Survey on Global Supply, Production and Distribution Chain Activities

Detailed information for 2021





Record number:


The purpose of this survey is to measure the extent of Canadian businesses' activities related to global supply, production and distribution chain. This information is used in the preparation of Canada's Balance of International Payments statistics, which is a major input in the compilation of the Canadian Gross Domestic Product (GDP). The information may also be used by Statistics Canada for other statistical and research purposes.

Data release - Scheduled for mid-2023


The survey collects information related to the purchase, production, and sale of goods abroad by Canadian businesses. It also gathers information on whether Canadian businesses perform manufacturing or processing work for other Canadian or foreign clients, and whether Canadian businesses hire other Canadian or foreign firms to perform the same type of work.

Reference period: Annual


  • Economic accounts
  • International trade
  • Manufacturing

Data sources and methodology

Target population

All Canadian businesses who are engaged in global production, supply and distribution activities.

Instrument design

From 2014 to 2016, questionnaire design specialists tested various versions of the questionnaire with a variety of respondents.


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

This is a sample survey of business establishments classified both geographically and to the North American Industry Classification System (NAICS) Canada 2017, with a cross-sectional design.

Sample size: 300

Sampling and sub-sampling: Many data sources were used in creating the sample. These sources provided signals on the nature of global production activities in which businesses are involved. Respondents were then chosen based on the type of global production activities they engaged in from their industries.

Data sources

Data collection for this reference period: 2022-04-01 to 2022-07-13

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected primarily through electronic questionnaire. Follow-up for non-response and for data validation is conducted by email, telephone or fax.

A strategy to replace survey data with administrative data has been introduced to reduce the respondent burden and survey costs.

The strategy involves using the first round sample data to identify the goods crossing the border without ownership change. Once the survey results become available, data replacement may be used to produce estimates on such goods at the national level for the compilation of the Canadian Gross Domestic Product (GDP).

Data integration combines data from multiple data sources including survey data collected from respondents, administrative data from the Canada Border Services Agency or other forms of auxiliary data when applicable. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data source providers into the formats, structures and levels required for processing. Administrative data are also used as an auxiliary source of data for editing and imputation when respondent data is not available.

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

Error detection

There are edits built into the data capture application which compare the captured data against unusual values, and check for logical inconsistencies. When an edit fails, the interviewer is prompted to correct the information (with the assistance of the respondent when necessary). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy.


When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include: deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables.

Imputation generates a complete and coherent micro data file that covers all survey variables.


The metadata will be provided upon release.

Quality evaluation

Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of: individual responses (especially for the largest companies), general economic conditions, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications, newspaper articles).

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.

In order to prevent any data disclosure, confidentiality analysis for financial and commodity variables is done using the G-CONFID system. G-CONFID is used for primary confidentiality as well as for the secondary suppression (residual disclosure). Direct disclosure or primary confidentiality occurs when the value in a tabulation cell is composed or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of 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, coding 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, verification of the survey data, and follow-up with respondents when needed to maximize response rates.

Measures of sampling error are calculated for each estimate. Also, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.

Both the sampling error and the non-response rate are combined into one quality rating code. This code uses letters that range from A to F where A means the data is of excellent quality and F means it is unreliable. Estimates with a quality of F are not published. These quality rating codes can be requested and should always be taken into consideration.

Quality indicator descriptions are: A - Excellent; B - Very good; C - Good; D - Acceptable; E - Use with caution; F - Too unreliable to publish.

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