureate:"
and so better understand their bearing upon the likely future productivity performance of the digital economy. Having persisted since 1989 in advancing the latter, regime transition interpretation of the so-called productivity paradox,
My approach to understanding the implications of the emerging digital economy continues to rest upon the idea that we are in the midst of a complex, contingent and temporally extended process of transition to a new, information intensive techno-economic regime;
therefore, that the supplanting of the Fordist regime by one developed around digital information processing and its distribution via electronic and electro-optical networks has turned out to be an affair in
the future may well bring a strong resurgence of the measured total factor productivity residual that could be attributed reasonably to the exploitation of digital information technologies.
The development and exploitation of digital information like previous profound historical transformations based upon new general purpose engines, turns out to entail a complicated techno-economic regime transition
particularly that of the diffusion of the electric dynamo, may justifiably be used as a source of insights into the dynamics of the digital economy and its productivity performance.
Gordon (1998a) presents more finely disaggregated data on labor productivity, which reveals the pervasiveness of the slowdown. 9 pronounced between the period 1948-66 and 1966-89.
and there is some basis for believing that during the past two decades these may well have become more pronounced in their effect on the accuracy of the official price deflators.
and improved access to marketing data are indeed enabling faster, less costly product innovation, manufacturing process redesign,
By combining this with data from Bailey and Gordon (1988) on the rising number of products stocked by the average U s. grocery supermarket,
It should not be surprising that the accuracy of a statistical system designed to record productivity in mass production
which is becoming increasingly widespread as digital information technologies diffuse throughout the economy, deserves further consideration. 3. 2 Leaving out investments in organizational change:
and the advent of digital information processing technologies in particular, having stimulated the creation of new software assets within the learning organizations,
and backup of electronic copies of documents all now enter into the task of producing a business letter.
and recorded are they likely to find their way into the adjunct studies that are performed to test the accuracy of more abstract productivity measurement systems. 26 The following draws upon a more detailed treatment of the productivity implications of the general purpose formulation computer technology
the spread of partially networked personal computers supported the development of new database and data entry tasks, new analytical and reporting tasks,
The primary bridge between these application environments was the widespread use of the IBM 3270, the DEC VT-100 and other standards for"intelligent"data display terminals, the basis for interactive data
One cannot simply infer the detailed future shape of the diffusion path in the case of the digital information revolution from the experience of previous analogous episodes;
which still lie before us in time. 6. Historical Perspectives on the Growth of Measured Productivity in the Digital economy 38 See David (1991a), Technical Appendix for this demonstration. 22 The historical trajectory of computer technology development, long overdue for change,
and related data logging devices were to be found in the hands of maintenance, restaurant, and factory workers.
These more"task specialized"devices have become sufficiently ubiquitous to provide the infrastructure for task-oriented data acquisition and display systems,
and maintenance of critical company data resources must be resolved, and these often are compelling enough to force redesign of the organizational structure, Thirdly,
Applications and their maintenance can be controlled by the technical support team who would previously have been responsible for the company's centralized data resources.
An analogous structural change has been envisaged, based on the development of digital information appliances--hand-held devices or other robust specialized tools that are carried on belts,
and telecommunications components that allow them to be linked through sophisticated networks to other such appliances, mainframe computers and distributed databases,
Prepared for the White house Conference on Understanding the Digital economy, WASHINGTON DC, May 25-6, 1999. David, Paul A,
and the Data Constraint, American Economic Review, Mar. 1994,84, pp. 1-23. Griliches, Zvi, Comments on Measurement Issues in Relating IT Expenditures to Productivity Growth, Economics of Innovation and New Technology, 3 (3-4), pp. 317-21,1995:
Firm-Level Evidence from Government and Private Data Sources, 1977-1993, Canadian Journal of Economics, 1998.
%and company database server (54.7%).53.4%of the companies that used Cloud computing did so by paying any service existing in servers of shared services suppliers.
according to the data from the first quarter 2014.67.7%of micro-companies had Internet access, and 99%of them used some broadband access solution.
we use a survey database that was collected by Romanian National Trade Registration Office-main legal entity with function of keeping the register of trade.
