7 APPENDICES
7.3 Appendix C: The description and source of the institutional variables and indicators used in
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7.3 Appendix C: The description and source of the institutional variables and indicators used in the
137 Quality of
Education
1) PISA (country level)
- Low achievers in Reading of 15-year-olds, 2006.
- Low achievers in Math of 15-year-olds, 2006.
- Low achievers in Science of 15-year-olds, 2006.
2) CREATIVE CLASS (SCIENTIFIC TALENT) (regional level)
- Annual employment in creative class / economically active population, 2008.
- Number of jobs in the creative workforce per active population, 2008.
Calculation: after subtracting from 100 the average of the three PISA variables, the average value was multiplied with the simple average of the two indicators of Creative Class (Scientific Talent). Box-cox transformation was used in the case of the average PISA variable, because the skewness of the original variable was higher than -1 (-2.119). Therefore λ =2. Also a second transformation was necessary to handle the skewness of the variable (again λ =2).
OECD – PISA database ESPON Database Portal (Theme:
Information and Society – Employment in Creative Class) ESPON Database Portal (Project:
CREA Update – Creative Workforce Update – Share of the
creative workforce) ESPON Database Portal (Theme:
Economy, finance and trade – Economically active population)
http://pisacountry.acer.edu.au/
http://database.espon.eu/db2/sear ch;jsessionid=db8d55d87de9e3a 650e2a3b1f293
Business Risk
BUSINESS EXTENT OF DISCLOSURE INDEX (country level)
Disclosure index measures the extent to which investors are protected through disclosure of ownership and financial information. The index ranges from 0 to 10, with higher values indicating greater disclosure, (0=least disclosure to 10=greatest disclosure), for year 2012.
The indicators distinguish three dimensions of investor protections: transparency of related-party transactions (extent of disclosure index), liability for self-dealing (extent of director liability index) and shareholders’ ability to sue officers and directors for misconduct (ease of shareholder suits index). The data come from a survey of corporate and securities lawyers and are based on securities regulations, company laws, civil procedure codes and court rules of evidence. Detailed description is available at: http://www.doingbusiness.org/methodology/protecting-investors (5 August 2013).
World Bank World Development Index
http://data.worldbank.org/indicat or/IC.BUS.DISC.XQ
Social Capital
1) SOCIAL CAPITAL (country level)
The sub-index measures countries’ performance in two areas: social cohesion and engagement; and community and family networks. This sub-index evaluates how factors such as volunteering, helping strangers, and donating to charitable organisations impact economic performance and life satisfaction. It also measures levels of trust, whether citizens believe they can rely on others, and assesses how marriage and religious attendance provide support networks beneficial to wellbeing. Empirical studies on social capital have shown that citizens’ wellbeing improves through social trust, family and community ties, and civic group membership. Similarly, societies with lower levels of social capital have been shown to experience lower levels of economic growth. And so the term ‘capital’ in ‘social capital’ highlights the contribution of social networks as an asset that produces economic and wellbeing returns (for year 2011).
The Social Capital sub-index contains 7 sub-indicators: (1) Donations, (2) Helping Strangers, (3) Formal Volunteering, (4) Marriage, (5) Perception of Social Support, (6) Religious Attendance, (7) Trust in Others. Data are available from 2011.
Detailed description of the variable is available at: http://webapi.prosperity.com/download/pdf/PI2012_MethodologyV4.pdf (5 August 2013)
2) TECHNOLOGICAL READINESS (regional level) - Households with access to broadband, 2011.
- Individuals who ordered goods or services over the Internet for private use, 2011.
- Households with access to Internet, 2011.
Calculation: re-scaled (converted to a scale of 0 to 10) Social Capital data were multiplied with the simple average of the three indicators of Technological Readiness.
LEGATUM Prosperity Index, Social Capital Eurostat Regional Database
http://www.prosperity.com/Expl oreData.aspx
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=isoc_r_ia cc_h&lang=en
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=isoc_r_bl t12_i&lang=en
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=isoc_r_br oad_h&lang=en
Open Society
1) PERSONAL FREEDOM (country level)
The Personal Freedom sub-index measures countries’ performance in two areas: individual freedom and social tolerance.
