Workplace and Employee Survey (WES)

Record number:

The overall goal of the survey is to examine the way in which employers and their employees respond to the changing competitive and technological environment.

Detailed information for 2006

Data release - February 5, 2009 (This last wave of Workplace and Employee Survey data only includes employer data since the employee portion of the survey was not conducted.)


The Workplace and Employee Survey (WES) is designed to explore a broad range of issues relating to employers and their employees. The survey aims to shed light on the relationships among competitiveness, innovation, technology use and human resource management on the employer side and technology use, training, job stability and earnings on the employee side.

The survey is unique in that employers and employees are linked at the micro data level; employees are selected from within sampled workplaces. Thus, information from both the supply and demand sides of the labour market is available to enrich studies on either side of the market.

To create the best conditions for growth in the knowledge-based economy, governments need to fine-tune their policies on education, training, innovation, labour adjustment, workplace practices, industrial relations and industry development. The results from the survey will help clarify many of these issues and will assist in policy development.

The Workplace and Employee Survey offers potential users several unique innovations: chief among these is the link between events occurring in workplaces and the outcomes for workers. In addition, being longitudinal, it allows for a clearer understanding of changes over time.

There are two reference periods used for the WES. Questions concerning employment breakdown use the last pay period of March for the reference year while other questions refer to the last 12-month period ending in March of the reference year.

For more information on data users and uses of the WES refer to


  • Adult education and training
  • Education, training and learning
  • Hours of work and work arrangements
  • Job training and educational attainment
  • Labour
  • Non-wage benefits
  • Wages, salaries and other earnings
  • Workplace organization, innovation, performance

Data sources and methodology

Target population

The target population for the employer component is defined as all business locations operating in Canada that have paid employees in March, with the following exceptions:

a) Employers in Yukon, Nunavut and Northwest Territories; and
b) Employers operating in crop production and animal production; fishing, hunting and trapping; private households, religious organizations and public administration.

The target population for the employee component is all employees working or on paid leave in March in the selected workplaces who receive a Canada Revenue Agency T-4 Supplementary form. If a person receives a T-4 slip from two different workplaces, then the person will be counted as two employees on the WES frame.

The survey population is the collection of all units for which the survey can realistically provide information. The survey population may differ from the target population due to operational difficulties in identifying all the units that belong to the target population.

The WES draws its sample from the Business Register (BR) maintained by the Business Register Division of Statistics Canada and from lists of employees provided by the surveyed employers.

The Business Register is a list of all businesses in Canada and is updated each month using data from various surveys, business profiling and administrative data.

Instrument design

In 1994, research on the possibility of an integrated approach to the collection and analysis of data on establishments and their employees was conducted by the Business and Labour Market Analysis Division. The findings were presented, a pre-test was funded and a WES working group was created. The group consulted with experts such as the EKOS group to determine the important research issues and a questionnaire was carved.

A pre-test of 50 businesses was conducted. There were consultations with an Advisory Subject Matter Group and based on the results of the pre-test and recommendations, changes were made to the questionnaire.

A pilot survey was conducted. There were consultations with experts such as the Subject Matter Advisory Group, Human Resources Development Canada, EKOS Group and based on their comments and suggestions changes were made to the questionnaires. Also, due to the improvement of the Business Register profiling the survey was simplified from a 3-stage process to a 2-stage process.

Ongoing scrutiny of the questionnaire by subject matter analysts, researchers, and interviewers alerts the WES team to any further modifications needed in the wording or order of questions.


This is a sample survey with a longitudinal design.

Frame description
The survey frame is a list of all statistical locations that carries contact and classification (e.g., industrial classification) information on the units. This list is used for sample design and selection; ultimately, it provides contact and classification information for the selected units.

Workplace Survey
The survey frame of the Workplace component of WES is created from the information available on the Statistics Canada Business Register.

Prior to sample selection, the business locations on the frame are stratified into relatively homogeneous groups called strata, which are then used for sample allocation and selection. The WES frame is stratified by industry (14), region (6), and size (3), which is defined using estimated employment. The size stratum boundaries are typically different for each industry/region combination. The cut-off points defining a particular size stratum are computed using a model-based approach. The sample is selected using Neyman allocation. This process partitions the target population into 252 strata. In 1999, 9,043 business locations were selected. In 2001, 1,792 locations were added for a total of 10,815. In 2003, 2,332 locations were added for a total of 13,147 business locations. In 2005, 2,022 locations were added for a total of 15,169 business locations.

