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

PRESENTATIONOutline

1. Background

2. Methodology of the Redesigned Survey

3. Advantages/Disadvantages of the Canadian Approach

4. Challenges of Collecting Electronic Data

5. Conclusion

1. BACKGROUND Commodity Flow Surveys in Canada

Shipments

Ship

Rail

Truck

from admin data (census)

from admin data (census)

TCOD

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

1. BACKGROUND Why 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

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

1. BACKGROUND Addressing 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!

$ 1 M

Revenue

Long Distance Local

Trucking companies Non-trucking companies

Canadian Companies ForeignCompanies

Old TCOD CoverageAdded Coverage in the new TCOD

1,828 1,462

2. REDESIGNED TCOD Coverage of the Old and New TCODs

(Number of Companies)

Other trucking activity

Hhld goods moving

Source: BR - 2004

351

2. REDESIGNED TCOD Key estimates to be produced

Key domains: Matrix: Origin x Destination x CommodityNFLD 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:

Key variables of interest: => Tonnage, Distance, Revenue

=> Sample size in each cell of the matrix is random

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 domainsOne solution: increase sample size

Old TCOD: 0.5 M shipments (sampling fraction: 0.8%)New TCOD: 7.4 M shipments (sampling fraction: 11.2%)

2. REDESIGNED TCOD Data Collection

A) Personal on-site visitsSimilar 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)

2. REDESIGNED TCOD Data 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)

2. REDESIGNED TCOD Data 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 costsImprove precision (through an increased sample size)

2. REDESIGNED TCOD Sample 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

2. REDESIGNED TCOD Domain 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

elsewhere 0

if )(

dhitjkydy

hitjk

hitjk

hit hitjh r

j

m

khitjkhitjkhitj

n

ihithi

H

h

dywwwwdY1 1

431

211

)()(ˆ

>> Variance estimation: Jackknife method

3. CANADIAN APPROACH vs. Other Commodity Flow Surveys

Most other commodity flow surveysCollect shipment information from the shippers

Canadian TCODCollects shipment information from the carriers

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

3. CANADIAN APPROACH Disadvantages

Incomplete coverage of trucking activity

On-site collection is very expensive

Variable “value of commodity” cannot be collected

4. COLLECTING ELECTRONIC DATA

Challenges

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

5. CONCLUSION Canadian 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!

Pour plus d’information, veuillez contacter

For more information please contact

www.statcan.ca

François Gagnon Francois.Gagnon@statcan.ca

Krista Cook Krista.Cook@statcan.ca