Monthly Electricity Supply and Disposition Survey (MELE)

Detailed information for July 2017

Status:

Active

Frequency:

Monthly

Record number:

2151

The purpose of this survey is to obtain information on the supply of, and/or demand for, energy in Canada.

Data release - September 26, 2017

Description

The purpose of this survey is to obtain information on the supply of, and/or demand for, energy in Canada. This information serves as an important indicator of Canadian economic performance, and is used by all levels of government in establishing informed policies in the energy area. The private sector also uses this information in the corporate decision-making process.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP program 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.

Collection period: During the month following the reference month.

Subjects

  • Energy
  • Energy consumption and disposition
  • Nuclear and electric power

Data sources and methodology

Target population

The universe consists of electric utilities and industrial establishments that generate and/or distribute electricity. Utilities and companies which have at least one plant with a total generating capacity of 500 KW, or one solar plant with a total generating capacity of over 100 KW are included. No threshold applies to distributors of electricity.

The frame is derived from the Electric Power Generating Stations Annual Survey (record number 2193) and the Electricity Supply and Disposition Annual Survey (record number 2194).

Instrument design

The survey questionnaire comprises volumes of electricity generated by utilities and industry, and the volume and value of receipts and deliveries by province, country and ultimate consumer.

The questionnaire is respondent completed.

Sampling

This survey is a 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 January 2017 is 1200 establishments.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The data are collected through an electronic questionnaire. 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 electrical generating capacity is above the minimum value (or "cut-off") for a particular energy source 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, 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.

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.

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.

Non-sampling error
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.

Coverage error
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame.

The Business Register (BR) is the common frame for all surveys using the IBSP model. The BR is a data service centre updated through a number of sources including administrative data files, feedback received from conducting Statistics Canada business surveys, and profiling activities including direct contact with companies to obtain information about their operations and Internet research findings. Using the BR ensures quality, while avoiding overlap between surveys and minimizing response burden to the greatest extent possible.

Documentation

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