Transcript Eurostat's Statistics on Science, Technology and Innovation (European Commission)

Eurostat's Statistics on
Science, Technology and Innovation
(European Commission)
Veijo Ritola
Head of Section
Science, Technology and Innovation Statistics
Eurostat – European Commission
Outline of the presentation
 Short introduction to Eurostat in general
 Short briefing to the current policy needs
 Six sub-categories of the Science, Technology and Innovation Statistics
 Research and Development
 Innovation
 Patents
 Careers of Doctorate Holders
 High Tech
 Human Resources in Science and Technology
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What is Eurostat?
 Eurostat is a Directorate General of the European
Commission - Commissioner Joaquín Almunia
 Eurostat is the central institution of the European
Statistical System (ESS) - a network of National
Statistical Institutes from all EU and EFTA Countries
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Institutions of the European Union
(simplified diagram)
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European Commission: Directorates-General and Services
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Eurostat’s organisation
 Director General - Walter Radermacher
 Deputy Director General - Marie Bohatá
 Staff approximately 870 people
 Seven Directorates
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Resources & Cooperation in the ESS
Quality, methodology and information systems
National and European accounts
External cooperation, communication and key indicators
Sectoral and regional statistics
Social and information society statistics
Business statistics
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Responsibilities of Eurostat
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Collect data from NSIs
Harmonise methods, definitions & classifications
Compile European aggregates – EU & Euro area
Disseminate statistics
 International relations – enlargement &
development
 Programme planning (coordinating national
programmes)
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Eurostat credibility is based on
 Independence
 Impartiality
 Objectivity
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Eurostat’s Website: http://ec.europa.eu/eurostat
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Science, Technology and Innovation statistics
Establishment and development of harmonised Community statistics
on Science, Technology and Innovation (STI) is important tool for
 Providing the necessary evidence basis for the definition, implementation
and analysis of Community policies on Science, Technology and Innovation
in Europe
 Regular monitoring the progress achieved towards development of
Knowledge-based economy (Lisbon objectives) and realisation of the
European Research Area
 Supplying the public and media with statistics needed to have an accurate
picture of science and technology in Europe and to evaluate the
performance of politicians and other actors
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POLICY NEEDS FOR STI STATISTICS
LISBON STRATEGY
Research
Assessment and support
to the EU actions and
policies
Growth
and jobs
Education
STATISTICS ON STI
Innovation
Analysing the progress
made towards Lisbon
goals and ERA initiatives
EUROPEAN RESEARCH AREA (ERA)
 Realising a single labour market for researchers
with high level of mobility
 Developing world-class research infrastructures
 Strengthening research institutions, engaged in
effective public-private cooperation
 Effective knowledge-sharing
 Optimising research programmes and priorities,
including the joint programming
 A wide opening of ERA to the world
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Six areas of STI
Statistics on
Research and
Development
Hugh-Tech
Industries and
Knowledge
Intensive Services
Human Resources
in Science and
Technology
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Statistics on
Innovation
Statistics on
Science,
Technology and
Innovation
Patent Statistics
Career of Doctorate
Holders
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RESEARCH AND DEVELOPMENT STATISTICS
LEGAL BASE
