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Results of the Wage Gap Pilot Program
Pay Equity Office
Results of the Wage Gap
Pilot Program
Results of the Wage Gap Pilot Program
Results of the Wage Gap Pilot Program
February 2015
978-1-4606-5195-7 [Print]
978-1-4606-5196-4 [HTML]
978-1-4606-5197-1 [PDF]
Contact
Pay Equity Commission
180 Dundas Street West, Suite 300
Toronto Ontario M7A 2S6
Telephone:
416-314-1896
Toll-Free: 1-800-387-8813
TTY: 416-212-3991
TTY Toll-Free: 1-855-253-8333
Facsimile: 416-314-8741
Email: [email protected] Website: www.payequity.gov.on.ca
Ce document est disponible en français
Results of the Wage Gap Pilot Program
Contents
1: Objectives ....................................................................................................................4
2: Background ..................................................................................................................4
3: Results .........................................................................................................................5
Response Rate ............................................................................................................5
Analysis .......................................................................................................................6
4: Conclusion ...................................................................................................................8
5: Appendix 1 – Data ..................................................................................................... 10
6: Appendix 2 – Decomposing the Wage Gap ............................................................... 11
Hourly Wage Results ................................................................................................. 11
Annual Wage Results ................................................................................................ 13
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1: Objectives
The Pay Equity Office (PEO or the office) conducted the Wage Gap Pilot Program in 2011
with the following objectives:
1. Outreach: to conduct outreach to private sector non-unionized employers with more
than 250 employees
2. Awareness: to raise awareness among these employers about their compensation
practices and possible wage gaps
3. Interest: to determine level of interest among these employers in their voluntary
participation to examine compensation practices
4. Opportunity to pilot PEO developed wage gap criteria: to field test PEO
developed criteria for assessing the presence of an apparent wage gap
Apparent wage gaps were noted among employers who participated. The data submitted
provides a snapshot of compensation practices at a point in time and is not representative
of all private sector employers in Ontario.
2: Background
The Pay Equity Office is responsible for enforcing the Pay Equity Act (R.S.O. 1990, c.P.7)
(the Act). The office works to educate the public about the Act and investigates and resolves
pay equity complaints. In addition, the office is empowered to conduct research into aspects
of compensation practices.
The PEO commissioned a selection of private sector, non-unionized employers through Dun
and Bradstreet. The following criteria were applied in the selection process:
1. Location: Ontario
2. Size: Corporate wide, 250+
3. Industries: manufacturing, real estate, wholesale trade and finance insurance.
Employers whom the Office had monitored in the previous 11 years, those with open cases
in the Office’s database as well as federally regulated employers not covered by pay equity
legislation in Ontario were filtered out of the final employer listing.
Surveys were mailed out to a total of 516 employers asking them to supply compensation
data as of December 31, 2010:
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Job title/position
Gender in that position
Employees’ current pay,
Salary range (if applicable)
Years of service
Five PEO developed criteria were applied to the incoming responses:
1. Clustering of jobs: explored the grouping of women in stereotypical female jobs
and alignment of jobs around wage/salary rates that might have been different
depending on the gender of the incumbent;
2. Natural breaks in clusters of jobs: examined the salary ranking of male and
female jobs and how there were grouped within identified pay bands;
3. Seniority vs. Job Rate: examined the job rate (highest rate of pay for identified
jobs) against seniority (years of service) to identify regular patterns of women
progressing at different rates compared to men;
4. Distribution of jobs: examined the distribution of female jobs across the
organization and how women were compensated compared to men within the
same job title; and
5. Job to job comparisons: compared male and female job titles that were estimated
to possess similar skill, effort, responsibility and working conditions.
3: Results1
Response Rate
Of the 516 letters sent to employers requesting their participation, 420 (81%) responded
with their compensation data. This high response rate underscores the importance of the
PEO’s outreach efforts and represents an encouraging level of engagement with respect to
employers’ interest in responding about their compensation practices.
1 Statistical analysis of data and statistical findings provided by Dr. Amy Peng, Associate Professor, Department of Economics, Ryerson University.
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Of the 420 employers who responded, 69 employers were excluded from further analysis for
administrative reasons e.g. duplicate files. The remaining 351 respondents formed the
sample for wage gap assessment.
Over two third of the respondents (68.5%) were from the manufacturing sector, reflective of
the size of Ontario’s largest employers.
Fifty-four percent (188 employers) were found to have an apparent wage gap based upon
the review of their compensation data using the five PEO criteria (wage gap assessment).
The office conducted statistical analysis to examine the gender wage gap and to examine
the influence of occupational tenure (years of service). To conduct this statistical analysis,
the sample included all firms with an “apparent wage gap” and where there was missing
information or miscoded observations, these firms were either adjusted or removed from the
sample (for detailed information, please see Appendix 1 – Data). The resulting dataset is
composed of 155 employers and 72,818 employees for analysis.
