Quarterly Motor Carriers of Freight Survey

Detailed information for second quarter 2007





Record number:


The principal objective of this survey is to provide information about the size, structure and economic performance of Canada's for-hire trucking industry.

Data release - September 7, 2007 ('top carriers'; data for 'all carriers' are available a few months later.)


The principal objective of this survey is to provide information about the size, structure and economic performance of Canada's for-hire trucking industry. Financial data are used as inputs to the Canadian System of National Accounts. Federal and provincial governments use the data to formulate policies and to monitor the trucking industry in Canada. Trucking companies and associations use the published statistics for benchmarking purposes.

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.

Reference period: January to March, April to June, July to September, October to December

Collection period: 2 weeks following the end of the quarter, for a period of 5 weeks.


  • Transportation
  • Transportation by road

Data sources and methodology

Target population

The target population includes all Canadian domiciled for-hire motor carriers (companies) of freight with annual operating revenues of $1 million or more.

Instrument design

The questionnaire for this survey has remained stable over the years, although the format and wording has been modified to maintain its relevance based on feedback from survey respondents and data users.


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

The survey population consists of all companies on Statistics Canada's business register, the Central Frame Data Base (CFDB), classified as for-hire trucking with an annual gross business income value of $1 million or more.

A sample of carriers is selected from the survey population for the first quarter of the reference year. Sample rotation, except among carriers that have a substantial impact on the survey estimates, was implemented for the 2000 reference year in order to reduce response burden. Rather than selecting an independent sample in the first quarter of every reference year, the previous year's fourth quarter sample is rotated to minimize the sample overlap from one year to the next. This sample is then updated each quarter so that it remains representative of the survey population. Each segment of the industry and each province and territory of Canada is represented in the sample.

The carriers on the first quarter survey frame are first grouped (stratified) according to their province of domicile and type of activity. Since 1997, the type of activity is defined according to the North American Industrial Classification System (NAICS). Then, within each province/type of activity combination, the carriers are divided into three size groups (strata). The size is measured by annual gross business income. For reasons of efficiency, carriers in the largest size stratum within each province/type of activity combination are included in the sample with certainty. Carriers in the remaining size strata are sampled according to a probability mechanism called simple random sampling, which gives every carrier within the same stratum an equal chance of selection. The quarterly sample size is about 800 units. For each of the second, third and fourth quarters of the reference year, the previous quarter sample is updated so that it remains representative of the updated survey population for that quarter. Units in the previous quarter sample no longer in the survey population are removed, and a sample of the units new to the survey population for that quarter (births) is added.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

At the end of each quarter, a questionnaire is sent to each sampled carrier. The data are collected by mail-back, facsimile or through computer assisted telephone interviews. The survey data are captured and checked for errors and inconsistencies. If required, some inconsistent, questionable or missing data are referred back to the carrier for clarification or revision.

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

Error detection

The survey data are captured and checked for errors and inconsistencies. If required, some inconsistent, questionable or missing data are referred back to the carrier for clarification or revision. Problems or missing data that cannot be resolved with the carrier are then replaced with consistent values (are imputed).


Problems or missing data, which cannot be resolved with the carrier, are replaced with consistent values (are imputed) using Statistics Canada's Generalized Edit and Imputation System. The system imputes the data using different imputation rules depending on the type of carrier and the type of data to be imputed.


Since only a sample of carriers is contacted, the individual values are weighted to represent the whole industry within the scope of the survey. The sampling weights of the respondents are adjusted to compensate for non response. The value for each carrier in the sample is multiplied by the sampling weight for that carrier, and then the weighted data from all sampled carriers belonging to a given estimation domain (e.g. general freight in Ontario) are summed to obtain the estimate. Variance estimates are obtained using the Taylor linearization method.

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

Quarterly estimates are provided for the most current quarter available. The data for the previous quarter are revised if necessary. Seasonal adjustments are not made to the data.

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.

Non-sampling errors are controlled through a careful design of the questionnaire, the use of a minimal number of simple concepts and consistency checks. Coverage error was minimized by using multiple sources to update the frame. Measures such as response rates are used as indicators of the possible extent of non-sampling errors.

The weighted response rate represents the proportion of the total revenue accounted for by units that responded to the survey. Of the sampled units contributing to the estimate, the weighted response rate was 69.3%, after accounting for firms that have gone out of business, have been reclassified to a different industry, are inactive, or are duplicates on the frame.

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.

CVs were calculated for each estimate. Generally, the more commonly reported variables obtained very good CVs (10% or less), while the less commonly reported variables were associated with higher but still acceptable CVs (under 25%). Some data might not be released because of poor data quality. The CVs are available upon request.

The qualities of CVs are rated as follows:

. Excellent 0.01% to 4.99%
. Very good 5.00% to 9.99%
. Good 10.00% to 14.99%
. Acceptable 15.00% to 24.99%
. Use with caution 25.00% to 34.99%
. Unreliable 35.00% or higher

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