 Framework legal act: Decision № 1608/2003/EC of the EP and of the Council
concerning the production and development of Community statistics on S&T
 Legal implementation measure: Commission Regulation № 753/2004
implementing Decision № 1608/2003/EC as regards statistics on S&T
R&D INDICATORS
 Intramural R&D expenditure (GERD)
 R&D personnel
 Government budget appropriations or outlays on R&D (GBAORD)
HARMONISED R&D CONCEPTS, DEFINITIONS AND CLASSIFICATIONS
 Proposed Standard Practice for Surveys on R&D - Frascati Manual,
OECD, 2002
available at: http://www.oecd.org/document/6/0,3343,en_2649_34451_33828550_1_1_1_1,00.html
DATA SOURCES IN MEMBER STATES
 Sample/census surveys, administrative sources or others of equivalent
quality, or their mixtures, subsidiary principle
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RESEARCH AND DEVELOPMENT STATISTICS
BREAKDOWNS OF R&D INDICATORS
(in accordance with standard classifications)
R&D personnel
GERD
 Sector of performance
 Source of funds
 Type of costs
 Type of R&D
 Fields of science (FOS)
 Socio-economic objectives (NABS)
 Economic activity (NACE)
 Size class
 Regions (NUTS)
 Sector of performance
 Occupation
 Qualification (ISCED)
 Gender
 Fields of science (FOS)
 Citizenship
 Age groups
 Economic activity (NACE)
 Size class
 Regions (NUTS)
GBAORD
 Socio-economic objectives (NABS)
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RESEARCH AND DEVELOPMENT STATISTICS
STANDARD CLASSIFICATIONS - available on Eurostat's Metadata Server RAMON
http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELS
TYPE OF R&D INDICATORS
 Obligatory
 Preliminary R&D (T+10) / Provisional GBAORD (T+6)
 Optional
 Final R&D (T+18) / Final GBAORD (T+12)
FREQUENCY OF INDICATORS
 Annual - GERD by sectors of performance, R&D personnel and Researchers in FTE
 Biannual (on each odd year) - vast majority of indicators
 Four yearly - gender disaggregation of some indicators
DEADLINES FOR DATA COLLECTION BY EUROSTAT
 Annually three rounds of data collection covering all data sets required,
including revisions of the time series:
In June: final R&D and provisional GBAORD data
In October: preliminary R&D yearly data
In December: final GBAORD data
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RESEARCH AND DEVELOPMENT STATISTICS
STANDARDISED APPROACH FOR DATA COLLECTION
JOINT OECD/EUROSTAT HARMONISED R&D QUESTIONNAIRE
 Comprises 3 modules:
Common Core OECD/Eurostat module
ESTAT supplementary module
OECD supplementary module
 Goes beyond the requirements of EU legal base
 Contains around 50 Tables in two Excel workbooks
 Data validation rules in place within the questionnaire
 Confidential data provision
 Received from 33 countries: 27 MSs; HR,TR, CH, IS, NO and RU
 Transmission media - eDAMIS
 Transmission format - Excel
EVALUATION OF DATA QUALITY
 Data validation by Eurostat at the delivery point
 National Quality Reports - covering standard quality criteria: Relevance, Accuracy,
Timelines and Punctuality, Accessibility and Clarity, Comparability, Coherence, Cost and
Burden
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RESEARCH AND DEVELOPMENT STATISTICS
DERIVED R&D VARIABLES (RATIO INDICATORS) produced by Eurostat
DERIVED R&D VARIABLES
 R&D expenditure as а percentage of GDP (R&D intensity)
For 2007: EU-27 = 1.85 % - still below the Lisbon target of 3%
In two MS: > 3 % - SE (3.60%) FI (3.47%)
In four MS: (2 % - 3%) - DE, FR, AT, DK
 GBAORD as а percentage of GDP
 GBAORD as а percentage general government expenditure
 R&D expenditure and GBAORD in Euro per inhabitant
 R&D personnel/Researchers as а percentage of active population
 R&D personnel/Researchers as а percentage of total employment
EU AGGREGATES calculated by Eurostat: EU-27, EU-15, EA-16
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RESEARCH AND DEVELOPMENT STATISTICS
CURRENT CHALLENGES
 DEVELOPMENT OF NEW INDICATORS FOR MONITORING EUROPEAN
RESEARCH AREA (ERA)
 National public funding to trans-nationally coordinated research
 National contributions to trans-national public R&D performers
(CERN, ILL, ERSF, EMBL, EMBO, ESO, JRC)
 National contributions to Europe-wide trans-national public R&D
programmes (ERA-NETs, ESA, EFDA, EUREKA, COST etc.)
