Use of Tax Data in the Unified Enterprise Survey (UES) Workshop on Use of Administrative Data in Economics Statistics Marie Brodeur Moscow November, 2006 Statistics Canada Statistique 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 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 Take-all Take-some 2 Take-some 1 Take-none Cell Must take units Royce-Maranda (RM) Exclusion Thresholds: To reduce response burden on small enterprises Sampling revenue

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

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 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 Mailout (38K CEs) Pre-Contact (17K Businesses) Edit / Verification (BLAISE) Receipt (75% target) Delinquent Follow-Up Capture / Imaging Clean Records Score Function

Post-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 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 Take-all Take-some 2 Take-some 1 Take-none Cell Must take units Royce-Maranda (RM) Exclusion Thresholds: To reduce response burden on small enterprises Sampling revenue

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 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

DISSIMINATION COLLECTION Chart of Accounts Sales Operating revenue Cost of sales Gross profit Expenses EBIT Outputs Inputs Value added Shipments Operating Surplus GDP LINK, BRIDGE, CONCORDANCE

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 CHART OF ACCOUNT Establishment 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)