developing a digital economy, the promotion of low-carbon, encouraging development of new products and modernizing education and training sector.
Recent data (April 2010) provided by ANCOM show a 12.4%increase in the number of fixed broadband connections at the end of 2009 versus the same period
These preliminary data indicate a penetration rate of broadband fixed connections at 13.1%of the population and
•Facilitate obtaining data, information and real-time reports •Application of general indicators regarding the development of Information
processing data, information and updated documents B. Streamline relations with public institutions •Increased access to electronic public services
•Reassessment of host status for data •Increased competitiveness and development of economic operators through
-Draft Law on single database (national electronic registers -Amendments to the Law nr. 161 of 2003, amendments specify measures to
1 Data available in December 2012. This table presents the available data for each indicator in the
latest update of each source Population and territory The Autonomous Community of the Balearic Islands is located in the Mediterranean sea along
October 2012 data The unemployment rate in Balearic islands in 2011 (21,96%)is well above the European average (9, 70%)and the OECD countries
 data  access  markets  and  market  roles  as  well
 data  safety  industry  development  research  The  key  ongoing
 data  collection  by  utility  personnel  to  gain  a
 data  handling  customer  contact  or  local  congestion Â
 data  safety, 16  industry  development  (mainly  ICT  electrical
 data  pro- †tection  for  consumers  (3  establishing
 data  han- †dling  The  challenges  of  distributed
 data  handling  and  communication  Differ- †ent  smart
http://www. tdeurope. eu/data/TD%20europe%20position%20paper%20on%20infrastructures%20and%20smar t%20grids%20010212. pdf
 data  of  the  customers  Will  there  be  distribution- †level
 data  gathering  data  handling  data  security  and  discussions
 on  data  privacy  On  the  other  hand  it
 might  as  advocates  sustain  allow  for  more  demand
 response  reliability  and  shorter  reaction  times  The  two
 data   Moreover  in  a  perspective  where  consumption
energieanbieter. de/data/uploads/20111010 bne positionspapier smart grids. pdf   Brandstã¤tt  C  Brunekreeft  G
APPROACH DATA METHOD Quantitative Statistical data from Spanish National Statistics Institute (INE) on number of establishments, GDP
employment and Input-Output regional economic accounts (www. ine. es Specialisation pattern mapping following Del
Qualitative data: all the regional RIS3 in Spain available from the Spanish Ministry of Finance and Public Administration (MINHAP
priorities at regional level (that is, sharing common databases sectorial/technological definitions, etc An establishment of a continuous forum to put together
follow-up and improve the strategy, a data set of policies (some of which could be developed jointly by different regions), the open economy dimension
Key socio economic data Aragã n is proud of its geostrategic location in the northern part of Spain between the Atlantic and Mediterranean
data facilitated by IDEPA. After that general introduction, we will discuss position in the last Regional
According to the latest population data, for the advancement of the Municipal Register to January 1 2011, Asturias has 1,
according to the latest data provided by the National Statistics Institute in Central Business Directory. This is a decrease from the previous year of 1. 3%.If
data, 2009 and 2010 The productive structure presents great weight of the industrial sector with 21.78%of the total
In view of the above data the main features of the Asturian economy and more specifically of its
With due caution with the data presented in Expert Assessment of RIS3 strategy for the region of Asturias, Spain †Miquel Barcelã 6
details in annex 5. 1. For every institution visited some basic data, functions and available figures are
Data Sede W3c 80 personas Presupuesto 2011: 4 Mâ Pieza clave de una futura estrategia del sector TIC
ï The strategy has been iniciated by IDEPA with the preparation of some data based on enquiries and information already available, that will be completed by thematic
ï By indirect data, like companies participating in Neotec programs (CDTI loans to based technology), EIBT certification by ANCES (National Association of CEEI€ s) or by
ï Cross-clustering capacity, entrepreneurship and the innovation capabilities of SMES and other strategic actions should be defined
reinforcing some priority clustering processes in a similar way than in some European regions c) Try to increase the participation of different private agents and social leaders in the process