Data collection was done over a 2 month period during September-October 2014. To reliably identify trends only respondents with long tenure
-organizational-or marketing-related innovation as defined by the Oslo manual (a set of integral guidelines for the collection of innovation data,
Digital Agenda Targets Progress report Digital Agenda Scoreboard 2015 1 Digitisation has been changing not just our economy
which is affecting all sectors of the economy and society the digital economy. These changes are happening at a scale
Digitisation has been good for Europe. Between 2001 and 2011 digitisation accounted for 30%of GDP growth in the EU. However, in other countries,
the contribution of ICT to growth was much higher. In recent years, it has become clear that one reason for the relative mediocre performance
Digital Agenda Scoreboard 2015 2 3 Total NGA coverage has reached now 68%in a gain of 6 points in the last year and of 39 points over the last five years.
At the end of 2014, Cable Docsis 3. 0 had the largest NGA coverage at 43, %followed by VDSL (38%)and FTTP (19%).
while VDSL coverage doubled in the last three years. There was a remarkable progress also in FTTP growing from 10%in 2011 to 19%in 2014.
rural coverage is only 25, %coming mainly from VDSL. Next Generation Access: the cities are covered mostly Digital Agenda Scoreboard 2015 0%20%40%60%80%100%20102011201220132014next Generation Access (NGA) broadband coverage in the EU, 2010-2014 Source:
IHS, VVA and Point Topic 0%10%20%30%40%50%60%70%80%90%100%ELITFRPLHRSKCZEUROBGIEESFISENOSIHUCYDEEEATUKPTLVISDKLULTNLBEMTTOTALRURALNEXT Generation Access (FTTP, VDSL
and Docsis 3. 0 cable) coverage, 2014 Source: IHS and VVA Roaming: the effects of legislation Digital Agenda Scoreboard 2015 4 0, 000,050, 100,150, 200,250, 300,350, 400,450, 5020072008200920102011201220132014average roaming price per minute for calls madeaverage price per minuteprice
energy use egovernment completed forms Targets possibly achieved in 2015 Non-usage (probable) Overall egovernment (possible) Too early to tell NGA coverage 100 Mbps take-up R&d in ICT
WP2 analysed available data to better understand the growth, impact and potential for social innovation in Europe.
which will require much more broad-scale data. WP3: Removing barriers to social innovation. The development and growth of social innovation is impeded by factors such as limited access to finances, poorly developed networks and intermediaries and limited skills and support structures.
Data and monitoring. Most of the future research questions we identified would benefit greatly from advanced databases containing information on social innovation, social needs, the social economy and its innovative potential, other environments of social innovation, relevant
actors and networks, technological innovations, etc. Civil society and the social economy as incubators. Our hypothesis that civil society provides a particularly fertile ground for the generation
and tap into existing data sources on national technological innovation systems. Social movements, power and politics.
and employs up to 10%of the total workforce in Germany. 47 In other countries (as is the case in Greece) there is no data to be found on employment in the social economy.
Thus we are still lacking more comprehensive and comparable data on the sector. The Third Sector Impact project that started early in 2014 will help to make this data available. 48 Nonetheless, the extent to
which social economy organisations are in fact innovators depends on numerous variables, e g. the size of the social economy and also on the welfare regime.
scope and impact of social innovation and adds complications to producing reliable data. Concerning metrics for social innovation
We should therefore try to harness relevant knowledge in the field and tap into existing data sources on national technological innovation systems.
It has become clear that survey-based data related to social innovation are necessary. Considering the importance of entrepreneurial activities as push-factors for social innovation, we need empirical survey data on organisations that are socially innovative
in order to better understand how social innovation emerges and how well it develops in societies. Figure 1:
and data sources on national technological innovation systems and make attempts to identify patterns in these systems. 3. Empirical testing of the proposed indicator system.
and contexts and could lead to more homogeneous data about social innovation and opportunities for social innovations in future.
and advocacy New flows of information (open data) Developing the knowledge base INTERMEDIARIES Social innovation networks Centres for information and evidence Hubs for diffusion and adoption Platforms for open
data/exchange of ideas Providing programmes/interventions Networking opportunities/events Information and brokerage support Knowledge transfer programmes Learning forums and insight legal advice, marketing services, fis cal and accounting services, HR advice
for example by civil organisations or the public sector who use data to better target pockets of social need
which are data-and analytics-heavy, and where high speed and global reach are important through reductions in transaction costs and increases in process efficiency.
