Monthly Wholesale Trade Survey (MWTS)

Monthly Wholesale Trade Survey (MWTS)
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Detailed information for December 2023

Status:

Active

Frequency:

Monthly

Record number:

2401

This survey provides information on the performance of the wholesale trade sector and when combined with other statistics, represents an important indicator of the Canadian economy.

Data release - January 26, 2024 (Early indicator); February 16, 2024

Description

This survey provides information on the performance of the wholesale trade sector and is an important indicator of the health of the Canadian economy. In addition, the business community uses the data to analyze market performance.

This survey presents estimates of monthly sales and inventory levels for wholesale merchants in Canada, each province and territory.

A variety of organizations, sector associations, and levels of government make use of the information. Governments are able to understand the role of wholesalers in the economy (5-6% of the Gross Domestic Product, depending on the year), which aid in the development of policies and tax incentives.

Reference period: Month

Collection period: Collection of the data begins approximately 7 working days after the end of the reference month and continues for the duration of that month.

Subjects

  • Retail and wholesale
  • Wholesale sales and inventories

Data sources and methodology

Target population

The target population for the Monthly Wholesale Trade Survey consists of all statistical establishments on the Business Register (BR) that are classified to the wholesale sector using the North American Industry Classification System (NAICS 2022).

The BR is a structured list of businesses engaged in the production of goods and services in Canada.

The statistical establishment is the level at which all accounting data required to measure production are available. The establishment, as a statistical unit, is defined as the most homogeneous unit of production for which the business maintains accounting records. From these records, it is possible to assemble all the data elements required to compile the full structure of the gross value of production (total sales or shipments and inventories), the cost of materials and services, and the labour and capital used in
production. In delineating the establishment, however, producing units may be grouped. An establishment comprises at least one location, but it can also be composed of many. Establishments may also be referred to as profit centres.

NAICS is the agreed upon common framework for the production of comparable statistics by the statistical agencies of Canada, Mexico and the United States. NAICS is based on a production-oriented, or supply-based, conceptual framework. The NAICS code range for the wholesale sector is 410000 to 419999.

The exclusions to the target population are establishments for which economic signals indicate a null or missing revenue and establishments in the following non-covered NAICS:

- 419 (business-to-business electronic markets, and agents and brokers)

Instrument design

The questionnaire collects monthly data on wholesale sales and the number of trading locations by province or territory and inventories of goods owned and intended for resale from a sample of wholesalers.

Sampling

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

The Business Register is a repository of information reflecting the Canadian business population and exists primarily for the purpose of supplying frames for all economic surveys in Statistics Canada. It is designed to provide a means of coordinating the coverage of business surveys and of achieving consistent classification of statistical reporting units. It also serves as a data source for the compilation of business demographic information.

The major sources of information for the Business Register are updates from the Statistics Canada survey program and from Canada Revenue Agency's (CRA) Business Number account files. This CRA administrative data source allows for the creation of a universe of all business entities.

The data provided in our products reflects counts of statistical locations by industrial activity (North American Industry Classification System), geography codes, and employment size ranges.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Collection of the data is performed by Statistics Canada's Regional Offices. Respondents are sent a questionnaire or are contacted by telephone to obtain their sales and inventory values, as well as to confirm the opening or closing of business trading locations. There is also follow-up of non-response. Collection of the data begins approximately 7 working days after the end of the reference month and continues for the duration of that month.

New entrants to the survey are introduced to the survey via an introductory letter that informs the respondent that a representative of Statistics Canada will be calling. This call is to introduce the respondent to the survey, confirm the respondent's business activity, establish and begin data collection, as well as to answer any questions that the respondent may have.

Significant effort is spent trying to minimize non-response during collection. Methods used, among others, are interviewer techniques such as probing and persuasion, repeated re-scheduling and call-backs to obtain the information, and procedures dealing with how to handle non-compliant (refusal) respondents.

If data are unavailable at the time of collection, a respondent's best estimates are accepted, and are subsequently revised once the actual data become available.

To minimize total non-response for all variables, partial responses are accepted. In addition, questionnaires are customized for the collection of certain variables, such as inventory, so that collection is timed for those months when the data are available.

Finally, to build trust and rapport between the interviewers and respondents, cases are generally assigned to the same interviewer each month. This action establishes a personal relationship between interviewer and respondent, and builds respondent trust.

Administrative data sources:
Reducing response burden is an ongoing challenge for Statistics Canada. In an attempt to alleviate response burden and survey costs, especially for smaller businesses, the sales data are derived for these establishments from GST files using a ratio estimator. The ratio estimator also increases the precision of the surveyed portion of the estimate.

For more information on the use of administrative data, refer to 'Monthly Wholesale Trade Survey: Use of Administrative Data'.

Inventories for establishments where sales are GST-based are derived using the Monthly Wholesale Trade Survey imputation system. The imputation system applies to the previous month values, the month-to-month and year-to-year changes in similar establishments which are surveyed.

View the Questionnaire(s) and reporting guide(s) .

Error detection

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 Wholesale Trade Survey, data editing is done at two different time periods.

First of all, editing is done during data collection. Edits during data collection are referred to as field edits and generally consist of validity and some simple consistency edits.

Follow-up with respondents occurs to validate potential erroneous data following any failed preliminary edit check of the data. Once validated, the collected data are regularly transmitted to the head office in Ottawa.

Secondly, editing known as statistical editing is also done after data collection. Statistical editing 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 the imputation stage. It should be noted that adjustments are not made at this stage to correct the reported outliers.

Edits checks are based on the Hidiroglou-Berthelot method whereby a ratio of the respondent's current month data over historical (i.e. last month, or same month last year) or administrative data is analyzed. When the respondent's ratio differs significantly from ratios of respondents who are similar in terms of industrial group and/or geography group, the response is deemed an outlier. Data that are flagged as an outlier will not be included in the imputation models (those based on ratios).

Imputation

Imputation in the Monthly Wholesale Trade Survey (MWTS) is the process used to assign replacement values for missing data. This is done by assigning values when they are missing on the record being edited to ensure that estimates are of high quality and that a plausible, internal consistency is created. Due to concerns of response burden, cost and timeliness, it is generally impossible to do all follow-ups with the respondents in order to resolve missing responses. Since it is desirable to produce a complete and consistent microdata file, imputation is used to handle the remaining missing cases.

In the MWTS, imputation for missing values can be based on either historical data or administrative data. The appropriate method is selected according to a strategy that is based on whether historical data are available, administrative data are available and/or which reference month is being processed.

There are three types of historical imputation methods. The first type is a general trend that uses one historical data source (previous month, data from next month or data from same month previous year). The second type is a regression model where data from previous month and same month previous year are used simultaneously. The third type uses the historical data as a direct replacement value for a non-respondent. Depending upon the particular reference month, there is an order of preference that exists so that a top quality imputation can result. The historical imputation method that was labeled as the third type above is always the last option in the order for each reference month.

The imputation method using administrative data is automatically selected when historical information is unavailable for a non-respondent. Trends are then applied to the administrative data source (monthly size) depending on whether the unit has a simple structure, e.g. enterprises with only one establishment, or a more complex structure.

Estimation

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. More specifically, the Monthly Wholesale Trade Survey (MWTS) uses a ratio estimator.

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 the ratio estimator. This is done by simply including the take-none portion in the control totals for the sample portion. By doing this, the weights for the sampled portion will be increased in such a way that the estimates will be adjusted to take into account the take-none portion.

The calculated weighted sales values are summed by domain, to produce the total sales estimates by each industrial group/geographic area combination and the total inventories by industrial group. 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.

Sales in volume: The value of wholesale trade is measured in two ways; including the effects of price change on sales and net of the effects of price change. The first measure is referred to as wholesale trade in current dollars and the latter as wholesale trade in volume. The method of calculating the current dollar estimate is to aggregate the weighted value of sales for all wholesale outlets. The method of calculating the volume estimate is to first adjust the sales values to a base year, using the price indexes, and then sum up the resulting values. See on this topic the following document 'Monthly Wholesale Trade Survey - Sales in volume' (in the 'Documentation' section below).

The measure of precision used for the MWTS to evaluate the quality of a population parameter estimate and to obtain valid inferences is the variance. The variance from the survey portion is derived directly from a stratified simple random sample without replacement.

Sample estimates may differ from the expected value of the estimates. However, since the estimate is based on a probability sample, the variability of the sample estimate with respect to its expected value can be measured. The variance of an estimate is a measure of the precision of the sample estimate and is defined as the average, over all possible samples, of the squared difference of the estimate from its expected value.

Quality evaluation

Prior to publication, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for large businesses), general economic conditions and historical trends.

Disclosure control

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

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. In addition, revisions are made once a year, with the release of the February data for the first time, 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. However, the revision period can be extended when historical revisions or restratification are done.

Since April 2008, the Monthly Wholesale Trade Survey 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 models), and of seasonally adjusting the raw time series. Finally, 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 allow 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 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.

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.

For more information on seasonal adjustment, see "Seasonal adjustment and identifying economic trends" (http://www5.statcan.gc.ca/olc-cel/olc.action?ObjId=11-010-X201000311141&ObjType=47&lang=en&limit=0) and 'Seasonally adjusted data - Frequently asked questions' (http://www.statcan.gc.ca/dai-quo/btd-add/btd-add-eng.htm).

Data accuracy

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 and response rates are major data quality measures used to validate results from the Monthly Wholesale Trade Survey.

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.

RESPONSE RATES
The average weighted response rate for 2022 is 85.2%.

NON-SAMPLING ERROR
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.

NON-RESPONSE BIAS
Significant effort is spent trying to minimize non-response during collection. Methods used, among others, are interviewer techniques such as probing and persuasion, repeated re-scheduling and call-backs to obtain the information, and procedures dealing with how to handle non-compliant (refusal) respondents.

If data are unavailable at the time of collection, a respondent's best estimates are also accepted, and are subsequently revised once the actual data become available.

To minimize total non-response for all variables, partial responses are accepted. In addition, questionnaires are customized for the collection of certain variables, such as inventory, so that collection is timed for those months when the data are available.

COVERAGE ERROR
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame.

Statistics Canada's Business Register (BR) provides the frame for the Monthly Wholesale Trade Survey. The BR is a data service centre updated through a number of sources including administrative data files, feedback received from conducting Statistics Canada business surveys, and profiling activities including direct contact with companies to obtain information about their operations and Internet research findings. Using the BR will ensure data quality, while avoiding overlap between surveys and minimizing response burden to the greatest extent possible.

OTHER NON-SAMPLING ERRORS
These errors may occur at various stages of data processing such as coding, data entry, verification, editing, weighting, tabulation, etc. Non-sampling errors are difficult to measure. More important, non-sampling errors require control at the level at which their presence does not impair the use and interpretation of the results. Measures have been undertaken to minimize the non-sampling errors. For example, units have been defined in a most precise manner and the most up-to-date listings have been used. Questionnaires have been carefully designed to minimize different interpretations. As well, detailed acceptance testing has been carried out for the different stages of editing and processing and every possible effort has been made to reduce the non-response rate as well as the response burden.

Documentation

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