As financial data is rarely available for SMES, we relied on the number of employees
and the collected data broadly categorized according to our three research questions. Within the first round of content analysis
and the coders reworked the data searching for significant quotations, which are presented below. Another goal of the third step was to filter out the most essential and topic-related findings among a variety of interesting ideas.
and try to comprehend the data as well as the most important findings. Next, the results are presented with meaningful statements from the interviews. 3. Results 3. 1. Current sources of innovation in SMES.
To convert these minimal pressure differences into a convenient tool for recording weather data the metal cells were brought into contact with a liquid that reacts to these small differences accurately
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
including cultural and organizational problems as the most important items. 4. DATA AND METHODS 4. 1 Survey description To analyze trends,
containing data on all Dutch firms. The data were collected in December 2005 over a period of three weeks, by means of computer assisted telephone interviewing (CATI.
All respondents were small business owners or managers and innovation decision-makers. Attempts to contact reference persons were made five times before considering persons as non-respondents.
The survey data contained a summary variable indicating customer involvement i e. a dummy coded 1
The survey data allowed distinguishing between employees that belong to the R&d department and those that are coming from other organizational parts of the company.
We first tested if our data were suitable for a component analysis, by calculating Measures of Sampling Adequacy (MSA) for the individual variables (Hair et al.
the data did not contain enough records to provide reliable insights about respondents'motives and challenges on this topic.
Evidence from Global Entrepreneurship Monitor Data H200809 25-7-2008 The Entrepreneurial Adjustment Process in Disequilibrium:
As forthechallengesofopeninnovationinsmes, our data setcontainsinformationonperceivedbarriersto adoptopeninnovationpractices. Theopeninnovation literaturehassofarwitnessedfewattemptstoexplorethis subject. Chesbroughandcrowther (2006) for example identifiedthenot-invented-here (NIH) syndromeandlack of internalcommitmentasmainhamperingfactors.
To convert these minimal pressure differences into a convenient tool for recording weather data the metal cells were brought into contact with a liquid that reacts to these small differences accurately
This assessment is based on converging data on some innovation inputs (R&d expenditure of firms), intermediate outputs (patents) and final outputs (international trade),
with data related to 2004 (European commission, 2005) and to 2009 (European commission, 2010. In the two categories of IT hardware and software, there were a few European companies that spent more than €1 billion on R&d in the year 2004.
Second, data on patents may be criticized as less relevant for some subsectors of IT, such as software,
The Key Figures 2007 Report, using data from the European Patent office, stated that: the US is ahead of the EU in four out of six high-tech areas:(
European commission, 2007: 54) Looking at patent data, it appears that in the patent class computer and automated business equipment the share of the EU-27 (the current 27 members of the EU) increases
Extending the analysis to 2005 on data from the Patent Cooperation Treaty (PCT), and using the larger definition of information and communication technologies (ICT),
Subsequent analyses, based on sector-level data, showed that a large part of the gap is due to large gains in productivity in the US market service sector,
Of particular importance is the stream of research originated by the construction of industry-level productivity data in the KLEMS project, supported by the European commission (O'Mahony and Timmer, 2009;
or data) at many levels, preserving its fundamental properties. This makes it possible to move increasingly far from the physical implementation on a hardware without losing the relevant aspects of the description.
This conceptualization led to the development of protocols that govern how data flows through the Internet,
The abstract nature of computer objects (e g. data, procedures) allowed a process of progressive transformation of many fields of reality,
The progressive digitalization of regions of reality (not only data but images, sound, movement, all sorts of physical parameters etc.
Several items of data are still missing, so the analysis must be done on different samples, variable by variable.
What follows is a purely descriptive treatment of data, with limited comment. Patterns of educational mobility We identified the location of the universities at
The data do not allow a full-scale analysis, because we do not have control samples of scientists in related fields.
Our data seem to suggest that computer science has been a gateway for cross-discipline mobility and cognitive recombination.