Data and monitoring It is clear that we require more and better data on social innovation, social needs, the social economy and its innovative potential, other environments of social innovation, relevant actors and networks, technological
Most of the future research questions we identified would benefit greatly from advanced databases containing information on these and other variables.
and nuanced databases would yield. Currently, different approaches to create such databases are on their way:
The research centre CRISES81 in Canada builds a relational database on social innovations; the SI-Drive project82 will produce a database of around 1000 global social innovations;
and the ICSEM project83 based in Belgium is in the process of building a database on different types of social enterprises all over the world.
These efforts are not coordinated at the moment. It will be a task for future research to develop a standard structure that allows such data to be combined
and compared. Civil society and the social economy as incubators Our hypothesis that civil society provides a particularly fertile ground for the generation
the connection between social economy organizations and social innovation requires more data for sound analyses.
All of this requires much more empirical data, in particular data separately considering socially innovative organisations. 38 SOCIAL INNOVATION THEORY
AND RESEARCH Effective collaborations It is evident that the nature of social innovations requires various actors to collaborate to make them successful (e g. for reasons of resource acquisition and allocation, for raising legitimacy, for reducing barriers or for spreading).
and tap into existing data sources on national technological innovation systems. Social movements, power and politics What can we learn from the literature on social movements?
Data have been collected by Bureau Van dijk Electronic Publishing Gmbh under supervision by Mark Schwerzel, Petra Steiner, Annelies Lenaerts and Roberto Herrero Lorenzo.
Our goal is to ensure that the data are accurate. However, the data should not be relied on as a substitute for your own research or independent advice.
We accept no responsibility or liability whatsoever for any loss or damage caused to any person as result of any error,
omission or misleading statement in the data or due to using the data or relying on the data.
If errors are brought to our attention we will try to correct them. EUR 26221 EN ISBN 978-92-79-33743-7 (print) 978-92-79-33742-0 (pdf) ISSN 1018-5593 (print
Scoreboard 5 Summary The 2013"EU Industrial R&d Investment Scoreboard"(the Scoreboard) contains economic and financial data for the world's top 2000 companies ranked by their investments in research and development (R&d.
The Scoreboard data are drawn from the latest available companies'accounts, i e. usually the fiscal year 2012 or 2012/131.
More salient facts observed from the analysis of 2012 and historic company data since 2003 include:
Figure S1 below shows the longerterm R&d trends for a subset of Scoreboard companies with available data for the past nine years.
For 1496 out of the top world 2000 companies in the Scoreboard with data for the whole period.
figures S2-S4 below show the longer-term R&d trends for subsets of Scoreboard companies with available data for the past nine years.
For 334 out of the top EU 527 companies in the Scoreboard with data for the whole period.
For 547 out of the top US 658 companies in the Scoreboard with data for the whole period.
For 324 out of the top Japanese 353 companies in the Scoreboard with data for the whole period.
For 350 EU and 566 US out of the top world 2000 companies in the Scoreboard with data for the whole period.
The relative size has been calculated as the ratio of sector R&d expenditures in EU over US considering the 136 companies with R&d data for the whole period. 12 The 2013 EU Industrial R&d Investment
Inflows of FDIS in R&d by main world regions 2003-2012 Data: FT fdi Markets database.
Source: The 2013 EU Industrial R&d Investment Scoreboard European commission, JRC/DG RTD. The 2013 EU Industrial R&d Investment Scoreboard 13 Introduction In 2013, we continued implementing changes in the EU Industrial R&d Investment Scoreboard (the Scoreboard) 2 aiming to enhance its capacity to monitor
increasing the geographic and time coverage and the number of companies. The target is to cover fast-growing medium-sized companies, particularly those in key sectors such as health and the ICT-related industries.
while maintaining an EU focus by complementing this coverage with the inclusion of the top 1000 R&d investing companies based in the EU4.
so that the companies'economic and financial data can be analysed over a longer period of time. For the second year, data are now being collected by Bureau Van dijk Electronic Publishing Gmbh,
following basically the same approach and methodology applied since the first Scoreboard edition in 2004.
