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ESSnet on the use of administrative data for business statistics

This presentation discusses the progress and future plans of the ESSnet project on using administrative data for business statistics. It covers topics such as existing practices, SBS and IFRS definitions, estimation of missing variables, timeliness issues, and quality indicators.

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ESSnet on the use of administrative data for business statistics

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  1. ESSnet on the use of administrative data for business statistics Alison Pritchard Economic Surveys Division, ONS, UK 27 October 2011

  2. Introductory and general remarks • This ESSnet’s 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; • Preparatory work was carried out initially in order to avoid duplication of effort; • This presentation covers progress made during the first 2 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. SBS v. IFRS definitions At first glance, many of the items in companies’ Balance Sheets and Profit and Loss accounts look very like SBS variables, but they are not exactly the same. • Need to compare the definitions in International Accounting Standards with those used for SBS. • Having established the differences in definitions, then compare SBS survey data with company accounts data, company by company, to find out whether these definitional differences are significant for SBS outputs.

  6. Estimation of missing SBS variables Key variables are available from most businesses’ accounts, but the detailed break-downs required for SBS are not. Therefore need to estimate for these missing variables, in order to make full use of admin/accounts data. Work has been done on the following SBS variables: • Change in stocks of goods for resale • Purchases for resale in the same condition • No. of employees in full-time equivalents • Payments for agency workers

  7. Timeliness issues for STS Administrative datasets often do not provide all the data we require at the STS delivery dates. Two cases, depending on the nature of the admin data available at each delivery date: • 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

  8. Case 1- representative data available Several approaches have been found for micro-imputation for turnover – • Using t-1 data, or t-12 data, or using a decision tree with ‘5 options’ with historical data for imputation, • Stratification at NACE 2 digit or 3/4 digit, • Adjusting for births and deaths. For employment, there is a macro-estimation model based on biases between 1st and definitive estimates in previous periods and the coverage of the admin dataset for current period t.

  9. Case 2 – only biased data available Four approaches are being worked on currently - • Regression estimator • Monthly survey calibrated by VAT quarter • 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

  10. Quality Indicators Findings from Stock-take research • Quality is seen as important • Various checks are conducted but these are not necessarily formal or regular, and are rarely published • Need for formal list of quality indicators relevant to statistics involving administrative data. Looking at input and process quality, with quality of output in mind. Quantitative indicators (at this stage)

  11. List of quality indicators Examples: Background information indicators • % of required variables derived indirectly from the admin data • % of common units in more than one admin source Quality indicators • Under-coverage • % of units in admin data which fail checks • % of units for which data have been adjusted

  12. Checklists In order to produce the quality indicators, it will be necessary to carry out several checks and record the results. We therefore have a work package which covers: • 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.

  13. Dissemination of results Information Centre The website address is: essnet.admindata.eu. • Workshops • STS workshop – 18/19 April 2012 in Cardiff, UK • current uses of admin data for STS, • relationship with admin data holder, • initial cleaning of admin data for STS, • dealing with the timeliness problem, and • recommended quality indicators. • SBS workshop – in Spring 2013

  14. Thank you for your attention – any questions?

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