 National contributions to bi- or multi-lateral public R&D programmes
established between MSs governments
 Total amount of Structural Funds for R&D (national and EU funding)
 Breakdown of R&D expenditure financed by abroad by type of source
(including EU/non-EU origin of source)
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RESEARCH AND DEVELOPMENT STATISTICS
CURRENT CHALLENGES
 DIRECT DATA COLLECTION FROM TRANS-NATIONAL
PUBLIC R&D PERFORMERS
 Launched by Eurostat on core R&D indicators
 DEVELOPMENT OF NEW R&D DATABASE
 Based on Eurostat standard tools - GSAST, EBB
 More efficient data treatment - automatic data validation, estimation,
conversion, aggregation, derivation, dissemination
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INNOVATION STATISTICS
LEGAL BASE
 Framework legal act: Decision № 1608/2003/EC of the EP and of the Council
concerning the production and development of Community statistics on S&T
 Legal implementation measure: Commission Regulation № 1450/2004
implementing Decision № 1608/2003/EC concerning the production and
development of Community statistics on innovation (amended by CR № 540/2009)
INDICATORS
EVERY TWO YEARS
 Innovation active enterprises
 Innovating enterprises that introduced new or
significantly improved products, new to the market
 Turnover from innovation, related to new or significantly
improved products, new to the market
 Turnover from innovation, related to new or significantly
improved products, new to the firm, but not new to the market
 Innovation active enterprises involved in innovation
cooperation - by type of cooperation
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INNOVATION STATISTICS
INDICATORS
EVERY FOUR YEARS
 Innovation expenditure (optional)
 Innovation active enterprises that indicated highly important
objectives of innovation - by type of objectives
 Innovation active enterprises that indicated highly important
sources of information for innovation - by type of source (optional)
 Enterprises facing important hampering factors - by type of
hampering factors
Beyond the variables listed above, MS compile additional statistics
(including their breakdowns) in accordance with the main themes
listed in the Oslo Manual (optional).
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INNOVATION STATISTICS
HARMONISED CONCEPTS, DEFINITIONS AND CLASSIFICATIONS
 Guidelines for Collecting and Interpreting Innovation Data
Manual, OECD, 2005
- Oslo
available at: http://lysander.sourceoecd.org/vl=1764186/cl=11/nw=1/rpsv/cgibin/fulltextew.pl?prpsv=/ij/oecdthemes/99980134/v2005n18/s1/p1l.idx
DATA SOURCES IN MEMBER STATES
 Combination of different sources - sample surveys, administrative data
or others of equivalent quality
TYPE OF INDICATORS
 Obligatory
 Optional
FREQUENCY OF INDICATORS
 Biannual, on each even year - 5 obligatory variables
 Four yearly - 7 obligatory and 2 optional variables (plus more)
DEADLINE FOR DATA COLLECTION BY EUROSTAT
 18 months after the end of the calendar year of the reference period
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INNOVATION STATISTICS
TYPES OF DATA TRANSMITTED
 Aggregated statistics - compulsory
 Individual (micro) data records - voluntary
 Confidential data provision
STANDARD TRANSMISSION FORMAT
 For aggregated data - Excel; For individual data - CSV file
 Data received from 29 countries: 27 MS, IS and NO
 Transmission media - eDAMIS
ACCESS TO MICRODATA
 Anonymised microdata: on CD
 Non-anonymised microdata: via the SAFE Centre in Eurostat
Information how to obtain microdata available at:
http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/cis
EVALUATION OF DATA QUALITY
 Data validation by Eurostat at the delivery point
 National Quality Reports - covering standard quality criteria: Relevance, Accuracy,
Timelines and Punctuality, Accessibility and Clarity, Comparability, Coherence, Cost and
Burden
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INNOVATION STATISTICS
STANDARDISED APPROACH FOR DATA COLLECTION
COMMUNITY INNOVATION SURVEY (CIS)
HARMONISED METHODOLOGICAL RECOMMENDATIONS
 Target population (NACE and size class coverage, statistical unit,
observation period)
 Survey methodology (sampling frame, type of survey, stratification
variables, sample size, sample selection and allocation)