We find the data illuminating. It is not surprising that top universities try to attract top scientists,
An easy way to comment these data is to remember that these are star scientists,
we are observing average data. Standard deviation informs us that even faster careers are observable. Indeed, for some people promotion to a higher level may occur within a year of the initial promotion!
It was not possible to normalize these data by age or seniority, given several missing items of data.
A crude approximation is offered in Table 9, suggesting that on average they may change country for each 30 years of age and each 15 years of professional seniority.
Our data seem to suggest that in the computer sciences the pattern of geographic mobility has been an ingredient of long-term success. Scientific productivity We offer a very rough descriptive analysis of the scientific production of top scientists.
so that any external control on the data self-declared in the CVS would require a long and dedicated investigation.
According to our data, top scientists move from the university that awarded their Bachelor degree to the USA,
Company Data. Brussels: Directorate-General Joint Research Centre. European commission 2007. Towards a European Research Area.
Section 2. 1) Consistent statistical data is missing Comparable international data about high-growth SMES are missing,
which data were available. A Eurobarometer study found that in several European countries the share of high-growth firms in the three years before 2009 was larger than 20%.
including key findings from recent literature, statistical data, theoretical ideas and empirical results. Chapter 4 analyses current policy developments, focusing on European and national policy approaches as well as specific issues related to entrepreneurship, access to finance, internationalisation and industry focus.
Matrix of main data sources for INNO-Grips Policy Brief 2 Quantitative focus Qualitative focus Primary data collection Representative enterprise survey (CATI
and Eurostat databases Data from industry associations Existing case studies from various sources Literature evaluation (desk research) 12 Research and development do not necessarily have to take place,
Data from various secondary sources is used here not only for exhibiting numbers of high-growth enterprises but also for other indicators such as venture capital provision.
Primary data collection The description of examples of successful support of high-growth innovative companies is a key element of this Policy Brief.
However, the study concludes that from the nature of the data collected and the limited number of examples of relevant policies precluded the formulation of any specific recommendations for Commission action,
unsatisfactory statistical data From a scientific point of view data availability is always unsatisfactory, but measurement of entrepreneurial activity, including high-growth SMES,
Internationally comparable data are scarce. The most notable initiative to make international data on entrepreneurship available may be the joint OECD-Eurostat Entrepreneurship Indicators Programme (EIP) launched in 2006.
Some key findings from the EIP are presented in the following, supplemented by data from other sources.
OECD The EIP provides data about high-growth enterprises which may be taken as a proxy for data about highgrowth innovative SMES.
Data are available for 15 countries divided by manufacturing and services. 19 The most recent data available at the time of authoring this Policy Brief were for 2006.
For this year, Bulgaria was on top for both manufacturing (8. 6%high-growth enterprises)
and services (8. 2%)see Exhibit 4. The following countries were Italy (8%/7. 9%),Estonia (7. 1%/5. 6),
%Brazil (6. 9%/5%)and the USA (5. 9%/19 See OECD (2009), pp. 28-31.
which data about high-growth enterprises were available, including Hungary, Sweden Spain, Norway, Luxembourg, Finland, and Romania.
Among the countries for which data are performed available, Bulgaria best (2. 3%gazelles in manufacturing, 1. 9%in services).
Eurobarometer A Eurobarometer survey in 2009 of more than 9, 000 companies provided data for all EU-27 countries. 20 As the denominator
and the data source is different from the OECD data, both datasets cannot be compared. It found that 12%of the companies had grown by over 20%on average per year in the previous three years, in terms of full-time employment or full-time equivalents.
Exhibit 3-3 shows the related data. 21 See Veugelers (2009), p. 2. The largest US companies were taken from the Financial times Global 500 of 2007, the largest European companies from the EU-IPTS Top 1000 of 2007,
Firm-level data was provided by the Zentrum für Europäische Wirtschaftsforschung (ZEW Mannheim, Germany. Policies for high-growth innovative SMES v1. 6 21 Exhibit 3-3:
for high-growth innovative SMES v1. 6 27 A Kauffmann Institute study of the US economy in 2010 with data for 2007 contained 5. 5 million firms.
