Several independent websites process this information into databases that allow cross-player comparisons and provide recommendations on how to progress in the game.
The government holds data about citizens in hundreds of databases, with individuals having little control over it.
and chart their own behaviour and actions. 20) Holistic services include phone based services such as New york's 311 service which provide a database that can be analysed for patterns of recurring problems and requests. 21) Tools
and experiences that has a database of 4, 000 ideas online, receives a quarter of a million visitors a year,
Variations will include toolkits, oral histories, databases, and manuals. One new initiative by Open Business is the creation of a database of open business models. 199) Barefoot consultants.
There is an important role for consultants and those with specialist knowledge who can act as knowledge brokers and advisers in the new systems.
and girls expertise Pilot Tool kit Development Finalize summit definition Validate Indicators Test Acceptance Our Work Portfolio M&e The Work of Others Global Health Agenda Girls Database
and more formal legal devices (like public databases). With the increasing mixing of voluntary and professional roles (for example around care for the elderly,
and controversial, infrastructure is the creation of a single database of children deemed‘at risk'in the UK.
which allow recipients to rate philanthropic foundations. 427) Providing extensive information on NGO performance, such as Guidestar's services and databases in many countries worldwide,
databases and the know-how of millions of individuals, is the ultimate source of all economic life. 15 Organizations,
As a sample frame for constructing the database, we used the register of SMES in the region that was offered by Suomen Asiakastieto,
databases and data collection tools...79 4. 1 Introduction 79 10 4. 2 Databases 79 Existing databases 79 4. 2. 1 Bibliometric databases 80 4
. 2. 2 Patent databases 82 4. 2. 3 Data availability according to EUMIDA 83 4. 2. 4 Expert view on data availability in non
-European countries 85 4. 2. 5 4. 3 Data collection instruments 87 Self-reported institutional data 88 4. 3. 1 4
cleaning 108 International databases 110 5. 3. 2 5. 3. 2. 1 Bibliometric data 111 5. 3. 2. 2 Patent data
their coverage in national databases...84 Table 4-2: Availability of U multirank data elements in countries'national databases according to experts in 6 countries (Argentina/AR, Australia/AU, Canada/CA, Saudi arabia/SA, South africa/ZA
, United states/US...86 Table 5-1: Regional distribution of participating institutions...99 Table 5-2:
and underlying database to produce authoritative expert institutional and field based rankings for particular groups of comparable institutions on dimensions particularly relevant to their activity profiles.
and citation databases mainly cover peer reviewed journal articles, while that type of scientific communication is prevalent only in a narrow set of disciplines (most natural sciences, some fields in medicine) but not in many others (engineering, other fields in medicine and natural sciences, humanities
Use of statistics from existing databases. National databases on higher education and research institutions cover different information based on national, different definitions of items and are
therefore not easily used in cross-national comparisons. International databases such as those of UNESCO, OECD and the EU show those comparability problems
but moreover they are focused on the national level and are therefore not useful for institutional
or field comparisons. 3 International databases with information at the institutional level or lower aggregation levels are currently available for specific subfields:
Regarding research output and impact, there are worldwide databases on journal publications and citations (the well-known Thomson Reuters and Scopus databases.
These databases, after thorough checking and adaptation, are used in the research-based global rankings. Their strengths and weaknesses were mentioned above.
Patent databases have not been used until now for global rankings. Self-reported data collected by higher education and research institutions participating in a ranking.
institutional data and choice of publication language (English) and channels (journals counted in the international bibliometric databases).
Rankings have to use multiple databases to bring in different perspectives on institutional performance. As much as possible available data sources should be used,
This will result in users creating their own specific and different rankings, according to their needs and wishes, from the entire database.
The other important components of the construction process for U multirank are the databases and the data collection tools that allow us to actually‘fill'the indicators.
The first step in the indicator selection process was a comprehensive inventory of potential indicators from the literature and from existing rankings and databases.
