Sawmills and Planing Mills

Detailed information for October 2005

Status:

Active

Frequency:

Monthly

Record number:

2134

This survey measures, on a monthly basis, the quantities of lumber that are produced and shipped by Canadian manufacturers.

Data release - December 21, 2005

Description

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

Subjects

  • Manufacturing
  • Wood, paper and printing

Data sources and methodology

Instrument design

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.

Sampling

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

The sample includes approximately 275 of the largest sawmills located in all provinces except Newfoundland and Prince Edward Island.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected each month from survey respondents using a mail-out / mail-back process. 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 or fax.

Under normal circumstance, data are collected, captured, edited, tabulated and published within 6-8 weeks after the end of the reference month.

View the Questionnaire(s) and reporting guide(s) .

Error detection

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.

Imputation

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.

Estimation

Final estimates of production, inventories and shipments by province are obtained by applying factors to data collected in the monthly Sawmills survey. This process is called benchmarking. The benchmark factors are ratios of the total quantity of lumber produced by sawmills as measured by the ASML to the total quantity of lumber produced by sawmills in the monthly Sawmills survey. These factors are calculated for each province based on the latest ASML commodity data available.

Quality evaluation

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.

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.

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.

Data accuracy

Since Monthly data for sawmills are benchmarked to the ASML data, and the ASML being a census, the estimates are not subject to sampling errors. However, the results are still subject to the non-sampling errors associated with non-response, inaccurate reporting, and processing. Errors relating to non-response can be measured. All attempts are made to control inaccurate reporting and processing errors.

Non-response error
Some respondents may be unable to provide data for numerous reasons (i.e. fire, theft, strike, economic hardship, etc.), while others may be late in responding. To minimize non-response, delinquent respondents are followed up rigorously by phone or fax. Data for non-responding units are imputed using industry trend and other related information. Data are revised usually once a year, at the same time as the new benchmark factors are produced to account for questionnaires that are received after the end of the monthly collection cycles since the previous revision.

Non-response error is calculated using the number of non-responses in the year divided by the number of total expected responses in the year for the units in the sample.

Non-response error in the final estimates will be somewhat higher given that the data that go into the calculation of the benchmark factor is also imputed.

Inaccurate response
Inaccuracy may result from poor questionnaire design or an inability on the part of respondents to provide the requested information or from misinterpretation of the survey questions. To reduce such errors the format and wording in the questionnaire are reviewed from time to time and modified based on feedback from survey respondents and data users. Respondents are also reminded of the importance of their contribution and of the accuracy of reported information.

Processing errors
These errors may occur at various stages in the processing of survey data such as data entry, verification, editing and tabulation. Data are examined for such errors using automated edits along with an analytical review by subject matter experts. Several checks are performed on the collected data to verify internal consistency and comparability over time.

Date modified: