Annual Survey of Forestry

Detailed information for 2003





Record number:


This survey collects the financial and commodity information used to compile statistics on Canada's logging industries.

Data release - December 22, 2005


As of 2004, this survey has been amalgamated with the Annual Survey of Manufactures to form the Annual Survey of Manufactures and Logging (record number 2103).

The Annual Survey of Logging and Forestry Support is a survey of the logging industry of Canada conducted annually since 1917. It is intended to cover all establishments engaged in logging operations, as well as associated activities that support logging operations (e.g. sales offices, warehouses).

Details collected include principal industrial statistics (such as shipments, employment, salaries and wages, cost of materials and supplies used, cost of purchased fuel and electricity used, inventories, goods purchased for resale, etc.) and commodity data.

Data collected from the Annual Survey of Logging and Forestry Support are important because they measure production in Canada's logging industries, as well as provide an indication of the well-being of each industry and its contribution to the Canadian economy. The data are used within Statistics Canada by the Canadian System of National Accounts, as well as by the business community, trade associations, federal and provincial departments and international organizations and associations. The data are used to profile logging industries, to undertake market studies, to forecast demand and to develop trade and tariff policies.

Statistical activity

The survey is administered as part of the Unified Enterprise Survey program (UES). The UES program has been designed to integrate, gradually over time, the approximately 200 separate business surveys into a single master survey program. The UES aims at collecting more industry and product detail at the provincial level than was previously possible while avoiding overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The unified 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.

Reference period: Fiscal year

Collection period: November of the reference year to October of the following year


  • Manufacturing
  • Wood, paper and printing

Data sources and methodology

Target population

The target population comprises all incorporated and non-incorporated establishments primarily engaged in logging activity under North American Industries Classification System (NAICS) 11331.

Instrument design

The Annual Survey of Forestry and Logging uses only one fully detailed questionnaire to collect data from respondents. This questionnaire is sent to establishments with shipments above certain thresholds that vary by province, by industry and by survey year.

The questionnaire requests standard financial information as well as data on the commodities that each establishment consumed or produced during the reference year. The list of commodities printed on each questionnaire is personalized for the surveyed establishment based, normally, on its response to the previous year's survey.

The questionnaires were developed in collaboration with data users in order to meet their statistical needs. Respondents and industry associations were also consulted through focus groups and individual meetings to ensure the information being asked was available and that the questionnaire could be filled out within a reasonable timeframe.


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

The frame used for sampling purposes is the Statistics Canada Business Register. The statistical unit is the establishment. The survey population includes all establishments in the logging industries above size thresholds that vary by industry, by province and by reference year.

A sample of establishments is selected from among units in the survey population based on a two phase probability design. Establishments are stratified by province, by industry and by revenue. "Take-alls" are selected based on being large and dominant within their industry. A "take-some" sample is also drawn. All in-sample units receive questionnaires. Whether a unit received a long form or a short form depends on the NAICS they belong to and, in some cases, on their size.

Selected data are obtained from administrative files for non-sampled units in the target population. The proportions of data collected with questionnaires and obtained from administrative files vary from one year to the next, from one province to another and from industry to industry. These proportions are based on the resources available as well as the survey's target coverage at the national, provincial and industry levels. Using administrative files, where possible, reduces both the survey response burden and data collection costs, while maintaining the necessary level of accuracy.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Mail out occurs in November of the reference year (for establishments with fiscal year-ends of April to October), in January (for establishments with fiscal year-ends of November and December) and in March (for establishments with fiscal year-ends of January to March) of the year following the reference year. The survey is usually directed to a financial officer within the business.

Respondents are asked to return the completed questionnaires within thirty days of receipt. Fax reminders are sent to respondents whose questionnaires are outstanding 45 days after the mail out. Collection is generally completed no later than October of the year following the reference year.