Empirical tests with two longitudinal data sets found that the profitable low growth firms are both more likely to reach the desirable state of high growth
and 26 by GIF2 at an average cost of 600,000 euro. 61 No valid data for jobs created,
It also tracks baseline data for its performance, such as employees, revenue growth and number of customers.
analyses of growth finance can hardly be based on solid data. Access to finance for entrepreneurs and young businesses, both debt and equity capital
is one area where there is scarce availability of comparable data across countries; often reliable data are not even available at the country level. 96 92 Definition of the European Venture capital Association,
see http://www. evca. eu/toolbox/glossary. aspx? id=982.93 See Deutsche bank Research (2010). 94 See Gallup (2009),
The Eurobarometer survey quoted in the following provides insightful data and it is based on almost 10,000 interviews,
and growth of EU's innovative companies confirms this. 119 Analysing empirical data for EU companies,
of Micro United Network Micro United Network Pte Ltd (http://www. microunited. com. sg) provides voice, video and data through internet protocol product distribution
which data are available when a combination of venture capital 150 See Cooper (2009). Policies for high-growth innovative SMES v1. 6 69 and IRAP assistance is available,
but there is no data available to measure the investment performance of this group of funds.
Even though they had the data, the review did not assess the presence of high growth firms
According to the OCS data most of the grants are provided to high growth SMES, though the OCS makes great efforts to increase the participation of firms belonging to traditional sectors.
Other items with outstandingly high percentages may confirm this interpretation of the data. 83%of the highgrowth companies said that good coaching by external consultants was no reason for growth.
Access to finance for entrepreneurs is an area with scarce comparable data across countries (see section 4. 2. 2)
The access to meta-data would be straightforward. It would empower the user to discover new knowledge
and open opportunities without having to process extensive data and information from various sources. In this way, the EEN could contribute more to SME growth and possibly high growth.
and interpreting innovation data. The Measurement of Scientific and Technological Activities. Third edition. A joint publication of OECD and Eurostat.
Section 2. 1) Consistent statistical data is missing Comparable international data about high-growth SMES are missing,
which data were available. A Eurobarometer study found that in several European countries the share of high-growth firms in the three years before 2009 was larger than 20%.
including key findings from recent literature, statistical data, theoretical ideas and empirical results. Chapter 4 analyses current policy developments, focusing on European and national policy approaches as well as specific issues related to entrepreneurship, access to finance, internationalisation and industry focus.
Matrix of main data sources for INNO-Grips Policy Brief 2 Quantitative focus Qualitative focus Primary data collection Representative enterprise survey (CATI
and Eurostat databases Data from industry associations Existing case studies from various sources Literature evaluation (desk research) 12 Research and development do not necessarily have to take place,
Data from various secondary sources is used here not only for exhibiting numbers of high-growth enterprises but also for other indicators such as venture capital provision.
Primary data collection The description of examples of successful support of high-growth innovative companies is a key element of this Policy Brief.
However, the study concludes that from the nature of the data collected and the limited number of examples of relevant policies precluded the formulation of any specific recommendations for Commission action,
unsatisfactory statistical data From a scientific point of view data availability is always unsatisfactory, but measurement of entrepreneurial activity, including high-growth SMES,
Internationally comparable data are scarce. The most notable initiative to make international data on entrepreneurship available may be the joint OECD-Eurostat Entrepreneurship Indicators Programme (EIP) launched in 2006.
Some key findings from the EIP are presented in the following, supplemented by data from other sources.
OECD The EIP provides data about high-growth enterprises which may be taken as a proxy for data about highgrowth innovative SMES.
Data are available for 15 countries divided by manufacturing and services. 19 The most recent data available at the time of authoring this Policy Brief were for 2006.