Literature review Review of existing rankings Review of existing databases First selection Stakeholder consultation Expert advice Second selection Pre-test Revision Selection
The required data to construct the indicator is either available in existing databases and/or in higher education and research institutions,
One may mention audio visual recordings, computer software and databases, technical drawings, designs or working models, major works in production or exhibition and/or award-winning
Expert Group on Assessment of University-Based Research (2010) Apart from using existing bibliometric databases,
These data refer to database years. Publishing in top-ranked, high impact journals reflects quality of research.
ISI databases available. Used in CWTS University-Industry Research Cooperation Scoreboard. 16 See also the brief section on the EUMIDA project,
For many of the indicators data are available in the institutional databases. Hardly any of such data can be found in national or international databases.
The various manifestations and results of internationalization are captured through the list of indicators shown in Table 3-5. The table includes some comments made during the consultation process that led to the selection of the indicators.
Data available in international data bases but bias towards certain disciplines and languages. 5 Number of joint degree programs The number of students in joint degree programs with foreign university (including integrated period at foreign university) as a percentage of total
While data may be found in international patent databases, the indicator is used not often and stakeholders did not particularly favor the indicator.
databases and data collection Multirank: databases and data collection Multirank: databases and data collection Multirank:
databases and data collection Multirank: databases and data collection Multirank: databases and data collection Multirank:
databases and data collection Multirank: databases and data collection Multirank: databases and data collection Multirank:
databases and data collection Multirank: databases and data collection Multirank: databases and data collection Multirank:
databases and data collection Multirank: databases and data collection Multirank: databases and data collection Multirank:
databases and data collection Multirank: databases and data collection tools tools 4. 1 Introduction In this chapter we will describe the databases
and data collection instruments used in constructing U multirank. The first part is an overview of existing databases mainly on bibliometrics and patents.
The second presents an explanation of the questionnaires and survey tools used for collecting data from the institutions (the self-reported data) at the institutional
and department levels and from students. 4. 2 Databases Existing databases 4. 2. 1one of the activities in the U multirank project was to review existing rankings
and explore their underlying databases. If existing databases can be relied on for quantifying the U multirank indicators this would be helpful in reducing the overall burden for institutions in handling the U-Multirank data requests.
However, from the overview of classifications and rankings presented in chapter 1 (section 1. 3) it is clear that international databases holding information at institution level
or at lower aggregation levels are currently available only for particular aspects of the dimensions Research and Knowledge Transfer.
For other aspects and dimensions, U multirank will have to rely on self-reported data. Regarding research output and impact, there are worldwide databases on journal publications and citations.
For knowledge transfer, the database of patents compiled by the European Patent office is available. In the next two subsections
available bibliometric and patent databases will be discussed. To further assess the availability of data covering individual higher education and research institutions,
the results of the EUMIDA project were taken also into account. 21 The EUMIDA project (see:
www. eumida. org) seeks to develop the foundations of a coherent data infrastructure (and database) at the level of individual higher education institutions.
Section 4. 2. 4 presents an overview of availability based on the outcomes of the EUMIDA project.
Our analysis on data availability was completed with a brief online consultation with the group of international experts connected to U multirank (see section 4. 2. 5). The international experts were asked to give their assessment of the 21 The U multirank project was granted access to the preliminary
outcomes of the EUMIDA project in order to learn about data availability in the countries covered by EUMIDA. 80 situation with respect to data availability in some of the non-EU countries included in U multirank Bibliometric databases 4. 2. 2there are a number of international databases
which can serve as a source of information on the research output of a higher education and research institution (or one of its departments).
An institution's quantity of research-based publications (per capita) reflects its research output and can also be seen as a measure of scientific merit or quality.
In particular, if its publications are cited highly within the international scientific communities this may characterize an institution as high-impact and high-quality.
The production of publications by a higher education and research institute not only reflects research activities in the sense of original scientific research,
but usually also the presence of underlying capacity and capabilities for engaging in sustainable levels of scientific research. 22 The research profile of a higher education
and research institution can be specified further by taking into account its engagement in various types of research collaboration.
