UES Overview - Millennium Indicators
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Transcript UES Overview - Millennium Indicators
Statistics Canada
Statistique Canada
Use of Tax Data in
the Unified Enterprise Survey (UES)
Workshop on Use of
Administrative Data in Economics Statistics
Marie Brodeur
Moscow
November, 2006
Overview of the presentation
1.
2.
3.
4.
5.
6.
7.
8.
UES Background
Integrated Approach Principles
Survey Characteristics
Business Register
Sampling
Achievements and Future Directions
Use of Tax Data
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 data
Creation of Enterprise Statistics Division
(ESD)
UES Pilot (RY 1997) -- 7 surveys
Gradual 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 Surveys
Over 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 sectors
Incorporated and unincorporated
businesses
Complex and simple enterprises
Structure
Legal
Operational
Statistical (Enterprise & Establishment)
Updated with Administrative Data
5. Sampling
Stratified Random Sample
Industry (NAICS 4)
Province
Size
1 Take-all stratum
2 Take-some strata (50% of units replaced
by tax)
Take-none strata (under Royce-Maranda
thresholds)
Stratification in One Look
Cell
Sampling revenue
Take-all
Take-some 2
Must
take
units
Take-some 1
Take-none
Royce-Maranda (RM)
Exclusion Thresholds:
•To reduce response
burden on small
enterprises
Sampling Process
BR
(2.3M businesses)
Survey Universe File
(2M businesses)
Sample Control File
(2M businesses)
UES Sample
(70K businesses)
Survey Interface File
38K CEs / Questionnaires
Tax Est’d
(1.4M)
55K
CEs
Tax Replacements
17K CEs
6. Achievements
Timeliness
Centralized Processing Systems
and Databases
Response Burden
Use of Tax Data
6a. Timeliness
Very problematic during start-up years
Many processing systems in development
Problems with questionnaires
Task force created in 2001
Target: 15 months after reference year
Since 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 imputation
Allocation and Estimation
Data Warehouse
Centralized Collection
Pre-Contact
(17K Businesses)
Mailout
(38K CEs)
Receipt
(75% target)
Score Function
Edit / Verification
(BLAISE)
“Clean” Records
Capture / Imaging
Delinquent Follow-Up
Post-Collection Processing
“Clean” Records
Pre-Grooming
Tax
Data
Central Data
Store
USTART
Edit & Imputation
Allocation /
Estimation
Subject Matter
Review & Correction
Tool
7. Use of Tax data
Significant process since 1997
Strategic 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 quality
Create efficiencies
Enhance work flows
Identify 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 using
T1(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 STC
T1 (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 detection
Eliminate duplication
Impute for missing values
Annualize in case of monthly data
Stratification in One Look
Cell
Sampling revenue
Take-all
Take-some 2
Must
take
units
Take-some 1
Take-none
Royce-Maranda (RM)
Exclusion Thresholds:
•To reduce response
burden on small
enterprises
RY2005 Methodology: Tax Replacement
T1
T2
Main sample
Main
sampleto
tobe
besurveyed
surveyed
Not eligible for tax : questionnaire
Characteristic survey (some Services surveys)
or questionnaire (all other divisions)
Tax replaced
ROYCE-MARANDA THRESHOLDS
T1 TakeNone:
Sample of
e-filers
T2 Take-None:
Census of General
Index of Financial
Information (GIFI)
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
Estimation
Below take-none
Direct 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 ACCOUNTS
Why does a Bureau of Statistics need one?
BUSINESS WORLD
Chart of Accounts (COA)
BUREAU OF STATISTICS
Chart of Accounts
COLLECTION
Sales
Operating
revenue
EBIT
Gross
Cost of profit Expenses
sales
LINK, BRIDGE, CONCORDANCE
DISSIMINATION
Shipments
Outputs
Inputs
Value
added
Operating
Surplus
GDP
Expected Benefits of a Chart of Accounts
Standardization in business data collection
Higher survey response
Increase in quality of data
Comparison of data from various sources
Increase efficiency in using administrative data
Links to Chart of Accounts
Establishment
CHART
OF
ACCOUNT
Legal entity
Legal entity
GST Data
Monthly tax data
Used to replace survey data for
monthly surveys
Implemented for manufacturing,
services and retail surveys
For 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)