Canadian Survey on Interprovincial Trade (CSIT)

Detailed information for 2023

The objective of the survey is to collect data on the trade of goods and services across provincial and territorial borders, as well as labour mobility in Canada. It is important to have data on the state of interprovincial activities to better understand the challenges for businesses in Canada and recommend policy changes.

Data release - February 14, 2025 (Partial data on the trade of goods and services across provincial and territorial borders); March 3, 2025 (Complete data)

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

Description

The objective of the survey is to collect data on the trade of goods and services across provincial and territorial borders, as well as labour mobility in Canada. It collects information on the obstacles faced by businesses engaged in interprovincial trade activities, the reasons why other businesses choose not to trade goods or services across provincial or territorial borders, and the challenges associated with hiring individuals with certifications or licences granted by another province or territory.

Statistics Canada is conducting this survey in collaboration with the Privy Council Office to better understand the difficulties faced by businesses conducting interprovincial trade, as well as the reasons why other businesses do not conduct interprovincial trade. It is important to have real data on the state of interprovincial activities to better understand the trends and prospects for a range of businesses in Canada. Various government departments will use this information to recommend policy changes to facilitate interprovincial trade in Canada.

Subjects

• Business performance and ownership
• Current conditions

Data sources and methodology

Target population

The target population is derived from Statistics Canada's Business Register (BR). The BR is an information database on the Canadian business population and serves as a frame for all Statistics Canada business surveys. It is a structured list of businesses engaged in the production of goods and services in Canada.

The target population for the Canadian Survey on Interprovincial Trade (CSIT) is all alive establishments on the BR which have five or more employees, have reported $50,000 or more in revenue, and belong to one of the following industries:
- 11 Agriculture, forestry, fishing and hunting;
- 21 Mining, quarrying, and oil and gas extraction;
- 23 Construction;
- 31-33 Manufacturing;
- 41 Wholesale Trade;
- 44-45 Retail Trade;
- 48-49 Transportation and Warehousing;
- 51 Information and Cultural Industries;
- 52 Finance and Insurance;
- 54 Professional, scientific, and technical services;
- 62 Health care and social assistance;
- 811 Repair and maintenance;
- 812 Personal and laundry services.

Instrument design

The collection instrument for this survey is an electronic questionnaire and Computer-Assisted Telephone Interviewing (CATI). The survey questionnaire was designed by Statistics Canada in collaboration with the Privy Council Office and other external stakeholders. The Questionnaire Design Resource Centre of Statistics Canada made suggestions to improve the questionnaire and then field-tested the questionnaire with fourteen different businesses. The results of those tests were used to further improve the questionnaire.

Sampling

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

This is a stratified random sample of alive establishments on the BR that have five or more employees, have reported $50,000 or more in revenue and belong in the following industries under the North American Industrial Classification System (NAICS) 2022:
- 11 Agriculture, forestry, fishing and hunting;
- 21 Mining, quarrying, and oil and gas extraction;
- 23 Construction;
- 31-33 Manufacturing;
- 41 Wholesale Trade;
- 44-45 Retail Trade;
- 48-49 Transportation and Warehousing;
- 51 Information and Cultural Industries;
- 52 Finance and Insurance;
- 54 Professional, scientific, and technical services;
- 62 Health care and social assistance;
- 811 Repair and maintenance;
- 812 Personal and laundry services.

The sampling unit for this survey is individual businesses in Canada.

The target population was stratified by geography and industry sector.

The survey frame was built using the May 2024 snapshot of Statistics Canada's Business Register.

The total sample size is 29,989 units.

Data sources

Data collection for this reference period: 2024-06-18 to 2024-10-15

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Data collection is conducted by electronic questionnaire (EQ) and Computer-Assisted Telephone Interviewing (CATI).

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

Error detection

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.

Imputation

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 donor inside the imputation class with similar characteristics. These imputation classes were formed based on statistical analysis using 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

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 target population. The response mechanism can be considered as a second-phase of sampling.
After the reweighting is performed, a calibration process is performed so that the weighted totals in each calibration group equal the population total within that group.

Estimation of proportions is done using the calibrated weights to calculate the population totals in the domains of interest.

Quality evaluation

Estimates were reviewed to ensure that the findings are sensical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.

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

This methodology type does not apply to this statistical program.

Data accuracy

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 42.2% and 57.8%, 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. Other examples include under or over-coverage of the population, differences in the interpretations of questions, and mistakes in recording and processing data. These errors are minimized through careful design of the survey questionnaire and verification of the survey data.