The Annual Survey of Forestry and Logging employs a "score function" strategy for collection, based on the value of shipments. Sampled units are divided into 3 follow-up categories (priorities 1, 2 and 3). The priority 1 records are re-contacted for non-response and all attempts are made to elicit a response as their contribution to the estimates is significant. In the case of priority 2 units, re-contact is based on scores with highest scored units being contacted first. The units included in this category are substitutable for one another. When a unit is a reluctant respondent, that unit is skipped and units next on the scored list are followed-up while ensuring that these units represent the contribution to coverage of the skipped unit. As a result, efforts in terms of time and money involved in re-contacting a reluctant respondent are reduced when a replacement unit(s) representing the same coverage can be re-contacted with relative ease. Follow-up continues until the pre-determined coverage level is reached. Full edits are applied to the units required to attain the coverage rate.

The remaining respondents and non-respondents (some priority 2 and all priority 3 units) are subject to minimal collection follow-up and edits. Failed edits are handled by an automatic post-collection edit and imputation system.

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

Error detection

Many reporting and data entry errors are identified using computer capture and edit procedures that are applied to the micro data. Historical edits (year-to-year comparisons) and consistency edits (totals equal sum of parts, proper units used, etc.) are applied at the data collection and capture stage and corrected with the aid of the respondent where possible.

Analysis of both financial and commodity data aggregates (by province and industry) is conducted to detect records which deviate from the expected range, either by exhibiting large year-over-year changes, or differing significantly from similar establishments. Analysts can "drill down" to review and possibly correct individual records if necessary.


The survey data are run through two different edit and imputation (E&I) systems: one for financial data and the other for commodity data.

The E&I system for financial data uses generic rules, which have been tailored to the needs of the survey. The system imputes data separately for each section of the questionnaire. It uses historical, donor and administrative data, as well as any data reported by the respondent to complete records that are missing information.

The E&I system for commodity data was developed using the Standard Economic Processing System (StEPS) software. StEPS is a product developed by the US Census Bureau written entirely in SAS and operating in a UNIX environment. Modules within StEPS include many interactive SAS/AF screens. Key modules within StEPS include Edit, Imputation, Estimation and Review and Correction. The commodity imputation process uses a number of methods such as raking, historical and donor imputation. Both values and quantities (where required) are imputed.


Financial data estimates for logging comprise two components: a weighted estimate that covers all units in the survey population and an aggregation of administrative data that covers all units in the take-none portion of the target population.

The reported (or imputed) values for each establishment in the sample are multiplied by the weight for that establishment and these weighted values are summed to produce estimates for the survey population.

For the take-none portion of the population, selected financial totals are obtained from simple aggregations of administrative records. These totals are used to derive other variables not available from administrative sources through data modeling. Each financial total is allocated across its sub-components based on relationships observed between the totals and other variables in data collected for the current period or in historical data available for the same NAICS and province.

For incorporated non-employers and non-incorporated establishments in the take-none portion of the target population, only total revenue is estimated by industry and province through the aggregation of tax data.

Commodity estimates are based on aggregations of data collected or imputed for long form questionnaires only.

Quality evaluation

The survey results are analyzed for comparability with trends in historical survey data for logging and in other data sources that measure logging activity in Canada. Logging data are also evaluated for comparability with data for manufacturing industries that manufacture their products from logs.

Disclosure control

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. 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 respondents 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

The most recent annual data are subject to a one year revision policy. Data are not seasonally adjusted.

Data accuracy

Although the methodology of this survey has been designed to promote data accuracy, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.

Examples of non-sampling errors include non-response, population under-coverage, differences in the interpretation of questions, incorrect information from respondents, as well as mistakes in recording, coding and processing data. These errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with delinquent respondents to maximize response rates. For reference year 2003, the weighted response rate for the Annual Survey of Forestry and Logging was 77.6%.

Sampling error occurs because the population estimate is derived from a sample of the population of interest rather than the entire population. The extent of sampling error depends on factors such as sample size, sampling design, and the method of estimation. Within resource constraints, all efforts are made to minimize sampling error.

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