 Collecting and processing the data (survey questionnaire, data collection
and data editing)
 Data quality (response rate, non- response survey, precision of results,
imputation, weighting and calibration)
 Transmission of data (types of data, output tabulation scheme, deadlines,
transmission tool)
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INNOVATION STATISTICS
COMMUNITY INNOVATION SURVEY (CIS)
STANDARD SURVEY QUESTIONNAIRE (CIS 2008)
1/
2/
3/
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5/
6/
7/
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10/
11/
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General information about the enterprise
Product innovation (good or service)
Process innovation
Ongoing or abandoned innovation activities for process and product innovations
Innovation activities and expenditures for process and product innovations
Sources of information and co-operation for innovation activities
Innovation objectives during 2006 - 2008
Organisational innovation
Marketing innovation
Innovations with environmental benefits
Basic economic information on the enterprise (turnover, employees)
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INNOVATION STATISTICS
CURRENT CHALLENGES
 REVISION OF THE REGULATION 1450/2004
 Extension to the organisational and marketing innovation
 Revision/extension of the economic activities covered
 Introduction of one-off modules
 Introduction of the quality annex
 From voluntary to mandatory microdata deliveries
 Frequency of the variables
 MODULE SELECTION FOR CIS 2010
 User driven innovation
 Creativity and skills to innovate
 TRACKING ENTERPRISES IN CONSECUTIVE MICRODATA SETS
 OBSERVATION PERIOD (2/3 YEARS)
 MEASUREMENT OF THE DESIGN IN THE INNOVATION SURVEYS
 EVALUATION OF THE NATIONAL QUESTIONNAIRES
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PATENT STATISTICS
PATENT STATISTICS
 Patent statistics measure
Research output
Innovation activities
Technological progress
Capacity to exploit knowledge
DATA SOURCES
 One single raw database (PATSTAT) compiled on the basis of input from
European Patent Office (EPO)
US Patent and Trademark Office (USPTO)
Japanese Patent Office (JPO)
HARMONISED R&D CONCEPTS, DEFINITIONS AND CLASSIFICATIONS
 Patent Statistics Manual, OECD,2009, available at:
http://www.oecd.org/document/29/0,3343,en_2649_34451_42168029_1_1_1_1,00.html
 International Patent Classification (IPC)
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PATENT STATISTICS
APPROACH FOR COMPILATION OF PATENT STATISTICS
 Data extracted from a single patent statistics raw database (PATSTAT),
held by the European Patent Office (EPO) and further edited, aggregated
and disseminated by Eurostat for all EU Member States, Candidate
Countries, EFTA members and other countries
Eurostat’s database contains data on:
Patents in high-technology fields
 Patent applications to the EPO
 Patents granted by the USPTO
 Triadic patent families (based on raw
patent data from OECD)
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High-tech patents
ICT patents
Biotechnology patents
Nanotechnology patents
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PATENT STATISTICS
TYPES OF INDICATORS
Patent applications to EPO by priority year
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Patent applications to the EPO by priority year at the national level
Patent applications to the EPO by priority year at the regional level
Ownership of inventions
European and international co-patenting
Patent citations
Patents granted by the USPTO by priority year
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Patents granted by the USPTO by priority year at the national level
Ownership of inventions
European and international co-patenting
Patent citations
Triadic patent families by earliest priority year
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PATENT STATISTICS
BREAKDOWNS OF PATENT INDICATORS
BREAKDOWNS
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Institutional sector
IPC sections and classes,
Economic activities (NACE classes)
Type of ownership
Inventors’/ applicants' country of residence
DERIVED PATENT VARIABLES (RATIO INDICATORS)
DERIVED VARIABLES FOR EPO AND USPTO PATENTS
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Per million inhabitants
