 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
2

 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
3

9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
4

9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
5





– 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
6

 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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
7




9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
8

9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
9

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
10

POLICY NEEDS FOR STI STATISTICS
LISBON STRATEGY STATISTICS ON STI
Research
Assessment and support to the EU actions and policies
Growth and jobs
Education Innovation
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
Analysing the progress made towards Lisbon goals and ERA initiatives
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation

Hugh-Tech
Industries and
Knowledge
Intensive Services
Six areas of STI
Statistics on
Research and
Development
Statistics on
Innovation
Statistics on
Science,
Technology and
Innovation
Patent Statistics
Human Resources in Science and
Technology
Career of Doctorate
Holders
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
12

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
13

RESEARCH AND DEVELOPMENT STATISTICS
BREAKDOWNS OF R&D INDICATORS
(in accordance with standard classifications)
 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)
 Socio-economic objectives (NABS)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
14

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
15

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
16

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
17

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
18

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
19

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
20

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).
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
21

INNOVATION STATISTICS
HARMONISED CONCEPTS, DEFINITIONS AND CLASSIFICATIONS
 Guidelines for Collecting and Interpreting Innovation Data
Manual , OECD, 2005 available at: http://lysander.sourceoecd.org/vl=1764186/cl=11/nw=1/rpsv/cgibin/fulltextew.pl?prpsv=/ij/oecdthemes/99980134/v2005n18/s1/p1l.idx
-
Oslo
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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
22

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
23

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
24

INNOVATION STATISTICS
COMMUNITY INNOVATION SURVEY (CIS)
STANDARD SURVEY QUESTIONNAIRE (CIS 2008)
1/ General information about the enterprise
2/ Product innovation (good or service)
3/ Process innovation
4/ Ongoing or abandoned innovation activities for process and product innovations
5/ Innovation activities and expenditures for process and product innovations
6/ Sources of information and co-operation for innovation activities
7/ Innovation objectives during 2006 - 2008
8/ Organisational innovation
9/ Marketing innovation
10/ Innovations with environmental benefits
11/ Basic economic information on the enterprise (turnover, employees)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
25

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
26

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
27

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:

Patent applications to the EPO

Patents granted by the USPTO
 Triadic patent families (based on raw patent data from OECD)
Patents in high-technology fields

High-tech patents

ICT patents
 Biotechnology patents
 Nanotechnology patents
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
28

PATENT STATISTICS
TYPES OF INDICATORS
Patent applications to EPO by priority year

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
 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
29

PATENT STATISTICS
BREAKDOWNS OF PATENT INDICATORS
BREAKDOWNS

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
 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
30

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
31

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
32

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.)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
33

CAREERS OF DOCTORATE HOLDERS
STANDARDISED APPROACH FOR DATA COLLECTION

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
34

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
Employment characteristics
 Occupation
 Researcher function / non -
 Earnings

Length of stay with current employer
Work perception
 Job qualification
 Perception to salary
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
35

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
36

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
37

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
38

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
39

HIGH-TECH STATISTICS
SECTORAL APPROACH BASED ON 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
– Knowledge intensive services
– Less knowledge intensive services
Classification is relative to
 variables used
 the data of the countries used
 the time the data refer to
 threshold set
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
40

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
Indicators
– Import/export in Mio Euro
– World shares
– Ratio of country’s high-tech trade in its total trade
– Share of intra-EU trade
 Data collection
– Traders’ customs declarations (extra-
EU27)
– Direct enterprise declarations (intra-
EU27)
 Data source and coverage
– Comext database - EU trade
– Comtrade database - World 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
41

HIGH-TECH STATISTICS
INDICATORS AND SOURCES FOR HIGH-TECH SECTORS (NACE)
SECTORAL APPROACH
 R&D personnel and expenditure
 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)
R&D survey
Labour Force Survey (LFS)
Community Innovation Survey (CIS)
Structural Business Survey (SBS)
Structure of Earnings Survey (SES)
European Private Equity and Venture
Capital Association (EVCA)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
42

HIGH-TECH STATISTICS
INDICATORS AND SOURCES FOR HIGH-TECH TRADE (SITC) –
PRODUCT APPROACH

Import and export of high-tech group of products
Comext / Comtrade
Patent indicators (IPC)

High-tech patents in high-technology fields and biotechnology patents
EPO, USPTO
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
43

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)
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
44

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
45

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
46

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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
47

HUMAN RESOURCES IN S&T (HRST)
APPROACHES IN COMPILATION OF HRST STATISTICS
From Labour Force Survey (LFS) From Education statistics
Data over employed and unemployed is used for stock and mobility statistics
Statistics over participants and graduates from tertiary level education is used for inflow statistics
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
48

HUMAN RESOURCES IN S&T (HRST)
MAIN INDICATORS
HRST STOCK


Sector of economic activity
Field of education studied



 HRST sub-category


Gender
 Age
Occupation
Unemployment rate
Nationality / country of birth
Region
HRST FLOWS


Job-to-job mobility
Tertiary level education
 participants
Tertiary level education
 graduates
Tertiary level education foreign students
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
49

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).
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
50

WHERE TO FIND S&T&I STATISTICS?
 WEB
– 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/
 PUBLICATIONS
– 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
9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
51

9/10/2009 Eurostat's Statistics on Science, Technology and Innovation
52