Monthly Survey of Food Services and Drinking Places (MSFSDP)
Detailed information for November 2019
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 - January 22, 2020
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 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 establishments that are classified as either food services or drinking places (NAICS 722) in the North American Industry Classification System (NAICS 2017).
This questionnaire collects data on monthly sales of food service establishments. 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 is a sample survey with a cross-sectional design.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Data collection, data capture, preliminary edit and follow-up are performed by regional office staff.
Data are collected through various methods, such as telephone, mail-out/mail-back and electronic questionnaire.
Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time.
Administrative data (from the Goods and Services Tax - GST) are the main source for the estimates for the take-none strata and are used as an auxiliary variable to improve the estimates in the take-some strata.
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.
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.
Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. Sales are estimated using ratio estimation. Ratio estimation consists of replacing the initial sampling weights (defined as the inverse of the probability of selection in the sample) by new weights in a manner that satisfies the constraints of calibration. Calibration ensures that the total of an auxiliary variable estimated using the sample must equal the sum of the auxiliary variable over the entire population, and that the new sampling weights are as close as possible (using a specific distance measure) to the initial sampling weights.
For example, suppose that the known population total of the auxiliary variable is equal to 100 and based on a sample the estimated total is equal to 90, so that we are underestimating by approximately 10%. Since we know the population total of the auxiliary variable, it would be reasonable to increase the weights of the sampled units so that the estimate would be exactly equal to it. Now since the variable of interest is related to the auxiliary variable, it is not unreasonable to believe that the estimate of the sales based on the same sample and weights as the estimate of the auxiliary variable may also be an underestimation by approximately 10%. If this is in fact the case, then the adjusted weights could be used to produce an alternative estimator of the total sales. This alternate estimator is called the ratio estimator. In essence, the ratio estimator tries to compensate for 'unlucky' samples and brings the estimate closer to the true total. The gain in variance will depend on the strength of the relationship between the variable of interest and the auxiliary data.
The take-none portion is taken into account by including its auxiliary variable data in the known population total. This causes the weights for the sampled portion to be increased in such a way that the estimates will include the take-none portion.
Like other Statistics Canada monthly surveys, the calculated weighted sales are summed by domain to produce the total sales estimates by each industrial group / geographic area combination. A domain is defined as the most recent classification values available from the Business Register for the unit and the survey reference period. These domains may differ from the original sampling strata because units may have changed size, industry or location. Changes in classification are reflected immediately in the estimates and do not accumulate over time.
The standard error and CV of the estimates are derived directly from the stratified simple random sample.
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.
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, 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 restratification are done.
The Monthly Survey of Food Services and Drinking Places data are seasonally adjusted using the X12-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. The annual totals of the seasonally adjusted series are forced to the annual totals of the original series.
The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions identified for 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. 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.
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 years the raw data was revised.
It is standard practice that every few years the sample is refreshed to ensure that the survey frame is up to date with births, deaths and other changes in the survey population. To that effect, a new sample was drawn in November 2015 and run in parallel with the old sample until May 2016. This new sample also reflected improvements made to the Business Register since the last sample refreshment (restratification) in December 2008. In addition the survey methodology has been refined to improve imputation of non-respondents, calendarization of reported data by respondents that do not report for a complete month, use of administrative (Goods and Services Tax) data, and modifications to seasonal adjustment options.
The methodology of this survey has been designed to control errors and to reduce their potential effects on estimates. However, the survey results remain subject to errors, of which sampling error is only one component of the total survey error. Sampling error results when observations are made only on a sample and not on the entire population.
All other errors arising from the various phases of a survey are referred to as non-sampling errors. 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.
Coefficients of variation (CV) and response rates are major data quality measures used to validate results from the Monthly Survey of Food Services and Drinking Places.
The coefficient of variation, defined as the standard error divided by the sample estimate, is a measure of precision in relative terms. Since the coefficient of variation is calculated from responses of individual units, it also measures some non-sampling errors.
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