Integrated Approach Processing Marie Brodeur Director General, Industry Statistics Branch, Statistics Canada St. Lucia February, 2014 SNA seminar in the Caribbean

Why A Centralized Process?  Best Practices  Standardization of Processes Cross Survey Comparisons Enterprise Centric Processing/Coherence Analysis  Efficient use of Resources  Transportable Knowledge Across Survey Programs 2

Pre-Grooming Allocation / Estimation Edit & Imputation Records from Collection Data Service Center Subject Matter Review & Correction Tool Tax Data Business Register UES Post-Collection Processing 3

Collection  Precontact (Dec-Jan) –Mostly for Business Register (BR) births; verification of contact information (name, address, …) –By phone (in a few cases, a letter or a fact sheet is sent)  Mail-out of questionnaires (Jan-March) –2 or 3 mail-out dates  Follow-up in case of non-response for some units (begins about a month after mail-out) –Phone call, r or fax  Mail-back of questionnaires  Verifications of received questionnaires / Edits –Is the questionnaire complete or are some key variables missing? (Edit follow-up by phone in some cases) 4

Centralized Collection Mailout Pre-Contact Edit / Verification Receipt (75% target) Delinquent Follow-Up Capture / Imaging “Clean” Records Prioritize 5

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  Direct Data Replacement  Calibration Estimates  Update Business Register  Allocation of survey data (use tax revenues, salaries and expenses)

 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 Processing Systems And Databases

Enterprise Portfolio Managers  Top 350 enterprises in Canada  Status Platinum, Gold, Silver, Bronze  Personal visits  Enterprise Profiling  Coordination of mail-out and collection  Enterprise/ Establishment coherence  Holistic Response Management Strategic Response Unit Escalation Process / Statistics Act 8

What Is E & I?  Editing Verify that parts add-up to total Ensure that there are no missing values where parts add up to total There must be consistency between related variables  Imputation Changing values in fields which fail edit rules with a view to ensuring that the resulting data satisfy all edit rules. In practice, reported data will rarely be changed Impute for missing data or partially responded data Impute entire records in the case of total non- response 9

Why Is E&I Necessary?  To produce a complete and consistent data file that accounts for all sampled units  Both units that did not respond to the survey must be imputed and units that did not provide a complete response must be imputed  Correct erroneous responses 10

E&I Terminology  Data Group Groupings (defined by SM) of records that will be kept together for imputation purposesGroupings (defined by SM) of records that will be kept together for imputation purposes These groupings are based on multi dimensions:These groupings are based on multi dimensions:  industry (NAICS)  geography (province)  Data groups that will be used for a specific survey will depend on: initial sample design (number of units sampled and the level of stratification used)initial sample design (number of units sampled and the level of stratification used) number of records that respond to the survey (a minimum of 5 or 10 records are required)number of records that respond to the survey (a minimum of 5 or 10 records are required) 11

BANFF E & I System  Impute for missing key variables as specified by subject matter (i.e. total revenue, total expenses)  Impute for other missing variables: Apply Historical Trend Apply Current Year Trend Use donor (for partial imputation) 12

BANFF Algorithms  DIFTREND - Historical trend imputation  CURRATIO - Current ratio imputation  PREVALUE – Value from the previous period for the same unit is imputed  PREAUX – Historical value of a proxy variable for the same unit  CURAUX – Current value of a proxy variable for the same unit 13

Allocation - Definition & Purpose Definition:  Allocation is the distribution of survey and administrative data from their acquisition level (Collection Entity) to the targeted statistical units (Establishments or Locations) as defined on the survey frame. Purpose:  To provide fully-processed micro data on a fiscal year basis, for establishments or locations in-sample for the UES  Determine the distribution of value added by province 14

Sample Survey Allocation 15

11/12/2015 Statistics Canada Statistique Canada 16 Multi-Mode Collection Quality Indicators and Scores Follow-Up Editing Imputation Estimation Sampling Rolling Estimates Interpretation & Dissemination Automated Processing Active Management Manual Editing Overview of the IBSP Rolling Estimates Approach

11/12/2015 Statistics Canada Statistique Canada 17 Active Management – Strategy Settings  A subset of all Key Estimates is selected  All Key Estimates are: Ranked from the most to the least important Weighted relatively using an importance factor Assigned a Quality Target  Targets are set in line with the importance factor.  Active Collection ends for a Key Estimate when the Quality Indicator meets the Quality Target.  Active management and sampling strategies are coherent by design.

 Quality Indicator (QI) QI= Sampling CV & Imputation CV & Pseudo Relative Bias  Measure of Impact (MI) Score Impact of a unit on the QI for a given estimate Units imputed from a poor model or with reported/imputed values far from their predicted values will have high MIs. 11/12/2015 Statistics Canada Statistique Canada 18 Active Management – Definitions

 Parallel run for 47 Business Surveys  Four Rolling Estimates iterations  Total CV calculated for all key estimates (8,600) at each iteration 11/12/2015 Statistics Canada Statistique Canada 19 Empirical Study – RY2011 Prototype

11/12/2015 Statistics Canada Statistique Canada 20