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

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

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

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

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

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