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ESSnet AdminData

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).

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ESSnet AdminData

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  1. 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)

  2. 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

  3. Who’s involved in WP3?

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

  5. 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

  6. 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

  7. 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

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

  9. 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

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

  11. 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

  12. 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

  13. 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

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

  15. Thank you for listening … duncan.elliott@ons.gov.uk luzi@istat.it siesto@istat.it d.vanelswijk@cbs.nl daliute.kavaliauskiene@stat.gov.lt brigitta.redling@destatis.de

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