Comparable statistics in the EU: ESS,
an example of an effective regional
statistical system
Claudia Junker, Eurostat, head of unit "Statistical
cooperation"
ASEAN regional workshop on strategic statistical
planning
28-29 November 2012, Jakarta
Outline
• Policy relevance of European
statistics
• Main actors of the ESS and beyond
• Planning and programming
• The European Statistics Code of
Practice
• Harmonisation
• Challenges leading to the "vision"
• The vision
• Conclusions
2
Policy relevance of European
statistics
• Europe 2020
• Enhanced economic surveillance – imbalance
scoreboard
• Stability and Growth Pact – fiscal surveillance
• Sustainability - GDP and beyond
• Regional cohesion, structural policy, CAP, etc.
• Enlargement process and ENP
3
Main actors of the European Statistical
System and beyond
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The NSIs of the Member States
Eurostat
Council and European parliament
European Statistical Advisory
Governance Board
• European Statistical Advisory
Committee
• The European Commission
4
Planning and programming
• The multi-annual European
Statistical Programme 2013-2017
- Legal basis for European statistics (adopted by
Council and Parliament)
- Provides the financial framework
- Reported on by Eurostat
- Focus on European statistics
• The annual work programmes of
Eurostat
- Adopted by the Commission
- Discussed with the Member States
• The work propgrammes of the
Member States
- Between 50 - 95% determined by European
statistics and legislation
5
The European Statistics Code of
Practice
• Sets standards for developing,
producing and publishing
European statistics
• Self-regulatory
• 15 Principles cover the
standards applicable to
• Institutional environment
• Statistical processes
• Statistical outputs
• 82 Indicators to measure
compliance
6
Examples of principles
• Professional independence
• Independence from political interference in developing,
producing and disseminating statistics is specified in law
• Heads of statistical authorities have sole responsibility for
deciding on statistical methods, standards, procedures
• Statistical work programmes are published and periodic
reports describe progress
• Appointment of heads of statistical authorities is based on
professional competence only and termination of service is
specified in law and cannot include reasons comprising
professional independence
• Commitment to quality
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•
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Quality policy is defined and available to public
Procedures in place to monitor quality
Product quality is monitored regularly
Regular review of key statistical outputs
Examples of principles
• Relevance
• Processes in place to consult users and monitor relevance, and
consider emerging needs
• Priority needs are being met and reflected in the work
programme
• User satisfaction is monitored on a regular basis
• Coherence and comparability
• Statistics are internally coherent and consistent
• Statistics are comparable over time
• Statistics are compiled on the basis of common standards with
respect to scope, definitions, units and classifications
• Statistics from different sources and of different periodicity are
compared and reconciled
• Cross-national comparability is ensured
Harmonisation
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•
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Regulations
Framework regulations
Methodology, handbooks
Gentlemen agreements
Common training programme
Working groups and task forces
And the vision…
Challenges leading to the vision
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Globalisation
Response burden
Costs
Competition
New expectations of users
Isolated identification of user needs
Isolated statistical regulations per domain
Inconsistencies in definitions
Insufficient standardisation
Variety of tools used
Current situation – “Stovepipe
approach”
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Weaknesses
Current situation
Weakness
 User needs are defined
in an isolated manner
 As a result, data
collections are isolated
as well
 Regulations are made
separately by statistical
domain
 Separate data
transmissions from NSIs
to Eurostat
 Variety of tools for data
validation and analysis
• no cross-checks for
synergies;
• Inconsistencies;
• Single-purpose use of data
• Differences in concepts,
breakdowns, reference
periods…
• Different channels and
formats, difficult follow-up;
• inconsistencies in metadata;
• Inefficiency, lack of
interoperability,
• difficult quality control
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Future business model (1)
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Principles of ESS joint strategy
1. User needs are at the heart – increase availability of
statistics (globalisation and multi-dimensional)
2. Use separate strategic approaches for “WHAT”
(products, services, priorities) and “HOW” (the vision)
3. Reduce costs while maintaining data quality
4. Develop close partnership between all the 28 NSIs
(MS, Estat) through appropriate dialogue and
networks
5. Reuse of statistics from other sources (web,
administrative sources)
6. Integration and standardisation of methods and tools
7. Legislation needs to focus on large domains and be
output oriented
8. Develop a strategic human resource policy (staff
skills, common training)
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Major aspects of implementation
• Horizontal integration: production of data according to
the responding unit (e.g. household, enterprise), not
by domain – no domain is specific!
• Vertical integration: joint structures, networks
(ESSnets)
• Standardisation: common tools for each step of the
data production process (e.g. common classifications,
common definition of variables, common validation
rules)
• Use and combination of different data sources (survey
data, administrative sources)
• Move from sending data (push mode) to retrieving
data from data warehouses (pull mode)
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Informati
on
Networ
k
Informati
on store
Process
Modular
productio
n
5.
Reference
1. Web
infrastruct
ure
2. EGR
Productio
n
Architectu
re
3. SIMstat
Optimal
cooperatio
n
6. ESS
validation
7. Admin
sources
4. ESS data
warehouses
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Examples of projects supporting the
VISION
ESS vision infrastructure projects – by project
• Use of administrative data (ADMIN)
• National accounts production system - services(NAPSS)
• ESS data warehouses (price and transport statistics)
(PRIX/TRANS)
• European System of business registers (ESBR)
• Single Market Statistics (SIMSTAT)
• Information Society – web infrastructure (ICT)
• Common data validation policy (VIPV)
• Census hub
• Framework legislation for business statistics
• Remote access to individual data
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Common data validation rules and
tools
Develop standard documentation for validation
Develop standard formats for data
Develop standard rules for data validation
Describe the standard rules
Validation at the most appropriate levels (closest
to the data, the sooner the better)
• Develop generic IT tools
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Census hub
• Use of SDMX as a standard
• Pull mode of data transmission
• Development of standard tools
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Conclusions
• Harmonisation is a long process
• Standardisation can support the process
• Willingness to harmonise is important based
on political requirements
• The more political statistical data become
the more harmonisation is needed
• Harmonisation is an exercise of balancing
interests
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