For this one can look at joint research publications involving international, regional and private sector partners.
Data on numbers and citations of research publications are covered relatively well in existing databases. Quantitative measurements and statistics based on information drawn from bibliographic records of publications are called usually‘bibliometric data'.
This part of the research literature is covered(‘indexed')by a number of international databases. In most cases the journals indexed are reviewed internationally peer,
U multirank therefore makes use of international bibliometric databases to compile some of its research performance indicators
Knowledge Transfer and Regional Engagement. 22 This is why research publication volume is a part of the U-Map indicators that reflect the activity profile of an institution. 81 Two of the most well-known databases that are available for carrying out
and Scopus. 23 Both are commercial databases that provide global coverage of the research literature
The Web of Science database is maintained by ISI, the Institute for Scientific Information, which was taken over by Thomson Reuters a few years ago.
The Scopus database was launched in 2004 by the publishing house Elsevier. It claims to be the largest abstract
and citation database containing both peer-reviewed research literature and web sources. It contains bibliometric information covering some 17
bibliometric data was derived from the October 2010 edition of the Web of Science bibliographical database.
An upgraded‘bibliometric version'of the database is housed and operated by the CWTS (being one of the CHERPA Network partners) under a full license from Thomson Reuters. This dedicated version includes the‘standardized institutional names'of higher education
and multidisciplinary database, has its pros and cons. The bulk of the research publications are issued in peer-reviewed international scientific and technical journals,
and no books or 23 Yet another database is Google Scholar. This is a service based on the automatic recording by Google's search engine of citations to any author's publications (of whatever type) included in other publications appearing on the worldwide web. 24 See:
The alternative source of bibliographical information, Elsevier's Scopus database, is likely to provide an extended coverage of the global research literature in those underrepresented fields of science.
For the following six indicators selected for inclusion in the U multirank pilot test (see chapter 6) one can derive data from the CWTS/Thomson Reuters Web of Science database:
Patent databases 4. 2. 3as part of the indicators in the Knowledge Transfer dimension, U multirank selected the number of patent applications for
and patents indicators may be derived from patent databases. For U multirank, patent data were retrieved from the European Patent office (EPO.
Its Worldwide Patent Statistical Database (version October 2009) 25, also known as PATSTAT, is designed and published on behalf of the OECD Taskforce on Patent Statistics.
83 The PATSTAT patent database is designed especially to assist in advanced statistical analysis of patent data.
Switzerland and Norway) and investigates the data available from national databases in as far as these are held/maintained by national statistical institutes, ministries or other organizations.
and whether information on these data elements may be found in national databases (statistical offices, ministries, rectors'associations, etc.).
The table illustrates that information on only a few U multirank data elements is available from national databases and,
their coverage in national databases Dimension EUMIDA and U multirank data element European countries where data element is available in national databases Teaching & Learning relative rate of graduate unemployment
whether data was available in national databases and/or in the institutions themselves. Table 4-2 shows that the Teaching and Learning dimension scores best in terms of data availability.
The same holds true to a lesser extent, for the dimension International Orientation, where little data is available in national databases.
Availability of U multirank data elements in countries'national databases according to experts in 6 countries (Argentina/AR, Australia/AU, Canada/CA, Saudi arabia/SA, South africa/ZA
, United states/US) Dimension U multirank data element Countries where data element is available in national databases Countries where data element is available in institutional database Teaching & Learning
In the Research dimension, Expenditure on Research and Research Publication Output data are represented best in national databases.
however, information is not really available in national databases. According to the experts consulted, more data can probably be found in institutional databases.
However, if that is the case, there is always a risk that different institutions may use different definitions
Even if there is information available in databases (national, institutional, or other), our experts stressed that it is not always easy to obtain that information (for instance in case of data relating to the dimension Regional Engagement).
