Monthly Survey of Food Services and Drinking Places (MSFSDP)
Detailed information for September 2023
This survey provides information to measure the economic performance and health of the food services and drinking places industry in the Canadian economy.
Data release - November 24, 2023
The Monthly Survey of Food Services and Drinking Places provides estimates of the value of sales of restaurants, caterers, and drinking places by province and territory and by industry at the North American Industry Classification System (NAICS) four or six digit codes level. These data are used by federal and provincial governments, private associations and food service businesses for consulting, marketing and planning purposes. The provincial and federal governments use the information to estimate provincial taxation shares.
Reference period: Month
Collection period: Collection of the data begins approximately 1-2 working days after the end of the reference month and continues for the duration of that month.
- Accommodation and food
- Business, consumer and property services
Data sources and methodology
The target population includes all statistical locations that are classified as either food services or drinking places (NAICS 722) in the North American Industry Classification System (NAICS 2022).
The observed population consists of all locations classified as either food services or drinking places (NAICS 722) according to NAICS 2022 found on the Statistics Canada Business Register as of the first day of the reference month.
A questionnaire is used on a portion of the observed population (mainly large food chains) to collect data on monthly sales of food and drinking service locations. The items on the questionnaire have remained unchanged for several years. Some minor modifications were made with the survey redesign of 2007 to facilitate its use and clarify a few elements. Associations representing the industry were consulted.
This survey is a census.
Responding to this survey is mandatory.
Data are collected directly from survey respondents for a portion of the observed population (mainly large food chains). For the remaining portion of the observed population, sales are derived from administrative data (from the Goods and Services Tax - GST), for the locations which collect GST. For the small locations which don't collect GST, data are derived from a model. The survey portion accounts for around 10% of locations (which represents around 30% of the annual revenue) while the administrative data from the GST and the modeled portion account respectively for around 63% and 27% of locations (which represent respectively around 65% and 5% of the annual revenue).
For the survey portion, data collection, data capture, preliminary edit and follow-up are performed by regional office staff. Data is 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 their impact and for data validation.
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 some locations.
Data integration combines data from multiple data sources including survey data collected from respondents, administrative data from the GST and modeled data. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data sources into the formats, structures and levels required for the data processing. Administrative data from the GST are used in a data replacement strategy for sales for most small and medium locations and a select group of large locations to avoid collection of these units. Administrative data are also used as an auxiliary source of data for editing and imputation when respondent data is not available.
A model is used to derive monthly sales for very small locations that do not report GST and it is based on the location's annual revenue.
View the Questionnaire(s) and reporting guide(s) .
Data editing is the application of checks to detect missing, invalid or inconsistent entries or to point to data records that are potentially in error. In the survey process for the Monthly Survey of Food Services and Drinking Places, data editing is done at two different time periods.
The first editing process is conducted during data collection. Once data are collected via the telephone, or electronic questionnaire or the receipt of completed mail-in questionnaires, the data are captured using customized data capture applications. All data are subjected to data editing.
Follow-up with respondents occurs to validate potential erroneous data following any failed preliminary edit check of the data. Once validated, the collected data is regularly transmitted to the head office in Ottawa.
Statistical editing is also done after data collection and is run prior to imputation in order to identify the data that will be used as a basis to impute non-respondents. Large outliers that could disrupt a monthly trend are excluded from trend calculations by the statistical edits. Reported data for the current reference month will go through various edit checks.
Edits are performed at a more aggregate level (industry by geography level) to detect records which deviate from the expected range, either by exhibiting large month-to-month change, or differing significantly from the remaining units. All data which fail these edits are subject to manual inspection and possible corrective action.
Similar validation, statistical editing and outlier detection are performed on the administrative data as well to detect potential erroneous data or unexpected month-to-month change.
Imputation is used to estimate for non-response and missing data. Imputation methods include the use of monthly trends from historical data and from administrative data (GST sales revenue). Historical and administrative data that fail statistical edits are considered outliers and are not used in the imputation process. The appropriate method is selected according to a strategy that is based on whether historical data is available, auxiliary data is available and/or which reference month is being processed.