For this year, Bulgaria was on top for both manufacturing (8. 6%high-growth enterprises)
and services (8. 2%)see Exhibit 4. The following countries were Italy (8%/7. 9%),Estonia (7. 1%/5. 6),
%Brazil (6. 9%/5%)and the USA (5. 9%/19 See OECD (2009), pp. 28-31.
which data about high-growth enterprises were available, including Hungary, Sweden Spain, Norway, Luxembourg, Finland, and Romania.
Among the countries for which data are performed available, Bulgaria best (2. 3%gazelles in manufacturing, 1. 9%in services).
Eurobarometer A Eurobarometer survey in 2009 of more than 9, 000 companies provided data for all EU-27 countries. 20 As the denominator
and the data source is different from the OECD data, both datasets cannot be compared. It found that 12%of the companies had grown by over 20%on average per year in the previous three years, in terms of full-time employment or full-time equivalents.
Exhibit 3-3 shows the related data. 21 See Veugelers (2009), p. 2. The largest US companies were taken from the Financial times Global 500 of 2007, the largest European companies from the EU-IPTS Top 1000 of 2007,
Firm-level data was provided by the Zentrum für Europäische Wirtschaftsforschung (ZEW Mannheim, Germany. Policies for high-growth innovative SMES v1. 6 21 Exhibit 3-3:
for high-growth innovative SMES v1. 6 27 A Kauffmann Institute study of the US economy in 2010 with data for 2007 contained 5. 5 million firms.
Empirical tests with two longitudinal data sets found that the profitable low growth firms are both more likely to reach the desirable state of high growth
and 26 by GIF2 at an average cost of 600,000 euro. 61 No valid data for jobs created,
It also tracks baseline data for its performance, such as employees, revenue growth and number of customers.
analyses of growth finance can hardly be based on solid data. Access to finance for entrepreneurs and young businesses, both debt and equity capital
is one area where there is scarce availability of comparable data across countries; often reliable data are not even available at the country level. 96 92 Definition of the European Venture capital Association,
see http://www. evca. eu/toolbox/glossary. aspx? id=982.93 See Deutsche bank Research (2010). 94 See Gallup (2009),
The Eurobarometer survey quoted in the following provides insightful data and it is based on almost 10,000 interviews,
and growth of EU's innovative companies confirms this. 119 Analysing empirical data for EU companies,
of Micro United Network Micro United Network Pte Ltd (http://www. microunited. com. sg) provides voice, video and data through internet protocol product distribution
which data are available when a combination of venture capital 150 See Cooper (2009). Policies for high-growth innovative SMES v1. 6 69 and IRAP assistance is available,
but there is no data available to measure the investment performance of this group of funds.
Even though they had the data, the review did not assess the presence of high growth firms
According to the OCS data most of the grants are provided to high growth SMES, though the OCS makes great efforts to increase the participation of firms belonging to traditional sectors.
Other items with outstandingly high percentages may confirm this interpretation of the data. 83%of the highgrowth companies said that good coaching by external consultants was no reason for growth.
Access to finance for entrepreneurs is an area with scarce comparable data across countries (see section 4. 2. 2)
The access to meta-data would be straightforward. It would empower the user to discover new knowledge
and open opportunities without having to process extensive data and information from various sources. In this way, the EEN could contribute more to SME growth and possibly high growth.
and interpreting innovation data. The Measurement of Scientific and Technological Activities. Third edition. A joint publication of OECD and Eurostat.
data! on! the! level! of! takecup! of! ecprocurement! on! total! regional! procurement! to! promote! healthy!
data! on! the! organizational! well! being! achievable! through! the! implementation! of! telework.''''Determinants (and (barriers (in (the (inner (context('The'inner'context'is'much'more'important'than'the'external'one'in'the'case'of'telework.'
data'on'the'level'of'takequp'of'eqprocurement'on'total'regional'procurement'for'promoting'healthy'competition.'