In fact, some of the key indicators are extracted from international bibliometric databases anyway and did need not data provision from the institutions.
these two dimensions are less prevalent in existing national and institutional databases and therefore presented some data availability problems.
All institutions had clear communication partners from the U multirank team. 4. 4 A concluding perspective This chapter, providing a quick survey of existing databases,
underlines that there are very few international databases/sources where data can be found for our type of rankings.
The only sources that are available are international databases holding bibliometric and patent data. This implies that
and data from international databases. 5. 2 The global sample A major task of the feasibility study was the selection of institutions to be included in the pilot study.
The existing set of higher education institutions in the U-Map database was included. This offered a clear indication of a broad variety of institutional profiles. 98 Some universities applied through the U multirank website to participate in the feasibility study.
the collection of self-reported data from the institutions involved in the study (including the student survey) and the collection of data on these same institutions from existing international databases on publications/citations and patents.
International databases 5. 3. 2the data collection regarding the bibliometric and patent indicators took place by studying the relevant international databases
and extracting from these databases the information to be applied to the institutions and fields in the sample. 111 5. 3. 2. 1 Bibliometric data As indicated in chapter 4,
we analysed the October 2010 edition of the Web of Science database (Wos) to compile the bibliometric data of the institutions involved in the sample.
statistics were produced that are represented sufficiently in the Wos database, either in the entire Wos or in the preselected Wos fields of science.
'The bibliometric data in the pilot version of U multirank database refer to one measurement per indicator.
The annual statistics refer to publication years (rather than database years. The computation routine for the field-normalized citation rate indicator involved collecting citations to each publication according to a variable citation window,
These data refer to database years. 114 The research publications in the three fields of our pilot study (business studies,
especially when the data is drawn from the Wos database for just a single (recent) publication year.
), for our analysis of patents we collected data from the October 2009 version of the international PATSTAT-database.
In this database the institutions participating in the sample were identified and studied in order to extract the institutional-level patent-data.
The extraction of institutional-level patent data is based on identification of the institute in the applicant field of the PATSTAT database (see appendix 7:
due to a lack of concordance with the field classification that is present in the patent database.
initiatives should also come from providers of (bibliometric) databases as well as stakeholder associations in the sector.
The indicators based on data from patent databases are feasible only for institutional ranking due to discrepancies in the definition and delineation of fields in the databases.
study was carried out via self-reporting from the institutions and analysis of international bibliometric and patent databases.
in bibliometric analysis the sets of publications produced by a specific institution (or a subunit of it) have to be identified in international bibliographic databases.
which the institution is detected automatically by lexical queries on the author's affiliation field (the address field) of the publications in the databases, by a query on keywords.
we were only able to identify our sample institutions in the database. Subunits for field analyses could not be found.
When this strategy leads to a substantial database within the next two years recruitment could be reinforced, at
static ranking but would only feed into the database, allowing the user to rank on the basis of the most current information.
with U multirank it is also possible to create so-called‘authoritative'ranking lists from the database.
For instance, an international public organization might be interested in using the database to promote a ranking of the international, research-intensive universities in order to compare a sample of comparable universities worldwide.
this might be an important means of generating revenue from database-derived products. On the other hand, in the first phase of implementation, U multirank should be perceived by all potential users as relevant for their individual needs.
The development of the European database resulting from EUMIDA should take into account the basic data needs of U multirank.
Some specific recommendations regarding the further development of the EUMIDA database can be made: First, there are some elements
U multirank implies a need for an international database of ranking data consisting of indicators which could be used as a flexible online tool
This database is a crucial starting point to identify and rank comparable universities. Developing a European data system
Furthermore it could also be used as a base for an international database and international rankings;
thus creating an increasing set of data systems to be combined into a joint database. How to deal with the top-down and bottom up-approach?
