Statistical Analyses of Compensation
South Florida Industry/OFCCP Liaison Group
May 19, 2010
Mary Dunn Baker, Ph.D.
Objectives
Reasons for conducting compensation analyses
Basic concepts underlying pay studies
Illustration of the compensation analysis process
• Hypothetical XYZ Corporation Pay Equity Study
Identification of explanatory factors
Potential perils of using “pooled” (organizationwide) models and the value of separate studies for
relevant subunits (job families or SSEGs)
Investigation of observed pay disparities
Evaluation of explanatory power of the model
Assessment of the meaningfulness of the study
results
2
Why Conduct Compensation Analyses?
Legal Reasons
Pay equity laws and regulations
Require that similarly situated demographic group
members receive equal pay for substantially equal work
Recent and likely future legislation provides greater incentives to file
claims
Lilly Ledbetter Fair Pay Act (Title VII)
3
Why Conduct Compensation Analyses?
Legal Reasons
Pay equity laws and regulations
Require that similarly situated demographic group
members receive equal pay for substantially equal work
Recent and likely future legislation provides greater incentives to file
claims
Lilly Ledbetter Fair Pay Act (Title VII)
Proposed Pay Check Fairness Act (Equal Pay Act)
Proposed Fair Pay Act of 2009 (FLSA)
4
Why Conduct Compensation Analyses?
Legal Reasons
Pay equity laws and regulations
Require that similarly situated demographic group
members receive equal pay for substantially equal work
Recent and likely future legislation provides greater incentives to file
claims
Lilly Ledbetter Fair Pay Act (Title VII)
Proposed Pay Check Fairness Act (Equal Pay Act)
Proposed Fair Pay Act of 2009 (FLSA)
Government regulatory agencies are devoting more attention to pay
disparities
Office of Federal Contract Compliance Programs (OFCCP)
Equal Employment Opportunity Commission (EEOC)
5
Why Conduct Compensation Analyses?
Legal Reasons
Pay equity laws and regulations
Require that similarly situated demographic group members receive
equal pay for substantially equal work
Recent and likely future legislation provides greater incentives to file claims
Lilly Ledbetter Fair Pay Act (Title VII)
Proposed Pay Check Fairness Act (Equal Pay Act)
Proposed Fair Pay Act of 2009 (FLSA)
Government regulatory agencies are devoting more attention to pay disparities
Office of Federal Contract Compliance Programs (OFCCP)
Equal Employment Opportunity Commission (EEOC)
Business Reasons
Attract and retain employees
Job satisfaction enhances performance/productivity
Avoid costs and distractions
Loss of federal contracts
Remedy current disparities with prospective pay adjustments
Compensate for past pay & benefits disparities (e.g., 2 years)
Continued oversight with periodic reporting
Negative publicity
6
Pay equity laws and regulations require that
similarly situated demographic group
members receive equal pay for substantially
equal work.
Similarly Situated:
Perform similar work (job content)
Similar skills/qualifications
Similar level of responsibility
Other pertinent factors (e.g., full-time
status)
7
Hypothetical Example – For Illustrative Purposes Only
XYZ Corporation
Salaried Employees
Total number of employees = 1,500
Number of male employees = 1,178
Number of female employees = 322
Overall average salary = $70,000
8
Hypothetical Example – For Illustrative Purposes Only
Distribution of Average Salaries
XYZ Corporation
16%
(34%) 68%
16%
(34%)
-1
0
1
$69,000
$70,000
$71,000
One S.D. = $1,000
Overall Average Salary = $70,000
9
Hypothetical Example – For Illustrative Purposes Only
Distribution of Average Salaries
XYZ Corporation
2.5%
(13.5%)