Analysis
There are 30,402 (41.75%) female employees and 42,398 (58.22%) male employees in the
sample. Employers have reported employee’s current pay in multiple ways (hourly pay,
weekly pay, annual pay or a combination of several pay frequencies).
Hourly Wages
Hourly pay is recorded for a total of 27,917 individuals. However, 10,649 of these also
record an annual rate of pay. 17,268 individuals for whom only an hourly pay amount is
recorded are used for the hourly analysis.
Figure 1: Distribution of Hourly Wages
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Figure 1 shows the distribution of hourly wages for male and female employees. As can be
observed, female’s hourly pay is concentrated at the lower range of the pay scale and
male’s hourly pay is concentrated at the higher range of the pay scale.
Approximately 20 percent of the male employees were paid less than $11 per hour while
just over 30 percent of the female employees earned less than $11 per hour.
The average hourly pay of the 9,620 males in this group is $20.84 while the average hourly
pay of the 7,648 females in this group is $16.49 (79.13% of average wage of males.) The
median (2nd Quartile) is $18.40 for males and $14.39 for females (78.2% of median wage of
males.) In the case of hourly pay, there is an average wage gap of $4.34 per hour.
Annual Wages
Annual pay is recorded for 55,537 individuals in the sample.
Figure 2: Distribution of Wages
Figure 2 shows the distribution of annual wages for both male and female employees.
Similar to the case for hourly wages, female wages are more concentrated towards the
lower end of the pay scale and the male wages are more concentrated towards the higher
end of the pay scale.
The average male annual wage is $74,943.42 and the average female annual wage is
$58,324.51 (77.82% of male’s wage). In the case of annual pay, there is an average wage
gap of $16,618.91.
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Occupational Tenure (Years of Service)
A majority of this dataset (81%) met the criteria for seniority versus job rate, indicating a
pattern of women progressing in their compensation at different rates when compared to
men.
In order to further investigate the gender wage gap in this dataset, a pooled regression was
conducted using years of service as an explanatory variable. Generally, in this approach,
wage gap factors include type of job, education, age, job tenure and other experience. In
this pilot project, the collection of individual characteristics was limited to occupational
tenure and detailed job characteristics was not requested (aside from job titles). As a result,
applying a gender wage gap decomposition model with a single variable will explain a small
portion of the gender wage gap. However, the model is illustrative of the suggested
approach for future pay equity research.
In the case of hourly pay, there is an average wage gap of $4.34 per hour. The average
years of service for males is 8.06 while the average for females is 7.34 years. For an
additional year of service, males earn $0.54 more per hour while females only earn $0.42
more. Differences in years of service alone can explain 8.9 percent of this gender wage
gap.
In the case of annual pay, there is an average wage gap of $16,618.91. The average years
of service for males is 10.34 years and the average years of service for females is 9.41
years. For an additional year of service, males earn $982.21 more while females only earn
$572.87 more. Differences in years of service alone can explain 5.5 percent of this gender
wage gap.
4: Conclusion
The Pay Equity Office Wage Gap Pilot Program has wielded successful results in meeting
its primary objectives.
From an outreach perspective, the program accessed Ontario’s largest private sector non-
union employers and increased awareness on the role of the office and drew attention to
reviewing compensation practices for impacts on gender wage gaps. The PEO will continue
to focus on activities that raise awareness regarding compensation practices.
From an interest perspective, the degree of participation in providing firm compensation
data was unprecedented. This resulted in a meaningful compensation dataset for statistical
analysis and an opportunity to note considerations for future pay equity research.
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The statistical analysis presented in this report has provided insight into Ontario’s largest
private sector non-union firms. We can see that in terms of occupational tenure, for every
additional year of service, there is a noted difference in pay. More research on causes of
Ontario’s wage gap can provide a clearer picture.
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5: Appendix 1 – Data
From 188 total firms that have an apparent wage gap, 33 firms were excluded from the
analysis for the following reasons:
Neither hourly wage nor annual wage was available
Years of service were unavailable
Hourly or annual pay rate was recorded as a range
Years of service was recorded as a negative or impossibly large value (fewest
years of service was -100 while longest was over 159 years)
Hourly or annual wage was unusually high (sales representative’s hourly wage
higher than a CEO’s hourly wage / annual wage over $1,000,000)
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6: Appendix 2 – Decomposing the Wage Gap
A standard approach used in economics for explaining the wage gap is the Blinder–Oaxaca
decomposition for linear regression models.
The Blinder–Oaxaca decomposition begins by fitting linear regression equations for male
and female wages:
𝑤𝑚 = 𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆𝑚 + 𝜀𝑚
𝑤𝑓 = 𝛼𝑓 + 𝛽𝑓𝑌𝐸𝐴𝑅𝑆𝑓 + 𝜀𝑓
The decomposition proceeds to explain the difference in the average male and average
female wage by introducing an intermediate hypothetical average female wage that uses
estimated male parameters with female average years.
�̅�𝑚 − �̅�𝑓 = �̅�𝑚 − �̅�𝑓∗ + �̅�𝑓
∗ − �̅�𝑓 = 𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 + 𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑔𝑎𝑝
�̅�𝑚 = 𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅ ̅𝑚
�̅�𝑓 = 𝛼𝑓 + 𝛽𝑓𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅�̅�
�̅�𝑓∗ = 𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅
�̅�
Simplifying shows that the explained part is given by
�̅�𝑚 − �̅�𝑓∗ = 𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅ ̅
𝑚 − (𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅�̅�) = 𝛽𝑚(𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅ ̅
𝑚 − 𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅�̅�)
For convenience, we can nest the two separate male and female wage equations into a
single equation to simplify estimation as follows:
𝑤𝑖 = 𝛼𝑚 + 𝛽𝑚𝑌𝐸𝐴𝑅𝑆𝑖 + 𝛼𝑓−𝑚𝑆𝐸𝑋𝑖 + 𝛽𝑓−𝑚𝑆𝐸𝑋𝑖𝑌𝐸𝐴𝑅𝑆𝑖 + 𝜀𝑖
In the computer estimation results, y represents the wage, x1 the years of service and x2
the sex (0=Male and 1 otherwise) for each of the individuals in the dataset.
Hourly Wage Results
The regression results for individuals with hourly wages are:
Call:
lm(formula = y ~ x1 + x2 + x1 * x2)
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Residuals:
Min 1Q Median 3Q Max
-21.939 -5.156 -2.555 2.061 264.224
Coefficients:
Estimate Std.Error t value Pr(>|t|)
(Intercept) 16.50135 0.14428 114.367 < 2e-16 ***
x1 0.53783 0.01231 43.679 < 2e-16 ***
x2 -3.10172 0.21837 -14.204 < 2e-16 ***
x1:x2 -0.11638 0.01985 -5.862 4.66e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 10.27 on 17264 degrees of freedom
Multiple R-squared: 0.1646, Adjusted R-squared: 0.1645
F-statistic: 1134 on 3 and 17264 DF, p-value: < 2.2e-16
These results imply that
𝛼𝑚 = 16.50135, 𝛽𝑚 = 0.53783, 𝛼𝑓 − 𝛼𝑚 = −3.10172, 𝛽𝑓 − 𝛽𝑚 = −0.11638
𝛼𝑓 = 𝛼𝑚 − 3.10172 = 13.39963
𝛽𝑓 = 𝛽𝑚 − 0.11638 = 0.42145
Hourly Summary:
Group Size Avg.Wage Avg.Years
Female 7648 16.49 7.34
Male 9620 20.84 8.06
Combined 17268 18.91 7.74
Thus, the explained portion of the hourly wage gap is given by
𝛽𝑚(𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅ ̅𝑚 − 𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅
�̅�) = 0.42145(8.06 − 7.34) = $0.30
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(Note: This value of $0.30 is less than the $0.38 used in the text due to rounding of years of
service.)
Annual Wage Results
The regression results for individuals with annual wages are:
Call:
lm(formula = y ~ x1 + x2 + x1 * x2)
Residuals:
Min 1Q Median 3Q Max
-79180 -20904 -7881 12321 905663
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64782.67 294.29 220.13 <2e-16 ***
x1 982.21 21.77 45.12 <2e-16 ***
x2 -11850.51 458.26 -25.86 <2e-16 ***
x1:x2 -409.34 35.82 -11.43 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 34300 on 55533 degrees of freedom
Multiple R-squared: 0.09153, Adjusted R-squared: 0.09148
F-statistic: 1865 on 3 and 55533 DF, p-value: < 2.2e-16
Annual Pay Summary:
Group Size Avg.Wage Avg.Years
Female 22766 58324.51 9.41
Male 32771 74943.42 10.34
Combined 55537 68130.91 9.96
Average annual wage difference of $16,618.91.
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Table 1: Parameter Estimates Based on Regression Results
Gender α β Average Years
Male 64782.67 982.21 10.34
Female 52932.16 572.87 9.41
𝛽𝑚(𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅ ̅𝑚 − 𝑌𝐸𝐴𝑅𝑆̅̅ ̅̅ ̅̅ ̅̅
�̅�) = 982.21(10.34 − 9.41) = $913.46
(Note: This value of $913.46 is less than the $915.34 used in the text due to rounding of
years of service.)