Canadian Survey on Business Conditions (CSBC)
Detailed information for the fourth quarter of 2022
The purpose of this survey is to collect information on businesses in Canada related to emerging issues. This iteration of the survey focuses on business expectations and business conditions in Canada.
Data release - November 25, 2022
Information collected is intended to help governments, chambers of commerce and business associations across Canada devise strategies and mechanisms to foster the survival and continuity of businesses in Canada.
- Business adaptation and adjustment
- Business performance and ownership
- Revenue and expenditures
- Wages, salaries and other earnings
Data sources and methodology
The target population for this survey is all active establishments on the Business Register (BR), which have an address in Canada, and which have employees. The following subsectors and sectors according to the North American Industry Classification System are excluded from the target population.
- 22: Utilities
- 523990: All other financial investment activities
- 55: Management of companies and enterprises
- 611: Educational services
- 6214: Out-patient care centres
- 6215: Medical and diagnostic laboratories
- 6219: Other ambulatory health care services
- 622: Hospitals
- 814: Private households
- 91: Public administration
Statistics Canada's BR is a central repository of information on businesses operating in Canada. It is used as the principal frame for many Statistics Canada's economic statistical programs. The BR provides consistent and standardized data at different statistical levels throughout the year.
The standardized business classification model developed at Statistics Canada comprises a four-level hierarchy of statistical entities:
- enterprise: the top of the hierarchy, which is associated with a complete (consolidated) set of financial statements;
- company: the level at which operating profit can be measured;
- establishment: the level at which the accounting data required to measure production are available (principal inputs, revenues, wages, etc.);
- location: the bottom of the hierarchy, which requires only the number of employees for delineation.
The collection instrument for this survey is an electronic questionnaire. The questionnaire is the result of collective input from stakeholders both internal and external to Statistics Canada.
This is a sample survey with a cross-sectional design.
The survey uses a stratified random sample of business establishments classified by geography, industry sector and size.
Data collection for this reference period: 2022-10-03 to 2022-11-07
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
The respondents are mailed or e-mailed a secure access code to respond to the electronic questionnaire.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of data processing activities. Prior to imputation, a series of edits are applied to the collected data to identify errors and inconsistencies. Errors and inconsistencies in the data are reviewed and resolved by referring to data for similar units in the survey and information from external sources. If a record cannot be resolved, it is flagged for imputation. Finally, edit rules are incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data are consistent.
After microdata verification, a variable was created for each of the survey variables to identify those that had either failed the verification rules or had missing values. Imputation was performed to reduce the amount of missing, inconsistent or incomplete data. The missing data were imputed using a randomly selected donor inside the imputation class. These imputation classes were formed based on statistical analysis performed with frame information or previous variables on the questionnaire.
A minimum number of units was required within each imputation class. When imputation classes were too small, larger classes were created by combining several classes together.
Imputation of survey variables was performed in an automated way using BANFF, a generalized system designed by Statistics Canada.
Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. For this survey, the sample used for estimation comes from a single-phase sampling process.
An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection. The weight calculated for each sampling unit indicates how many other units it represents.
However, since some of the selected units did not answer the survey, reweighting is performed on the responding units so that their final weights still represent the whole target population. The response mechanism can be considered as a second-phase of the sampling process.
After the reweighting is performed, a calibration process is performed so that the weighted totals per calibration groups equal the population totals.
Estimation of proportions is done using the calibrated weights to calculate the population totals in the domains of interest.
Estimates were reviewed to ensure that the findings are logical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.
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
This methodology type does not apply to this statistical program.
There are two types of errors which can impact the data: sampling errors and non-sampling errors.
Estimates are subject to sampling error. This error can be expressed as a standard error. For example, the proportion of businesses in the target population that would respond YES to a given question is estimated to be 50%, with a standard error of 4%. In repeated sampling, the estimate would be expected to fall between 46% and 54%, nineteen times out of twenty. The following rules based on the standard error (SE) are used to assign a measure of quality to all of the estimates of percentages.
A = Excellent (0.00% to less than 2.50%)
B = Very good (2.50% to less than 5.00%)
C = Good (5.00% to less than 7.50%)
D = Acceptable (7.50% to less than 10.00%)
E = Use with caution (10.00% to less than 15.00%)
F = Too unreliable to be published (Greater than or equal to 15%, data are suppressed)
Non-sampling errors may occur for various reasons during the collection and processing of the data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire and verification of the survey data.
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