Use of Tax Data inUse of Tax Data inthe Unified Enterprise Survey (UES)the Unified Enterprise Survey (UES)
Workshop on Use of Administrative Data in Economics Statistics
Marie BrodeurMoscow
November, 2006
Statistics CanadaStatistique Canada
Overview of the presentation
1. UES Background 2. Integrated Approach Principles 3. Survey Characteristics 4. Business Register 5. Sampling 6. Achievements and Future Directions 7. Use of Tax Data 8. Research and development
1. UES Background
Major project to improve provincial statistics (1996)
Reliable Annual Provincial Data for the Allocation of HST Revenues (SNA I-O Tables)
More detailed Industry & Commodity dataCreation of Enterprise Statistics Division
(ESD)UES Pilot (RY 1997) -- 7 surveysGradual Expansion of Surveys; Covers 65%
of GDP
2. Integrated Approach Principles
Use of Single, Unduplicated Frame -- the BR
Expanded coverage
Common Sample Design Methodology
Integrated Questionnaire -- common / simple language; harmonized concepts / variables
Centralized Data Collection at the Statistical Establishment level
2. Integrated Approach Principles (continued)
Common Generic Processing Systems and Methods
Centralized Warehouse
Head Office Survey
Maximum Use of Tax Data
Annual Profiling of Large Enterprises
Enterprise Portfolio Managers
3. Survey Characteristics
Separate Enterprise & Establishment Surveys
Over 50 Establishment SurveysOver 55,000 collection entities
representing about 68,000 establishments (17K replaced by tax for RY 2005)
Centralized Collection -- $3.5 million budget
Smallest businesses estimated through tax
4. Business Register (BR)
BR covers all sectorsIncorporated and unincorporated
businessesComplex and simple enterprisesStructure
LegalOperationalStatistical (Enterprise &
Establishment)
Updated with Administrative Data
5. Sampling
Stratified Random Sample Industry (NAICS 4) Province Size
1 Take-all stratum2 Take-some strata (50% of units
replaced by tax)Take-none strata (under Royce-
Maranda thresholds)
Stratification in One Look
Take-all
Take-some 2
Take-some 1
Take-none
Cell
Must takeunits Royce-Maranda (RM)
Exclusion Thresholds:
•To reduce response burden on small enterprises
Sam
plin
g re
venu
e
Sampling ProcessSampling Process
Survey Universe File(2M businesses)
Sample Control File(2M businesses)
Survey Interface File38K CEs / Questionnaires
Tax Est’d (1.4M)
UES Sample (70K businesses)
Tax Replacements17K CEs
55K CEs
BR(2.3M businesses)
6. Achievements
TimelinessCentralized Processing
Systems and DatabasesResponse BurdenUse of Tax Data
6a. Timeliness
Very problematic during start-up yearsMany processing systems in
developmentProblems with questionnaires
Task force created in 2001Target: 15 months after reference yearSince RY 2003, all surveys between 12-
15 month period
6b. Centralized Processing Systems and Databases
Develop centralized systems Move away from stand-alone Single point of access for security
Integrated Questionnaire Metadata System
Edit and imputationAllocation and EstimationData Warehouse
Centralized CollectionCentralized Collection
Mailout(38K CEs)
Pre-Contact(17K Businesses)
Edit / Verification(BLAISE)
Receipt(75% target)
Delinquent Follow-Up
Capture / Imaging
“Clean” Records
Score Function
Post-Collection ProcessingPost-Collection Processing
Pre-Grooming
Allocation / Estimation
Edit & Imputation
“Clean” Records
Central Data Store
Subject Matter Review & Correction
Tool
Tax Data
USTART
7. Use of Tax data
Significant process since 1997Strategic Streamlining Initiative Result
Almost 65% of units replaced by tax data
Impact of 27% in the total estimate
Streamlining Initiatives at STC
Announced in 2002 Objectives
Maintaining qualityCreate efficiencies Enhance work flowsIdentify trade-offs
Expand the use of tax data for survey replacement
T1\T2 Project
Objective is to substitute 50% of simple establishments.
Direct Data Replacement for annual surveys usingT1(unincorporated)T2 (incorporated)
Facilitated by the Chart of Accounts (COA).
Types of Administrative (Tax) Data
From the Canadian Revenue Agency (CRA)
Agreement between CRA and STCT1 (unincorporated businesses)T2 (incorporated businesses)T4 (pay slips)GST (goods and service tax)PD7 (payroll deduction accounts)
Processing of Tax Data
Edit erroneous reports Outlier detectionEliminate duplicationImpute for missing valuesAnnualize in case of monthly
data
Stratification in One Look
Take-all
Take-some 2
Take-some 1
Take-none
Cell
Must takeunits Royce-Maranda (RM)
Exclusion Thresholds:
•To reduce response burden on small enterprises
Sam
plin
g re
venu
e
RY2005 Methodology: Tax Replacement
T2
T1 Take-None:
Sample of e-filers
T2 Take-None:
Census of General Index of Financial Information (GIFI)
ROYCE-MARANDA THRESHOLDS
Main sample to be surveyed
Not eligible for tax : questionnaire
Tax replaced
Characteristic survey (some Services surveys) or questionnaire (all other divisions)
T1
Main sample to be surveyed
UES: Use of Tax Data
Validation (comparison)Verify dubious collected data against
the equivalent tax data record Imputation
One of the methods used for non-response
EstimationBelow take-noneDirect Data Replacement Some annual surveys 100% tax (Taxi
& Limousines, Survey of Mapping) Update Business Register Allocation of survey data ( use tax
revenues, salaries and expenses)
CHART OF ACCOUNTSWhy does a Bureau of Statistics need one?
BUSINESS WORLD
Chart of Accounts (COA)
BUREAU OF STATISTICS
DISSIMINATION
COLLECTION
Chart of Accounts
SalesOperatingrevenue Cost of
sales
Grossprofit Expenses
EBIT
OutputsInputs
Valueadded
Shipments OperatingSurplus
GDP
LINK, BRIDGE, CONCORDANCE
Expected Benefits of a Chart of Accounts
Standardization in business data collectionHigher survey responseIncrease in quality of dataComparison of data from various sourcesIncrease efficiency in using administrative
data
Links to Chart of Accounts
CHART OF
ACCOUNTEstablishment
Legal entity
Legal entity
GST Data
Monthly tax dataUsed to replace survey data for
monthly surveysImplemented for manufacturing,
services and retail surveysFor RY 2005 used for analytical
comparisons for annual Services Surveys
Research and Development
Data Integration Project make a more efficient use of tax data
Development of new quality indicators (e.g. Rates, coefficients of variation)