For-Hire Trucking Survey

Detailed information for first and second quarters 2002

The purpose of this survey is to measure outputs of the Canadian for-hire trucking industry by providing estimates of inter-city commodity movements.

Data release - April 17, 2003

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

The For-hire Trucking Survey has been re-designed and will be replaced by the Trucking Commodity Origin and Destination Survey beginning with reference year 2004.

The purpose of this survey is to measure outputs of the Canadian for-hire trucking industry by providing estimates of inter-city commodity movements. Information is provided for shipments, revenue, weight, and distance. The survey complements the results of the Motor Carriers of Freight Survey by providing additional information. The data are used by federal and provincial governments, trucking associations, members of the industry, universities and research institutions. The statistics are also used by planning boards to determine the amount of traffic on certain highways.

Statistical activity

This statistical activity is part of a set of surveys measuring various aspects of activities related to the movement of people and goods. These surveys are grouped as follows:

Transportation by air includes records related to the movement of aircraft, passengers and cargo by air for both Canadian and foreign air carriers operating in Canada as well as the financial and operating characteristics of Canadian air carriers. These data are produced by the Aviation Statistics Centre.

Transportation by rail includes records relating to rail transportation in Canada, and between the United States and Canada.

Transportation by road includes records relating to all road transport in Canada. In addition to surveying carriers and owners of registered motor vehicles, certain programs rely on aggregation of provincial and territorial administrative records.

Subjects

• Transportation
• Transportation by road

Data sources and methodology

Target population

Canada-based for-hire trucking companies with annual operating revenues of one million dollars or more, the major part of which is derived from long-distance deliveries.

Instrument design

This methodology does not apply.

Sampling

This is a sample survey with a cross-sectional design.

The For-hire Trucking Survey is a sample survey and the sample design is based on a two-stage sample of approximately 800,000 shipments made by the inter-city for-hire carriers. Once the population has been stratified according to areas of operation, type of services, commodities carried and revenue class, the first-stage consists of selecting, in each stratum, a number of firms corresponding to the desired number of firms determined at the sample selection stage. The sample of firms is then converted to a sample of Document Storage Location Point (DSLP) by including in the latter sample all DSLP of the selected firms. The second stage of the sample design consists of selecting a systematic sample of shipments from the files of each selected DSLP.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The data collection is carried out by interviewers from Statistics Canada Regional Offices across Canada. The interviewers visit each selected DSLP determine the apparent size of the files (number of shipments) and, using the appropriate sampling interval from a table, transcribe the data from the shipping documents.

The information about shipments is either transcribed from documents, or obtained from computer tapes provided by some respondents. The following information is collected for each sampled shipment: the origin and destination of the shipment, a description of the commodity or commodities carried, the shipment weight and the transportation revenue earned.

View the Questionnaire(s) and reporting guide(s).

Error detection

Once all necessary information for the survey is collected, a series of verifications takes place to ensure that the records are consistent and that collection and capture of the data did not introduce errors. Missing values and data found in error are imputed.

Imputation

Several automatic imputations are done using historical, hot deck and library search methods.

Estimation

Since the sample is selected in two steps, the sampling weight is also calculated in two steps. The first-stage sampling weight is calculated for each carrier in the first-stage sample. Then the second-stage sampling weight is calculated for each shipment selected from the carrier in the first-stage sample. Finally these two weights are multiplied together to obtain the final weight. The weighted values obtained by multiplying the final weights and the collected values are aggregated to produce the estimates.

Quality evaluation

The survey results are analyzed before dissemination. In general this includes a detailed review of the individual responses (especially for the largest enterprises), a review of general economic conditions as well as historic trends and comparisons with industry averages and ratios.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential.

If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data. Data for a specific industry or variable may be suppressed (along with that of a second industry or variable) if the number of enterprises in the population is too low.

Revisions and seasonal adjustment

The survey methodology changed over the time. The methodology described is the current one used for the survey, which was implemented for the reference year 1997.

Time series analysis would require consistent and comparable data over time. The data series of this survey have been subject to break in the series due to methodology changes that occurred.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.

Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.

Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.