'and'publish'data'on'the'organizational! wellcbeing'achievable'through'the'implementation'of'telework.''5. Enhance'the'quality'and'the'quantity'of!
o consolidation of imperfect data, o spatiotemporal analysis, o modelling/simulation of material degradation, o joint reconstruction within and across collections,
The report, together with any data and indicators published therein, can be downloaded from the Kaleidoszkóp website using the following link:
All these data are indicative of very large disparities between the country's various regions. From the point of view of the RDI, Hungary has a peculiar geographic structure,
and data promoting a better understanding of the above. 5 Introd uction...6 1. The position of Centra l Hungar y within the RDI landscape of Hungar y...8 1. 1 Disproportions within RDI measuring concentration...
Our objective is to provide an analysis of the R&d and innovation related performance and potential of particular regions, based on comparative facts and data.
Pending the availability of relevant data, provided that these were a useful source of information
as well as in the interests of the reliable interpretation and presentation of underlying statistical data. Subsequent chapters are devoted to comparing data on other regions and counties,
even though Budapest and the Central Hungary region are mentioned in this part, and moreover, in several chapters.
Our analysis uses the latest available data: in the case of R&d this covers 2011,
however regional GDP data for 2011 are only preliminary data; other labour market data relate to 2012,
or were taken from census findings for 2011. We also deploy a number of regional econometric methods, the findings
This accounts for the slight discrepancy between the data we present in Chapters 1. 4 and 3. 7 on the one hand,
and HCSO data on the other. Census statistics provide a treasure trove of comprehensive data for our analysis,
and in our view show a strong correlation with innovation potential, examples of which are foreign language proficiency, the percentage rate of higher education graduates within the total population,
and labour activity data in general. We also did some correlation and regression analysis of innovative sectors/industries,
namely that with the exception of Central Hungary, we are practically unable to correlate sectoral and regional data for any other region in a way that would ensure compliance with the statistical golden rule on the traceability of the data provider.
or fewer data providers made up a group of this kind, and so pursuant to prevailing regulations we were allowed not to display their data.
This in itself clearly illustrates the current state of research and development in Hungary: stakeholders are very thinly spread in many regions and sectors/industries,
Being the case this has limited unfortunately our ability to include more data than what finally ended up on these pages,
as a result of which the depth of sectoral data suffered most of all. If this publication therefore gives the impression that it does not provide a comprehensive account in respect to the regions outside Central Hungary,
then it should be noted that the relevant data do exist, but they cannot be made public due to the above reasons.
As it was impossible to display the full array of relevant RDI data in the core text of this document many of the tables,
Based on the data of 19 counties and of Budapest itself for 2010, we may conclude that the concentration values of indicators for directly measuring R&d (0. 4 in terms of total R&d expenditure
Available data indicate a high concentration in most cases: according to preliminary data for 2011, the per capita GDP (calculated by purchasing power parity) of Budapest is more than double the national average (EU R 16,484 per capita),
representing EU R 35,583 per capita. For the same reason, the per capita GDP of Central Hungary (EU R 26,574) is significantly above the national average.
i e. statistics comprising the unique data of 19 counties and Budapest. Concentration converging to 0 indicates a diffuse, even distribution of objects designated by the indicator in question.
graphs and tables presented in this document contain data of various aggregation levels, on the understanding that these data are not always absolute figures.
The possibly most detailed and absolute data are usually found on the National Innovation Office RDI Observatory's website:
http://www. kaleidoszkop. nih. gov. hu/(provided that their publication is prohibited not due to data protection) 6 Gross Expenditure on Research and development:
The National Innovation Office RDI Observatory's own calculations based on HCSO and Eurostat data 10 1. The position of Ce ntral Hungary within the RDI landscape of Hungary
The National Innovation Office RDI Observatory's own calculations based on HCSO data. In Ce ntral Hungary 2/3 of total R&d expenditure is use d by the business enterprise se ctor,
The data presented here9 demonstrate the weight of certain indicators characterising companies engaging in R&d within the central region (we also included data for Budapest and Pest County separately.
and we included data for Budapest and Pest County separately. 10 The Community Innovation Survey (CIS) commissioned by the European union every two years analyses the innovation activity of companies.
