Annual Survey on End-Use of Refined Petroleum Products (AEND)

Detailed information for 2017

Status:

Active

Frequency:

Annual

Record number:

2168

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 governmental agencies to fulfill their regulatory responsibilities. The private sector also uses this information in the corporate decision-making process.

Data release - November 9, 2018

Description

This survey collects sales (end-use) information from all refineries in Canada for 11 refined petroleum products (or product groups) including motor gasoline, aviation fuels, light and heavy fuel oils. Sales information is reported for establishments classified to various industries including selected manufacturing and transportation industries, agriculture, residential and others.

Survey data are made available under the authority of the Statistics Act to other federal departments and provincial authorities through data sharing agreements subject to the embodied principles of data confidentiality. Data are intended for use by survey respondents, industry associations, industry analysts, federal and provincial governments, the press and general public to assess trends in demand for products.

Statistical activity

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

Reference period: Calendar year

Subjects

  • Energy
  • Energy consumption and disposition
  • Petroleum products

Data sources and methodology

Target population

The target population is comprised of all establishments in Canada engaged in transforming crude petroleum and coal into intermediate and end-products classified to NAICS 324 Petroleum and Coal Products Manufacturing under the North American Industry Classification System (NAICS 2012). The observed population are those establishments in the target population for which business information is available on Statistics Canada's Business Register.

Instrument design

This survey has been designed to minimize different interpretations. The survey was field tested with respondents to ensure the questions, concepts and terminology were appropriate. Statistics Canada's Questionnaire Design and Resource Centre (QDRC) performed qualitative tests of the questionnaire by conducting cognitive interviews with 4 companies in Alberta and British Columbia.

Sampling

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

Data sources

Data collection for this reference period: 2018-01-18 to 2018-04-18

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Excel Spreadsheet is sent by electronic file transfer to respondents. Import related data from the International Accounts and Trade Division is used to supplement and ensure end-use allocations of full Canadian supply/demand of fuels.

Where applicable, import related data from the International Accounts and Trade Division is integrated into the industry aggregate by commodity.

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 are being surveyed. Estimation of totals is done by simple aggregation of the values of all estimation units 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

In order to ensure the accuracy and consistency of the data such as domestic sales of refined petroleum products the results of this survey are reconciled with other energy programs. These include the Monthly Refined Petroleum Products Survey, Secondary Distributor of Refined Petroleum Products Survey, and the Energy Supply and Demand.

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

This methodology type does not apply to this statistical program.

Data accuracy

The response rate for this survey is 94.7%.

For a 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.

If changes are received from respondents, the data are incorporated and the disseminated data are revised.

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