(34.0%) 95% (34.0%)
(13.5%)
2.5%
-2
-1
0
1
2
$68,000
$69,000
$70,000
$71,000
$72,000
One S.D. = $1,000
Two S.D. = $2,000
Overall Average Salary = $70,000
10
Hypothetical Example – For Illustrative Purposes Only
Female/Male Average Pay Difference
Example #1
Number of Standard
Deviations From
Overall Average
1.0 Std
Dev Diff
Average
Salary
-2.5
$67,500
-2.0
$68,000
-1.5
$68,500
-1.0
$69,000
-0.5
$69,500
0
$70,000
0.5
$70,500
1.0
$71,000
1.5
$71,500
2.0
$72,000
2.5
$72,500
Female Average
Male Average
11
Hypothetical Example – For Illustrative Purposes Only
Female/Male Average Pay Difference
Example #2
Number of Standard
Deviations From
Overall Average
2.5 Std
Dev Diff
Average
Salary
-2.5
$67,500
-2.0
$68,000
-1.5
$68,500
-1.0
$69,000
-0.5
$69,500
0
$70,000
0.5
$70,500
1.0
$71,000
1.5
$71,500
2.0
$72,000
2.5
$72,500
Female Average
Male Average
12
Difference Between Female/Male Salaries
Statistically Insignificant
Statistically
Significant
Statistically
Significant
Likely to Occur by Chance
Unlikely to Occur
by Chance
-3
Unlikely to Occur
by Chance
-2
-1
0
1
2
3
Number of Standard Deviations
13
Hypothetical Example - For Illustrative Purposes Only
Statistical Test of Difference
Between Female and Male Average Salaries
$80,000
XYZ Corporation
$72,000
Average Salary
$60,000
$59,000
$40,000
$20,000
$0
Difference = -$13,000
Number of Standard Deviations = -13.00
Female
Male
14
Interpretation of Results of Statistical Tests of
Differences Between Average Salaries
A statistically significant difference indicates that the
observed difference is not likely to have occurred by
chance in a neutral salary-setting process.
If not chance, then…
Protected status is a factor that influences pay;
AND/OR
The difference is attributable to group differences in
factors that influence salaries and for which the
analysis has not accounted.
15
General Economic Determinants of Pay
Level and type of work performed
• Occupation
• Complexity of tasks
• Amount of responsibility
16
General Economic Determinants of Pay
Level and type of work performed
• Occupation
• Complexity of tasks
• Amount of responsibility
Amount and type of education, skills &
training
Amount and type of work experience
17
General Economic Determinants of Pay
Level and type of work performed
• Occupation
• Complexity of tasks
• Amount of responsibility
Amount and type of education, skills &
training
Amount and type of work experience
Employer’s compensation philosophy,
practices, & budget
18
Hypothetical Example - For Illustrative Purposes Only
Gender Distributions Across FLSA Categories
XYZ Corporation
Percent of Employees
80%
71.6%
60%
55.9%
44.1%
40%
28.4%
20%
0%
Non-exempt
Exempt
Female
Male
19
Hypothetical Example - For Ilustrative Purposes Only
Average Salary by Grade
XYZ Corporation
Average Salary (in thousands)
$160
$145
$140
$120
$110
$100
$80
$70
$60
$40
$47
$55
$38
$27
$20
$0
1
2
Non-exempt
3
4
5
6
7
Exempt
20
Hypothetical Example - For Illustrative Purposes Only
Gender Distributions Across Pay Grades
XYZ Non-exempt Employees
Percent of Employees
60%
55.0%
50.7%
50%
45.0%
43.3%
40%
30%
20%
10%
6.0%
0.0%
0%
Grade
1
Average Salary $27
(in thousands)
2
$38
Female
3
$47
Male
21
Hypothetical Example - For Illustrative Purposes Only
Gender Distributions Across Pay Grades
XYZ Exempt Employees
50%
Percent of Employees
45%
45.0%
39.1%
40%
36%
36.0%
31.1%
30%
22.8%
20%
17.9%
10%
7%
7.0%
1.1%
0%
4
Grade
Average Salary $55
(in thousands)
5
$70
Female
6
$110
7
$145
Male
22
Hypothetical Example – For Illustrative Purposes Only
Types of Work Performed
XYZ Corporation
Non-exempt:
Clerical
Manufacturing Operations
Technical Support
Exempt:
Engineering Support
Engineering
Finance