Please see the main methodological limitations summarised in Box 1 and detailed methodological notes in Annex 2. The capacity of data collection is being improved by gathering information about the ownership structure of the Scoreboard parent companies
Companies'behaviour and performance can be analysed over longer time periods using our history database that contains information on the top R&d companies since 2003.
An analysis of the main indicators of the company data aggregated by world regions is included together with the performance of companies over the period 2004-2012.
Finally, chapter 6 presents an analysis based on data about foreign direct investments (FDIS) made by the Scoreboard companies.
and the listing of companies ranked by their level of R&d investment is provided in Annex 3. The complete data set is freely accessible online at:
http://iri. jrc. ec. europa. eu/scoreboard13. html In the next edition, this website will allow user-friendly and interactive access to the individual company data
The 2013 EU Industrial R&d Investment Scoreboard 17 Box 1. Methodological caveats Users of Scoreboard data should take into account the methodological limitations summarised here,
when comparing data from different currency areas. The Scoreboard data are expressed nominal and in Euros with all foreign currencies converted at the exchange rate of the year-end closing date (31.12.2012).
The variation in the exchange rates from the previous year directly affects the ranking of companies,
When analysing data aggregated by country or sector, be aware that in many cases, the aggregate indicator depends on the figures of a few firms.
Every Scoreboard comprises data of several financial years allowing analysis of trends for the same sample of companies.
It comprises an analysis of the company data aggregated by main world region for the period 2004-2012.
The 2000 Scoreboard companies invested €538. 8 billion in R&d, 6. 2%more than in 2011,6 Due to data availability some companies may be missed
which data are fully available. Source: The 2013 EU Industrial R&d Investment Scoreboard. European commission, JRC/DG RTD.
These figures are based on our history database comprising R&d and economic indicators over the whole 2004-2012 period for 1017 companies (EU 248, US 358 and Japan 241.
for 388 EU out of the 2000 companies with R&d and net sales data for the whole period Source:
for 547 US out of the 2000 companies with R&d and net sales data for the whole period Source:
The R&d data are broken down into groups of industrial sectors with characteristic R&d intensities (see definition in Box 1. 1). The following points can be observed regarding the overall R&d changes in the period 2004-2012
for 324 Japanese out of the 2000 companies with R&d and net sales data for the whole period Source:
It is important to remember that data reported by the Scoreboard companies do not inform about the actual geographic distribution of the number of employees.
and Japanese companies and those from the Rest of the World that reported employment data for the whole period 2004-12.
Zephir database by Bureau Van dijk. 34 The 2013 EU Industrial R&d Scoreboard Long-term performance of top R&d companies This section analyses the behaviour of the top companies over the last 10
years based on our history database containing company data for the period 2002-2012. Results of companies showing outstanding R&d and economic results are underlined.
These figures are based on our history database comprising R&d and economic indicators over the whole 2004-2012 period from the EU 1000 dataset, including 135 from Germany, 81 from France and 122 from the UK.
for 135 German out of the EU1000 companies with data for the whole period*Profitability expressed as companies'profits as percentage of net sales Source:
for 81 French out of the EU1000 companies with data for the whole period*Profitability expressed as companies'profits as percentage of net sales Source:
for 122 UK out of the EU1000 companies with data for the whole period.**Profitability expressed as companies'profits as percentage of net sales Source:
12%in 2004 (data from Evaluatepharma's 2013 report). But before discussing the details and the companies involved we need to describe the main features of the business environment in
Company Country Table 5. 4 shows key data for Gilead Sciences Celgene, Life Technologies, Illumina, United Therapeutics, Alkermes, Emergent Biosolutions, Viropharma, BTG, Acorda Therapeutics, Genus, Genomic Health, Spectrum Pharmaceuticals and Luminex.
