Annual Survey of Service Industries: Database, Directory and Specialty Publishers
Detailed information for 2007
This survey collects the financial and operating data needed to develop national and regional economic policies and programs.
Data release - This survey does not release any data for confidentiality reasons.
Results of this survey are not made available to the public, due to industry concentration and Statistics Canada's provisions to protect the confidentiality of individual respondents.
This survey collects the financial and operating data needed to produce statistics on the Database, Directory and Specialty Publishing industry in Canada. These data are aggregated with information from other sources to produce official estimates of national and provincial economic production in Canada. The estimates are used by government for national and regional programs and policy planning.
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.
This survey is part of the Service Industries Program. The survey data gathered are used to compile aggregate statistics for over thirty service industry groupings. Financial data, including revenue, expense and profit statistics are available for all of the surveys in the program. In addition, many compile and disseminate industry-specific information.
Reference period: Calendar year
Collection period: January to August
- Business, consumer and property services
- Business performance and ownership
- Financial statements and performance
- Information and culture
Data sources and methodology
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Database, Directory and Specialty Publishers (businesses that publish calendars, colouring books, greeting cards, posters, etc.) according to the North American Industry Classification System (NAICS) during the reference year.
The survey questionnaires comprise financial characteristics such as sources of revenue, details of expenses and employment characteristics. Based on contacts with respondents and data users, some modifications have been incorporated to the questionnaires in order to reflect the nature of the industry surveyed. The changes were field tested to ensure that they were reasonable and sustainable.
This survey is a census with a cross-sectional design.
This methodology does not apply.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
The basic objective of the survey is to produce estimates for the whole industry, incorporated and unincorporated businesses. The data come from two different sources: a census of all businesses with revenue above or equal to a certain threshold (Note: the threshold varies between surveys and sometimes between provinces in the same survey) and administrative data for businesses with revenue below the specified threshold. It should be noted that only financial information is obtained from administrative sources, e.g., revenue, expenses, depreciation and salaries, wages and benefits. Characteristics such as revenue by type of service are collected only for surveyed establishments.
Data are collected through a mail-out/mail-back process, while providing respondents with the option of telephone or electronic filing methods.
Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of both collection and data processing activities. Automated 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.
Imputation generates a complete and coherent microdata file that covers all survey variables.
This methodology type does not apply to this statistical program.
Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for the largest companies), general economic conditions and coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications or newspaper articles).
Statistics Canada is prohibited by law from releasing any information it collects that 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.
In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.