Information Systems
Manufacturing Management
23
Hypothetical Example - for Illustrative Purposes Only
Average Salary by Job Family
XYZ Corporation
Average Salary (in Thousands)
$120
$100
$95
$80
$75
$80
$64
$60
$40
$54
$38
$39
Clerical
Mfg
Oper
$44
$20
$0
Tech
Supp
Non-exempt
Info
Sys
Mfg
Mgt
Finance
Eng
Supp
Eng
Exempt
24
Hypothetical Example - For Illustrative Purposes Only
Gender Distributions Across Job Families
XYZ Corporation
50.0%
Percent of Employees
43.5%
40.0%
30.0%
27.3%
23.3%
21.7%
20.0%
14.0%
12.4%
10.5%
10.0%
8.7% 8.5%
8.4%
5.2%
4.3% 4.9%
4.2%
1.6%
1.5%
0.0%
Clerical
Average Salary
(in thousands)
$38
Mfg
Oper
$39
Tech
Supp
$44
Info
Sys
$54
Female
Mfg
Mgt
$64
Finance
$75
Eng
Supp
$80
Eng
$95
Male
25
Hypothetical Example - For Illustrative Purposes Only
Average Years of Company Service
XYZ Corporation
14
Average Years of Service
12
11.2
10.9
10
8
6
4
2
0
Female
Male
26
Multiple Regression Analysis
A statistical tool that allows the analyst to:
Quantify the protected/non-protected salary
difference after “filtering out” differences that are
attributable to other measurable factors that
influence pay.
Determine whether any remaining estimated
protected/non-protected salary difference is
statistically significant.
27
Hypothetical Example - For Illustrative Purposes Only
Estimated Multiple Regression Equation
PREDICTED SALARY
=
+
+
+
+
+
+
+
+
+
$ Constant
$ Grade 1
$ Grade 2
$ Grade 3
.
.
$ Grade 7
$ ADM/CLER
$ MFG OPER
$ TECH SUPP
.
.
$ ENG
$ Per Year of Co Service
28
Hypothetical Example - For Illustrative Purposes Only
Actual Salary v. Predicted Salary
$150,000
$125,000
Actual Salary
$100,000
$75,000
$50,000
$25,000
$0
$0
$25,000
$50,000
$75,000
$100,000
$125,000
$150,000
Predicted Salary
29
Hypothetical Example – For Illustrative Purposes Only
Company-wide Salary Regression Analysis
XYZ Corporation
Model
1
Explanatory Factors
Female
Female/
Male
Salary
Difference
Number of
Standard
Deviations
-$13,000
-13.00*
*Statistically significant.
30
Hypothetical Example – For Illustrative Purposes Only
Company-wide Salary Regression Analysis
XYZ Corporation
Model
Explanatory Factors
Female/
Male
Salary
Difference
Number of
Standard
Deviations
1
Female
-$13,000
-13.00*
2
Model 1 plus Grade
-$ 3,800
- 4.29*
*Statistically significant.
31
Hypothetical Example – For Illustrative Purposes Only
Company-wide Salary Regression Analysis
XYZ Corporation
Model
Explanatory Factors
Female/
Male
Salary
Difference
Number of
Standard
Deviations
1
Female
-$13,000
-13.00*
2
Model 1 plus Grade
-$ 3,800
- 4.29*
3
Model 2 plus Job Family
-$ 2,650
- 3.10*
*Statistically significant.
32
Hypothetical Example – For Illustrative Purposes Only
Company-wide Salary Regression Analysis
XYZ Corporation
Model
Explanatory Factors
Female/
Male
Salary
Difference
Number of
Standard
Deviations
1
Female
-$13,000
-13.00*
2
Model 1 plus Grade
-$ 3,800
- 4.29*
3
Model 2 plus Job Family
-$ 2,650
- 3.10*
4
Model 3 plus Years of Company Service
-$ 2,700
- 3.12*
*Statistically significant.
33
Hypothetical Example – For Illustrative Purposes Only
Financial Exposure Calculations
Based on Company-wide Regression Model
XYZ Corporation
Female/
Male
Average
Salary
Difference
Number
of Female
Employees
Current
Salary
Adjustment
Expenditures
-$2,700
322
$869,400
34
Hypothetical Example – For Illustrative Purposes Only
Financial Exposure Calculations
Based on Company-wide Regression Model
XYZ Corporation
Female/
Male
Average
Salary
Difference
-$2,700
Number
of Female
Employees
Current
Salary
Adjustment
Expenditures
2 Years
Back
Pay
2 Years of
Benefits
Loss (8%)
Total*
322
$869,400
$1,738,800
$139,104
$2,747,304
* Does not include interest on back pay and benefits.
