1. ESSnet Admin data T HE U SE OF ADMINISTRATIVE AND A CCOUNTS D ATA FOR B USINESS S TATISTICS Presented by: Pieter Vlag Statistics Netherlands

2. Aim of ESSnet The ESSnet has been set up in order to develop best practices and make recommendations in the uses of administrative and accounts data in the production of business statistics. 2009 – 2012

3. List of Partners ESSnet Partners Co-ordinator Office for National StatisticsONSUnited Kingdom participants Instituto Nacional de EstatísticaINEPortugal Centraal Bureau voor de StatistiekCBSNetherlands Statistics LithuaniaSLLithuania Istituto Nazionale di StatisticaISTATItaly Eesti StatistikaESEstonia Federal Statistical OfficeDESTATISGermany Statistiek en Economische InformatieSIEBelgium

4. List of Partners ESSnet – internal organisation WP 0Co-ordination and Administration WP 1 Existing practices WP 2 Checklists (to be started 2010) WP 3 Estimation Variables which cannot be obtained from Adm. Sources WP 4 Short term statistics: (timeliness of) Admin data WP 5 Integrating data from different sources (postponed until 2011) WP 6 Quality indicators WP 7 Statistics and Accounting Standards (to be started 2010) WP 8 Creation of an Information Centre WP 9 Training and exchange of Best Practice

5. List of Partners ESSnet – internal organisation (2) WP 0Alison Pritchard (UK) WP 1Luigi Constanzo (IT) WP 2- WP 3Danny van Elswijk (NL) WP 4Pieter Vlag (NL) WP 5Jutta Oertel (GE) WP 6John-Mark Frost (UK) WP 7Antanina Valiuliene (LT) WP 8Heli Jaago (EE) WP 9Humberto Pereira (PT)

6. Scope of work package- What is timeliness Division into two problems: For monthly and quarterly (STS) estimates the register data are incomplete because 1. The administrative data are by definition incomplete for monthly and/or quarterly reporting periods (EXAMPLE: in many EU-countries small enterprises may declare VAT on a quarterly/yearly base) 2.The administrative data are incomplete because are not yet available for (flash and regular) STS-estimates (EXAMPLE: first STS-estimate = T+ 30; register data complete T+40) MOST COMMON: combination of 1. and 2.

7. Description of work Describe best practices for estimation of STS-variables from adm. sources if`due to the timeliness problem - turnover variable is not yet (completely) availabe in VAT registration. - employment variable (wages, persons employed) are not yet (completely) available in social security administrative sources Transforming best practices into recommendations (“research” subject) Best practices for revision strategy: How do you revise data when more data are available ESSnet Admin Data Workpackage : STS-estimates

8. OBSERVATION: Survey data: NSIs do have control about timeliness Admin data: NSIs don’t have (complete) control about timeliness -Practical implications: a.admin data for monthly and quarterly STS-estimates are (often) incomplete b.availability of admin data for STS-estimates differs per country CHALLENGE: Are recommendations possible, taking into account the ‘national’ differences ?

9. ESSnet Admin Data Workpackage : STS-estimates ACTIONS a.Case study between UK and NL (for details see paper) b.Contacts and visits to other countries (DE, EST, FI, IT, LT) to check whether experiences and solutions for NL-UK are recognizable. c.Development of a framework to be worked out in 2010-2011 by DE, EST, FI, LT, NL, UK

10. CASE STUDY NETHERLANDS until 2009: VAT reporter yearlyif VAT-remittance < € 1883,= year quarterly if VAT-remittance <= € 7000,= quarter monthly if VAT-remittamce > € 7000,= quarter + “special cases” 1.1.2009 threshold M -> Q increased to € 15000,= from 2009 – Q3 year if VAT-remittance < € 1883,= year Quarter general month voluntary + “special cases” ? TIMELINESS <= 30 days after reporting period

11. CASE STUDY NETHERLANDS Quarterly turnover estimates - benchmarking system (prototype producion) Monthly estimates – in research a.Survey largest enterprises + nowcasting (approach Stat. Finland) b.Survey largest enterprises + quarterly system (modified) c.Survey largest enterprises + small survey other ent., low aggregation levels after quarter (approach Stat. Sweden) VAT t-12VAT t datadata + est. MM MQ QM QQ Mx Qx xM xQ panels stopping starting

12. CASE STUDY UNITED KINGDOM -Monthly declarations (limited) + 3 three-month periods (“staggers”) -Timeliness: 40 days after declaration period

13. CASE STUDY UNITED KINGDOM - Research to be done (small enterprises) CONCLUSION: after splining the ‘staggers’ into monthly data -> similar methodological problems as in the Netherlands -If representative: approach Dutch benchmark methodology possible - If not: nowcasting or small survey

14. OTHER COUNTRIES Estonia: Review position – good knowledge of admin data Germany: Use of VAT for quarterly turnover estimates (section H,J – M,N partly) -> focus: improvement + starters/stoppers Italy: OROS-system (employment) Lithuania: a.VAT for monthly estimates with regression estimator (auxilliary) information) b.employment register Finland (consultant): a.expertise in use of VAT for STS (quarter + month) b.collaboration: t+30days monthly estimates

15. Framework to be worked out by DE, EST, IT, LT, NL, UK Inventory Legislation, availability NSI, stability time-series Analyses Work UK - NL adm. Data Example VAT 1 1 VAT can be replaced by empl VAT no complete coverage VAT Almost complete coverage transf to STS periods VAT only VAT < threshold without benchmarking with benchmarking Benchmarking for quality 1st res. Benchmarking for transf. Q -> M not representative or cannot be modelled representative or can be modelled no VAT possible Nowcasting (GREG-type) Est. Survey (t,t-x) (GREG-type) Est. VAT (t,t-x)

16. Example: The Netherlands 2010 - VAT Inventory Legislation, availability NSI, Stability time-series Analyses Like deliverable I - 2009 adm. Data Example VAT VAT no complete coverage VAT Almost complete coverage transf to STS periods VAT only VAT < threshold without benchmarking with benchmarking Benchmarking for quality 1st res. Benchmarking for transf. Q -> M not representative or cannot be modelled representative or can be modelled no VAT possible Nowcasting (GREG-type) Est. Survey (t,t-x) (GREG-type) Est. VAT (t,t-x) Quarter – deliverable II - 2009 Month

17. Strategy – next step Quarterly turnover estimates (adm. data almost complete coverage): Germany describes its method -> comparison with NL and FI Employment: Italy describes its method -> comparison with LT Monthly turnover estimates (adm. data with no complete coverage): Netherlands describes its ideas -> comparison with IT and FI Regression estimator + revising results Lithuania describes its method -> “ comparison ” NL Practices: F, NO, DK

18. Challenge a.Establishing the link between availability admin data and STS- estimates b.When established, comparing practices to improve them c.Providing recommendations