The data is taken mainly from the companies'own websites. The first is Abcam, a biotech
It provides comprehensive technical data sheets and quality control for these products which are marketed all through its website.
Matching the first 1500 Scoreboard companies13 with data on greenfield FDIS14, the objective is to show how the top world R&d spenders are locating
the developer is hired to finish the entire project without owner input. 13 Sample corresponding to the 2012 EU Industrial R&d Investment Scoreboard edition. 14 Greenfield investment data is derived from the 2013 fdi
Markets database (a service from the Financial times Limited 2013), which accounts for more than 110,000 greenfield investment projects around the world for the period 2003-2011.
which data is available for the period 2003-2012. Figure 6. 7 reports the number of projects by type of FDI (R&d versus manufacturing and other types of FDIS16) and R&d intensity (high, medium-high, medium-low,
The data for the Scoreboard are taken from companies'publicly available audited accounts. As in more than 99%of cases these accounts do not include information on the place where R&d is performed actually
therefore, fundamentally different20 from that of statistical offices or the OECD when preparing Business enterprise Expenditure on R&d (BERD) data,
The Scoreboard data are primarily of interest to those concerned with benchmarking company commitments and performance (e g. companies, investors and policymakers),
while BERD data are used primarily by economists, governments and international organisations interested in the R&d performance of territorial units defined by political boundaries.
which provides reliable up-to-date information on R&d investment and other economic and financial data, with a unique EU-focus.
The data in the Scoreboard are published as a four-year time-series to allow further trend analyses to be carried out, for instance,
The sources of data also differ: the Scoreboard collects data from audited financial accounts and reports whereas BERD typically takes a stratified sample,
covering all large companies and a representative sample of smaller companies. Additional differences concern the definition of R&d intensity (BERD uses the percentage of R&d in value added,
The 2013 EU Industrial R&d Investment Scoreboard 77 Annex 2-Methodological notes The data for the ranking of the 2013 EU Industrial R&d Scoreboard (the Scoreboard) have been collected from companies
Bvd data for the years prior to 2012 have been checked with the corresponding data of the previous Scoreboards adjusted for the corresponding exchange rates of the annual reports.
Main characteristics of the data The data correspond to companies'latest published accounts, intended to be their 2012 fiscal year accounts,
Therefore, the current set represents a heterogeneous set of timed data. In order to maximise completeness and avoid double counting,
The data used for the Scoreboard are different from data provided by statistical offices e g.
BERD data. The Scoreboard refers to all R&d financed by a particular company from its own funds,
Further, the Scoreboard collects data from audited financial accounts and reports. BERD typically takes a stratified sample,
For companies outside the Euro area, all currency amounts have been translated at the Euro exchange rates ruling at 31 december 2012 as shown in Table A3. 1. The exchange rate conversion also applies to the historical data.
The original domestic currency data can be derived simply by reversing the translations at the rates above.
and is Table A3. 1. Euro exchange rates applied to Scoreboard data of companies based in different currency areas (as of 31 dec 2012).
which data exist for both R&d and net sales in the specified year. The calculation of R&d intensity in the Scoreboard is different from than in official statistics, e g.
only if data exist for both the current and previous year. At the aggregate level, 1yr growth is calculated only by aggregating those companies for
which data exist for both the current and previous year. 6. Three-year growth is the compound annual growth over the previous three years,
only if data exist for the current and base years. At the aggregate level, 3yr growth is calculated only by aggregating those companies for
which data exist for the current and base years. 7. Capital expenditure (Capex) is used expenditure by a company to acquire
2013 EU Industrial R&d Investment Scoreboard 85 Annex 4-Access to the full dataset The 2013 Scoreboard comprises two data samples:
The following links provide access to the two Scoreboard data samples containing the main economic and financial indicators and main statistics over the past four years.
The Scoreboard contains economic and financial data for the world's top 2000 companies ranked by their investments in research and development (R&d.
The Scoreboard data are drawn from the latest available companies'accounts, i e. usually the fiscal year 2012 or 2012/13.
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