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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

Statistics Canada

Statistique Canada

Unified Enterprise Survey New Horizons

International Conference on Establishment Surveys

Daniela Ravindra and Marie Brodeur

Montreal, June 2007


1. Background

2. Principles

3. Survey Characteristics

4. Sampling and Use of Tax Data

5. Processing

6. Analysis and Dissemination

7. Achievements

background to the integrated approach
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)
integrated approach principles
Integrated Approach Principles
  • Use of a single unduplicated frame -- the BR
  • Common sample design methodology
  • Questionnaire
          • harmonized concepts / variables
          • Generic portion
  • Centralized Data Collection
integrated approach principles continued
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
survey characteristics
Survey Characteristics
  • All annual surveys
  • Establishment Surveys
  • Currently 64 surveys but plans to add more
  • Smallest businesses estimated through tax
  • Stratified Random Sample
    • Industry
    • Province
    • Size
      • 1 Take-all stratum
      • 2 Take-some strata (50% of units replaced by tax)
      • Take-none strata
sampling process
Sampling Process

Survey Universe File

(2M businesses)


(2.3M businesses)

Tax Est’d


Sample Control File

(2M businesses)

55K CEs

UES Sample

(70K businesses)

Survey Interface File

38K CEs / Questionnaires

Tax Replacements

17K CEs


UES : Use of Tax Data in Sampling

T2 and other


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.



Sample of T1

T2 TN:

Census of T2

Take-none : Weighted Sample of T1 and Census of T2

centralized collection
Centralized Collection


Score Function


Edit / Verification



(75% target)

“Clean” Records

Capture / Imaging

Delinquent Follow-Up

centralized processing systems and databases
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
post collection processing
Post-Collection Processing

“Clean” Records

Tax Data

Central Data




Edit & Imputation

Subject Matter

Review & Correction


Allocation /


ues use of tax data in post collection
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
ues use of tax data in post collection continued
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
analysis and dissemination
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
  • Timeliness improved
  • Efficient, streamlined systems
  • Common database
  • Response burden reduced
  • More coherent data
  • More efficient use of resources