Annual Survey of Forestry

Detailed information for 1999

This survey collects the financial and operating data needed to produce statistics for the logging and forestry support industry.

Data release - December 3, 2001

• Description
• Data sources and methodology
• Data accuracy

Description

The Annual Survey of Forestry is a survey of the Forestry Industries of Canada conducted annually since 1917. It is intended to cover all forestry establishments.

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, including shipments or consumption of particular products.

Data collected from the Annual Survey of Forestry are important because they measure production in this sector in Canada, providing an indication of the well-being of each industry, and its contribution to the Canadian economy. The data are used internally 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 for profiling the forestry sector, for market studies, for forecasting demand, for the development of trade and tariff policies and for the creation of guidelines or general programs.

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 September of the following year

Subjects

• Manufacturing
• Wood, paper and printing

Data sources and methodology

Target population

The target population for the Annual Survey of Forestry comprises establishments primarily engaged in the Logging Industry: North American Industry Classification System (NAICS) 113310.

• Additional documentation

Instrument design

The survey uses three types of questionnaire. The long form questionnaire, the short form questionnaire and the questionnaire for head offices.

The questionnaires were developed in collaboration with our users in order to meet their statistical needs and in conjunction with the division in charge of questionnaire design in order to comply with departmental standards. Respondents were also consulted to ensure that the information was available and that the questionnaire could be filled out within a reasonable timeframe.

The main data collected by the Annual Survey of Forestry are:

- Inventories
- Cost of materials and components
- Shipments
- Other revenue from products and services
- Employees: number, salaries and wages
- Commodity data

The System of National Accounts uses a substantial amount of data collected by the Annual Survey of Forestry. Information in the input/output tables is derived from the commodity data.

Sampling

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

The target population consists of establishments primarily engaged in forestry, with value of shipments of $30,000 or more. The survey includes unincorporated establishments, and their activity is estimated in the results. The sample frame is from the Business Register. The population size is approximately 8,000 establishments.

The statistical unit is the establishment. One company may have several establishments. The survey sends questionnaires to each establishment in a company, in addition to the questionnaire sent to the head office.

The proportion of long form questionnaires, short form questionnaires and use of financial data varies from one year to the next, from one province to another and from industry to industry. These proportions are based on the resource envelope as well as the survey's target coverage at the national, provincial and industry levels.

Data sources

Responding to this survey is mandatory.

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

Data from the Annual Survey of Forestry are derived from two sources: questionnaires that are mailed out and administrative files. The survey uses three different types of questionnaires in order to collect data: a long form questionnaire, a short form questionnaire and a questionnaire for head offices. Since the survey collects a wide range of information, the response burden is substantial. Using different questionnaires and administrative files reduces both the response burden and data collection costs, while maintaining the necessary level of accuracy.

The long form is a fully detailed questionnaire sent to large establishments in order to collect detailed information on primary statistics as well as commodity input and output. Only a fraction of establishments receive a long form questionnaire, but these represent about 90% of the total value of shipments. Note that these proportions tend to be lower at the provincial level for 6-figure NAICS industries. The long form questionnaire collects data for commodities classified according to the Standard Classification of Goods (SCG).

A short form questionnaire is used to collect information for small establishments. Establishments that receive a short form questionnaire are selected based on revenue and may vary from one province to the next. Administrative data from the Canada Customs and Revenue Agency are used to complete the information collected with short form questionnaires and, in particular, to reduce the response burden.

Short form questionnaires and administrative files from the Canada Customs and Revenue Agency do not provide separate figures for administrative employees and production workers. Since most employees of small establishments are production workers, all employees are included with production workers. This has the effect of deflating somewhat the figures for administrative employees while inflating figures for production workers by similar amounts.

The North American Industry Classification System has been used since 1998. This system is used to classify establishments by type of production.

The Standard Classification of Goods (SCG) has been used since 1988 to classify goods. This classification is the Canadian adaptation of the international commodity classification known as the Harmonized System (HS). The adoption, in 1998, of the SCG/HS has not only improved the comparability of these figures with other Canadian data series, such as imports and exports, but also with the statistics of other countries around the world.

Error detection

Of the two main detection methods for the Annual Survey of Forestry, the method most often used is comparison with the previous year to detect substantial differences

Microdata are compared with results from previous years to detect substantial inconsistencies. For long form questionnaires, financial and commodity data are compared to detect errors for both commodities and financial data, since total commodities must be equal to the variable reported for input, shipments or energy consumption. Data from short form questionnaires are compared with the previous year, but also with data from the Canada Customs and Revenue Agency to detect inconsistencies between the two.

The number of establishments at the time of data compilation is also another method of error detection. The number of establishments in the various national industries must be compared so that all surveyed establishments are included in the survey, which may considerably affect survey results.

Estimation

Data are not estimated. Values always represent collected data, with the exception of imputations for missing or erroneous data. As previously mentioned, commodity data are compiled from long form questionnaires only. They do not represent all the establishments in the survey, although the long form questionnaires represent about 90% of the value of shipments.

Quality evaluation

Comparison with results from the previous year is the most commonly used method. Analysis of trends for the last few years can also determine data quality, as can comparison with other data sources, such as: the Survey of Employment, Payroll and Hours (SEPH) or current surveys on products

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.

Confidentiality analysis includes the detection of possible direct disclosure, which occurs when the value in a tabulation cell is composed of a few respondents or when the cell is dominated by a few companies.

Data accuracy

The Annual Survey of Forestry collects data for all establishments with revenues of $30,000 or over. No estimates are made for establishments with revenues of less than $30,000. The amount estimated for non-response represents about 10% of the total value of shipments in Canada. However, this percentage may vary from one province to another. As with any other survey, errors influence program quality. The purpose of the survey is to represent the importance of the sector in the Canadian economy and in each province.

Commodity data must be used with caution, as the analysis is more restricted in scope.