Results and next steps from the ESSnet Admin Data

1. Results and next steps from the ESSnet Admin Data Alison Pritchard Business Outputs & Developments, Office for National Statistics, UK 4 December 2012

2. Introductory and general remarks • Part of MEETS Programme • Focus is mainly on the statistical outputs required by the STS and SBS Regulations; • There are 8 participating NSIs – UK, NL, LT, IT, EE, DE and BE; • The agreed work programme is very ambitious, so it’s been divided between 8 workpackages; • This presentation covers progress made during the first 3 years and future plans.

3. Aims of the ESSnet Admin Data “The use of administrative and accounts data for business statistics has country specific problems as well as problems common for most of the MSs. The common problems concern the methods of quality checking, editing and estimation of missing variables. One of the ways to help NSIs solve common problems is to create an ESSnet where several MSs interested in the topic can collaborate on the common task, and then disseminate the results to non-participating members.”

4. Overview of existing practices in MSs The areas covered are: SBS, STS, Prodcom and Business Registers (only because these are used as the sampling frame for business statistics). • Collection of all relevant literature in one place; • Searchable database of literature and current practices of European NSIs; • Glossary of terms relevant to the use of administrative data for business statistics; • All the results of the 2010 review have been published on this ESSnet’s Information Centre (essnet.admindata.eu).

5. Overview of existing practices in MSs Want the searchable online database to provide up-to-date information. • Expanding glossary a little now, working with MEMOBUST colleagues; • Already updated literature repository; • Minimum burden on MSs because just asking them to tell us about developments since 2010; • Keen to ensure that searchable database is available to NSIs after end of ESSnet Admin Data.

6. SBS compared with IFRS definitions • Building on information collected by the Eurostat funded Taxonomy Project; • Structural Business Statistics variables, because financial statements tend to be produced annually. • Many items in companies’ financial statements look like SBS variables, but not precisely the same. • Work is continuing, but first recommendations are: • (a) SBS turnover definition should be amended to exclude excise duties;(b) Turnover;Changes in stocks of goods and services; and Total purchases can be obtained directly from the Notes to company accounts.

7. Estimation of missing SBS variables • Change in stocks of goods for resale – if total change is available from financial statements, robust regression modelling works well for the breakdowns. Mass imputation (using nearest neighbour donor imputation) works fairly well when a small number of survey responses are available but no admin data. • Purchases for resale in the same condition – simple ratio estimation with VAT turnover data works well provided a small number of survey responses are available.

8. Estimation of missing SBS variables • No. of employees in full-time equivalents – simple multiplication of total employees by a conversion factor (obtained from hours worked or hours paid survey data) works very well in practice; • Payments for agency workers – no helpful admin data found; • Investment in tangible goods – current work on how to estimate for details of capital investment and disposals using admin data; • Production value - future work on how to estimate production value.

9. Timeliness issues for STS Admin data are often received too late for STS delivery dates. There are two cases, based on how much admin data are available: • admin data are fairly complete and can be considered as representative - • STS estimates can be based only on admin data • 2. admin data are incomplete and cannot be considered as representative - • STS estimates can use admin data combined with data from a survey of large enterprises

10. Timely admin data are not representative Work is continuing to make firm recommendations for best methods to use, depending on the precise availability of admin data to the NSI. Three approaches are being worked on currently - • Monthly survey calibrated by VAT quarterly data; • Time-series analyses for separate estimation for small enterprises; • Model-based: by comparing the growth rate for large enterprises with that for the entire population.

11. Quality Indicators • Quantitative quality indicators, e.g. • Under-coverage • % of units in admin data which fail checks • % of units for which data have been adjusted • Background information indicators, e.g. • % of required variables derived indirectly from the admin data • % of common units in more than one admin source • Composite indicators • Qualitative quality indicators

12. Checklists In order to produce the quality indicators, NSIs must carry out several checks and record the results. We are therefore producing a handbook covering: • Issues to consider before acquiring a new admin dataset, and if an existing admin dataset changes (e.g. a quarterly delivery becomes a monthly delivery); and • Best practice methods for initial cleaning of admin datasets when received by the NSI. Information Centre The website address is: essnet.admindata.eu.

13. Future Workshops SBS workshop – 20/21 March 2013 in Lisbon, PT • current uses of admin data for SBS, • relationship with admin data holder, • initial cleaning of admin data for SBS, • practical methods for making use of company accounts data • recommended quality indicators Final STS workshop – 7 May 2013 in Tallinn, EE

14. Thank you for your attention – any questions?