Eurostat's Statistics on

1
Eurostat's Statistics on Science, Technology and
Innovation (European Commission)Veijo Ritola
Head of Section Science, Technology and
Innovation StatisticsEurostat European
Commission
2
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

3
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

4
Institutions of the European Union(simplified
diagram)
5
European Commission Directorates-General and
Services
6
Eurostats organisation

• Director General - Walter Radermacher
• Deputy Director General - Marie Bohatá
• Staff approximately 870 people
• Seven Directorates
• 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

7
Responsibilities of Eurostat

• 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)

8
Eurostat credibility is based on

• Independence
• Impartiality
• Objectivity

9
Eurostats Website http//ec.europa.eu/eurostat
10
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
11
POLICY NEEDS FOR STI STATISTICS
LISBON STRATEGY

• STATISTICS ON STI

Research
Assessment and support to the EU actions and
policies Analysing the progress made towards
Lisbon goals and ERA initiatives
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

11
12
Six areas of STI
13
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 ST ? Legal implementation
measure Commission Regulation ? 753/2004
implementing Decision ? 1608/2003/EC as
regards statistics on ST RD INDICATORS
? Intramural RD expenditure (GERD) ?
RD personnel ? Government budget
appropriations or outlays on RD (GBAORD)
HARMONISED RD CONCEPTS, DEFINITIONS AND
CLASSIFICATIONS ? Proposed Standard
Practice for Surveys on RD - Frascati Manual,
OECD, 2002 available at
http//www.oecd.org/document/6/0,3343,en_2649_3445
1_33828550_1_1_1_1,00.htmlDATA SOURCES IN
MEMBER STATES ? Sample/census surveys,
administrative sources or others of equivalent
quality, or their mixtures, subsidiary
principle
14
RESEARCH AND DEVELOPMENT
STATISTICS
BREAKDOWNS OF RD INDICATORS
(in accordance with standard
classifications)
RD personnel
? Sector of performance ? Occupation ?
Qualification (ISCED) ? Gender ? Fields of
science (FOS) ? Citizenship ? Age groups ?
Economic activity (NACE) ? Size class ? Regions
(NUTS)

• ? Sector of performance
• ? Source of funds
• ? Type of costs
• ? Type of RD
• ? Fields of science (FOS)
• ? Socio-economic objectives (NABS)
• ? Economic activity (NACE)
• ? Size class
• Regions (NUTS)

GERD
GBAORD
? Socio-economic objectives (NABS)
15
RESEARCH AND DEVELOPMENT
STATISTICS STANDARD CLASSIFICATIONS - available
on Eurostat's Metadata Server RAMONhttp//ec.euro
pa.eu/eurostat/ramon/index.cfm?TargetUrlDSP_PUB_W
ELS TYPE OF RD INDICATORS ? Obligatory
? Preliminary RD (T10) / Provisional
GBAORD (T6) ? Optional ?
Final RD (T18) / Final GBAORD (T12)
FREQUENCY OF INDICATORS ? Annual - GERD by
sectors of performance, RD 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 RD and provisional GBAORD data
In October preliminary RD yearly data
In December final GBAORD data
16
RESEARCH AND DEVELOPMENT
STATISTICS STANDARDISED APPROACH
FOR DATA COLLECTIONJOINT OECD/EUROSTAT
HARMONISED RD 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 -
ExcelEVALUATION 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
17
RESEARCH AND
DEVELOPMENT STATISTICS DERIVED RD
VARIABLES (RATIO INDICATORS) produced by Eurostat
EU AGGREGATES
calculated by Eurostat EU-27, EU-15, EA-16


