Monthly Refined Petroleum Products (MRPP)

Detailed information for September 2024

Status:

Active

Frequency:

Monthly

Record number:

2150

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

Data release - October 23, 2024

Description

This monthly survey collects data on the activities of all Canadian refineries and oil sands processing plants involved in the production of refined petroleum products and of selected major blending terminals of these products. This data is required for integration into the input-output sector of the Canadian System of National Accounts. Data is made available under the authority of the Statistics Act to other federal departments and provincial authorities through data sharing agreements subject to embodied principles of data confidentiality. Data is intended for use by survey respondents, industry associations, industry analysts, the press and the general public to assess trends in the Canadian refining sector.

Reference period: Month

Collection period: 1 to 10 days following the end of the survey reference month.

Subjects

Energy
Energy consumption and disposition
Petroleum products

Data sources and methodology

Target population

The universe is comprised of all refineries, oil sands processing plants and major terminals of refined petroleum products in Canada. The list of survey respondents is updated regularly to ensure a total coverage of refining operations and a sufficiently wide coverage of major terminals of these products. Industries covered include those classified to the North American Industry Classification System (NAICS 2017) sub-sector 324 Petroleum and coal product manufacturing comprised of establishments primarily engaged in transforming crude petroleum and coal into intermediate and end-products. It also includes industries in sub-sector 412 Petroleum and petroleum products merchant wholesalers comprised of establishments primarily engaged in wholesaling crude oil, liquefied petroleum gases heating oil and refined petroleum products.

Additional documentation

Instrument design

A pilot survey was conducted with refineries and terminals across Canada and the survey was field tested with respondents to ensure the questions, concepts and terminology were appropriate.

Sampling

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

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

An Email invitation is sent to respondents to download an excel spreadsheet based questionnaire for completion and to provide access to a secure portal to upload the data to Statistic Canada.

The questionnaire is available in French and English.

Data will be integrated and processed through the Integrated Business Statistics program (IBSP).

Non response follow up will consist of email reminders followed by phone calls from Statistics Canada's regional offices.

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

To be provided when data are released.

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