i) Strategic actions fostering cross-clustering capacity, entrepreneurship and the innovation capabilities of SMES should be defined
Economic data ï¿The (Gross domestic product) GDP of Cantabria was 13.289,89 millions of â in 2011
Economic data ï¿GDP distribution ï¿Services 61 %ï¿Industry and energy 17 %ï¿Construction:
Economic data ï¿Industry GDP distribution ï¿34.72%Metal processing ï¿12.51%Food, beverages and
%Economic data ï¿Services GDP distribution ï¿26,87%Business services ï¿13,76%Tourism and hospitality
%Economic data ï¿The main export destinations ï¿France ï¿Germany ï¿Italy ï¿U k
cobertura que permita el acceso a una velocidad de 30 megabits por segundo (Mbps) o superior, al
cobertura que permita el acceso a una velocidad de 30 megabits por segundo (Mbps) o superior, al
cobertura que permita el acceso a una velocidad de 30 megabits por segundo (Mbps) o superior, al
objetivos de Europa 2020, la sim mecanismos de ejecuciã n mã¡s coherentes, Ã nfasis sobre resultados y eficacia y la
is a significant increase in the R&d activity of the enterprises as compared with the data of one decade ago, in
%Figure 5. Main data of †HAMHIT€ Companies in Castilla y Leã n Source: INE
%According to the data published by EUROSTAT, Castilla y Leã n has reduced in 17.8 percentage points the gap in GDP with the European union since the incorporation of
•Based on objective data and including solid supervision and evaluation systems 1. 2 METHODOLOGY The elaboration of the Castilla y Leã n RIS3 has followed the six-step methodology
The latest available data assigns economic returns for Castilla y Leã n participation in national R&d programmes
) Concerning Internet access technology, data for the first quarter of 2013 in Castilla y Leã n are very positive,
Additional noteworthy data is the positive evolution of technology by the youngest part of the population, especially in the 10
independent contractors), where usage data and ICT availability continue to be low with minor annual economic growth.
Usage data from companies using the Castilla y Leã n Online Government is better than the usage of these services by citizens:
Open Data; new models for collaboration with other companies Citizens •Existence of constantly increasingly
areas, such as mobile applications and technology, cyber security, Big data, Internet of the Future, Cloud computing, all of which are crosscutting technologies for any economic
6. 2 Develop the digital economy for companies growth and competitiveness 6. 3 Boost egovernment and improve the efficacy, efficiency,
1 Latest data available from 2010 2 National Statistics Institute 3 Scopus Database, Elsevier 4 Application of historical data queried from the Spanish Foreign
Trade Institute. Ministry of Economy and Competitiveness 5 European Statistics Office 6 Secretariat of State for Telecommunications and Information
Society. Ministry of Industry, Energy, and Tourism 7 Data corresponding to the first six months of 2013
8 At least once a week during the last three months 9 Data corresponding to 2013
ME 035 10 FINANCIAL PLAN The development of Strategy will involve both public and private resources.
 Data  demand  for  contents  more  usable  technologies  closer
 data  mining  etc   •†Robotics   •†Intelligent
3. 1. Sample and data We used a cross-sectional dataset of Europe-based SMES across all
dataset is motivated by the concern for the reliability of yearly data on invention commercialization strategies, provided that our sample
the collection of yearly data on ï rms'invention commercialization strategies (i e. yearly data on whether each ï rm had sold or licensed
its inventions to other ï rms or had embodied its inventions into products). ) Having conducted an accurate and extensive pilot search
on multiple data sources (including Business & Industry, Factiva Zephyr and Securities Data Corporation databases as well as com
-pany web sites and specialized websites) we discovered that collect -ing yearly data for small private companies was problematic since
these ï rms do not receive systematic media coverage. Therefore it is not possible to ï nd each licensing agreement or each product
Sim -ilarly, if a company has launched products based on its inventions this information is likely to appear at least once on the materials we
-point of yearly data variability. The choice of using a cross-sectional dataset is in fact in line with other studies in the ï eld such as Arora
Company names identiï ed from the patent database have been matched with company names from the Amadeus database (Bureau
Van dijk; hence both listed and non-listed companies were incl -uded in our sample. Checks for misspelling of company names were
employed to collect the data that we used in this study. Data on ï rms'vertical integration and invention trade were collected and