Per million labour force
Relative to Gross domestic product (GDP) in euro
Relative to Gross domestic expenditure on R&D (GERD)
Relative to Expenditure on R&D in Business enterprise sector
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PATENT STATISTICS
FIELDS OF INVESTIGATION
 PATENTS IN NUCLEAR TECHNOLOGY
Nuclear Reactor Technique
Radiation Acceleration Technique
 PATENTS IN WIND ENERGY
Wind Motors
Relevant surrounding techniques (Circuit arrangements or systems for supplying or
distributing electric powers, Control or regulation of electric motors, generators, or
dynamo-electric converters, Dynamo-electric machines)
 PATENTS IN ENVIRONMENTAL RELATED ENERGY
Environmental Related
Renewable Energy
Automobile Pollution Control Technology
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PATENT STATISTICS
CURRENT CHALLENGES
 CREATE NEW INDICATORS AND MORE BREAKDOWNS
Specific technological sectors
Triadic patent families
Regional level
 SEARCH WAYS TO COMBINE PATENT STATISTICS WITH THE
BUSINESS DATA
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CAREERS OF DOCTORATE HOLDERS
CDH 2006 VOLUNTARY SURVEY (NO LEGAL BASE)
 Widely supported project (EU Commission, OECD, UNESCO)
 Measuring the mobility, careers and expectations of research educated
people
PARTICIPATING COUNTRIES
 21 EU MSs, Australia, Switzerland, Iceland, Norway and USA
REFERENCE YEAR
 2006 (except for Belgium, Netherlands, Norway: 2005, Italy, Malta: 2007)
CARRIED OUT
 In 2007 - 2008
DATA SOURCES IN MS
 Variety of sources for compiling the target population (registers,
administrative data, census of population etc.)
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CAREERS OF DOCTORATE HOLDERS
STANDARDISED APPROACH FOR DATA COLLECTION
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CORE MODEL QUESTIONNAIRE
INSTRUCTION MANUAL FOR COMPLETING THE QUESTIONNAIRE
METHODOLOGICAL GUIDELINES
OUTPUT INDICATORS TEMPLATE
VARIABLES IN PROPOSED TABULATIONS - definitions and sources
CORE MODEL QUESTIONNAIRE
 Module EDU - Doctoral education
 Module REC - Recent graduates
 Module POS - POSTDOCS
 Module EMP - Employment situation
 Module MOB - International mobility
 Module CAR - Career related experience and scientific productivity
 Module PER - Personal characteristics
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CAREERS OF DOCTORATE HOLDERS
MAIN CHARACTERISTICS
Personal characteristics
 Gender
 Age
 Country of birth
 Type of citizenship/residential status
Educational characteristics
 Country of doctorate award
 Field of doctorate award
Work perception
Employment characteristics
 Occupation
 Researcher function / non  Earnings
 Length of stay with current employer
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 Job qualification
 Perception to salary
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CAREERS OF DOCTORATE HOLDERS
GROSSING-UP - applied by all countries except for Belgium, Czech
Republic, Poland, Romania and Slovak Republic
FIRST RESULTS
 Presented in the December 2008 Brussels meeting
 Lack of comparability, mainly due to coverage inconsistencies
 Additional request for ‘restricted’ data on specific set of output tables
Restriction 1: ISCED6 graduates aged below 70 years old
Restriction 2: ISCED6 graduates awarded after 1990
 Revised data was gathered in March 2009 - comparability issues
are still apparent
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CAREERS OF DOCTORATE HOLDERS
SELECTED FINDINGS
 Male doctorate holders are in general more than female doctorate
holders (more than 60% in most of the countries)
 Most doctorate holders have been awarded in the reporting country
(exceptions are CY IS MT)
 Most popular occupation is teaching profession
 Doctorate holders are most employed as researchers than nonresearchers in all countries (exceptions are BE NL RO)
 Doctorate holders are generally far better paid compared to the total
population (SES 2006 results)
 Doctorate holders tend to stay with the same employer for more than
5 years and in many countries for more than 10 years (except for DK)
 Most employed doctorate holders have a job that is related to their
doctoral degree (except for AT)
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CAREERS OF DOCTORATE HOLDERS
