Annual Non-store Retail Survey (RTNS)

Detailed information for 2014

This survey provides information that is comparable to data available for other industries, such as: production and employment by type of non-store retailer by province, and proportion of Gross Domestic Product.

Data release - July 18, 2016

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

Description

The Annual Non-Store Retail Survey, is an industry-based survey, meaning that it covers only businesses that primarily sell through non-store methods, and collects data for all the sales of these businesses. This survey provides information that is comparable to data available for other industries, such as: production and employment by type of non-store retailer by province, and proportion of Gross Domestic Product. Businesses, associations, analysts and governments will use this information to monitor the impact of the non-store retail industry in Canada.

Non-store retailers, like store retailers, are organized to serve the general public, but their retailing methods differ. The establishments of this sub sector reach customers and market merchandise with methods such as, infomercials, the publishing of catalogues, door-to-door solicitation, in-home demonstration, temporary merchandise stalls and distribution by vending machines.

Except for the following conditions, internet retailing activities are included in the Annual Retail Trade Survey:

- where the store retailer financially accounts and reports for their store versus internet sales as two separate activities, store sales become part of the Annual Retail Trade Survey estimates and internet sales become part of the Annual Non-Store Retail Survey estimates

- internet retail sales, when performed as an exclusive activity, are always included as part of the Annual Non-store Retail Survey

- internet purchases from a non-Canadian retail site are always excluded from the estimates.

The non-store retailers sub sector also includes establishments engaged in the home delivery of products. This includes fuel dealers and newspaper delivery businesses.

The methods of transaction and delivery of merchandise vary by type of non-store retailers. For example, non-store retailers that reach their customers using information technologies can receive payment at the time of purchase or at the time of delivery, and the delivery of the merchandise may be done by the retailer or by a third party, such as the post office or a courier. In contrast, non-store retailers that reach their customers by door-to-door solicitation, in-home demonstration, temporary displaying of merchandise (stalls) and vending machines typically receive payment and deliver the merchandise to the customer at the time of the purchase.

The non-store retail industry includes self-employed individuals who work as agents on commission for a particular company. Retail commission agents are also known as sales contractors, distributors, or sales representatives, and usually represent only one company.

Agents on commission primarily sell merchandise owned by others, that is, they do not take legal title to the goods they handle. Therefore, they cannot report inventories or goods purchased for resale and don't necessarily have employees. Most of their revenue is derived from commissions, though they may also have other sources of revenue.

The Annual Non-Store Retail Survey is released with The Annual Retail Survey (record number 2447) which covers store-based retailers.

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

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 September of the year after the reference period.

Subjects

• Retail and wholesale
• Retail sales by type of product

Data sources and methodology

Target population

The target population consists of all establishments classified to the Non-Store Retail sector (NAICS 454) according to the North American Industry Classification System (NAICS 2012) during the reference year.

The observed population consists of all establishments classified to the Non-Store Retail sector (NAICS 454) according to the 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 questionnaires comprise generic modules that have been designed to cover the non-store retailing industry. These modules include revenues, expenses, and commodities. The questionnaires also include industry-specific modules designed to ask for financial and non-financial characteristics that pertain specifically to this industry.

In order to reduce respondent burden, smaller firms receive a characteristics questionnaire (shortened version) which only include the industry-specific modules. For smaller firms, revenue and expense data are extracted from administrative files.

The questionnaire was developed in consultation with potential respondents, data users and questionnaire design specialists.

Sampling

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

The frame is the list of active enterprises and establishments that were selected for Statistics Canada's Business Activity, Expenditure and Output Survey. This frame provides basic information about each firm, including address, industry classification, and information from administrative data sources. This information, initially coming from Statistics Canada's Business Register, has also been updated and expanded through Statistics Canada's Business Activity, Expenditure and Output Survey.

Prior to the selection of a random sample, enterprises are classified into homogeneous groups (i.e., groups with the same NAICS codes and same geography) based on the characteristics of their establishments. Then, each group is divided into sub-groups (i.e. small, medium, large) called strata based on the annual revenue of the enterprise.

Following that, a sample, of a predetermined size, is allocated into each stratum, with the objective of optimizing the overall quality of the survey while respecting the available resources. The sample allocation can result in two kinds of strata: take-all strata where all units are sampled with certainty, and take-some strata where a sample of units are randomly selected.

The total sample size for this survey is approximately 1,000 enterprises.

Data sources

Data collection for this reference period: 2015-04-27 to 2015-10-23

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.

Estimation

The sample used for estimation comes from a single-phase sampling process. An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection that is conditional on the realized sample size. The weight calculated for each sampling unit indicates how many other units it represents. The final weights are usually either one or greater than one. Sampling units which are "Take-all" (also called "must-take") have sampling weights of one and only represent themselves.

Estimation of totals is done by simple aggregation of the weighted values of all estimation units that are found in the domain of estimation. Estimates are computed for several domains of estimation such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. It should be noted that this classification information may differ from the original sampling classification since records may have changed in size, industry or location. Changes in classification are reflected immediately in the estimates.

When some enterprises have reported data combining many units located in more than one province or territory, or in more than one industrial classification, data allocation is required. Factors based on information from sources such as tax files and Business Register profiles are used to allocate the data reported on the combined report among the various estimation units where this enterprise is in operation. The characteristics of the estimation units are used to derive the domains of estimation, including the industrial classification and the geography.

Units with larger than expected size are seen as misclassified and their weight is adjusted so that they only represent themselves (large units found in a stratum of small units for example).

The weights can be modified and adjusted using updated information from taxation data. Using a statistical technique called calibration, the final set of weights is adjusted in such a way that the sample represents as closely as possible the taxation data of the population of this industry.

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.

Data accuracy

All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed to maximize response rates.

Measures of sampling error are calculated for each estimate. Also, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.

Both the sampling error and the non-response rate are combined into one quality rating code. This code uses letters that ranges from A to F where A means the data is of excellent quality and F means it is unreliable. These quality rating codes can be requested and should always be taken into consideration.