triangulated through an extensive search of press releases, including Business & Industry, Factiva, Zephyr and the Securities Data Corpora
-tion (SDC) databases as well as from company web sites. In cases where this information was not available from current companies
'websites, or if the companies'websites were no longer active, the Internet Archive's Wayback Machine was used to visit the past
) Data on ï rms'inventive portfolios was collected using Patstat. Data on ï rms'age were obtained from
company websites and Internet archives. Amadeus was employed to collect data on ï rms'proï tability and size across the whole time
-frame covered by this study. Finally, to obtain data on the strength of the appropriability regime across the different European countries
included in this study sample, this paper referred to publications by Park (2008) and Ginarte and Park (1997
Amadeus database having been granted at least 1 patent that had been applied at the EPO ofï ce in 1996†2001,
-tive performance, we refer to patent data. However, patents substan -tially vary in their economic and technological value (Griliches, 1984
Industry, Factiva, Zephyr and the Securities Data Corporation (SDC databases as well as company websites and specialized websites) to
identify announcements and reports mentioning the name of the ï rm. We used the Internet Archive's Wayback Machine to visit the
i e. whether (1) the data are representative of the overall populations of technology specialists in Europe in the time period considered;(
limited data are available publicly on the population of SMES who are technology specialists. Hence, we believe that the selected sample is
However, we acknowledge that the panel data on technology commercialization strategies of small private ï rms
since these data are not available on public or commercial dataset. Future research may consider the possi
whilst the use of secondary data allows conducting such investigation only to a very limited extent, the use of surveys or in
Econometric Analysis of Cross-section and Panel Data. The MIT Press, Cambridge, Massachussets Yadav, M. S.,Prabhu, J. C.,Chandy, R. K.,2007.
Sample and data Variables Dependent variables Independent variable Control variables Results Discussion Implications to practice Implications to theory
In addition, country notes present statistics and policy data on SMES, entrepreneurship and innovation for 40 economies, including OECD countries, Brazil, China, Estonia, Indonesia, Israel
Sourceoecd is the OECD online library of books, periodicals and statistical databases For more information about this award-winning service and free trials ask your librarian,
and you can include excerpts from OECD publications, databases and multimedia products in your own documents, presentations, blogs, websites and teaching materials, provided that suitable acknowledgment of OECD as source
Italy (the spatial clustering analysis and annexes in Chapter 3) and Andrea Piccaluga, Scuola Superiore Sant†Anna, Pisa, Italy,
data and policy information from 40 economies around the world, and so provides an insight into the
In addition to presenting the data, the report also explores the policy imperatives in three major yet insufficiently recognised action areas that are new to much of the policy world.
data and highlighting current policy issues of greatest concern. It draws in particular on the expertise
Notes on the country data...128 Chapter 3. Knowledge Flows...131 Introduction...132 How knowledge affects entrepreneurship...
The geographical clustering of knowledge-intensive activities...136 The role of local knowledge flows for spatial agglomerations
The â€oeorbis†Database...158 Annex 3. A2. The LISA Methodology...161 Chapter 4. Entrepreneurship Skills...
â Presents a set of country-level data on SMES, entrepreneurship and innovation performance, and a review of major policies and new policy developments in the field
The data also show substantial SMES, ENTREPRENEURSHIP AND INNOVATION Â OECD 201016 EXECUTIVE SUMMARY shares of total activity accounted for by each of the sub-categories of micro, small-and
The data suggest that SMES innovate less than large firms across a range of categories including product innovation, process innovation, non-technological innovation, new to
There is strong spatial clustering in knowledge-driven sectors, i e. those where R&d intensity basic university research and highly-skilled workers are most important.
and data is not commonly available for non-technological innovation as a proportion of firm employment or turnover.
Chapter 2 provides data on SME innovation performance and constraints across 40 economies and examines the major and new policies that have been introduced.