UPCOMING CHALLENGES
 Voluntary countries participation in CDH 2009. Financial support (grants)
from Eurostat
 Revision of the CDH technical documents - end of September 2009
 CDH 2009 national data collection:
 Preparation phase at country level - end of 2009
 Data collection - 2010
 Output tables to UIS/OECD/Eurostat before end 2010
 Data publication and analysis
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HIGH-TECH STATISTICS
MAIN APPROACHES IN COMPILATION OF HIGH-TECH STATISTICS
SECTORAL APPROACH
 Sectors identified following the
Statistical Classification of
Economic Activities in the
European Community (NACE)
PRODUCT APPROACH
 Products identified following the
Standard International Trade
Classification (SITC)
Sectors identified according
to the technological intensity:
R&D expenditure/value added
Products identified according
to the high value of R&D intensity:
R&D expenditure/total sales
PATENTS
High-tech and biotechnology
patents identified according to
International Patent Classification
(IPC 8th edition)
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HIGH-TECH STATISTICS
SECTORAL APPROACH BASED ON NACE
NACE
 common EU classification
of economic activities
 covers a whole range of
economic activities
 4-digit level
Manufacturing and services
classified according to:
 the level of technological intensity
R&D expenditure/value added
 the share of the highest educated staff
 Manufacturing sector
– High-technology
manufacturing
– Medium-high technology
manufacturing
– Medium-low technology
manufacturing
– Low-technology manufacturing
 Services
Classification is relative to
 variables used
 the data of the countries used
 the time the data refer to
 threshold set
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– Knowledge intensive services
– Less knowledge intensive
services
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HIGH-TECH STATISTICS
PRODUCT APPROACH BASED ON SITC
HIGH-TECH PRODUCTS
Aerospace
Armament
Computers-Office machines
Electronics-Telecommunication
Pharmacy
Scientific instruments
Electrical machinery
Non-electrical machinery
Chemistry
 Data collection
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Traders’ customs declarations (extraEU27)
Direct enterprise declarations (intraEU27)
Indicators
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Import/export in Mio Euro
World shares
Ratio of country’s high-tech
trade in its total trade
Share of intra-EU trade
Classification is less relative
as the products are assumed to be more
homogeneous (than the sectors) and
therefore less dependent on the set
of countries used
 Data source and coverage
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Comext database - EU trade
Comtrade database - World trade
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HIGH-TECH STATISTICS
INDICATORS AND SOURCES FOR HIGH-TECH SECTORS (NACE)
SECTORAL APPROACH
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R&D personnel and expenditure
R&D survey
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Employment statistics for high-tech
sectors
Innovation activities
Structural business statistics (number of
enterprises, turnover, value added at
factor costs, production value, social
security costs etc)
Mean annual earnings by sex, age and
level of education
Venture capital investment by stage of
development (for all sectors)
Labour Force Survey (LFS)
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Community Innovation Survey (CIS)
Structural Business Survey (SBS)
Structure of Earnings Survey (SES)
European Private Equity and Venture
Capital Association (EVCA)
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HIGH-TECH STATISTICS
INDICATORS AND SOURCES FOR HIGH-TECH TRADE (SITC) –
PRODUCT APPROACH
 Import and export of high-tech
Comext / Comtrade
group of products
Patent indicators (IPC)
EPO, USPTO
 High-tech patents in high-technology
fields and biotechnology patents
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HIGH-TECH STATISTICS
UPCOMING CHALLENGES
 Establishment of transitional definitions to accommodate the revised
NACE Rev.2 source data
More in-depth revision waits the R&D intensity data with NACE 2
(2011) and more recent OECD's input-output tables (2009-2010)
 Updating the High-Tech classifications