35
Hypothetical Example - For Illustrative Purposes Only
“Pooled” Models May Yield Misleading Results
Company-wide Female/Male Salary Difference = -$2,000
Female/Male Salary Differences by Job Family
$4,000
$3,000
Female/Male Salary Difference
$2,500
$2,000
$500
$0
-$2,000
-$3,000
-$4,000
-$4,000
-$5,000
-$6,000
Job Family
1
2
3
4
5
6
36
Hypothetical Example - For Illustrative Purposes Only
“Pooled” Models May Mask Significant Pay Disparities That Do Exist
Company-wide Female/Male Salary Difference = $0
Female/Male Faculty Salary Differences by Job Family
$8,000
$6,000 *
Female/Male Salary Difference
$6,000
$4,000
$5,500 *
$3,000*
$2,000
$0
-$2,000
-$2,500*
-$4,000
-$6,000
-$8,000
Job Family
-$5,000*
-$7,000*
1
2
3
4
5
6
*Statistically significant.
37
Hypothetical Example – For Illustrative Purposes Only
“Pooled” v. Separate Analyses?
Separate salary regression analyses for each job family are required
when the impact of pay factors varies across job families.
For example:
Job
Family
Additional
Year of
Service
Adm/Clerical
Engineering
Average
$500
$1,500
$1,000
38
Hypothetical Example - For Illustrative Purposes Only
Female/Male Salary Differences by Job Family
Separate Regression Analysis for Each Job Family
XYZ Corporation
Female/Male Salary Difference
$2,000
$1,562
$1,322
$649
$0
-$2,000
-$1,799
-$1,765*
-$2,731
-$3,495
-$4,000
-$6,000
-$6,590*
-$8,000
No. Std. Devs:
Clerical
Mfg
Oper
Tech
Supp
Info
Sys
1.52
-1.83
-2.16
-0.99
Mfg Finance Eng
Mgt
Supp
Eng
0.51
0.56
-3.49
-1.73
*Statistically significant.
39
Hypothetical Example – For Illustrative Purposes Only
Outcome of Salary Analysis by Job Family
XYZ Corporation
Percent of Female Employees
60%
55.0%
50%
40%
30%
26.4%
18.6%
20%
10%
0%
0%
Paid Significantly
Less Than Men
Paid Insignificantly
Less Than Men
Paid Insignificantly
More Than Men
Paid Significantly
More Than Men
40
Investigate Significant Differences:
Are Observed Compensation Disparities
Attributable to:
a few “outliers” ?
OR
a general pattern underpaying the protected
group?
factors not included in the model?
41
Hypothetical Example - For Illustrative Purposes Only
Actual Salary v. Predicted Salary
Technical Support
2 Std Dev
Range
Positive
Outlier
$75,000
Actual Salary
$60,000
$45,000
♦
$30,000
Negative
Outlier
♦
♦
$15,000
♦
♦
$0
$0
$15,000
$30,000
$45,000
$60,000
$75,000
Predicted Salary
Males
Females
42
Hypothetical Example – For Illustrative Purposes Only
Impact of “Outliers” on Tech Support
Female/Male Pay Difference
$2,000
XYZ Corporation
$1,000
$0
-$658
-$1,000
-$2,000
No. of Std. Devs:
-$1,765
Including "Outliers"
-2.16*
Excluding "Outliers"
-0.52
*Statistically significant.
