pay equity office

Results of the Wage Gap Pilot Program

Pay Equity Office

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

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Facsimile: 416-314-8741

Email: [email protected] Website: www.payequity.gov.on.ca

Ce document est disponible en français

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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


February 2015 | Results of the Wage Gap Pilot Program 4

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:

http://www.e-laws.gov.on.ca/html/statutes/english/elaws_statutes_90p07_e.htm



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.



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



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.



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.



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.



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)



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)



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



(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.



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.)

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