The National Innovation Office RDI Observatory's own calculations based on HCSO data The relative share of the rest of the country The relative share of Pest County The relative share
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Manufacture of pharmaceuticals shows extremely high levels of concentration,
The National Innovation Office RDI Observatory's own calculations based on HCSO data. The relative share of the rest of the country The relative share of Pest County The relative share of Budapest 91,3%0%10%20%30%40%50%60%70
The National Innovation Office RDI Observatory's own calculations based on HCSO data. By national comparison, Budapest and Pest County do not have a significant weight within R&d performance associated with the manufacture of electrical equipment:
The National Innovation Office RDI Observatory's own calculations based on HCSO data. 0%10%20%30%40%50%60%70%80%90
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Budapest has a dominant position within the R&d performance of the information
The National Innovation Office RDI Observatory's own calculations based on HCSO data. The Central Hungary region accounts for a significant proportion of professional, scientific and technical activities and although it might come as somewhat of a surprise,
GDP per capita by county (EUR, PPP based on preliminary data for 2011. Source: The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former. 2 Capturing a snapshot of Central Hungary
and Budapest is only part of the regional aspect of RDI, but in order to see the full picture it is vital to include a description of every region.
EU R 16,484 based preliminary HCSO data for 2011. If we look at individual regions, then we find that Western Transdanubia is above the national average,
Total R&d expenditure as a percentage of GDP by county (based on preliminary GDP data for 2011.
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
By comparing the same data at a regional level, we find that apart from Central Hungary only the two Northern
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
as these indicators can tell us more about real living standards than GDP or any other macroeconomic data.
The National Innovation Office RDI Observatory's map imaging based on HCSO data. Regions other than Central Hungary employ no more than 40%of all Hungarian researchers (what is more, this value goes down to 34.2%when converted into FTE),
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former. 25 3. Innovation potential of unemployment is significantly lower for counties of the Transdanubian region
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former.
The National Innovation Office RDI Observatory's own calculations based on HCSO data. The competence assessment,
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Lecturers Students 50.8%12.2%10.1%9. 9%5. 9%5. 8%5. 3%Central Hungary Central Transdanubia Northern Hungary Northern
The National Innovation Office RDI Observatory's own calculations based on HCSO data. 3. 4 The link between national migration and R&d In recent years, national migration
data including both domestic and international migration statistics). Central Hungary has already been mentioned, other than that-compared to other counties-Gyor-Moson-Sopron stands out with an impressive positive migration balance,
and it is obvious from the migration data, that most counties with a positive balance are in the western part of Hungary,
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former. 628 165 149 143 65 54 30 0
The National Innovation Office RDI Observatory's own calculations based on HCSO data. 17 Baranya Borsod-Abaúj-Zemplén Veszprém Csongrád Hajdú-Bihar-30
The National Innovation Office RDI Observatory's own calculations based on HCSO data for 2010. CF Manufacture of pharmaceuticals;
County level correlation analysis based on GEOX and HCSO data for 2010. CF Manufacture of pharmaceuticals;
The National Innovation Office RDI Observatory's own calculations based on HCSO data for 2010. Average rank Pest Gyor-Moson-Sopronborsod-Abaúj-Zemplénbács-Kiskunfejér Hajdú-Biharcsongrádbaranya Komárom-Esztergomveszprém Szabolcs-Szatmár-Beregjász-Nagykun-Szolnokheveszalasomogybékés Vastolnanógrád 1
The National Innovation Office RDI Observatory's own calculations based on HCSO data for 2010. Average rank 2. 2 2. 4 2. 7 3. 9 4. 8 4. 8 0123456 Central Transdanubia Southern Great Plain Northern Great
The correlation calculation is used a procedure to determine how close the correlation is between various probability variables (indicators and/or data.
