ESS: A Regional Statistical System in the EU
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
2
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
3
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
4
Main actors of the European Statistical
System and beyond
•
The NSIs of the Member States
•
Eurostat
•
Council and European parliament
•
European Statistical Advisory
Governance Board
•
European Statistical Advisory
Committee
•
The European Commission
5
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
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
•
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
•
Regulations
•
Framework regulations
•
Methodology, handbooks
•
Gentlemen agreements
•
Common training programme
•
Working groups and task forces
And the vision…
Challenges leading to the vision
•
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”
11
Weaknesses
Current situation
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
Weakness
•
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
12
Future business model (1)
13
14
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)
15
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)
16
Examples of projects supporting the
VISION
ESS vision infrastructure projects – by project
•
Use of administrative data (ADMIN)
•
National accounts production system - services(NAPS-
S)
•
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
17
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
18
Census hub
•
Use of SDMX as a standard
•
Pull mode of data transmission
•
Development of standard tools
19
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
20
The European Statistical System (ESS) is a model of an effective regional statistical system in the EU, as exemplified by its policy relevance, main actors, planning and programming, adherence to the European Statistics Code of Practice, and key principles such as professional independence. The ESS plays a vital role in areas like economic surveillance, regional cohesion, and sustainability, contributing to the overall vision of harmonized statistical practices in the European region.
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Presentation Transcript
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 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 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 Regulations Framework regulations Methodology, handbooks Gentlemen agreements Common training programme Working groups and task forces And the vision
Challenges leading to the vision 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 11
Weaknesses Current situation 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 Weakness 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 12
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) 14
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) 15
Informati on Process Modular productio n 5. Reference Optimal cooperatio n Networ k Informati on store 6. ESS validation 2. EGR 1. Web infrastruct ure Productio n Architectu re 7. Admin sources 3. SIMstat 4. ESS data warehouses 16
Examples of projects supporting the VISION ESS vision infrastructure projects by project Use of administrative data (ADMIN) National accounts production system - services(NAPS- S) 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 17
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 18
Census hub Use of SDMX as a standard Pull mode of data transmission Development of standard tools 19
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 20