All sampled units are assigned a sampling weight (a raising factor is attached to each sampled unit to obtain estimates for the population from a sample). For example, if two units are selected at random and with equal probability out of a population of ten units, then each selected unit will represent five units in the population, and it will have a sampling weight of five.

The 2005 WES survey collected data from 6,693 out of the 7,864 sampled employers. The remaining employers were either out-of-business, seasonally inactive, holding companies, or out-of-scope. The majority of non-respondents were owner-operators with no paid help and in possession of a payroll deduction account.

The initial sample selected in 1999 is followed over time and is supplemented at two-year intervals with a sample of births selected from units added to the Business Register since the last survey occasion. Stratification of units remains constant over the life of the initial panel (set at 8 years). Whenever possible, the same sampling fractions are applied to all panels. Sometimes the sampling fractions are adjusted to offset stratum erosion, or to compensate for upswings or downswings in the economy. For 2001, they were revised slightly upward. This resulted in a birth panel of 1,792 workplaces. For 2003 this resulted in a birth panel of 2,332 workplaces and for 2005 this resulted in 2,022 workplaces.

Employee Survey
The frame of the employee component of WES is based on lists of employees made available to interviewers by the selected workplaces. A maximum of twenty four employees are sampled using a probability mechanism. In workplaces with fewer than four employees, all employees are selected.

Sample Size - Employer
1999 - 6,322
2000 - 6,068
2001 - 6,207
2002 - 5,818
2003 - 6,565
2004 - 6,159
2005 -- 6,693
2006 - 6,312

Sample Size - Employee
1999 - 23,540
2000 - 20,167
2001 - 20,352
2002 - 16,813
2003 - 20,834
2004 - 16,804
2005 -- 24,197

Employees will be followed for two years only, due to the difficulty of integrating new employers into the location sample as workers change companies. As such, fresh samples of employees will be drawn on every second survey occasion (i.e. first, third, fifth).

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data collection, data capture, preliminary editing and follow-up of non-respondents are all done in Statistics Canada Regional Offices. In 1999, workplace data were collected in person. As of 2000, computer assisted telephone interviews are conducted. For about 20% of the surveyed units (mostly large workplaces), more than one contact person is required.

For the employee component, telephone interviews are conducted with persons who agree to participate in the survey by filling out and mailing in an employee participation form.

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

Error detection

The use of CATI for data collection greatly reduces the number of response and typographical errors. The system incorporates basic data validation and verification of known relationships such as full time and part time employment not exceeding total employment. To detect errors that have eluded the CATI application, both micro and macro level analysis of questionable responses is performed to protect the coherence of the data.


There are three types of nonresponse in WES: unit nonresponse, item nonresponse and wave nonresponse. Unit nonresponse occurs if it is not possible to obtain the survey information for all variables of a selected unit (workplace or employee) due to a refusal or the impossibility to make a contact. Item nonresponse occurs if we are able to obtain only partial information from a selected unit. This could be due to a refusal or the impossibility to respond to some questions or inconsistencies in the data collected. Finally, wave nonresponse occurs when we have at least partial information at a previous wave for a selected unit but no information at the current wave. In the current nonresponse treatment strategy, a weight adjustment for the respondents is computed to deal with unit nonresponse while item and wave nonresponse are treated using different imputation methods. Cross-sectional versions of these methods are used for units appearing at the current wave for the first time. Otherwise, if historical data are available, longitudinal versions are used.

In the case of item nonresponse, some processing and editing is done before proceeding to imputation in order to remove inconsistencies in the data collected. Editing is based on a set of rules that must or should likely be satisfied. This process leads to either creating additional missing values or imputing by deduction the values that should have been reported. This type of imputation is used when a single missing field can be deduced uniquely from the given information. For example, if one component of a sum is missing and the remaining components including the sum are present, then the missing component can be determined uniquely.

Once this process is completed, the remaining missing values are imputed using one of the four methods described below. To avoid producing inconsistencies in the imputed data, most interrelated fields are imputed as a block. Since there are a number of questions falling into this category, a post-imputation system has been developed to preserve all inter-field relationships.