In the‘bottom-up'approach national rankings could feed their data into the international database, the U multirank unit will be able to pre-fill the data collection instruments
Finalisation of the various U multirank instruments 1. Full development of the database and web tool.
and the international U multirank database should be realized. Roll out of U multirank across EU+countries 5. Invitation of EU+higher education institutions and data collection.
Elements of a new project phase Work package Products Deadline Database and web tool Functioning database Functioning web tool prototype 06/2012 Standards
In addition, efficiency refers to the coordination of different European initiatives to create international databases (such as E3m, EUMIDA.
e g. media companies (interested in publishing rankings), consulting companies in the higher education context and data providers (such as the producers of bibliometric databases).
Methodological development and updates Communication activities Implementation of (technical) infrastructure Development of a database Provision of tools for data collection Data collection (again including communication) 170 Data analysis (including self-collected
and graduate surveys or the use of databases charged with license fees, e g. bibliometric and patent data.
IT Indicators/databases used (e g. license costs) Development of a database Staff Basic IT costs Provision of tools for data collection Staff Basic IT costs (incl. online survey systems
and databases Data analysis Staff Number of countries and institutions covered Range of indicators and databases License fees of databases (e g. bibliometric) Publication Staff Basic IT costs Features of web tool
to present results Information services for users Staff Basic IT costs Number of countries and institutions covered Range of indicators and databases Scope of information services Internal organization
and should enlarge this database internationally, targeting the institutions required to reach sufficient coverage for all relevant profiles.
New evidence from the KEINS database''.''Research Evaluation, 17 (2): 87-102. Magerman T, Grouwels J.,Song X. & Van Looy B. 2009.
for enterprise systems mostly relying on traditional relational data base management systems. As for drivers, cloud computing is represented in Fig. 1. 1,
Mapreduce4 and 3 Several classifications of the Nosql databases have been proposed in literature 39. Here we mention Key-/Value-Stores (a map/dictionary allows clients to insert
and Column-Oriented databases (data are stored and processed by column instead of row). An example of the former is Amazon's Dynamo;
and Cassandra represent Column-Oriented databases. For further details we refer the reader to 39,40. 4 Mapreduce exploit, on the one hand,(i) a map function,
for improving fraud detection as tax evasion control through the integration of a large number of public administration databases;
In this case, the volume of data is reduced to a limited view on the asset actually stored in databases.
and strategy points for big data lifecycle phases Lifecycle phase Factors Recommendations Strategy points Storage Technology Consolidate corporate databases (internal)
The third case study, based on a Cloudera case history 33, focuses again on the relevance of consolidation and integration for retrieving valuable information from Big data, with a specific attention to data base technologies.
and for a savvy and sustainable choice of the right mix of technologies to consolidate corporate databases (internal)
Proceedings of extending database technology (EDBT), ACM. March 22 24, Sweden, pp 530 533 14. Davenport TH, Barth P, Bean R (2012) How‘‘Big data''is different.
Han J, Haihong E, Le G, Du J (2011) Survey on Nosql database. In: Proceedings of the 6th international conference on pervasive computing and applications, pp 363 366 40.
Strauch C (2010) Nosql databases. Lecture notes on Stuttgart Media, Stuttgart, pp 1 8 41.
which is simplified a model large-scale distributed database. Finally, the last one is the Map Reduce1 programming model that can be modified according to the characteristics of the applications that Google is running on its servers.
often interfacing different preexisting platforms and heterogeneous databases. To solve the above issues, managers can follow an integrated
,{ZIP CODE, date of birth, gender} allow to identify 87%of US citizens using public data base (as for gender, age,
keeping databases that are being used by different users in different locations synchronized and up to date; reducing the costs of transportation, phone calls,
51 Collaboration, 194 Collaborative management tools, 113,114 Collaborative software, 123 Collaborative working environment (CWE), 123 Collective Intelligence, 68 Column-oriented databases, 6
A more transparent, inclusive and Europe-wide database and network of initiatives would greatly assist in promoting innovation-driven entrepreneurship. 6 Enhancing Europe's Competitiveness Partner:
while working within a large organization) in consulting SMES in innovation management based on an extensive European benchmarking database.
