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François Gagnon and Krista Cook Statistics Canada ICES III, Montreal, June 2007

Collecting Electronic Data From the Carriers: the Key to Success in the Canadian Trucking Commodity Origin and Destination Survey. François Gagnon and Krista Cook Statistics Canada ICES III, Montreal, June 2007. PRESENTATION Outline. 1. Background 2. Methodology of the Redesigned Survey

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François Gagnon and Krista Cook Statistics Canada ICES III, Montreal, June 2007

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  1. Collecting Electronic Data From the Carriers: the Key to Success in the Canadian Trucking Commodity Origin and Destination Survey François Gagnon and Krista Cook Statistics Canada ICES III, Montreal, June 2007

  2. PRESENTATIONOutline 1. Background 2. Methodology of the Redesigned Survey 3. Advantages/Disadvantages of the Canadian Approach 4. Challenges of Collecting Electronic Data 5. Conclusion

  3. 1. BACKGROUNDCommodity Flow Surveys in Canada from admin data (census) Ship from admin data (census) Shipments Rail Truck TCOD

  4. 1. BACKGROUNDWhat is TCOD? • Purpose : To measure trucking commodity movements • Unit of interest : Shipments • Variables collected for each shipment : • commodity carried, tonnage • origin and destination of shipment • distance, transportation revenues • Outputs : Estimates and CVs, microdata file • Input to : System of National Accounts • Main user & Co-sponsor: Transport Canada

  5. 1. BACKGROUNDWhy a redesign? • TCOD was developed in the early 1970s • In 2000, Statistics Canada approved a multi-year project to redesign the survey • To improve data quality • To better meet the new requirements of the users - Constraint: no additional production costs

  6. 1. BACKGROUNDAddressing data coverage needs Needs identified and decisions made • Trucking industry • Long-distance & local •  $1M (in terms of company revenue) • < $1M (in terms of company revenue) • Trucking activity in non-trucking businesses (Private trucking) • Foreign companies : no frame for now

  7. 1. BACKGROUNDAddressing other needs • Annual data • Provincial & Territorial estimates • Improve precision • Other variables such as “value of shipment”: not available on shipping documents => Improve coverage + precision + detail AT NO ADDITIONAL COST: a good challenge!

  8. 2. REDESIGNED TCODCoverage of the Old and New TCODs (Number of Companies) Trucking companies Non-trucking companies Revenue 1,828 351 1,462 $ 1 M  Other trucking activity Hhld goods moving Local Long Distance Foreign Canadian Companies Companies Old TCOD Coverage Added Coverage in the new TCOD Source: BR - 2004

  9. NFLD P.E.I. … B.C. 051: 051: 051: 051: 061: 061: 061: 061: NFLD … … … … … … … … 991: 991: 991: 991: 051: 051: 051: 051: 061: 061: 061: 061: P.E.I. … … … … … … … … 991: 991: 991: 991: 051: 051: 051: 051: … 061: 061: 061: 061: … … … … … … … … 991: 991: 991: 991: 051: 051: 051: 051: 061: 061: 061: 061: B.C. … … … … … … … … 991: 991: 991: 991: 2. REDESIGNED TCODKey estimates to be produced Key domains: Matrix: Origin x Destination x Commodity => Sample size in each cell of the matrix is random Key variables of interest: => Tonnage, Distance, Revenue

  10. 2. REDESIGNED TCOD Need for a larger sample size • Main challenge of commodity flow surveys: • No efficient stratification possible to control sample size by estimation domain (O/D/Commodity cells) => random sample size in O/D/Commodity cells => poor precision in many estimation domains • One solution: increase sample size • Old TCOD: 0.5 M shipments (sampling fraction: 0.8%) • New TCOD: 7.4 M shipments (sampling fraction: 11.2%)

  11. 2. REDESIGNED TCODData Collection A) Personal on-site visits • Similar process to the old TCOD • Improved CAPI application • 79% of the sampled companies (was 91%) • reduction of the overall collection costs (since this collection method is expensive) • 0.2 M shipments (comparable to the old TCOD)

  12. 2. REDESIGNED TCODData Collection B) Profiling using CATI • Used for all companies with < 50 combinations of Origin/Destination/Type of commodity • 21% of the sampled companies (was 9%) • 3.7 M shipments in the sample (49% of the sample) => Profiling allows to: • Reduce collection costs • Improve precision (through an increased sample size)

  13. 2. REDESIGNED TCODData Collection C) Electronic Data Reporting (EDR) ► 1st years of the new TCOD - for the same 7 large companies - 100% of their data (only 5% in the old TCOD) - 3.6 M shipments (48% of the total sample) - automation of coding + imputation ► Future years: - potentially 200+ companies => EDR will allow to: • Reduce collection costs • Improve precision (through an increased sample size)

  14. 2. REDESIGNED TCODSample Design 4-Stage Design: • 1st stage: Stratified SRSWOR of companies • Must-take strata for Profile & EDR companies > 2nd stage: Sample of a period of time (e.g., a 6-month period) > 3rd stage: Systematic sample of shipping documents > 4th stage: Systematic sample of shipments

  15. 2. REDESIGNED TCODDomain Estimation where: yhitjk= value of the variable of interest for the shipment k on shipping document j from the survey period t of company i in stratum h d =domain of interest >> Variance estimation: Jackknife method

  16. 3. CANADIAN APPROACH vs. Other Commodity Flow Surveys • Most other commodity flow surveys • Collect shipment information from the shippers • Canadian TCOD • Collects shipment information from the carriers

  17. 3. CANADIAN APPROACH Advantages • Survey population clearly defined: • no subjective decision on which industries (NAICS) to include • Collection via EDR & profiles • large increase of sample size at a minimal cost • reduces sampling errors • estimates at a more detailed level • On-site collection • reduces non-sampling errors • higher response rate => reduces nonresponse bias

  18. 3. CANADIAN APPROACH Disadvantages • Incomplete coverage of trucking activity • On-site collection is very expensive • Variable “value of commodity” cannot be collected

  19. 4. COLLECTING ELECTRONIC DATAChallenges • Companies’ data vs. TCOD variables • file formats + concepts • Security of electronic data • Automation of the processing • coding of commodities and origin/destination • imputation of commodities

  20. 5. CONCLUSIONCanadian Approach Collection from the carriers: • Larger sampling fraction => reduces sampling errors • On-site collection: => reduces non-sampling errors => higher response rate • Electronic data collection: huge potential to be developed in future years!

  21. François Gagnon Francois.Gagnon@statcan.ca • Krista Cook Krista.Cook@statcan.ca

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