Electric Utility Financial Report Annual (AEUF)

Detailed information for 2022

The survey collects financial data from electric utilities in Canada.

The information is used as input to the Canadian System of National Accounts. Federal (Canada Energy Regulator) and provincial agencies are also provided with data on a regular basis.

Data release - May 17, 2024

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

Description

The survey collects financial data from electric utilities in Canada.

The information is used as input to the Canadian System of National Accounts. Federal (Canada Energy Regulator) and provincial agencies are also provided with data on a regular basis.

Reference period: Calendar year

Subjects

• Business performance and ownership
• Energy
• Financial statements and performance
• Nuclear and electric power

Data sources and methodology

Target population

The universe consists of all electric utilities in Canada with revenues from the sale of electricity of $5 million or more, or with revenues from interprovincial trade in electricity. The frame is derived from revenue data reported in the Electricity Supply and Disposition Annual Survey (survey number 2194).

Instrument design

The survey questionnaire comprises a balance sheet of assets with liabilities and equity. Also reported are direct taxes, employees, wages and salaries, and an account of operating revenues and expenses.

The questionnaire is respondent completed.

Sampling

This survey is a census.

This survey is a cut-off census with a cross-sectional design.

The sampling unit is the enterprise as defined on the Statistics Canada Business Register.

The sample size for reference period 2020 is 208 units.

Data sources

Data collection for this reference period: 2023-05-08 to 2023-09-29

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The data is collected through a mail-out /mail-back process. Follow-ups are conducted by phone, fax or e-mail as needed.

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

Error detection

Error detection is an integral part of both collection and data processing activities. Edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g. Value > 0, Value > -500, Value = 0), linear equality edits (e.g. Value1 + Value2 = Total Value), linear inequality edits (e.g. Value1 >= Value2), and equivalency edits (e.g. Value1 = Value2). When errors are found, they can be corrected using the failed edit follow up process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.

Imputation

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.

Estimation

All units in the observed population whose revenues from the sale of electricity is $5 million or more, or with revenues from interprovincial trade in electricity are surveyed. The cut-off or threshold for inclusion is selected to reduce response burden on those units in the population whose contribution to domain totals is deemed too small to be significant. Estimation of totals is done by simple aggregation of the values of all estimation units above the cut-off that are found in the domain of estimation. Estimates are computed for several domains of interest such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. It should be noted that this classification information may differ from the original sampling classification since records may have changed in size, industry or location. Changes in classification are reflected immediately in the estimates.

Quality evaluation

Prior to the data release, 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.

Micro data is only shared or disclosed to organizations with whom Statistics Canada has an official data sharing agreement in place. All company records are removed for any respondent who has written the Chief Statistician to object to the sharing of their data.

A generalized system (G-Confid) is used to assess and apply disclosure control to the tabular estimates. Sensitivity rules are applied to the data and a suppression pattern is created which indicates which cells may be published and which cannot be disclosed for reasons of confidentiality. The system is able to automatically assess tabular relationships and map cells to various sources, as well as consider previous periods' patterns and revisions to ensure completeness and consistency of confidentiality.

Revisions and seasonal adjustment

There is no seasonal adjustment. Data from previous years may be revised based on updated information.

Data accuracy

For a cut-off census, the main source of error in statistical estimates is due to non-response. Non-response bias is minimized by making special effort during data collection to encourage non-respondents to reply to the questionnaire. In cases where imputation is required, imputed data is carefully reviewed to ensure validity and consistency with current and any previously reported data that is available.