Frameworks for Data Collectionâ€, OECD Statistics Working papers, 2008/1, OECD Publishing, Paris doi: 10.1787/243164686763 SMES, ENTREPRENEURSHIP AND INNOVATION  OECD 2010 41
New Evidence from Micro Data, Ch. 1, pp. 15-82, University of Chicago Press, Chicago
by structural data on the SME sector and selected indicators showing SME innovation performance, perceived barriers to innovative activities, and financing
available, data are presented also for accession countries (Chile, Estonia, Israel, Russia and Slovenia) and enhanced engagement countries (Brazil, China, India, Indonesia and South
Data presented in the chapter come from three main sources â OECD Structural and Demographic Business Statistics Database
â Innovation Surveys (e g. the Community Innovation Surveys; national innovation surveys â OECD Product Market Regulation Indicators
Data are drawn from the OECD dataset Business Statistics by Size Class, which is part of the OECD Structural and Demographic Business Statistics Database.
The dataset comprises five dimensions: country, industry, year, size class and variable. The variables presented in this chapter are:
The PMR database comprises three broad sets of indicators state control, barriers to entrepreneurship, and barriers to trade and investment.
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812706506652 B. Innovation performance of SMES and large firms, 2007-081
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812710434422 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812733856326 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812772716231 B. Innovation performance of SMES and large firms, 2002-041
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812823002327 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812824001404 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/812888716138 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813015740451 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-064
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813110872302 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2002-043
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813116208667 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813126285000 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813138862470 B. Innovation performance of SMES and large firms, 2002-041 C. Types of innovation co-operation, 2002-043
1. Data only reflect enterprises with 3 or more persons engaged. 2. As%of all firms within size class
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813224252704 B. Innovation performance of SMES and large firms, 2004-062 C. Types of innovation co-operation, 2004-065
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813326326305 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2002-044
1. For manufacturing, data only reflect enterprises with 4 or more persons engaged. 2. As%of SMES with new product sales
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813327663628 B. Innovation performance of SMES and large firms, 2002-04
1. For manufacturing, data only reflect enterprises with 5 or more persons engaged. 2. As%of all firms within size class
Regulation Database statlink 2 http://dx. doi. org/10.1787/813331118285 B. Innovation performance of SMES and large firms, 2002-042 C. Administrative burdens on start-ups4
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813367102180 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813468327202 B. Source of finance of SMES and large firms, 2002-04 C. Administrative burdens on start-ups1
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813475131677 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813487217851 B. Innovation performance of SMES and large firms, 2005-071
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813517714027 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813521857686 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813540502857 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813544660727 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-064
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813612045268 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-064
Database statlink 2 http://dx. doi. org/10.1787/813624112502 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2002-044
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813727458672 B. Type of innovation by SMES, 20071
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813835033115 B. Innovation performance of SMES and large firms, 2004-061 C. Types of innovation co-operation, 2004-063
OECD, Product Market Regulation Database statlink 2 http://dx. doi. org/10.1787/813847876385 B. Innovation performance of SMES and large firms, 20071 C. Types of innovation co-operation, 2004-062
transactions carried out by electronic data interchange. Several other actions, including the establishment of Cyber Laws, the setting up of the Cyber Regulations Appellate Tribunal,
which include data relating to the Golan heights, East Jerusalem and Israeli settlements in the West bank
OECD, PMR Database; OECD Economic Surveys: Israel 2009 statlink 2 http://dx. doi. org/10.1787/813271727207
Notes on the Country Data The structural data on businesses presented in the chapter follow the International
Standard Industrial Classification (ISIC) Rev. 3, based on the following nomenclature A. Agriculture, hunting and forestry
Most data presented refer to the nonfinancial business economy, i e. ISIC Rev. 3/NACE Sections C to I and K and is subdivided into Industry (Sections C, D, E and F) and Services
The following text gives details on the completeness of the data for each country Australia:
The geographical clustering of knowledge-intensive activities Activities can cluster for different reasons, such as availability of intermediate suppliers
findings underline the importance of knowledge-driven clustering in knowledge-intensive industries. They are reflected also in the results of a recent OECD study of seven
Given the limits of official data sources for local-level analysis, we turn to firm-level
information from the commercial ORBIS database on the location, nature and performance of local innovation clusters.