Presently both main High-Tech classifications (in terms of economic
activities and in terms of products) are based on 'old' reference data
for very limited set of (more developed) countries
 Development of new sectoral classification based on the knowledge
intensity, measured through LFS data on the share of tertiary educated
employed, by economic activity (NACE)
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HUMAN RESOURCES IN S&T (HRST)
HRST STATISTICS
 HRST statistics review the supply of and demand for highly qualified
staff in a broad sense
 Statistics show stocks and flows of HRST at EU, national and regional
level
DATA SOURCES
 Data extracted from two Eurostat sources (Labour force survey and
Statistics on education) and edited, aggregated and disseminated by
Eurostat for all EU27 (+)
HARMONISED CONCEPTS, DEFINITIONS AND CLASSIFICATIONS
 Manual on the measurement of Human Resources devoted to S&T Canberra Manual, OECD, 1995
available at: http://www.oecd.org/dataoecd/34/0/2096025.pdf
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HUMAN RESOURCES IN S&T (HRST)
DEFINITION
 Definition based on the cross tabulation of education and occupation,
used often as proxy for ‘researchers’
 Human Resources in S&T are all individuals who fulfil at least one
of the following conditions:
 Have successfully completed tertiary-level education
and/or
 Work in S&T occupation as professionals or technicians, where
the above qualifications are normally required
 The conditions of the above educational or occupational requirements are
considered according to internationally harmonised standards:
- International Standard Classification of Occupation - ISCO
- International Standard Classification of Education - ISCED
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HUMAN RESOURCES IN S&T (HRST)
HRST SUB - CATEGORIES
HRSTC - individuals who have successfully completed tertiary-level
education and work in an S&T occupation as professionals
or technicians
HRSTE - individuals who have successfully completed tertiary-level
education
HRSTO - individuals who work in an S&T occupation as professionals
or technicians
HRSTU - individuals who have successfully completed tertiary-level
education but are unemployed
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HUMAN RESOURCES IN S&T (HRST)
APPROACHES IN COMPILATION OF HRST STATISTICS
From Labour Force Survey (LFS)
Data over
employed
and
unemployed
is used for stock
and mobility statistics
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From Education statistics
Statistics over
participants
and
graduates
from tertiary level education
is used for inflow statistics
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HUMAN RESOURCES IN S&T (HRST)
MAIN INDICATORS
HRST STOCK
 HRST sub-category
 Gender
 Age
 Occupation
 Sector of economic activity
 Field of education studied
 Unemployment rate
 Nationality / country of birth
 Region
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HRST FLOWS
 Job-to-job mobility
 Tertiary level education
participants
 Tertiary level education
graduates
 Tertiary level education
foreign students
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HUMAN RESOURCES IN S&T (HRST)
UPCOMING CHALLENGES
 Updating the Canberra Manual
HRST concept and definitions are based on the OECD's Canberra
Manual which was published more than 20 years ago. Since then
both underlying classifications has been revised, International
Standard Classification of Occupation (ISCO) and International
Standard Classification of Education (ISCED97).
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WHERE TO FIND S&T&I STATISTICS?
 WEB
 PUBLICATIONS
– Eurostat/Science, Technology
and Innovation
http://epp.eurostat.ec.europa.eu
– OECD database
http://www.oecd.org/statsportal/
– DG Research
http://ec.europa.eu/research/
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– Eurostat collections
Statistical Book on Science,
technology and innovation – 2009
Pocketbook on Science, technology
and innovation – 2008
Statistics in Focus
News release
– DG Research
Key figures on Science, technology
and competitiveness 2008/2009
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Thank you !
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