Annual Survey of Service Industries: Database, Directory and Specialty Publishers (DDSP)

Detailed information for 2016

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.

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology

Description

This survey collects the financial and operating data needed to produce statistics on the Database, Directory and Specialty Publishers industry in Canada.

Data collected from businesses are aggregated with information from other sources to produce official estimates of national and provincial economic production for this industry.

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.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure, and content.

The integrated 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. The combined results produce more coherent and accurate statistics on the economy.

• Additional documentation

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: The calendar year, or the 12-month fiscal period for which the final day occurs on or between April 1st of the reference year and March 31st of the following year.

Collection period: April through October of the year after the reference period

Subjects

• Business, consumer and property services
• Business performance and ownership
• Financial statements and performance
• Information and culture

Data sources and methodology

Target population

The target population consists of all establishments classified to the Database, directory and specialty publishers (NAICS 51114) North American Industry Classification System (NAICS 2012) during the reference year.

The observed population consists of all establishments classified to the Database, directory and specialty publishers (NAICS 51114) North American Industry Classification System (NAICS 2012) found on Statistics Canada Business Register as of the last day of the reference year (including establishments active for a part of the reference year).

• Additional documentation

Instrument design

The survey questionnaire contains generic modules designed to cover several service industries. These include revenue and expense modules.

In order to reduce response burden, most of the firms receive a characteristic questionnaire (shortened version) that is industry-specific which does not include the revenue and expense modules. This shortened version is designed to collect both financial and non-financial characteristics, while revenue and expense data are extracted from administrative files.

Sampling

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

Data sources

Data collection for this reference period: 2017-04-27 to 2017-10-27

Responding to this survey is mandatory.

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

Data are collected primarily through electronic questionnaire, which can be responded to in either official language. Respondents also have the option of receiving a paper questionnaire, replying by telephone interview or using other electronic filing methods. Follow-up is conducted via email, telephone or fax and dynamically prioritized on the basis of weighted response rates and for data validation on discrepancies from predicted values.

Administrative data

A strategy to replace survey data with tax data has been introduced to reduce the response burden and survey costs. The strategy involves using tax data instead of survey data for most of the simple units (for example, a single location and a single activity).

As part of the Integrated Business Statistics Program (IBSP), T1 tax data are used for unincorporated businesses and T2 tax data for incorporated businesses. Data replacement may be used to correct outliers or to replace partially or completely missing data. Tax data may also be used to reconcile survey data.

Data integration combines data from multiple data sources including survey data collected from respondents, administrative data from the Canada Revenue Agency or other forms of auxiliary data when applicable. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data source providers into the formats, structures and levels required for IBSP processing. Administrative data are used in a data replacement strategy for a large number of financial variables for most small and medium enterprises and a select group of large enterprises to avoid collection of these variables. Administrative data are also used as an auxiliary source of data for editing and imputation when respondent data are not available.

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

Error detection

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.

Imputation

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.

Quality evaluation

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).

Disclosure control

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.