The Personal Freedom sub-index captures the effects of freedom of choice, expression, movement, and belief, on a country’s per capita GDP and the subjective wellbeing of its citizens. It also assesses how levels of tolerance of ethnic minorities and immigrants impact countries’ economic growth and citizens’ life satisfaction. Societies that foster strong civil rights and
Charron et al.(2011) EU QoG Corruption Index (EQI)
LEGATUM Prosperity Index, Personal Freedom
http://www.qog.pol.gu.se/data/da tadownloads/qogeuregionaldata/
http://www.prosperity.com/Expl oreData.aspx
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freedoms have been shown to enjoy increases in levels of satisfaction among their citizens. When citizens’ personal liberties are protected, a country benefits from higher levels of national income (for year 2011).
The Personal Freedom sub-index contains 5 sub-indicators: (1) Civil Liberties, (2) Civil Liberty and Free Choice, (3) Satisfaction with Freedom of Choice, (4) Tolerance of Immigrants, (5) Tolerance of Minorities.
Detailed description of the variable is available at: http://webapi.prosperity.com/download/pdf/PI2012_MethodologyV4.pdf (5 August 2013)
2) CORRUPTION (regional level)
Data based on a standardized variable combining education (EdCor: region's aggregated score from survey question on the extent to which corruption persists in the education system in the region/area), health (HelCor: region's aggregated score from survey question on the extent to which corruption persists in the health care system in the region/area, and general public corruption (OtherCor: egion's aggregated score from survey question on the extent to which respondents felt other citizens in the region/area use bribery to obtain public services) in addition to law enforcement (LawCor: region's aggregated score from survey question on the extent to which corruption persists in the law enforcement in the region/area) and the payment of bribes (HelBribe: region's aggregated score from survey question asking whether the respondents were forced to pay a bribe in the last 12 months to obtain any health care in the region/area. . Data are from 2009-2010.
[Source: Nicholas Charron , Lewis Dijkstra & Victor Lapuente (2013): Regional Governance Matters: Quality of Government within European Union Member States, Regional Studies, DOI:10.1080/00343404.2013.770141
To link to this article: http://www.qog.pol.gu.se/digitalAssets/1446/1446579_regional-studies-article.pdf (9 August 2013)]
Detailed description of the variable is available at: http://www.qog.pol.gu.se/digitalAssets/1362/1362471_eqi---correlates-codebook.pdf (9 August 2013)
Calculation: re-scaled (converted to a scale of 0 to 10) Corruption data were multiplied with re-scaled (converted to a scale of 0 to 10) Personal Freedom data.
Business Environment
1) BUSINESS FREEDOM (country level)
Business freedom is a quantitative measure of the ability to start, operate, and close a business that represents the overall burden of regulation as well as the efficiency of government in the regulatory process. The business freedom score for each country is a number between 0 and 100, with 100 equaling the most free business environment. The score is based on 10 factors, all weighted equally, using data from the World Bank’s Doing Business report. Each factor is converted to a scale of 0 to 100, after which the average of the converted values is computed. The result represents the country’s business freedom score, 2013. Detailed description of the variable is available at: http://www.heritage.org/index/business-freedom (5 August 2013).
2) EU QoG INDEX (regional level)
Data shows quality of government. Data based on a study on regional variation in quality of government within the EU The dataset covers all 27 EU countries as well as 172 NUTS 1 and NUTS 2 regions within 18 of the 27 countries, thus the data is given for 181 separate units. The data for region ns was collected via a large survey of roughly 34,000 respondents in Europe in December of 2009. The national level estimates are taken from the World Bank Governance Indicators. The regional estimates are comprised of 16 separate indicators. Data are from 2009-2010.