The ORBIS database provides insights about the spatial pattern of business demography and performance and is based on a highly disaggregated
Selected clusters are compared then using data on business demography and business performance. These findings represent some of the
and selection biases in the original data source. 3 SMES, ENTREPRENEURSHIP AND INNOVATION Â OECD 2010136
the NUTS classification and the location information included in the original data source countries such as Denmark, Luxembourg, The netherlands,
OECD elaboration based on ORBIS database available from Bureau Van dijk Low LQ Medium-Low LQ SMES, ENTREPRENEURSHIP AND INNOVATION Â OECD 2010 137
which might also infer a bias in the original data sources. Secondly, KISA firms often tend to
OECD elaboration based on ORBIS database available from Bureau Van dijk Low LQ Medium-Low LQ Medium-High LQ High LQ
data source, territorial grid and methodology adopted for Figure 3. 3. This map provides empirical evidence on the uneven distribution of US firms in knowledge-intensive services
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software 1st range (0) 2nd range (891) 3rd range (516) 4th range (493) 5th range (488
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software High-High low-Low Low-High High-low
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software 1st range (0) 2nd range (790) 3rd range (400
OECD elaboration based on ORBIS database available from Bureau Van dijk (Geoda software High-High low-Low Low-High High-low
performance at the local level calculated experimentally from the ORBIS database can lead to an international analysis of the strength of clusters based on a composite indicator
the case of the US clusters, given data source constraints for this country, the composite
OECD elaboration based on ORBIS database available from Bureau Van dijk statlink 2 http://dx. doi. org/10.1787/814040554856
OECD elaboration based on ORBIS database available from Bureau Van dijk Ranking Name of business cluster Country of residence
OECD elaboration based on ORBIS database available from Bureau Van dijk statlink 2 http://dx. doi. org/10.1787/814047837382
OECD elaboration based on ORBIS database available from Bureau Van dijk Ranking Name of business cluster Country of residence
The above section illustrated the phenomenon of spatial clustering of economic activity in knowledge intensive sectors.
suggests that local knowledge transfers are important to this clustering process. This literature stresses the fact that knowledge does not spill over long distance †which means
exists between knowledge spillovers, spatial clustering and innovative output (Giuliani 2005). ) This is especially true for knowledge-driven sectors,
comparable data on this phenomenon. Patents and numbers of spin-off companies are relatively easy to count,
In addition, data collection is regular in some countries but sporadic in others SMES, ENTREPRENEURSHIP AND INNOVATION Â OECD 2010 145
Data show that KTOS have a much longer tradition in the United states than in Europe
Finally, data on university spin-offs in the two different contexts diverge much more slightly, with nearly
Data, however, show that knowledge transfer is still incipient in China. Universities have a great number of patents (126 per KTO),
3. An overview on the ORBIS database is given in Annex 3. A1 4. Patent protection can be sought abroad
The â€oeorbis†Database The scope of ORBIS for territorial analysis The ORBIS database, developed and maintained by Bureau Van dijk (BVD),
is a source of business micro data. The database includes around 40 million companies, has a
geographical coverage of up to 200 countries, and can consider all sectors of economic activity. There are no exclusion thresholds in terms of enterprise size, unless national
limitations reduce the coverage of administrative data sources. Given national data source constraints, there is plenty of information at the firm level about sector, legal status
ownership and an array of financial and economic variables. The target population consists of firms with a corporate legal status,
which means that micro firms with less than ten employees may be excluded largely from this database
The value of the ORBIS database for territorial analysis rests on the possibility to rearrange firm-level data according to detailed company location.
The information on company location relates to the complete address, which includes street, city and postal
code. A wide range of entrepreneurship, economic performance and financial indicators can be calculated at the local level from the ORBIS database
â Business demographic indicators, e g. business birth rate; survival rate; distribution of firms by age, etc
ORBIS database at different levels of industry or geographical breakdown using standard formulas, the calculation of business demographic indicators raises methodological
Limited information available in the ORBIS database on complex business demography events, such as mergers and acquisitions, makes the definition of a company
real exit rates, as it is a continuously expanding database in terms of both international and national coverage. In this respect, information regarding the company†s incorporation
database †i e. the date of company incorporation and the entry of a new company in the
Potential biases of territorial data calculated from commercial databases The key territorial information included in a commercial database with firm-level data
is the company†s complete postal address. Since this information is provided at the maximum available territorial detail, it can be rearranged easily according to various
territorial data. The first concerns use of the company instead of the local unit as the
company activities to a single location leads to incoherent territorial data in the case of
Besides location bias, other characteristics of commercial databases can indirectly alter the consistency of territorial data calculated from this sort of source
Coverage restrictions concern the under-coverage of the set of companies extracted from the database with respect to the relevant target population, where the latter is
conventionally assumed to include all active enterprises resident in a given country Under-coverage is induced generally by threshold effects in which firms under a certain
size are included not in the original data sets or by additional restrictions on revealing information on small areas imposed by the database provider
A structural bias is a systematic deviation from the target population in the sample distribution of key economic variables (number of firms, turnover, employees, value added
number of factors such as restrictions in the database and poor data quality consistency across industries, regions,
database, for instance the exclusion of all companies with some specific legal status. It occurs when the selection effect is correlated significantly with variables of interest for
Territorial data in the form of absolute values are affected by coverage restrictions structural bias and selection bias.
This is because simple aggregations of micro data by relevant territorial units completely mirror the characteristics of input data
Normalised territorial indicators, of which location quotients (LQ) are the most relevant example, may mitigate the negative impact of coverage restrictions and structural
neutralise these sources of bias in the input data Dynamic territorial indicators, such as employment or labour productivity growth
indicators is altered by an uneven spatial distribution of the sample of micro data as compared to the target population,
databases are upgraded sometimes in terms of coverage and data quality. This database upgrading may induce a structural break that can alter the spatial distribution of
companies by increasing or decreasing the magnitude of a static territorial bias Spatial) econometric modelling may also represent a possible way to deal with
different types of territorial bias in the data. In particular, if model regressors absorb some sources of bias, this econometric approach may provide interesting and unbiased results
databases to carry out nonstandard territorial analysis with an insight into the methodological problems that may affect the consistency of these territorial data.
The conclusion is that normalised static territorial indicators and dynamic territorial indicators are more robust with respect to different sources of territorial bias.
In the case of uneven spatial clustering global spatial indicators, such as Moran†S i, are found to be less useful and local indicators
Moran†S i and LISA differ in data employed and analytical scope. Moran†S i is a global
and the overall pattern in the data is summarised in a single statistic. In contrast, LISA calculates a local version of Moran†S i for each areal unit in the
data. In particular, LISA shows statistically significant groupings of neighbouring areas with high and low values around each region in the study area.
suggest clustering of similar values (positive spatial correlation), whereas the HL and LH locations indicate spatial outliers (negative spatial correlation
Also in the case of workers in existing SMES, data confirm the existence of a skills and training problem holding back innovation.
Across the EU-15 countries, data from the Eurostat Continuing vocational training Survey show that employees in enterprises with less than 50 employees receive
using population survey data. The GEM report found that 1. 2 million people, which corresponds to 3. 2%of the working-age UK population,
entrepreneurship, recent UK data released by the Third Sector in July 2009 www. cabinetoffice. gov. uk/media/231495/factoids. pdf) refer to an estimated average (2005-07
515 organisations that applied (data elaborated for OECD by the Korea Labor Institute and the Research Institute of Social Enterprise
In the United states, the Johns Hopkins Nonprofit Economic Data Project (NED) is generating information on the dynamics of the nonprofit sector by analysing diverse
datasets on nonprofit organisations, including data on nonprofit finances, employment and wages, and volunteering. The website of the project (www. ccss. jhu. edu
In addition, country notes present statistics and policy data on SMES, entrepreneurship and innovation for 40 economies, including OECD countries, Brazil, China, Estonia, Indonesia, Israel
Sourceoecd is the OECD online library of books, periodicals and statistical databases For more information about this award-winning service and free trials ask your librarian,
Notes on the Country Data Chapter 3. Knowledge Flows Introduction How knowledge affects entrepreneurship The systemic approach to innovation
The geographical clustering of knowledge-intensive activities European union Figure 3. 1. Distribution of HTM firms in the European union (Quantiles based on LQS
The â€oeorbis†Database Annex 3. A2 The LISA Methodology Chapter 4 Entrepreneurship Skills The importance of entrepreneurship skills for SMES and start-ups
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011