Annual Coal Mines Survey (ACMS)
Detailed information for 2017
The survey collects financial data from coal mine operations in Canada.
Data release - July 11, 2019
The survey collects annual data from all coal mines operating in Canada with sales in excess of one million dollars.
The information is used as input to the System of National Accounts. Provincial agencies are also provided with data on a regular basis.
Reference period: Calendar year
Collection period: Data are collected within 5 months after the reference year.
- Business performance and ownership
- Financial statements and performance
Data sources and methodology
The universe consists of all coal mines operating in Canada with sales of $1,000,000 or over during the year.
The questionnaire comprises operating revenues, number of employees, salaries and wages, cost of fuel and electricity, process supplies and other statistics of a general nature.
The questionnaire is respondent completed.
This survey is a census with a cross-sectional design.
Data are collected for all units of the target population, therefore no sampling is done.
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
The 'preliminary' annual estimates of coal production (volume and value) are based on monthly data collected by the Coal Monthly survey (record no. 2147); they are produced to satisfy a requirement of Natural Resources Canada for early estimates.
The data are collected by Head Office through a mail-out/mail-back process. Follow ups are conducted by phone or fax as needed to reach the target response rate.
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 revenues from the sale of coal is $1 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.
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).
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.
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.