[Source: Nicholas Charron , Lewis Dijkstra & Victor Lapuente (2013): Regional Governance Matters: Quality of Government within European Union Member States, Regional Studies, DOI:10.1080/00343404.2013.770141 To link to this article: http://www.qog.pol.gu.se/digitalAssets/1446/1446579_regional-studies-article.pdf (9 August 2013)]
Detailed description of the variable is available at:
http://www.qog.pol.gu.se/data/datadownloads/qogeuregionaldata/ (5 August 2013).
Calculation: Business Freedom indicator was multiplied with the re-scaled EU QoG index.
Heritage Foundation EU QoG Index
http://www.heritage.org/index/ex plore
http://ec.europa.eu/regional_poli cy/sources/docgener/work/2012_
02_governance.pdf
Absorptive Capacity
1) FIRM-LEVEL TECHNOLOGY ABSORPTION (country level)
This data is taken from the WEF Global Competitiveness Report. Technological readiness is the 9th pillar of the Global Competitiveness Index (GCI). The pillar contains two sub-indicators: (1) Technological adoption and (2) ICT use. In today’s globalized world, technology is increasingly essential for firms to compete and prosper. The Technological readiness pillar measures the agility with which an economy adopts existing technologies to enhance the productivity of its industries, with
World Economic Forum Competitiveness Report
2012-2013, 489. p.
Eurostat Regional Database
http://www3.weforum.org/docs/
WEF_GlobalCompetitivenessRe port_2012-13.pdf
http://epp.eurostat.ec.europa.eu/t
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specific emphasis on its capacity to fully leverage information
and communication technologies (ICT) in daily activities and production processes for increased efficiency and enabling innovation for competitiveness. The variable of Firm-level technology absorption is a part of the Technological readiness pillar. The variable answer the question to what extent do businesses in a country absorb new technology (1 = not at all; 7 = aggressively absorb). Weighted average of 2011–12 data.
Detailed description of the variable is available at: http://www3.weforum.org/docs/CSI/2012-13/GCR_Chapter1.1_2012-13.pdf (9 August 2013)
2) EMPLOYMENT IN KNOWLEDGE INTENSIVE AND HIGH TECHNOLOGY ADOPTIONS (regional level)
• Employment in high-Technology Adoptions (high-tech manufacturing and knowledge-intensive services) by NUTS 2 region (2007-2008).
• Employment in technology and knowledge-intensive sectors by NUTS 2 region and gender (from 2008 onwards, NACE Rev. 2) (2011)
• Researchers, all sectors by NUTS 2 region, % of total employment (2009).
• Annual data on Human resources in science and technology (HRST) and sub-groups by NUTS 2 region (2011).
Calculation: Firm-level Technology Absorption variable was multiplied with the average of variables related to employment in knowledge-intensive and high-Technology Adoptions.
gm/table.do?tab=table&init=1&l anguage=en&pcode=tgs00039&
plugin=1
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=htec_emp _reg2&lang=en
http://epp.eurostat.ec.europa.eu/t gm/table.do?tab=table&init=1&l anguage=en&pcode=tgs00043&
plugin=1
http://epp.eurostat.ec.europa.eu/t gm/table.do?tab=table&init=1&l anguage=en&pcode=tgs00038&
plugin=1
Education &
Training
HIGHER EDUCATION AND TRAINING AND LIFE-LONG LEARNING (regional level)
- Share of population aged 25-64 years with higher educational attainment, 2011. [Source: Eurostat Regional Database: Persons aged 25-64 with tertiary education attainment by sex and NUTS 2 regions (from 2000 onwards) - %]
- Share of population aged 25-64 years participating in education and training, 2011. [Source: Eurostat Regional Database: Participation of adults aged 25-64 in education and training by NUTS 2 regions (from 2000 onwards) -
%]
Calculation: The sum of the two variables is used.