43
Hypothetical Example - For Illustrative Purposes Only
Actual Salary v. Predicted Salary
Finance
$150,000
2 Std Dev
Range
$125,000
Actual Salary
$100,000
$75,000
$50,000
$25,000
$0
$0
$25,000
$50,000
$75,000
Predicted Salary
Males
$100,000
$125,000
$150,000
Females
44
Hypothetical Example - For Illustrative Purposes Only
Average Years of XYZ Service
Finance
14
Average Years of Service
12.3
12
11.4
10
9.4
8
6.5
6
4.9
4
2.9
2
0
Total Years
Years in Grade
Female
Years at Lower Grades
Male
45
Hypothetical Example – For Illustrative Purposes Only
Years of Prior Professional Experience by Gender
Average Years of Prior Professional Experience
10.0
Finance
8.0
6.0
7.4
5.3
4.0
2.0
0.0
Female
Male
46
Hypothetical Example - For Illustrative Purposes Only
Gender Distributions Across Education Levels
Finance
100%
Percent of Employees
85%
80%
75%
60%
40%
20%
0%
15%
10%
15%
0%
No College Degree
Bachelor's Degree
Female
Graduate Degree
Male
47
Hypothetical Example – For Illustrative Purposes Only
Female/Male Salary Difference
Female/Male Salary Difference
$2,000
Finance
Initial v. Enhanced Model
$0
-$2,000
-$3,332 *
-$4,000
-$6,000
-$6,590 *
-$8,000
No. of Std. Devs:
Initial Model
-3.49
Enhanced Model
-2.22
*Statistically significant.
48
Hypothetical Example – For Illustrative Purposes Only
Financial Exposure Calculations
Based on Enhanced Regression Model
Finance
Female/
Male
Average
Salary
Difference
Number
of Female
Employees
Current
Salary
Adjustment
Expenditures
-$3,332
45
$149,940
49
Hypothetical Example – For Illustrative Purposes Only
Financial Exposure Calculations
Based on Enhanced Regression Model
Finance
Female/
Male
Average
Salary
Difference
-$3,332
Number
of Female
Employees
Current
Salary
Adjustment
Expenditures
2 Years
Back
Pay
2 Years of
Benefits
Loss (8%)
Total*
45
$149,940
$299,880
$23,990
$473,810
* Does not include interest on back pay and benefits.
50
Evaluation of Compensation Models
Pooled v. Separate Models for each SSEG
• Reasonableness of SSEGs
Influential Observations
Omitted Variables
• Explanatory Power
51
How well does the model fit the data?
Explanatory power of the model is measured by
Adjusted R Square.
•Percent of variation in pay explained by model
•Ranges from 0% to 100%
Accounting/Finance Models’ Adjusted R Square Values
Initial
53%
Enhanced
92%
•“Low” Adjusted R Square
•omitted factors
•little variation in characteristics across employees
•factors are not related to pay
52
Evaluation of Compensation Models
Pooled v. Separate Models for each SSEG
• Reasonableness of SSEGs
Influential Observations
Omitted Variables
• Explanatory Power
Tainted Variables
53
Potentially Tainted Variables
Factors under employer’s control about which
decisions are allegedly biased (e.g. performance ratings).
Inclusion of tainted variables “covers up” statistical
evidence of discrimination.
Female/Male Pay Difference
Number of Std. Devs.
With
Without
Performance Performance
Variables
Variables
-$2,500
-$978
-3.21
-1.75
54
Evaluation of Compensation Models
Pooled v. Separate Models for each SSEG
• Reasonableness of SSEGs
Influential Observations
Omitted Variables
• Explanatory Power
Tainted Variables
Measurement Error
• Consistent Definition of Compensation
• Inaccurate/Incomplete Data
• Computer Programming Errors
• Proxy Variables
55
Proxy Variables
Approximation of the value of a factor that cannot be directly
measured.
For example, Years of Prior Work Experience is
often estimated using:
Age at Hire
Years Between Highest Degree and Hire Date
Starting Salary
56
Evaluation of Compensation Models
Pooled v. Separate Models for each SSEG
• Reasonableness of SSEGs
Influential Observations
Omitted Variables
• Explanatory Power
Tainted Variables
Measurement Error
• Consistent Definition of Compensation
• Inaccurate/Incomplete Data
• Computer Programming Errors
• Proxy Variables
Statistical v. Practical Significance
57
Hypothetical Example – For Illustrative Purposes Only
Significance of pay difference depends, in part,
on the number of observations.
