Monthly Restaurants, Caterers and Taverns Survey

Detailed information for January 2002

Status:

Active

Frequency:

Monthly

Record number:

2419

This survey collects sales and receipts data from a sample of restaurants, caterers and taverns to estimate provincial taxation shares.

Data release - March 18, 2002

Description

This survey collects sales and receipts data from a sample of restaurants, caterers and taverns in Canada. This data is 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: 10th to 25th of each month

Subjects

  • Accommodation and food
  • Business, consumer and property services

Data sources and methodology

Target population

The target population consists of all statistical establishments with employees (sometimes referred to as firms or units) classified as food services and drinking places (NAICS 722) according to the North American Industry Classification System (NAICS).

More details are included in the document "Restratification and Methodology Changes in the Monthly Restaurants, Caterers and Taverns Survey (MRCTS)" accessible through a hyperlink in the Documentation section below.

Sampling

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

The Monthly Restaurant, Caterers and Taverns Survey sample design is a stratified simple random sample. The frame is derived from Statistic Canada's Central Frame Data Base (CFDB). The stratification is based on kind-of-business and provinces/territories. Kind-of-business groups are a grouping of 4 and 5 digit 1997 NAICS codes. Each kind-of-business/province combination is further subdivided into 3 substrata, based on size. The first of these substrata is a take-all stratum (census) and contains all complex businesses as well as all businesses in a kind-of-business/province combination whose revenues exceed a given threshold. The other two strata are take-some (partially sampled) and contain medium and small businesses respectively. Businesses are assigned to these strata based on estimates of their revenues found on the CFDB.

The sample size is about 3,300 companies.

Error detection

There are edits built into the data capture application to check the entered data for unusual values, as well as to check for logical inconsistencies. Whenever an edit fails, the interviewer is prompted to correct the information (with the help of the respondent when necessary).

Imputation

Imputation is applied to missing records. The imputation system automatically selects the appropriate method depending on the availability of the data. Possible imputation methods are based on month-to-month trends, year-to-year trends, historical data, annual data, etc. Records that fail statistical edits are considered as outliers and are not used in calculating imputation variables (such as monthly trends) used by the imputation system.

Estimation

Sales are estimated by increasing the in-sample sales results by an estimation weight. An initial weight equal to the inverse of the original probability of selection is assigned to each entity. The weights are subsequently adjusted for achieved sample size, in order to inflate the estimate to represent the entire current population. The calculated weighted sales values are summed by domain, to produce the total sales estimates by each kind-of-business/province level combination. A domain is defined as the most recent classification values available from the CFDB for the statistical entity and the survey reference period. These domains may differ from the original sampling strata because records may have changed size, industry, or location. Changes in classification are reflected immediately in the estimates and do not accumulate over time.

The variance is derived directly from a stratified simple random sample.

Quality evaluation

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. 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.

Prior to publication, 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 historical trends.

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

Raw data are revised, on a monthly basis for the month 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.

Data are not seasonally adjusted.

Data accuracy

Coefficients of variation (CV) and response rates are major data quality measures used to validate results from the Monthly Restaurants, Caterers and Taverns Survey.

The coefficient of variation is used to measure the sampling error of the estimates. The coefficient of variation, at the national level for total sales, has ranged, on a monthly basis, from 2.8% to 3.3%.

The response rate, which is about 75%, is a measure of the proportion of those sample units that have responded in time for inclusion in the estimate.

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