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 planning28-29 November 2012, Jakarta

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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

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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

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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

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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

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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”

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WeaknessesCurrent 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

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Future business model (1)

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Principles of ESS joint strategy1. 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 quality4. 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 tools7. Legislation needs to focus on large domains and be

output oriented8. 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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Information

Network

Process

Information store

1. Web infrastruct

ure

2. EGR

3. SIMstat

4. ESS data warehouses

Optimal cooperati

on

6. ESS validation

7. Admin sources

Modular productio

n5.

Reference

Production

Architecture

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

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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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