Part-time work and family-friendly practices in canadian workplaces - June 2003

Appendix A: Concepts and Methods

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Objectives

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.

Sample sizes and response rates

WES was conducted for the first time during the summer (employer survey part) and fall of 1999 (employee survey part). Just over 6,350 workplaces and about 24,600 employees responded to the survey, representing response rates of 94% and 83%, respectively. The employer sample is longitudinal—the sampled locations will be followed over time, with the periodic addition of samples of new locations to maintain a representative cross section. 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). This longitudinal aspect will allow researchers to study both employer and employee outcomes over time in the evolving workplace.

Appendix A: Table 1. Sample sizes and estimated populations

	Workplaces		Employment
Industry/Workplace size/Region	Number of respondents	Estimated population	Number of respondents	Estimated population
Overall	6,351	735,911	24,597	10,777,543
Industry
Forestry, mining, oil and gas extraction	313	13,359	1,193	190,453
Labour intensive tertiary manufacturing	406	20,584	1,620	497,409
Primary product manufacturing	318	7,648	1,434	392,872
Secondary product manufacturing	292	11,762	1,191	371,888
Capital intensive tertiary manufacturing	359	17,059	1,469	585,253
Construction	607	54,659	2,095	419,373
Transportation, warehousing, wholesale trade	706	84,820	2,877	1,114,182
Communication and other utilities	413	9,712	1,376	243,601
Retail trade and consumer services	515	249,409	1,864	2,596,439
Finance and insurance	498	34,153	1,893	512,159
Real estate, rental and leasing operations	364	24,429	1,143	189,303
Business services	467	83,245	1,830	1,006,460
Education and health services	751	109,404	3,193	2,340,519
Information and cultural industries	342	15,669	1,419	317,632
Workplace size
1-19 employees	2,872	640,077	6,154	3,471,168
20-99 employees	1,743	83,412	8,356	3,260,557
100-499 employees	1,249	10,735	6,810	1,960,109
500 employees or more	487	1,687	3,277	2,085,708
Region
Atlantic	777	63,152	3,003	709,303
Quebec	1,432	153,277	5,745	2,560,682
Ontario	1,626	276,920	6,187	4,352,265
Manitoba	423	27,888	1,641	402,138
Saskatchewan	329	29,333	1,217	322,333
Alberta	839	80,063	3,183	1,076,019
British Columbia	925	105,279	3,621	1,354,803
Source: Workplace and Employee Survey, 1999.

 

Appendix A: Table 2. Response rates

Category	Employer response rate (%)	Employee response rate (%)
Overall	94.0	83.1
Industry
Forestry, mining, oil and gas extraction	97.0	87.1
Labour intensive tertiary manufacturing	91.0	81.3
Primary product manufacturing	95.3	85.7
Secondary product manufacturing	94.7	85.7
Capital intensive tertiary manufacturing	94.5	84.4
Construction	94.3	83.8
Transportation, warehousing, wholesale trade	92.6	84.5
Communication and other utilities	98.0	82.9
Retail trade and consumer services	93.3	82.2
Finance and insurance	96.5	87.5
Real estate, rental and leasing operations	97.3	87.8
Business services	94.2	85.7
Education and health services	96.8	86.5
Information and cultural industries	98.1	87.9
Workplace size
1-19 employees	96.9	85.0
20-99 employees	95.1	86.8
100-499 employees	92.4	85.0
500 employees or more	93.4	81.6
Region
Atlantic	96.3	88.8
Quebec	92.4	82.5
Ontario	95.6	84.2
Manitoba	96.4	87.7
Saskatchewan	96.7	86.3
Alberta	94.9	85.0
British Columbia	96.2	85.1
Source: Workplace and Employee Survey, 1999.

Target population

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

a) Employers in Yukon, Northwest Territories and Nunavut

b) Employers operating in crop production and animal production; fishing, hunting and trapping; private households and public administration.

The target population for the employee component is all employees working in the selected workplaces who receive a Customs Canada and 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.

Survey population

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.

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, profiling of businesses and administrative sources.

Reference period

The reference period for WES is mainly the 12-month period ending March 1999. Some questions in the workplace portion covered the last pay period ending before March 1999.

Sample design

The survey frame is a list of all units 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 information for the selected units.

i) Workplace survey

The survey frame for the workplace component of WES was created from the information available on the Statistics Canada Business Register.

Prior to sample selection, the business locations on the frame were stratified into relatively homogeneous groups called strata, which were then used for sample allocation and selection. The WES frame was stratified by industry (14), region (6), and size (3), which was defined using estimated employment. The size stratum boundaries were typically different for each industry/region combination. The cut-off points defining a particular size stratum were computed using a model-based approach. The sample was selected using Neyman allocation. This process generated 252 strata with 9,144 sampled business locations.