Young Global Leader Building a database and evaluating data with a clearly defined process Google Ventures uses algorithms with data from academic literature or from due diligences.
Developing a transparent, inclusive, Europe-wide database and network of initiatives for entrepreneurship Partner: Developing entrepreneurship initiatives that achieve both scale
Europe-wide database and network of initiatives for entrepreneurship Establishing a visible, inclusive network of public and private initiatives is considered to be somewhat important or very important by 89%of survey participants (Figure 23).
7. See Eurostat database. 8. Note: Slovakia is not part of the analysis because as of 2010 (and therefore during the assessed time interval) it has applied a new statistical methodology;
World Development Indicators database (accessed May 12, 2014). 6 Estimates are for fiscal year 2014. NASSCOM, India IT-BPM Overview, available at http://www. nasscom. in/impact-indias-growth (accessed August 3, 2014.
World Development Indicators database (accessed August 3, 2014). The GDP estimate is for 2013 and sourced from IMF 2014c.2014 World Economic Forum 1. 1:
World Economic Outlook Database, April. Available at http://www. imf. org/external/pubs/ft/weo/2014/01/weodata/index. aspx..
World Development Indicators 2014 database. Available at http://data. worldbank. org/products/wdi. World Economic Forum. 2009.
IMF World Economic Outlook Database April 2014. GDP per capita (PPP $), 2013 (log) scale GCI 2014 2015 score (1 7) 2014 World Economic Forum The Global Competitiveness Report
World Economic Outlook Database, April. Available at http://www. imf. org/external/pubs/ft/weo/2014/01/weodata/index. aspx.
World Development Indicators 2014 database. Available at http://data. worldbank. org/products/wdi. 2014 World Economic Forum The Global Competitiveness Report 2014 2015 49 1. 1:
International labor organization, ILOSTAT database available at http://www. ilo. org/ilostat/faces/home/statisticaldata/bulk-download?
CIESIN uses time series of the World Database on Protected Areas (WDPA) developed by the United nations Environment Programme (UNEP) World Conservation Monitoring Centre (WCMC) in 2011
The International Union for Conservation of Nature (IUCN), Environmental law Centre ELIS Treaty Database 2014 World Economic Forum 1. 2:
World bank, World Development Indicators database, http://data. worldbank. org (retrieved June 20, 2014) S17 Fish stocks overexploited Fraction of the country's exclusive economic zone with overexploited and collapsed stocks 2011 or most recent The Sea Around Us (SAU) project‘s Stock
Population-weighted average exposure values were calculated using population data from the Global Rural Urban Mapping Project (2011) database.
International monetary fund, World Economic Outlook database, April 2014 edition. Note: Not all charts are drawn to scale.*
all data in the Key indicators'section are sourced from the April 2014 edition of the International monetary fund (IMF)' s World Economic Outlook (WEO) Database:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables 400 The Global Competitiveness Report 2014 2015 2014 World Economic Forum 2. 2:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables 400 The Global Competitiveness Report 2014 2015 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 China...
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables The Global Competitiveness Report 2014 2015 401 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 Luxembourg...
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables 402 The Global Competitiveness Report 2014 2015 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 United states...
International monetary fund, World Economic Outlook Database (April 2014 edition) 2. 2: Data Tables The Global Competitiveness Report 2014 2015 403 2014 World Economic Forum 2014 World Economic Forum Pillar 1 Institutions Data
International Telecommunication Union, ITU World Telecommunication/ICT Indicators Database 2014 (June 2014 edition) 2. 2:
International Telecommunication Union, ITU World Telecommunication/ICT Indicators Database 2014 (June 2014 edition) 2. 2:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables 438 The Global Competitiveness Report 2014 2015 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 Timor-Leste1...
