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Comparable statistics in the EU: ESS, an example of an effective regional statistical system Claudia Junker, Eurostat, head of unit "Statistical cooperation"

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Presentation on theme: "Comparable statistics in the EU: ESS, an example of an effective regional statistical system Claudia Junker, Eurostat, head of unit "Statistical cooperation""— Presentation transcript:

1 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 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 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 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 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

6 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

7 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

8 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

9 Harmonisation Regulations Framework regulations Methodology, handbooks Gentlemen agreements Common training programme Working groups and task forces And the vision…

10 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

11 Current situation – “Stovepipe approach” 11

12 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

13 Future business model (1) 13

14 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 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 16 Informati on Networ k Process Informati on store 1. Web infrastruct ure 2. EGR 3. SIMstat 4. ESS data warehouses Optimal cooperatio n 6. ESS validation 7. Admin sources Modular productio n 5. Reference Productio n Architectu re

17 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

18 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

19 Census hub Use of SDMX as a standard Pull mode of data transmission Development of standard tools 19

20 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

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