? RD expenditure as ? percentage of GDP
(RD intensity) For 2007 EU-27 1.85 -
still below the Lisbon target of 3 In
two MS gt 3 - SE (3.60) FI (3.47) In
four MS (2 - 3) - DE, FR, AT, DK ? GBAORD
as ? percentage of GDP ? GBAORD as ?
percentage general government expenditure ?
RD expenditure and GBAORD in Euro per
inhabitant ? RD personnel/Researchers as ?
percentage of active population ? RD
personnel/Researchers as ? percentage of total
employment
DERIVED RD VARIABLES
18
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 RD performers
(CERN, ILL, ERSF, EMBL, EMBO, ESO, JRC)
? National contributions to Europe-wide
trans-national public RD programmes
(ERA-NETs, ESA, EFDA, EUREKA, COST etc.)
? National contributions to bi- or multi-lateral
public RD programmes established
between MSs governments ? Total amount of
Structural Funds for RD (national and EU
funding) ? Breakdown of RD expenditure
financed by abroad by type of source
(including EU/non-EU origin of source)
19
RESEARCH AND DEVELOPMENT
STATISTICS CURRENT CHALLENGES ? DIRECT
DATA COLLECTION FROM TRANS-NATIONAL PUBLIC
RD PERFORMERS ? Launched by Eurostat on
core RD indicators? DEVELOPMENT OF NEW RD
DATABASE ? Based on Eurostat standard
tools - GSAST, EBB ? More efficient data
treatment - automatic data validation,
estimation, conversion, aggregation,
derivation, dissemination
20

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 ST ? 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
? 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
EVERY TWO YEARS
21

INNOVATION STATISTICS INDICATORS
Beyond the variables listed above, MS compile
additional statistics (including their
breakdowns) in accordance with the main themes
listed in the Oslo Manual (optional).
? 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
EVERY FOUR YEARS
22

INNOVATION STATISTICS HARMONISED
CONCEPTS, DEFINITIONS AND CLASSIFICATIONS ?
Guidelines for Collecting and Interpreting
Innovation Data - Oslo Manual, OECD,
2005 available at http//lysander.sourceoecd.org
/vl1764186/cl11/nw1/rpsv/cgi-bin/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
23

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/porta
l/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
24

INNOVATION STATISTICS
STANDARDISED APPROACH FOR DATA COLLECTION
COMMUNITY INNOVATION SURVEY
(CIS)
? 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)
HARMONISED METHODOLOGICAL RECOMMENDATIONS
25

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)
26

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
27

PATENT STATISTICS PATENT STATISTICS ?
Patent statistics measure Research
output Innovation activities
Technological progress Capacity to
exploit knowledgeDATA 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 RD CONCEPTS, DEFINITIONS AND
CLASSIFICATIONS ? Patent Statistics Manual,
OECD,2009, available at
http//www.oecd.org/document/29/0,3343,en_2649_344
51_42168029_1_1_1_1,00.html ? International
Patent Classification (IPC)
28
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

• Patents in high-technology fields
• High-tech patents
• ICT patents
• Biotechnology patents
• Nanotechnology patents

• Eurostats database contains data on
• Patent applications to the EPO
• Patents granted by the USPTO
• Triadic patent families (based on raw
• patent data from OECD)

29

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
30

PATENT STATISTICS BREAKDOWNS
OF PATENT INDICATORS DERIVED
PATENT VARIABLES (RATIO INDICATORS)
? Institutional sector ? IPC sections and
classes, ? Economic activities (NACE classes)
? Type of ownership ? Inventors/
applicants' country of residence
BREAKDOWNS
? Per million inhabitants ? Per million
labour force ? Relative to Gross domestic
product (GDP) in euro ? Relative to Gross
domestic expenditure on RD (GERD) ? Relative
to Expenditure on RD in Business enterprise
sector
DERIVED VARIABLES FOR EPO AND USPTO PATENTS
31
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
32

PATENT STATISTICSCURRENT 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
33
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.)
34
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

• ? 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

CORE MODEL QUESTIONNAIRE
35
CAREERS OF DOCTORATE HOLDERS
MAIN
CHARACTERISTICS
Personal characteristics
Educational characteristics

• ? Gender
• ? Age
• ? Country of birth
• ? Type of citizenship/residential status

• ? Country of doctorate award
• ? Field of doctorate award

Work perception
Employment characteristics

• ? Job qualification
• ? Perception to salary

• ? Occupation
• ? Researcher function / non -
• ? Earnings
• ? Length of stay with current employer