Eurostat Regional Database
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=edat_lfse _11&lang=en
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=trng_lfse _04&lang=en
Business Strategy
1) NATURE OF COMPETITIVE ADVANTAGE (country level)
This data is taken from the WEF Global Competitiveness Report. Business sophistication is the 11th pillar of the Global Competitiveness Index (GCI). There is no doubt that sophisticated business practices are conducive to higher efficiency in the production of goods and services. Business sophistication concerns two elements that are intricately linked: the quality of a country’s overall business networks and the quality of individual firms’ operations and strategies. These factors
are particularly important for countries at an advanced stage of development when, to a large extent, the more basic sources of productivity improvements have been exhausted. The quality of a country’s business networks and supporting industries, as measured by the quantity and quality of local suppliers and the extent of their interaction, is important for a variety of reasons. When companies and suppliers from a particular sector are interconnected in geographically proximate groups, called clusters, efficiency is heightened, greater opportunities for innovation in processes and products are created, and barriers to entry for new firms are reduced. Individual firms’ advanced operations and strategies (branding, marketing, distribution, advanced production processes, and the production of unique and sophisticated products) spill over into the economy and lead to sophisticated and modern business processes across the country’s business sectors. The variable of Nature of competitive advantage is a part of the Technological readiness pillar. The data captures answers to the question:
“What is the nature of competitive advantage of your country’s companies in international markets based upon?” (1 = low-cost or natural resources; 7 = unique products and processes). Weighted average of 2011–12 data.
Detailed description of the variable is available at: http://www3.weforum.org/docs/CSI/2012-13/GCR_Chapter1.1_2012-13.pdf (9 August 2013)
2) BUSINESS SOPHISTICATION (regional level)
- Share of employment in sophisticated sectors, 2011. [Employment in the J, K sectors as % of total employment,
World Economic Forum Competitiveness Report
2012-2013, 489. p.
Eurostat Regional Database (Total – All NACE activities, J – Information and communication, K – Financial and insurance
activities) EU Regional Competitiveness
Index, 2010
http://www3.weforum.org/docs/
WEF_GlobalCompetitivenessRe port_2012-13.pdf
http://appsso.eurostat.ec.europa.e u/nui/submitViewTableAction.d o;jsessionid=9ea7d07d30e456f8 33bccf984d05b62de462168ef89f .e34MbxeSaxaSc40LbNiMbxeN aNeKe0
http://appsso.eurostat.ec.europa.e u/nui/submitViewTableAction.d o
http://www.urenio.org/wp-content/uploads/2010/09/Region al-Competitive-Index-EU-JRC2010.pdf
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J: Information and Communication, K: Financing and insurance activities.]
- Share of Gross value added (GVA) in sophisticated sectors, 2007. [GVA in the J, K sectors as % of total GVA, J:
Information and Communication, K: Financing and insurance activities.]
- New foreign firms per one million inhabitants, 2005-2007.
Calculation: The Nature of competitive advantage was multiplied with the unweighted average of the three indicators of the Business Sophistication variable. Box-cox transformation was used in the case of the ‘Employment in JK Sectors’ sub-indicator, because the skewness of the original variables were higher than 1 (1.499). Therefore λ = -0.05.
Technology Transfer
1) INNOVATION SUB-INDEX (same variables were used as in the EU Regional Competitiveness Index (2010), but data were updated) (regional level)
- Total patent applications: Patent applications to the EPO by priority year by NUTS 2 regions. Number of applications per one million inhabitants (2008-2009 average).
- Scientific publication: Publications per one million inhabitants (Thomson Reuters Web of Science & CWTS database (Leiden University). Average of years 2005-2006.
- High-tech inventors: High-tech patent applications to the EPO by priority year by NUTS 2 regions. Number of applications per one million inhabitants (2008-2009 average).
- ICT inventors: PCT patent applications (fractional count by inventor and priority year) in ICT, 2010.
- Biotechnology inventors: PCT patent applications (fractional count by inventor and priority year) in biotech, 2010.
