Annual Survey of Secondary Distributors of Refined Petroleum Products (SRPP)
Detailed information for 2018
The purpose of this survey is to obtain information on the volume of refined petroleum products distributed by secondary distributors in Canada. It supplements energy consumption data collected from the refineries in the Annual Survey on End-Use of Refined Petroleum Product (AEND, record # 2168).
Data release - November 7, 2019
The Annual Survey of Secondary Distributors of Refined Petroleum Products (SRPP) is a cost recovery survey sponsored by Natural Resources Canada (NRCan) and Environment and Climate Change Canada (ECCC).
By definition, secondary distributors of refined petroleum products (RPPs) are commercial entities that act as buyers and resellers between the supplier and end user for RPPs.
Secondary distributors have emerged in the marketplace in the last few years as refineries have accelerated the divestment of the distribution of their products by relying on fuel resellers/secondary distributors.
Prior to the introduction of the SRPP in 2009, the Annual Survey on End-Use of Refined Petroleum Product (AEND, record # 2168) questionnaire alone was unable to clearly allocate the energy demand by end-user as represented by secondary distributors. Refineries would allocate sales made to secondary distributors to the commercial and institutional sector, a heterogeneous grouping of approximately 20 industries in the service sector, resulting in inflated energy consumption statistics for this particular sector. The need for a supplemental survey was necessary in order to more accurately capture this consumption data in the Canadian economy.
Together with AEND, a refinery census survey, the SRPP survey collects energy consumption data which is then integrated into Statistics Canada's annual Report on Energy Supply and Demand (RESD). The RESD data are used extensively by ECCC to derive greenhouse gas inventory calculations and by NRCan's Office of Energy Efficiency to produce the Energy Efficiency Handbook. The RESD is the primary source of information provided to the International Energy Agency (IEA) in order to meet Canada's international reporting requirements.
The SRPP survey is a pioneering survey in that it captures biofuels data that has been blended with the RPPs being sold as well as the source of the biofuel used. These data, alongside the RPP data sold by secondary distributors represent an important segment in the Canadian economy.
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 (January 1st to December 31st)
- Petroleum products
Data sources and methodology
The target population is comprised of all establishments in Canada engaged in all motor gasoline, diesel fuel, heating fuel and heavy fuel reselling (either retail or wholesale) establishments operating in Canada for at least one day between January and December of a calendar year classified to NAICS 454311, 454319 (excluding firewood dealers) and a portion of establishments coded to NAICS 412110 under the North American Industry Classification System. The observed population is comprised of those establishments in the target population for which business information is available on Statistics Canada's Business Register and whose revenue exceeds a minimum threshold or cut-off. The cut-off excludes from the population all establishments that comprise the bottom 10% of an industry and/or province grouping and is implemented to reduce response burden for small establishments.
Establishments mainly engaged in the retail or wholesale distribution and sales of crude oil, liquefied petroleum gases (LPG), aviation fuel, asphalt, and lubricating oils and greases are excluded from the target population of this survey. Also excluded from this survey are establishments covered by the AEND survey.
The electronic questionnaire used for 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 12 small, medium and large size companies in Alberta, Ontario and Quebec.
This survey is a census with a cross-sectional design.
The sampling unit for this cut-off census is the enterprise as defined on the Statistics Canada Business Register. The sample size for reference year 2018 is 500 establishments.
Data collection for this reference period: 2018-02-14 to 2018-03-31
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data are collected via electronic questionnaires. Survey reminders as well as non-response follow up are conducted via email, fax, and telephone. Telephone interviews to help complete the survey are offered and conducted by the regional office in either English or French.
View the Questionnaire(s) and reporting guide(s) .
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.
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.
All units in the observed population whose revenue is above the minimum value (or "cutoff") for a particular industry and/or geographic grouping 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 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.
Survey data are routinely reconciled with other energy surveys such as the Monthly Refined Petroleum Products Survey (MRPP, record # 2150) and the Annual Survey on End-Use of Refined Petroleum Products (AEND, record #2168). Other sources for data confrontation are import statistics from the International Accounts and Trade Division (IATD) at Statistics Canada.
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.
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.
If changes are received from respondents, the data are incorporated and the disseminated data are revised.
- Date modified: