Wholesale Trade Survey (Monthly) (MWTS)
Detailed information for December 2003
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 - February 26, 2004
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 analyse 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.
- Retail and wholesale
- Wholesale sales and inventories
Data sources and methodology
The wholesale trade sector comprises establishments primarily engaged in wholesaling merchandise and providing related logistics, marketing and support services. The wholesale process is generally an intermediate step in the distribution of merchandise; many wholesalers are therefore organized to sell merchandise in large quantities to retailers, and business and institutional clients. However, some wholesalers, in particular those that supply non-consumer capital goods, sell merchandise in single units to final users.
Wholesale merchants buy and sell merchandise on their own account, that is they take title to the goods they sell. In addition to the sale of goods, they may provide, or arrange for the provision of, logistics, marketing and support services, such as packaging and labeling, inventory management, shipping, handling of warranty claims, in-store or co-op promotions, and product training. Dealers of machinery and equipment, such as dealers of farm machinery and heavy duty trucks, also fall within this category.
The items on the questionnaire have remained unchanged for several years. However, should modifications become necessary, proposed changes would go through a review committee, and a field test with respondents and data users to ensure its relevancy.
This is a sample survey with a cross-sectional design.
The sample of the monthly wholesale trade survey is drawn from Statistics Canada's Central Frame Data Base (CFDB), part of which comprises businesses engaged in wholesale activity.
The target population is all wholesale merchant establishments, excluding those engaged in the wholesaling of grain and petroleum products. The sampling unit is the statistical company. The sample is approximately 8,500 units.
Each stratum is first defined by the 1980 Standard Industrial Classification and geographic region. Each combination of industry and geography is divided into three substrata according to their size. The first substratum includes both large and complex businesses and is self-representing as businesses are included in the sample with certainty (a census) while the other two strata are only partially sampled. Some thresholds that separate the substrata were modified to reflect economic growth since the last redesign in 1988.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Respondents are sent either a questionnaire or contacted by telephone to obtain their monthly sales and inventories.
Staff within Statistics Canada's Regional Offices performs the telephone interviews, data capture activities, and follow-up of non-respondents. As well, preliminary editing of the captured data, and subsequent telephone follow-ups which may result due to edit failures, are performed within the Regional Offices. The edited data are transmitted regularly to the head office in Ottawa.
View the Questionnaire(s) and reporting guide(s).
There are edits built into the data capture application to compare the entered data against 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). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy.
Once the data are received back at head office an extensive series of processing steps are undertaken to thoroughly verify each record received. Edits are performed at a more aggregate level (trade group by geographic level) to detect records which deviate from the expected, either by exhibiting large month-to-month change, or differing significantly from the remaining companies. All data failing these edits are subject to manual inspection and possible corrective action.
Imputation is applied to missing records. The imputation system automatically selects the appropriate method depending on the availability of the data. Possible imputation methods may be 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.
In addition, there sometimes can be an identifiable lag between the time a company opens and its appearance on the survey frame. To compensate for the effect this time lag has on monthly estimates, sales for sample births are imputed back to the actual date of birth or the beginning of the current year (if the actual date of birth is prior to the current year).
Wholesale sales and inventory levels are estimated by increasing the in-sample sales and inventory 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 values are summed by domain, to produce the total estimates by each trade group / geographic area 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 methodology of this survey has been designed to control errors and to reduce the potential effects of these. However, the results of the survey 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.
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
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 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 January data for the first time, for all months in the previous year. 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 never exceeds three years.
The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions identified for the raw data. Also, since the seasonal adjusted estimates are calculated using an auto-regressive integrated moving average model (ARIMA), the trend is 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, all the seasonally adjusted parameters are revised to incorporate the most recent data. For example, trading day weights are adjusted. Revised seasonally adjusted estimates for each month in the previous three calendar years are released at the same time as the annual revision to the raw data.
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 non-sampling error. 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.
Although every effort is made to keep non-sampling errors to a minimum, they always exist. Measures such as response rate and response fraction can be used as indicators of the possible extent of non-sampling errors.
The response fraction is the proportion of the estimate which is based upon reported data. For example, a cell with 20 active sample units in which 10 respond for a particular month would have a response rate of 50%. However, if the 10 reporting units represented $9 million out of a total of $12 million, the response fraction would be 75%. Thus 25% of the estimate would come from imputed data. The response rate is a measure of the proportion of those sample units that have responded in time for inclusion in the estimate.
The average weighted response rates for monthly wholesale trade are:
Sampling error can be measured by the standard error (or standard deviation) of the estimate. The coefficient of variation (CV) is the estimated standard error percentage of the survey estimate. Estimates with smaller CVs are more reliable than estimates with larger CVs.
The average estimated coefficient of variation are:
1.1 Sales (Canada)
1.8 Inventories (Canada)