36
CAREERS OF DOCTORATE HOLDERS
GROSSING-UP - applied by all countries except
for Belgium, Czech Republic, Poland, Romania and
Slovak RepublicFIRST 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
37
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 non-
researchers 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)
38
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
39
MAIN APPROACHES IN COMPILATION OF HIGH-TECH
STATISTICS
HIGH-TECH STATISTICS
PRODUCT APPROACH
SECTORAL APPROACH

• Products identified following the Standard
  International Trade Classification (SITC)

• Sectors identified following the Statistical
  Classification of Economic Activities in the
  European Community (NACE)

Sectors identified according to the
technological intensity RD expenditure/value
added
Products identified according to the high value
of RD intensity RD expenditure/total sales
PATENTS
High-tech and biotechnology patents identified
according to International Patent
Classification (IPC 8th edition)
40
SECTORAL APPROACH BASED ON NACE
HIGH-TECH STATISTICS

• NACE
• ? common EU classification of economic
  activities
• ? covers a whole range of economic activities
• ? 4-digit level

• ? Manufacturing sector
• High-technology manufacturing
• Medium-high technology manufacturing
• Medium-low technology manufacturing
• Low-technology manufacturing
• ? Services
• Knowledge intensive services
• Less knowledge intensive services

• Manufacturing and services
• classified according to
• the level of technological intensity
• RD expenditure/value added
• the share of the highest educated staff

• Classification is relative to
• variables used
• the data of the countries used
• the time the data refer to
• threshold set

41
PRODUCT APPROACH BASED ON SITC
HIGH-TECH STATISTICS

• 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 countrys 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 collection
• Traders customs declarations (extra-EU27)
• Direct enterprise declarations (intra-EU27)
• ? Data source and coverage
• Comext database - EU trade
• Comtrade database - World trade

42
INDICATORS AND SOURCES FOR HIGH-TECH SECTORS
(NACE) SECTORAL APPROACH
HIGH-TECH STATISTICS

• RD 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)

• RD 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)

43
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)
EPO, USPTO
? High-tech patents in high-technology fields and
biotechnology patents
44

HIGH-TECH STATISTICS UPCOMING
CHALLENGES ? Establishment of
transitional definitions to accommodate the
revised NACE Rev.2 source data More
in-depth revision waits the RD 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)
45
HUMAN RESOURCES
IN ST (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 levelDATA
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 ST -
Canberra Manual, OECD, 1995 available at
http//www.oecd.org/dataoecd/34/0/2096025.pdf
46
HUMAN RESOURCES
IN ST (HRST) DEFINITION ? Definition
based on the cross tabulation of education and
occupation, used often as proxy for
researchers ? Human Resources in ST are
all individuals who fulfil at least one
of the following conditions
? 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
? Have successfully completed tertiary-level
education and/or ? Work in ST occupation as
professionals or technicians, where the
above qualifications are normally required
47

HUMAN RESOURCES IN ST (HRST)
HRST SUB - CATEGORIES
HRSTC - individuals who have successfully
completed tertiary-level education
and work in an ST occupation as professionals
or technicians
HRSTE - individuals who have successfully
completed tertiary-level education
HRSTO - individuals who work in an ST occupation
as professionals or technicians
HRSTU - individuals who have successfully
completed tertiary-level education
but are unemployed
48
APPROACHES IN COMPILATION OF
HRST STATISTICS
HUMAN RESOURCES IN ST (HRST)
From Education statistics
From Labour Force Survey (LFS)
Statistics over participants and graduates from
tertiary level education is used for inflow
statistics
Data over employed and unemployed is used for
stock and mobility statistics
49
HUMAN RESOURCES IN
ST (HRST)
MAIN INDICATORS
? HRST sub-category ? Gender ? Age ?
Occupation ? Sector of economic activity ?
Field of education studied ? Unemployment rate
? Nationality / country of birth ? Region
HRST STOCK
? Job-to-job mobility ? Tertiary level
education participants ? Tertiary level
education graduates ? Tertiary level
education foreign students
HRST FLOWS
50
HUMAN RESOURCES
IN ST (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).
51
WHERE TO FIND STI 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

52
Thank you !