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Unified Enterprise Survey New Horizons

Statistics Canada Statistique Canada. Unified Enterprise Survey New Horizons. International Conference on Establishment Surveys Daniela Ravindra and Marie Brodeur Montreal, June 2007. Outline. 1. Background 2. Principles

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Unified Enterprise Survey New Horizons

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  1. Statistics Canada Statistique Canada Unified Enterprise Survey New Horizons International Conference on Establishment Surveys Daniela Ravindra and Marie Brodeur Montreal, June 2007

  2. Outline 1. Background 2. Principles 3. Survey Characteristics 4. Sampling and Use of Tax Data 5. Processing 6. Analysis and Dissemination 7. Achievements

  3. Background to the Integrated Approach • Established in 1997 • Goal to integrate all annual business surveys • Objectives: • Improve coherence • Improve breath and depth of data • Not increase response burden • Created one central area for processing (ESD)

  4. Integrated Approach Principles • Use of a single unduplicated frame -- the BR • Common sample design methodology • Questionnaire • harmonized concepts / variables • Generic portion • Centralized Data Collection

  5. Integrated Approach Principles (continued) • Integrated metadata system • Common generic processing systems and methods (all residing in ESD) • Centralized data warehouse • Head Office Survey • Maximum use of tax data • Coherence analysis for large enterprises • Regular profiling of large enterprises • Holistic approach to response management

  6. Survey Characteristics • All annual surveys • Establishment Surveys • Currently 64 surveys but plans to add more • Smallest businesses estimated through tax

  7. Sampling • Stratified Random Sample • Industry • Province • Size • 1 Take-all stratum • 2 Take-some strata (50% of units replaced by tax) • Take-none strata

  8. Sampling Process Survey Universe File (2M businesses) BR (2.3M businesses) Tax Est’d (1.4M) Sample Control File (2M businesses) 55K CEs UES Sample (70K businesses) Survey Interface File 38K CEs / Questionnaires Tax Replacements 17K CEs

  9. UES : Use of Tax Data in Sampling T2 and other T1 Main sample to be surveyed (sample size of about 47k, from which 4k are T1) Sample substitution (TRP) for pre-identified T1 (unincorporated) and T2 (Incorporated) units. THRESHOLDS T1TN: Sample of T1 T2 TN: Census of T2 Take-none : Weighted Sample of T1 and Census of T2

  10. Centralized Collection Pre-Contact Score Function Mailout Edit / Verification (BLAISE) Receipt (75% target) “Clean” Records Capture / Imaging Delinquent Follow-Up

  11. Centralized Processing Systems and Databases • Develop centralized systems • Single point of access for security • Move away from stand-alone • Increase efficiency of resource use • Integrated Questionnaire Metadata System • Edit and imputation • Use generalized system • Allocation • Developed for complex units • In the process of standardizing the approach • Estimation

  12. Post-Collection Processing “Clean” Records Tax Data Central Data Store Pre-Grooming USTART Edit & Imputation Subject Matter Review & Correction Tool Allocation / Estimation

  13. UES: Use of Tax Data in Post-Collection • Validation (comparison) • Verify dubious collected data against it’s equivalent tax data record • Imputation • One of the methods used for non-response • Estimation • Weighted TRP units, T2 take-none, weighted T1 take-none, T1 adjustments for units not on the business register

  14. UES: Use of Tax Data in Post-Collection (continued) • Not always a direct correspondence between tax and survey variables: differences in concepts and definitions • Developed a common mapping to bring them together • Standard income statement called Chart of Accounts • Map survey and tax data to it

  15. Analysis and Dissemination • Analysis conducted by subject-matter specialists • Use of common analytical tools • Estimates released no later than 15 months after reference date • Previous year’s data is revised when working on current year

  16. Achievements • Timeliness improved • Efficient, streamlined systems • Common database • Response burden reduced • More coherent data • More efficient use of resources

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