Annual Survey of Service Industries: Food Services and Drinking Places
Detailed information for 2007
This survey collects the financial and operating data needed to develop national and regional economic policies and programs.
Data release - April 7, 2009
This annual sample survey collects data required to produce economic statistics for the Food Services and Drinking Places 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.
Survey estimates are made available to businesses, governments, investors, associations, and the public. The data are used to monitor industry growth, measure performance, and make comparisons to other data sources to better understand this industry.
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 September
- Accommodation and food
- Business, consumer and property services
- Business performance and ownership
- Financial statements and performance
Data sources and methodology
The target population consists of all establishments classified to the Food Services and Drinking Places industry (NAICS 722) according to the North American Industry Classification System (NAICS) during the reference year. This industry comprises establishments primarily engaged in preparing meals, snacks and beverages, to customer order, for immediate consumption on and off the premises.
The survey questionnaires comprise generic modules that have been designed to cover several service industries. These modules include revenues, expenses, and employment, as well as an industry-specific module designed to ask for financial and non-financial characteristics that pertain specifically to this industry.
In order to reduce respondent burden, smaller firms received a characteristics questionnaire (shortened version), which excluded the revenue and expense modules. For these firms, revenue and expense data are extracted from administrative files. Firms which operate in more than one industry or province received a full questionnaire.
This is a sample survey with a cross-sectional design.
The frame is the list of establishments from which the portion eligible for sampling is determined and the sample is taken. The frame provides basic information about each firm including: address, industry classification and information from administrative data sources. The frame is maintained by Statistics Canada's Business Register and is updated using administrative data.
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified to this industry according to the North American Industry Classification System (NAICS) during the reference year observed.
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 sample of all businesses with revenue above or equal to a certain threshold (note: the threshold varies between surveys and sometimes between industries and provinces in the same survey) for which either survey or administrative data may be used; and administrative data only for businesses with revenue below the specified threshold. It should be noted that only financial information is available from businesses below the threshold; e.g., revenue, and expenses such as depreciation and salaries, wages and benefits. Characteristics such as client base and revenue by type of service are collected only for surveyed establishments.
Prior to the selection of a random sample, establishments are classified into homogeneous groups (i.e., groups with the same NAICS codes and same geography (province/territory)). Quality requirements are targeted, and then each group is divided into sub-groups called strata: take-all, must-take, and take-some.
The take-all stratum represents the largest firms in terms of performance (based on revenue) in an industry. The must-take stratum is comprised of units selected on the basis of complex structural characteristics (multi-establishment, multi-legal, multi-NAICS, or multi-province enterprises). All take-all and must-take firms are selected to the sample. Units in the take-some strata are subject to simple random sampling.
The effective sample size for reference year 2007 was 3,002 collection entities.
Data collection for this reference period: 2008-01-23 to 2008-08-05
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Data are collected through a mail-out/mail-back process, while providing respondents with the option of telephone or other 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).
Data are examined for inconsistencies and errors using automated edits coupled with analytical review. Every effort is made to minimize the non-sampling errors of omission, duplication, reporting and processing. Several checks are performed on the collected data. These checks look for internal consistency such as: section totals must be equal to the components; if employees are reported, personnel costs must be greater than zero; the main source of income must be consistent with the assigned NAICS code; identification of extreme values; etc.
Where information is missing, imputation is performed using a "nearest neighbour" procedure (donor imputation), using historical data where available, using averages based on responses from a set of similar establishments, or using administrative data as a proxy for reported data.
As part of the estimation process, survey data are weighted and combined with administrative data to produce final industry estimates.
Prior to dissemination, combined survey results are analyzed for overall quality; in general, this includes a detailed review of individual responses (especially for the largest companies), an assessment of the general economic conditions portrayed by the data, historic trends, and comparisons with other data sources.
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.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
While considerable effort is made to ensure high standards throughout all stages of collection and processing, 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.
Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.
The weighted response rate represents the proportion of the total revenue accounted for by units that responded to the survey. Of the sampled units contributing to the estimate, the weighted response rate was 86.2%, after accounting for firms that have gone out of business, have been reclassified to a different industry, are inactive, or are duplicates on the frame.
Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.
For the 2007 Food Services and Drinking Places survey, CVs were calculated for each estimate. Generally, the more commonly reported variables obtained very good CVs (10% or less), while the less commonly reported variables were associated with higher but still acceptable CVs (under 25%). Some data might not be released because of poor data quality. The CVs are available upon request.
The qualities of CVs are rated as follows:
. Excellent 0.01% to 4.99%
. Very good 5.00% to 9.99%
. Good 10.00% to 14.99%
. Acceptable 15.00% to 24.99%
. Use with caution 25.00% to 34.99%
. Unreliable 35.00% or higher