Data coming from administrative files are also subject to imputation in case of non-response. Furthermore, calendarization is performed on units which report their administrative data quarterly or annually to provide a value corresponding to the reference month.
Since the Monthly Survey of Food Services and Drinking Places is a census, as all locations have a value of sales, either from collection, administrative data or modeled data, an estimate of total sales is obtained by aggregating the values from all units belonging in a specific domain. A domain can be defined as the entire population, an industry group, a province/territory or the intersection between an industry group and a province/territory. To determine to which domain a location belongs in, the most recent classification values available from the Business Register for the unit and the survey reference period are used. Changes in classification are reflected immediately in the estimates.
Prior to dissemination, combined survey results are analyzed for comparability. In general, this includes a detailed review of individual responses, general economic conditions, historic trends, and comparisons with other data sources.
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
Revisions in the raw data are required to correct known non-sampling errors. These normally include replacing imputed data with reported data, corrections to previously reported data, and estimates for new births that were not known at the time of the original estimates.
Raw (unadjusted) data are revised, on a monthly basis, for the two months immediately prior to the current reference month being published. That is, when data for December are being published for the first time, there will also be revisions, if necessary, to the raw data for November and October.
In addition, revisions are made once a year, usually with the initial release of the March data, for all months in the previous years. The purpose is to correct any significant problems that have been found that apply for an extended period. The actual period of revision depends on the nature of the problem identified, but rarely exceeds three years. The revision period can be extended when historical revisions or major changes to the survey methodology are done.
The Monthly Survey of Food Services and Drinking Places data are seasonally adjusted using the X-12-ARIMA method. This consists of extrapolating a year's worth of raw data with the ARIMA model (auto-regressive integrated moving average model), and of seasonally adjusting the raw time series to remove seasonal and calendar effects. The annual totals of the seasonally adjusted series are forced to the annual totals of the original series. A seasonally adjusted time series is a time series that has been modified to eliminate the effect of seasonal and calendar influences. For this reason, the seasonally adjusted data allows for more meaningful comparisons of economic conditions from month to month.
The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions applied to the raw data. Also, the seasonally adjusted estimates are calculated using X-12-ARIMA, and are sensitive to the most recent values reported in the raw data. For this reason, with the release of each month of new data, the seasonally adjusted values for the previous three months are revised.
Once a year, seasonal adjustments options are reviewed to take into account the most recent data. Revised seasonally adjusted estimates for each month in the previous years are released at the same time as the annual revision to the raw data. The actual period of revision depends on the number of years the raw data was revised.
The methodology of this survey has been designed to control errors and to reduce their potential effects on estimates. These errors can be divided into sampling errors and non-sampling errors. The Monthly Survey of Food Services and Drinking Places is not subject to sampling errors since data are obtained in one way or another for all locations in the observed population (this is a census).
Non-sampling errors can result from many causes. For example, these types of errors can occur when a respondent provides incorrect information or does not answer certain questions; when a unit in the target population is omitted or covered more than once; when a unit that is out of scope for the survey is included by mistake or when errors occur in data processing, such as coding or capture errors. While the impact of non-sampling errors is difficult to evaluate, certain measures such as response and imputation rates can be used as indicators of the potential level of non-sampling error. Also, variance due to imputation can be calculated to provide a more accurate measure of quality of estimation in the presence of imputation since it takes in account the extent of the imputation as well as the accuracy of the imputation models.
Variance due to imputation has been calculated for each of the different sources and the coefficient of variation (CV), which is a measure of precision in relative terms, has been derived. The CV is defined as the standard error (square root of the variance) divided by the estimate.
The CV is often expressed in terms of quality indicator to ease the interpretation. The quality indicator uses letters from A to F where A stands for excellent quality and F stands for unreliable. Estimates with a quality indicator of F are not published.