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

The inaugural WES survey collected data from 6,351 out of the 9,144 sampled employers. The remaining employers were a combination of workplaces determined to be 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.

ii) Employee survey

The frame for the employee component of WES was based on lists of employees made available to interviewers by the selected workplaces. A maximum of twelve employees was sampled using a probability mechanism. In workplaces with fewer than four employees, all employees were selected.

Data collection

Data collection, data capture, preliminary editing and follow-up of non-respondents were all done in Statistics Canada Regional Offices. Interviewers in person collected the workplace survey data. The workplace questionnaire covered a wide range of topics. For about 20% of the surveyed units (mostly large workplaces), more than one respondent was required to complete the questionnaire. For the employee component, telephone interviews were conducted with persons who had agreed to participate in the survey by filling out and mailing in an employee participation form.

Statistical edit and imputation

Following collection, all data were analyzed extensively. Extreme values were listed for manual inspection in order of priority determined by the size of the deviation from average behaviour and the size of their contribution to the overall estimate.

Respondents who opted not to participate in the survey—total nonresponse—were removed and the weights of the remaining units were adjusted upward to preserve the representativity of the sample. For respondents who did not provide all required fields—item non-response— a statistical technique called imputation was used to fill in the missing values for both employers and employees. The particular method that was selected for this purpose, weighted hot-deck, is based on first identifying respondents at a certain level called imputation class, and then from within the imputation class a donor is selected using a probability mechanism. The donor's value is then transferred to the missing field of the non-respondent.

The WES components were treated independently even if some questions on the employee questionnaire could have been imputed from the related workplace questionnaire.

Estimation

The reported (or imputed) values for each workplace and employee in the sample are multiplied by the weight for that workplace or employee; these weighted values are summed up to produce estimates. An initial weight equal to the inverse of the original probability of selection is assigned to each unit. To calculate variance estimates, the initial survey weights are adjusted to force the estimated totals in each industry/region group to agree with the known population totals. These adjusted weights are then used in forming estimates of means or totals of variables collected by the survey.

Variables for which population totals are known are called auxiliary variables. They are used to calibrate survey estimates to increase their precision. Each business location is calibrated to known population totals at the industry/region level. The auxiliary variable used for WES is total employment obtained from the Survey of Employment, Payrolls and Hours.

Estimates are computed for many domains of interest such as industry and region.

Data quality

Any survey is subject to errors. 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. Errors can arise due to the use of a sample instead of a complete census, from mistakes made by respondents or interviewers during the collection of data, from errors made in keying in the data, from imputation of a consistent but not necessarily correct value, or from other sources.

Sampling errors

The true sampling error is unknown; however, it can be estimated from the sample itself by using a statistical measure called the standard error. When the standard error is expressed as a percent of the estimate, it is known as the relative standard error or coefficient of variation.

Non-sampling errors

Some non-sampling errors will cancel out over many observations, but systematically occurring errors (i.e. those that do not tend to cancel) will contribute to a bias in the estimates. For example, if respondents consistently tend to underestimate their sales, then the resulting estimate of the total sales will be below the true population total. Such a bias is not reflected in the estimates of standard error. As the sample size increases, the sampling error decreases. However, this is not necessarily true for the non-sampling error.

Coverage errors

Coverage errors arise when the survey frame does not adequately cover the target population. As a result, certain units belonging to the target population are either excluded (under-coverage), or counted more than once (over-coverage). In addition, out-of-scope units may be present on the survey frame (over-coverage).

Response errors

Response errors occur when a respondent provides incorrect information due to misinterpretation of the survey questions or lack of correct information, gives wrong information by mistake, or is reluctant to disclose the correct information. Gross response errors are likely to be caught during editing, but others may simply go through undetected.

Non-response errors

Non-response errors can occur when a respondent does not respond at all (total non-response) or responds only to some questions (partial non-response). These errors can have a serious impact on estimates if the non-respondents are systematically different from the respondents in survey characteristics and/or the non-response rate is high.

Processing errors

Errors that occur during the processing of data represent another component of the non-sampling error. Processing errors can arise during data capture, coding, editing, imputation, outlier treatment and other types of data handling. A coding error occurs when a field is coded erroneously because of misinterpretation of coding procedures or bad judgement. A data capture error occurs when data are misinterpreted or keyed in incorrectly.

Joint interpretation of measures of error

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.

Confidentiality

The information presented in this publication has been 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.

Response/non-response

a) Response rate: includes all units, which responded by providing "usable information" during the collection phase.

b) Refusal rate: includes those units, which were contacted but refused to participate in the survey.