Estimation is the survey step that consists of approximating unknown parameters using only a part of the population, called the sample, and of making inferences about these unknown parameters; that is, drawing conclusions about the population from only a sample of that population. Examples of usual population parameters of interest include population totals, means and ratios. There may also be an interest in the estimation of model parameters such as linear or logistic regression model coefficients.

Estimates are obtained by attaching a final weight to each unit (workplace or employee) in the sample. The basic weighting principle is to weight each unit by the inverse of its probability of inclusion in the sample. This leads to the initial design weight, which is often interpreted as the number of times that each sampled unit should be duplicated to represent the whole population. Because of many reasons, such as refusals or the impossibility to contact some of the sampled units, the observed sample is of smaller size than the original sample selected. To compensate for nonresponse, imputation and nonresponse weight adjustment are used. Nonresponse weight adjustment consists of adjusting the design weight of each responding unit by a nonresponse adjustment factor. Then, another weight adjustment is performed to deal with the problem of stratum jumpers (large workplaces believed to be small at the time of the survey design and vice-versa), which leads to an adjusted design weight. Finally, calibration is used to obtain final weights. The basic idea of calibration is to find final weights as close as possible to the adjusted design weights and such that constraints are satisfied. The goal of these constraints is: i) to ensure consistency with total employment by industry/region obtained from the Survey of Employment, Payroll and Hours (SEPH); and ii) to improve the efficiency of the estimates.

Quality evaluation

To validate estimates of key financial variables such as revenues and expenditures, comparisons were made with the United Enterprise Survey, the Annual Retail and Wholesale Trade Survey, and the Census of Manufacturing. Other data sources such as LEAP were used to assess survey coverage and death rates. On the employee side, comparisons were made with wage data collected by the Survey of Labour and Income Dynamics and the Labour Force Survey. Other variables were scrutinized as well. Most of these data verification activities took place during the revision of the 1999 wave. Since then, data are vigorously validated and edited each year of the survey to ensure sufficient data quality.

Disclosure control

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. 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.

The release of the workplace portion of WES to the Research Data Centres marks the first occasion that business data have been made available outside of Statistics Canada. Despite all external users of WES data being sworn to uphold the confidentiality of the files, further steps are taken to ensure disclosure avoidance. All obvious workplace identifiers are removed from the files and a number of large or unique respondents are suppressed. The procedure is done in two steps.

In the first step one computes an average rank of a record based on its contribution to the estimates of totals for a number of key variables. The top five ranked records in each industry are analyzed to assess their likelihood of being identified. The second step involves a multivariate technique called Principal Component Analysis whereby data are reduced to at most three dimensions -- principal components -- such that the characteristics of the original data are preserved. Any records whose principal components set them apart from the rest of the observations are reviewed. This can also be done visually by rotating a 3-D representation of the principal components to identify units that lie outside of the main data cloud. The records deemed unique by this step are combined with those obtained in the first step and suppressed. The suppression pattern is reviewed at two year intervals coinciding with the sample top-up.

The confidentiality of the employee portion is less problematic in that the sampling weights tend to mask the identity of respondent. Note that the employees associated with the suppressed workplaces are also suppressed.

The information presented in publications is reviewed to ensure that the confidentiality of individual responses is respected. Any estimate that could reveal the identity of a specific respondent is declared confidential, and consequently not published.

Data accuracy

While considerable effort is made to ensure a high standard throughout all survey operations, the resulting estimates are inevitably subject to a certain degree of error. This is true in every survey. The total survey error can be divided into two main components: the sampling error and the nonsampling errors. The sampling error is due to the fact that estimates are computed using only a sample of the whole population instead of a complete census while the nonsampling errors are due to all other causes such as an imperfect frame, measurement errors or nonresponse.

The WES sample was designed to be efficient for estimating totals at an industry by region by size level within the available budget. The projected coefficients of variation were around 5% for industry and 10% for industry by region for variables highly correlated with employment. When estimates are produced, they are compared to the projected precision. Approximately 60% of all estimates of totals exceeded expectation with another 25% being within the Statistics Canada publishable cut-off of 33%. The remaining 15% were not publishable by our standards. These were mostly estimates not highly correlated with employment. All estimates falling into the unpublishable category are validated.

The measure of non-response error and the coefficient of variation must be considered jointly to assess the quality of the estimates. The lower the coefficient of variation and the higher the response fraction, the better will be the published estimate.

The 2006 response rates:

Workplace -- 74.9