Annual Passenger Bus and Urban Transit Survey (PBUT)
Detailed information for 2019
The purpose of the Annual Passenger Bus and Urban Transit Survey is to collect annual financial, operating and employment data on bus companies operating in Canada. It also includes municipalities and government agencies that operate urban transit and commuter services.
Data release - TBD
The objective of the survey is to produce financial, operating and characteristic estimates related to passenger bus and urban transit companies operating in Canada. The resulting estimates are used as input to the Canadian System of National Accounts, by Transport Canada, other federal and provincial departments, and by transportation companies, consulting firms, universities and foreign governments. The information is used for the analysis of transportation activity, for marketing and economic studies, as well as industry performance measures.
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: Calendar year
Collection period: April through September of the year after the reference period
- Transportation by road
Data sources and methodology
The target population consists of all establishments with operating activities in the bus transportation industry during the reference year, to following codes of the North American Industry Classification System Canada 2012: 485110 (Urban transit systems), 485210 (Interurban and rural bus transportation), 485410 (School and employee bus transportation), 485510 (Charter bus industry), 485990 (Other transit and ground passenger transportation) and 487110 (Scenic and sightseeing transportation, land).
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. This information has also been updated and expanded through survey specific information maintained by survey analysts.
The questionnaire was originally designed in consultation with survey design specialists and industry focus groups. The questionnaire was redesigned in 2015 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 2012.
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 800 enterprises.
Data collection for this reference period: 2020-06-15 to 2020-10-14
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Electronic questionnaires, mailout of paper questionnaires, telephone interviews and data extracted from administrative files.
Data are collected directly from respondents by means of an electronic and/or paper questionnaire.
Collection focal points within Statistics Canada oversee the completion of the questionnaire by respondents. This involves mailing out survey questionnaires and 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.
The survey collected data and the administrative data from the Canadian Urban Transit Association are first combined depending on the availability, and the Tax data are then used in imputation for compensate non-responses. All the matchings between the different types of data are direct.
View the Questionnaire(s) and reporting guide(s) .
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. Material errors are thereby identified and corrected.
Various manual methods for imputation, such as donor imputation, ratio analysis and trend analysis are utilized. Tax data are also used when applicable.
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 and province, based on the most recent classification information for the statistical entity and the survey reference period.
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.
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 survey, seasonal adjustments are not applicable.
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.