Calculation: unweighted average of the five innovation related variables. Box-cox transformation was used in the case of the indicator, because the skewness of the original indicator was higher than 1 (1.139). Therefore λ = -0.05
Eurostat Regional Database OECD Regional Database
(Innovation Indicators) EU Regional Competitiveness
Index, 2010
http://epp.eurostat.ec.europa.eu/t gm/table.do?tab=table&init=1&l anguage=en&pcode=tgs00040&
plugin=1
http://epp.eurostat.ec.europa.eu/t gm/table.do?tab=table&init=1&l anguage=en&pcode=tgs00041&
plugin=1
http://stats.oecd.org/Index.aspx?
datasetcode=REG_DEMO_TL2#
http://www.urenio.org/wp-content/uploads/2010/09/Region al-Competitive-Index-EU-JRC2010.pdf
Technology Development
GERD (regional level)
Gross Domestic Expenditure in Research & Development (GERD) as a percentage of GDP, for year 2009. (regional level) Calculation: Box-cox transformation was used in the case of the GERD variable, because the skewness of the original variable was higher than 1 (1.095). Therefore λ = -0.05.
Eurostat Regional Database
http://appsso.eurostat.ec.europa.e u/nui/show.do?dataset=rd_e_ger dreg&lang=en
Clustering
CLUSTERS (regional level) Cluster Mix Index
1. Average EU wage per cluster is calculated across the reporting countries, weighted by the total number of employees they have in that cluster category at the national level.
2. Wages are normalized so that the cluster with the lowest average EU wage (that happened to be footwear) is equal to 1.
3. For each region, a wage cluster mix index is created by taking the sum across all clusters of the regional share in employment per cluster times the relevant cluster wage index calculated above. This gives a number for each region; the higher it is the more the region benefits from the cluster mix effect rather than strong performance within any individual cluster.
DGRegion Individual Datataset (not-published)
Connectivity
INFRASTRUCTURE SUB-INDEX (regional level)
- Motorway density (average pop/area). EU27=100, Eurostat/DG TREN/EuroGeographics/National Statistical Institutes, 2006.
- Railway density (average pop/area), EU27=100, Eurostat/DG TREN/EuroGeographics/National Statistical Institutes, 2007.
- Number of passenger flights, daily number of passenger flights (accessible within 90-minute drive), Eurostat/EuroGeographics/National Statitical Institutes, 2007.
Calculation: Average of the variables of motorway density, railway density and number of passenger flights. Box-cox transformation was used in the case of three transportation variables, because the skewness of the original variable was higher than 1 (1.674). Therefore λ = -0.05.
EU Regional Competitiveness Index, 2010
http://www.urenio.org/wp-content/uploads/2010/09/Region al-Competitive-Index-EU-JRC2010.pdf
141 Financial
Institutions
1) DEPTH OF CAPITAL MARKET (country level)
The Depth of Capital Market is one of the six sub-indices of the Venture Capital and Private Equity index. This variable is a complex measure of the size and liquidity of the stock market, level of IPO, M&A and debt and credit market activity. Note that there were some methodological changes over the 2006-2012 time period so comparisons across years are not perfect.
The data set was provided by Alexander Groh, 2013.
Detailed description about the indicator is available at: http://www.wall-street.ro/files/102434-82.pdf (5 August 2013).
2) CONCENTRATION OF FINANCIAL SERVICES (regional level)
Regional employment in financial services sector as percentage of total regional employment (for different years between 2005-2011).
Calculation: Depth of Capital Market country level data were multiplied with the Concentration of Financial Services variable. Box-cox transformation was used in the case of three transportation variables, because the skewness of the original variable was higher than 1 (2.505). Therefore λ = -0.05.
Groh, A, H.Liechtenstein and K.
Lieser 2012 The Global Venture Capital and Private Equity Country Attractiveness Index
2012 Annual, Cluster Observatory (Financial services – employees, Regional
employment)
http://www.wall-street.ro/files/102434-82.pdf http://www.clusterobservatory.eu /index.html#!view=regionalmap ping;i=V16140;y=2011;r=NC10;
rsl=2;rp=NC10;s=CC20-fin;sp=CC20-STND;p=table http://www.clusterobservatory.eu /index.html#!view=regionalmap ping;i=V16140,C20220;y=2011;
r=NC10;rsl=2;rp=NC10;sp=CC2 0-STND;p=table
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