Spearman's rank correlation allows us to compute the coordinated movement of characteristics measured on an ordained (ranked) data scale
(or transformed into such a data scale). In the example we ranked various innovative industries/sectors according to
with the only difference being that the regional calculation is based on regional data. The county by county distribution of companies engaged in the sectors of the national economy such as the manufacturing of computer, electronic and optical products, electrical energy, gas and steam supply and air conditioning, water supply, professional
The National Innovation Office RDI Observatory's own calculations based on HCSO data and map imaging of the former. 22 We defined corporate research units as any
This accounts for the slight discrepancy between our own and HCSO data. 37 3. Innovation potential 23 It must be noted that we do not know
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Central Hungarynorthern Great Plaincentral Transdanubia Southern Great Plainnorthern Hungarywestern Transdanubiasouthern Transdanubia 7. 7 6. 8 5. 2 4. 8 3
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Figure 36: Number of Accredited Innovation Clusters by region, with an indication of how many members they have.
The National Innovation Office RDI Observatory's own calculations based on MAG (Hungarian Economic Development Centre) data and map imaging of the former. 0 20 40 60
KMOP and AIK tenders The following data show the regional distribution of tender amounts awarded under GOP,
We found no big surprises analysing these data, and we again came to the conclusion that Central Hungary stands out among other regions,
The National Innovation Office RDI Observatory's own calculations based on EMIR (European Market Infrastructure Regulation) data and map imaging of the former. 40 4. Re gional
The National Innovation Office RDI Observatory's own calculations based on PKR data and map imaging of the former.
Statistical data of FP7 participants who signed a grant agreement during the period Figure 41:
The National Innovation Office RDI Observatory's own calculations based on E-CORDA data and map imaging of the former. 26 43 4. Re gional distribution of grants
which analyses six RDI-relevant data in three different years before creating a normalised complex index (by comparing it to the maximum value of the dataset.
and comprises quantitative and qualitative data at the same time. zzrdi Infrastructure complex index: this index provides a clear indication of the availability of the material and nonmaterial infrastructural resources needed for any kind of RDI activity.
In the table of basic data provided in Appendix 1 we made it clear which indicator is classed under which complex index,
(i e. data of that region whichever scored the highest value in the indicator in question), in other words the maximum value serves as a benchmark.
All these data are indicative of very large disparities between the country's various regions. It is common knowledge that the unemployment data for different regions can vary greatly,
and the number of unemployed graduates in different counties cannot be explained conclusively either by the size
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 0 200 400 600 800 1 000 1 200 2000
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 0 100 200 300 400 500 600 2000 2001 2002
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 50 Appendices0 100 200 300 400 500 600 700 2000
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 0 100 200 300 400 500 600 2000 2001 2002
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 0 100 200 300 400 500 600 700 800 900
National Innovation Office RDI Observatory's own calculations based on Eurostat data. 51 Appendices 2000 2001 2002 2003 2004 2005 2006 2007
National Innovation Office RDI Observatory's own calculations based on Eurostat data. Unemployed graduates, capita (left axis) Unemployed non-graduates, capita (left axis) The proportion of graduates within total unemployment,%(right axis) 14.7%11.6%8
The National Innovation Office RDI Observatory's own calculations based on HCSO data. 3. Geographic distribution of unemployment according to qualifications 52 Appendices Unemployed graduates, capita
The National Innovation Office RDI Observatory's own calculations based on HCSO data. Table 1: Rank correlation matrix;
Data source: HCSO) We highlighted strong correlations (above 0. 7) in green. CF Manufacture of pharmaceuticals;
Data source: HCSO) We highlighted strong positive correlations (above 0. 7) in green, and negative correlations in red.
and any data and indicators published therein, can be downloaded from the Kaleidoszkóp website: http://www. kaleidoszkop. nih. gov. hu/54 References Borsi, Balázs Mikita, József (2013:
as well as data and analyses supporting RDI policy related decision-making. With the help of this database, RDI stakeholders can be involved in diagnosing problems as may exist within the sector
All Kaleidoszkóp system data and service functionalities are meant to assist public sector institutions and other organisations in their networking,
zzgeneric and specific sectoral RDI analyses and statistics zzquality data sources informing analysis zzinformation on public funded RDI projects zzregister of Hungarian research infrastructure facilities
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