ESSnet AdminData Methods of estimation for business statistics variables that cannot be obtained from administrative data sources (WP3) Duncan Elliott (UK), Danny van Elswijk (NL), Orietta Luzi (IT), Giampiero Siesto (IT), Brigitta Redling (DE), Daliute Kavaliauskiene (LT)

Aim of work package three Estimation for business statistics variables that cannot be obtained from administrative data sources No similar variables and definitional differences Admin data from a public authority Focus on SBS and STS variables Objective: reduce burden for business

Who’s involved in WP3?

Planned work 2009 – 2010 Literature Review Identification of variables Organising partnerships Development of estimation methods Testing Review

Some terminology Admin data: administrative and accounts data Register Administrative: for administrative purposes Statistical: processed for statistical purposes Survey Sample: use of sample returns Register based: direct use of administrative and derived variables

Business statistics variables Selection of Structural Business Statistics and Short Term Statistics WP3 members researched availability of admin data Variables selected for initial research Payments for agency workers Changes in stocks of goods and services Total purchases of goods and services Number of employees in FTE New Orders SBS STS

Quality and regulatory requirements SBS (selected variables) Timeliness: 18 months from reference period Period: annual Details: class level and region by division Quality information Definition of each variable STS (new orders) Timeliness: one month and 20 or one month and 35 days from reference period Details: reduced NACE code or division

Reducing burden Reduce number of enterprises sampled Reduce number of questions Reduce periodicity units sampled or questions asked

Estimation methods Reducing burden from a more extensive use of admin data to reduce sampling fractions Potential for only a small reduction Larger reductions of burden Design: ‘take none’ and/or ‘ask some strata Estimation: Direct and Synthetic Modelling at aggregate and/or unit level

Example take some take none sample Derive model from sample Apply model to ‘take none’ stratum or all non- sampled units

Quality of estimation Accuracy Bias Developing a decent model Assessing quality of estimates Relevance for other Member States Testing using past data Other quality issues addressed by other WPs

Future work and challenges (1) ‘Changes in stocks’ and ‘Purchases’ Applying models to ‘take none’ stratum Problems: modelling highly skewed data and data with high frequency of zeros, bias ‘New Orders’ Applying models to ‘take none’ stratum Problems: bias, timeliness of admin data

Future work and challenges (2) ‘Payments for agency workers’ Sample employment agencies Estimation using VAT Problems: definitional issues for industrial and regional details ‘Number of employees in FTE’ Combination of admin and sample survey sources Problems: definitional issues, timeliness and periodicity

Beyond 2010 Review of 2009 – 2010 work Selection and analysis of further variables Report on recommendations and best practice

Thank you for listening …