International monetary fund, World Economic Outlook Database (April 2014 edition; World bank, At-a-Glance Table; Organisation for Economic Co-operation and Development (OECD), Economic Outlook 2014;
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources NOTE: For inflation rates between 0. 5 and 2. 9 percent, a country receives the highest possible score of 7. Outside this range,
International monetary fund, World Economic Outlook Database (April 2014 edition) and Public Information Notices (various issues; African Development Bank, Organisation for Economic Co-operation and Development (OECD),
World trade organization, Statistical Database: Time series on Merchandise and Commercial Services (accessed July 02,2014; International monetary fund, World Economic Outlook Database (April 2014 edition;
national sources 1 2011 2 2012 2. 2: Data Tables The Global Competitiveness Report 2014 2015 479 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 MEAN 4. 5 7
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 2. 2: Data Tables 516 The Global Competitiveness Report 2014 2015 2014 World Economic Forum RANK COUNTRY/ECONOMY VALUE 1 Hong kong SAR...
World trade organization, Online Statistics Database (accessed June 18, 2014; International monetary fund, World Economic Outlook Database (April 2014 edition;
national sources 1 2011 2 2012 2. 2: Data Tables The Global Competitiveness Report 2014 2015 517 2014 World Economic Forum 2014 World Economic Forum Pillar 11 Business sophistication
Organisation for Economic Co-operation and Development (OECD), Patent Database,(situation as of June 2014;
International monetary fund, World Economic Outlook Database (April 2014 edition; World Economic Forum's calculations. For more details about the treatment of Hong kong SAR and Taiwan (China), refer to the section Technical Notes and Sources. 2. 2:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 0. 02 Population Total population in millions 2013 Sources:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 0. 03 GDP per capita Gross domestic product per capita in current US dollars 2013 Sources:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 0. 04 GDP as a share of world GDP Gross domestic product based on purchasing power parity as a percentage of world GDP 2013 Sources:
International monetary fund, World Economic Outlook Database (April 2014 edition) Pillar 1: Institutions 1. 01 Property rights In your country, how strong is the protection of property rights,
International Telecommunication Union, ITU World Telecommunication/ICT Indicators Database 2014 (June 2014 edition) 2. 09 Fixed telephone lines Number of active fixed
International Telecommunication Union, ITU World Telecommunication/ICT Indicators Database 2014 (June 2014 edition) Pillar 3:
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 3. 02 Gross national savings Gross national savings as a percentage of GDP 2013 or most recent year available Aggregate national savings
International monetary fund, World Economic Outlook Database (April 2014 edition; World bank, At-a-Glance Table; Organisation for Economic Co-operation and Development (OECD), Economic Outlook 2014;
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 3. 04 Government debt Gross general government debt as a percentage of GDP 2013 or most recent year available Gross debt consists of all
International monetary fund, World Economic Outlook Database (April 2014 edition) and Public Information Notices (various issues; African Development Bank, Organisation for Economic Co-operation and Development (OECD),
World trade organization, Statistical Database: Time series on Merchandise and Commercial Services (accessed July 02,2014; International monetary fund, World Economic Outlook Database (April 2014 edition;
national sources 6. 15 Degree of customer orientation In your country, how well do companies treat customers?
International monetary fund, World Economic Outlook Database (April 2014 edition; national sources 10.04 Exports as a percentage of GDP Exports of goods and services as a percentage of gross domestic product 2013 Total exports is the sum of total exports of merchandise and commercial services.
World trade organization, Online Statistics Database (accessed June 18, 2014; International monetary fund, World Economic Outlook Database (April 2014 edition;
national sources Pillar 11: Business sophistication 11.01 Local supplier quantity In your country, how numerous are local suppliers?
Organisation for Economic Co-operation and Development (OECD), Patent Database,(situation as of June 2014;
International monetary fund, World Economic Outlook Database (April 2014 edition; World Economic Forum's calculations 2014 World Economic Forum 2014 World Economic Forum The Global Competitiveness Report 2014 2015 547 About the Authors Beñat
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