Detailed information for October 2021
The monthly survey, Sawmills, measures quantities of lumber produced and shipped by Canadian manufacturers.
Data release - January 7, 2022
This survey measures, on a monthly basis, the quantities of lumber that are produced and shipped by Canadian manufacturers.
The quantities of lumber produced and shipped are used as an indicator of the economic condition of the Wood industry and trends in the construction market, as an input to Canada's Gross Domestic Product and as an input into macro- and micro-economic studies to determine market shares and industry trends. The data are also used by the business community, trade associations (including the Council of Forest Industries and l'Association des manufacturiers de bois de sciage du Québec), federal and provincial departments and international organizations.
Reference period: Month
Collection period: During the month following the reference month.
- Wood, paper and printing
Data sources and methodology
The target population for this survey includes all sawmills in Canada classified to the North American Industry Classification System code 321111, that produce lumber and/or wood-chips as a product or by-product.
The questionnaire for this survey has remained stable over the years, although the format and wording has been modified to maintain its relevance based on feedback from survey respondents and data users.
This is a sample survey with a cross-sectional design.
A sample of establishments is selected from among units in the survey population based on a cut off sampling plan.
All establishments above the thresholds are now selected in the sample as a sample selection of establishments above the thresholds was previously done.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Data are collected each month from survey respondents using an electronic questionnaire process as well as a mail-out / mail-back process upon request. Data capture and preliminary editing are performed simultaneously to ensure validity of the data. Businesses from whom no response has been received or whose data may contain errors are followed-up by telephone, email or fax.
Under normal circumstances, data are collected, captured, edited, tabulated and published within 6-8 weeks after the end of the reference month.
To estimate the contribution of units below sampling thresholds, the system derives ratios from Goods and Services Tax (GST) files using a statistical model. The model accounts for the difference between units above the threshold and those below the threshold as well as the time lag between the reference period of the survey and the reference period of the GST file.
View the Questionnaire(s) and reporting guide(s) .
In order to detect errors and internal inconsistencies, automated edits are applied to captured data to verify that totals equal the sum of components and that the data are consistent with the previous month's data. Data that fail the edits are subject to manual inspection and possible corrective action.
In addition, subject matter experts analyze the data at a more aggregate level to detect and verify any large month-to-month or year-over-year changes for the industry.
Missing data for the current month are imputed automatically using a number of statistical techniques that use survey data collected during the current cycle as well as auxiliary information sources. These auxiliary sources include survey data from a previous cycle (historical), donor questionnaires and administrative data. Opening stocks are set equal to the value of the closing stocks from the previous month. Closing stocks are calculated by adding production to opening stocks and then subtracting shipments and waste values. The option exists for the subject matter analyst to manually override these imputations with better estimates based on pertinent knowledge about the industry or the business.
As part of the estimation process, survey data are aggregated and combined with administrative data to produce final industry estimates.
Survey results are analyzed to ensure comparability with patterns observed in the historical data series and the economic condition of the industry. Information available from other sources, such as the Monthly Survey of Manufacturing (record number 2101), the Building Permits survey (record number 2802), the media, other government organizations and industry associations, are also used in the validation process.
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.
Direct disclosure may occur when the value in a tabulation cell is composed of a few responses or when the cell is dominated by a few companies. Residual disclosure may occur when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
Monthly, preliminary estimates are provided for the reference month and revised estimates, based on late responses, are provided for the previous month.
Once every year (normally in July), the monthly Sawmills series are revised. These revisions incorporate any data that may have been received after the close of the collection cycle during the previous reference year.
The revised estimates are published in the CANSIM Directory.
The methodology of the monthly survey Sawmills is designed to control errors and to reduce their potential effects on estimates. The total error is composed of sampling error and non-sampling error. Sampling error results when observations are made only on a sample and not on the entire population. Non-sampling error results from various other causes. Examples of these causes are when a sawmill provides incorrect information or does not answer certain questions, when a sawmill is omitted or counted more than once, and when coding or capture errors occur in data processing.
Prior to publication, survey estimates are compared for consistency. There is also a detailed review of individual responses (especially for large sawmills), general economic conditions and historical trends.
A common measure of sampling error in a survey estimate is the coefficient of variation (CV). The CV of an estimate is defined as its standard error divided by the estimate itself, and expressed as a percentage. Since the CV is calculated from survey data, the CV is itself only an estimate, subject to sampling error and non-sampling error.
The CV is calculated as:
CV (X) = S(X) * 100% / X
where X is the estimate and S(X) is the standard error of X.
The CV can be used to calculate a range within which the quantity being estimated is expected to lie. This range is called a confidence interval. If non-sampling error is negligible, there is a 68% chance that the true quantity lies within 1 CV (as a percentage) of the estimate, and a 95% chance that the true quantity lies within 2 CV (as a percentage) of the estimate.
For example, if an estimate of 12,000,000 has a CV of 2%, the standard error is 240,000 (because this is the estimate multiplied by the CV). There is a 68% chance that the true quantity lies between (12,000,000 - 240,000) = 11,760,000 and (12,000,000 + 240,000) = 12,240,000. Also, there is a 95% chance that the true quantity lies between (12,000,000 - 480,000) = 11,520,000 and (12,000,000 + 480,000) = 12,480,000.
The industry includes smaller sawmills which are not included in the survey. An estimate of the production of these small sawmills is calculated using their GST tax data, and this estimate is included in the overall estimates for the industry. The contribution of these small sawmills is relatively very small, and is assumed to have a negligible impact on the CV of the overall survey estimate for each quantity.