Annual For-Hire Trucking Survey (AFHTS)

Detailed information for 2019





Record number:


The purpose of this survey is to measure the size, structure and economic performance of the trucking industry and to analyze its impact on the Canadian economy.

Data release - April 14, 2021


The purpose of this survey is to measure the size, structure and economic performance of the trucking industry and to analyze its impact on the Canadian economy.

This is a new survey. While its content is as brief as possible, it includes detailed information on business types, operating statistics (tonnage, distance travelled, number of shipments, etc.), financial statistics, operating breakdowns (e.g. distribution of trucking revenue by type of trucking activity), fleet and employments.

It will be the most complete source of information on the financial performance and characteristics of the trucking industry in Canada. The results will be used as inputs to the Canadian System of National Accounts. Federal and provincial governments will use the data to formulate policies and to monitor the trucking industry in Canada. Trucking companies and associations will use the published statistics for benchmarking purposes.

The survey covers all businesses located in Canada classified to "Truck Transportation" according to the North American Industrial Classification System (NAICS 484). It excludes foreign-based trucking establishments operating in Canada and non-trucking establishments with their own fleets (private trucking).

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content.

The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.

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: The 12-month fiscal period for which the final day occurs on or between April 1st of the reference year and March 31st of the following year.

Collection period: March through July


  • Transportation
  • Transportation by road

Data sources and methodology

Target population

The target population consists of all establishments with operating activities in the trucking industry during the reference year, classified to following codes of the North American Industry Classification System Canada 2017: 4841 (General freight trucking) and 4842 (Specialized freight trucking).

The observed population is the list of active enterprises and establishments that were selected from Statistics Canada's Business Register. This database provides basic information about each firm, including address, industry classification, and information from administrative data sources.

Instrument design

The questionnaire was originally designed in consultation with survey design specialists and industry focus groups. The questionnaire was designed in 2020 to reflect the growing needs of stakeholders. Survey changes were carried out in consultation with survey design specialists and were reviewed by internal committees, as well as by several industry respondents.


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

A stratified random sample of enterprises grouped by province and classified to the North American Industry Classification System (NAICS) Canada 2017.

Prior to the selection of a random sample, enterprises are classified into homogeneous groups (i.e., groups with the same NAICS codes and same geography [province/territory]) based on the characteristics of their establishments. Then, each group is divided into sub-groups (i.e. small, medium, large) called strata based on the annual revenue of the enterprise.

Following the stratification, a sample, of a predetermined size, is allocated into each stratum, with the objective of optimizing the overall quality of the survey while respecting the available resources. The sample allocation can result in two kinds of strata: take-all strata where all units are sampled with certainty, and take-some strata where a sample of units are randomly selected.

The total sample size for this survey is approximately 2,500 enterprises.

Data sources

Data collection for this reference period: 2020-08-31 to 2020-12-10

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Collection focal points within Statistics Canada oversee the completion of the questionnaire by respondents. This involves providing assistance in their completion when requested, and following up via telephone, e-mail and fax in order to resolve edit problems associated with returned questionnaires or to collect data from respondents who have not returned the questionnaire.

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

Error detection

A series of edits are used during the data collection phase of the survey as well as during the analysis of data. Ratio analysis, comparative analysis, outlier detection are utilized.

At the micro level, several checks are performed on the data to verify internal consistency and identify extreme values. At the macro level, the data are subjected to a detailed quality review process, including a comparative analysis to prior year when applicable. Material errors are thereby identified and corrected.


Various manual methods for imputation, such as donor imputation, ratio analysis and trend analysis are utilized.


The Generalized Estimation System developed at Statistics Canada is used to produce the domain estimates and quality indicators. It is a SAS based application for producing estimates for domains of a population based on a sample and auxiliary information. Estimates are computed at several levels of interest, such as North American Industry Classification System, province, and size, based on the most recent classification information for the statistical entity and the survey reference period.

Quality evaluation

Survey results are analyzed at both the micro and macro level. At the micro level, checks are performed on the data to verify internal consistency and identify extreme values. At the macro level, the data are subjected to a detailed quality review process, including a comparative analysis to prior year. Material errors are thereby identified and corrected.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that 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.

Revisions and seasonal adjustment

Annual estimates are provided for the reference year. The data for the previous reference year are revised if necessary. As this is an annual program, seasonal adjustments are not applicable.

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 is calculated, 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. 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. The total revenue estimates for the whole industry were judged to be excellent at the national level (under 5%) and good to excellent at the provincial/territorial level (under 15%). 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%
Too unreliable to be published: 35.00% or higher

In addition to increase variance, non-response can result in biaised estimates if non-respondents have different characteristics from respondents. Non-response is addressed through a follow-up with respondent, imputation and validation of microdata.

Coverage error was minimized by keeping the frame up to date using survey and administrative sources. Coverage rates are monitored during sampling process.


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