Workplace and Employee Survey

Marie Drolet

WES Research Manager

Business and Labour Market Analysis Division

Statistics Canada

marie.drolet@statcan.ca

Outline of today’s presentation

General survey overview• Why a linked survey?

• Survey content

• Potential research questions

• Collection

• Methodology

• Data Access

Comparative Research: • Can the workplace explain gender pay differentials?

Goals of WES

To develop an ongoing survey that will link workers and workplaces at the micro levelprovide information from both the demand and

supply sides of the labour market ==> enriching research studiesprovide longitudinal information allowing

researchers to control for both individual and workplace effects that are not possible in other data sets

improve survey infrastructure

Survey ContentEmployee outcomes:Hours polarization;Wages;Training received;

Workplace characteristics:Technology implemented;Operating revenues, expenditures, payroll,Employment; hiring, vacanciesBusiness strategies;Work organizationCompensation schemes;Training provided;Occupation mix Organizational change;Subjective measures of productivity, profitabilityType of competition

Worker/job characteristics;Education;Age/gender;Occupation, management responsibilities;Work history, tenure;Family characteristics;Unionization;Use of technology;Participation in decision making;Wages and fringe benefits;Work schedule/arrangements;Training taken

Workplace outcomes;Employment, revenue growth;Organizational change;Implementation of technologies.

Potential research questions Are unionized workers more actively involved in

workplace decision-making and employee participation programs?

Are there industrial sectors that are replacing less skilled workers with higher skilled workers?

What are the characteristics of workers leaving their jobs, thereby creating job vacancies?

Do alternative work practices such as job rotation and participation in work groups reduce quit rates?

Methodology:Target Population

All business locations in Canada that have paid employees EXCEPT employers in

• Yukon, Northwest Territories

• agricultural, fishing, hunting, trapping,

• public administration

• religious organizations

• military

Employee content• receives a T4 slip from Revenue Canada

Methodology:Sampling frame

Stratified 2 stage design Workplace component

• physical location of business with paid workers

• frame stratified by industry, region, size

• size cut-offs are different for industry / region combinations ==> model based approach

• sampling weights assigned to each unit = the inverse probability of selection with adjustment*

Worker Component• lists of employee made available by employers

Methodology:Longitudinal strategy for workplaces

*** up to 6 years

Methodology:Longitudinal strategy for workers

*** 2 years

Methodology:Weighting and Estimation

To produce estimates from a sample that relates to a population of interest requires the use of survey weights

Weights are adjusted for• complete non-response

• stratum jumpers

• calibration or “benchmark” to known totals from the Survey of Employment, Payroll and Hours

• linked analysis weights adjusted for live units with no responding employees

Methodology:How to compute variances that take into

account the complex survey design of WES? Not done in most statistical packages Use bootstrap weights provided

• (100 in total for each observation, each based on 50 iterations)

• idea: re-sampling technique to capture variability

To calculate the variance• produce an estimate based on each set of bootstrap

weights

• compute the variance estimate

• apply an adjustment to make the variance design consistent (50/100)

Methodology:Why is taking account of the complex survey

design of WES SOOOO important?

NOT taking into account of the design effect results in an UNDERESTIMATION of the variance

Hypothesis testing and constructing confidence intervals requires accurate standard errors

May erroneously report that a statistic is significantly different from zero when it is not.

