Transcript Explaining the Gender Wage Gap: The role of worker and

Workplace and Employee Survey
Marie Drolet
WES Research Manager
Business and Labour Market Analysis Division
Statistics Canada
[email protected]
Outline of today’s presentation
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General survey overview
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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
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To develop an ongoing survey that will
 link workers and workplaces at the micro level
provide information from both the demand and
supply sides of the labour market
==> enriching research studies
provide longitudinal information allowing
researchers to control for both individual and
workplace effects that are not possible in other
data sets
 improve survey infrastructure
Survey Content
Employee outcomes:
Hours polarization;
Wages;
Training received;
Workplace characteristics:
Technology implemented;
Operating revenues,
expenditures, payroll,
Employment; hiring, vacancies
Business strategies;
Work organization
Compensation schemes;
Training provided;
Occupation mix
Organizational change;
Subjective measures of
productivity, profitability
Type 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
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Are unionized workers more actively involved in
workplace decision-making and employee
participation programs?
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Are there industrial sectors that are replacing less
skilled workers with higher skilled workers?
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What are the characteristics of workers leaving
their jobs, thereby creating job vacancies?
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Do alternative work practices such as job rotation
and participation in work groups reduce quit rates?
Methodology:
Target Population
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All business locations in Canada that have
paid employees EXCEPT employers in
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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
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• 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
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To produce estimates from a sample that
relates to a population of interest requires
the use of survey weights
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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?
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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
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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?
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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
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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
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Remote Access
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proposal sent to WES research manager
dummy data set sent to researcher
programs sent to Statistics Canada
designed primarily for multivariate analyses
Important websites
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Statistics Canada: www.statcan.ca
WES site
• General Information:
– http://www.statcan.ca/english/survey/business/workpl
ace/workplace.htm (English)
– http://www.statcan.ca/francais/survey/business/work
place/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/71-584MIE/free.htm
– http://www.statcan.ca/francais/freepub/71-584MIF/free_f.htm
Can the workplace explain Canadian
gender pay differentials?
Marie Drolet
Canadian Public Policy, Summer 2002
Evolving Workplace Series, No
Objectives
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To move beyond ‘traditional’ analyses by
incorporating workplace characteristics in
the wage outcomes of men and women
household data = focus on worker
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To determine the contribution of the
workplace in ‘explaining’ the gender
wage gap
linked employee-employer data = focus on
gender segregation
Determinants of wages
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Usual suspects
• human capital, demographic, job
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New WES variables
• high performance workplace practices
» self directed workgroups
» performance pay
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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?
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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 different
Men
Women
Average workplace rate
of 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%
$257
$247
Over-educated
36.4%
38.4%
Under-educated
16.3%
14.5%
Training expenditures
Main Finding #1:
Women are concentrated in low wage
workplaces
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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
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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 & Includes industry &
occupation
occupation
Total
100.0
100.0
Worker
characteristics
10.7
18.7
Workplace
characteristics
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 Includes industry
& occupation
& occupation
Industry
19.7
Workplace part-time rate
17.7
10.9*
Self-directed workgroups
2.5
2.1
Performance-based pay
2.3
2.2
Foreign held
2.4
2.2
Non-profit
-1.0
1.3
Timing of labour
2.2
1.4
Training expenditures
0.3
0.2
Job-education mismatch
1.8
1.0
Main Finding #5:
Despite addition of new variables, a
substantial portion of gap is unexplained
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In most detailed specification about 40% of the gap
is unexplained
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Adjusted: women earn 92% of male average hourly
wage rate
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Differs from analyses using SLID
• unexplained: 51%
• adjusted: 89%
Important from a public policy
perspective
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Current research necessary since policies tend
to address different components of the gap
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Knowledge of contribution of workplace to
gender pay differentials is essential since
workplace contributes more to explaining
differentials than the worker