Rules of Thumb: NO

Response Rates

Employer Employee

1999 96.5% 83.3%

2000 95.8% 86.8%

2001 92.7% 87.8%

Data Access Research Data Centres

• SSRHC application

• evaluated by 3 member peer committee

• judged on scientific merit, viability of methods, appropriateness of data

• deemed employees of STC

• research falls within the mandate of STC

Remote Access• proposal sent to WES research manager

• dummy data set sent to researcher

• programs sent to Statistics Canada

• designed primarily for multivariate analyses

Important websites Statistics Canada: www.statcan.ca WES site

• General Information:– http://www.statcan.ca/english/survey/business/workplace/

workplace.htm (English)– http://www.statcan.ca/francais/survey/business/

workplace/workplace_f.htm (French)

• Questionnaires (1999-2002)– http://www.statcan.ca/english/concepts/wes.htm– http://www.statcan.ca/francais/concepts/wes_f.htm

• Workplace Evolving Series – http://www.statcan.ca/english/freepub/71-584-MIE/free.htm

– http://www.statcan.ca/francais/freepub/71-584-MIF/free_f.htm

Can the workplace explain Canadian gender pay differentials?

Marie Drolet

Canadian Public Policy, Summer 2002Evolving Workplace Series, No

Objectives

To move beyond ‘traditional’ analyses by incorporating workplace characteristics in the wage outcomes of men and womenhousehold data = focus on worker

To determine the contribution of the workplace in ‘explaining’ the gender wage gaplinked employee-employer data = focus on

gender segregation

Determinants of wages Usual suspects

• human capital, demographic, job

New WES variables• high performance workplace practices

» self directed workgroups

» performance pay

• foreign ownership

• non profit organizations

• quantity and timing of labour demanded and supplied

• required & actual education match

• training costs per employee

• workplace rate of part-time employment

• industry / occupation / firm size

Do wages differ by workplace characteristics?

expected robust association between wages and ‘established’ variables

impact of workplace variables(-) percent working part-time

(+) training expenditures per employee

(+) foreign ownership

(+) self directed workgroups

(+) workers receiving performance-based pay

(+) quantity and timing of labour

(+) undereducated

(-) overeducated

(NS) non-profit organizations

Are the workplaces of men and women different?

* significantly differentMen Women

Average workplace rateof part-time employment*

19.7% 33.7%

Self-directed workgroup* 36.3% 29.0%Non-profit* 13.9% 28.6%Foreign held* 11.0% 5.5%Training expenditures $257 $247Over-educated 36.4% 38.4%Under-educated 16.3% 14.5%

Main Finding #1:Women are concentrated in low wage

workplaces

When usual suspects are taken into account …. Pooled OLS model:

• women earn 15% less men when there are NO controls for the workplace

Workplace fixed effects model*• women earn 8% less than men when controls for

the workplace are included

Main Finding #2:Industry measure in WES ‘explains’ more of

gender pay differentials Using ONLY Worker characteristics +

industry & occupation– 58% of gender wage gap ‘explained’– industry ‘explains’ 34% of gender wage gap– significantly different from other studies

• Using Survey of Labour and Income Dynamics 1997: – 50% is explained

– industry accounts for 15%

Main Finding #3:The workplace accounts for more of the gap

than the worker

Excludes industry &occupation

Includes industry &occupation

Total 100.0 100.0

Workercharacteristics

10.7 18.7

Workplacecharacteristics

25.8 41.4

“Unexplained” 63.5 39.9

Main Finding #4:The contribution of specific workplace characteristics in ‘explaining’ the gap

based on Oaxaca decomposition method, base = male pay structure

Excludes industry& occupation

Includes industry& occupation

IndustryWorkplace part-time rate 17.7

19.710.9*

Self-directed workgroups 2.5 2.1Performance-based pay 2.3 2.2

Foreign held 2.4 2.2Non-profit -1.0 1.3Timing of labour 2.2 1.4Training expenditures 0.3 0.2Job-education mismatch 1.8 1.0

Main Finding #5:Despite addition of new variables, a

substantial portion of gap is unexplained

In most detailed specification about 40% of the gap is unexplained

Adjusted: women earn 92% of male average hourly wage rate

Differs from analyses using SLID• unexplained: 51%

• adjusted: 89%

Important from a public policy perspective

Current research necessary since policies tend to address different components of the gap

Knowledge of contribution of workplace to gender pay differentials is essential since workplace contributes more to explaining differentials than the worker