-$50 Female/Male Pay Difference
Number of Std Devs
-0.80
-1.39
-1.89
-2.67
Number of Employees
50
100
200
500
Consider “practical” as well as statistical significance.
58
Hypothetical Example – For Illustrative Purposes Only
Significance of pay difference depends, in part,
on the number of observations.
-$5,000 Female/Male Pay Difference
Number of Std Devs
-1.11
-1.47
-1.92
-2.18
-3.98
Number of Employees
30
40
50
60
100
Consider “practical” as well as statistical significance.
59
Common Reasons for Observed
Compensation Disparities
“Artificial” Disparities
Organization-wide studies
Inaccurate data
Failure to account for important
factors
60
Common Reasons for Observed
Compensation Disparities (con’t)
Compensation Practices/Guidelines
Pay management by percentages rather than dollars
61
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Pre-hire
$50,000
Hire Year
$55,000
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
10%
New Hire
$60,500
Difference
-$5,000
10%
New Hire
-$5,500
62
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
Difference
Pre-hire
$50,000
-$5,000
Hire Year
$55,000
10%
New Hire
$60,500
10%
New Hire
-$5,500
Year 2
$56,650
3%
Merit
$62,315
3%
Merit
-$5,665
Year 3
$61,182
8%
Promo
$67,300
8%
Promo
-$6,118
63
Common Reasons for Observed
Compensation Disparities (con’t)
Compensation Practices/Guidelines
Pay management by percentages rather than dollars
Demotion with no decrease in pay
64
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
Difference
Pre-hire
$50,000
-$5,000
Hire Year
$55,000
10%
New Hire
$60,500
10%
New Hire
-$5,500
Year 2
$56,650
3%
Merit
$62,315
3%
Merit
-$5,665
Year 3
$61,182
8%
Promo
$67,300
8%
Promo
-$6,118
Year 4
$61,182
0%
No Budget
$74,030
10%
Chair
-$12,848
Year 5
$61,182
0%
No Budget
$71,809
-3%
Removed
-$10,627
65
Common Reasons for Observed
Compensation Disparities (con’t)
Compensation Practices/Guidelines
Pay management by percentages rather than dollars
Demotion with no decrease in pay
Performance ratings
66
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
Difference
Pre-hire
$50,000
-$5,000
Hire Year
$55,000
10%
New Hire
$60,500
10%
New Hire
-$5,500
Year 2
$56,650
3%
Merit
$62,315
3%
Merit
-$5,665
Year 3
$61,182
8%
Promo
$67,300
8%
Promo
-$6,118
Year 4
$61,182
0%
No Budget
$74,030
10%
Chair
-$12,848
Year 5
$61,182
0%
No Budget
$71,809
-3%
Removed
-$10,627
Year 6
$62,406
2%
Avg Perf
$74,682
4%
Excel Perf
-$12,276
67
Common Reasons for Observed
Compensation Disparities (con’t)
Compensation Practices/Guidelines
Pay management by percentages rather than dollars
Demotion with no decrease in pay
Performance ratings
Matching offers of higher pay from other employers
68
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
Difference
Pre-hire
$50,000
-$5,000
Hire Year
$55,000
10%
New Hire
$60,500
10%
New Hire
-$5,500
Year 2
$56,650
3%
Merit
$62,315
3%
Merit
-$5,665
Year 3
$61,182
8%
Promo
$67,300
8%
Promo
-$6,118
Year 4
$61,182
0%
No Budget
$74,030
10%
Chair
-$12,848
Year 5
$61,182
0%
No Budget
$71,809
-3%
Removed
-$10,627
Year 6
$62,406
2%
Avg Perf
$74,682
4%
Excel Perf
-$12,276
Year 7
$62,406
0%
No Budget
$78,416
5%
Match Offer
-$16,010
69
Common Reasons for Observed
Compensation Disparities (con’t)
Compensation Practices/Guidelines
Pay management by percentages rather than dollars
Demotion with no decrease in pay
Performance ratings
Matching offers of higher pay from other employers
Salary compression
70
Impact of Compensation Guidelines on
Female/Male Pay Difference
ABC University
Female
Salary
Female
Percent
Change
Reason
For
Change
Male
Salary
Male
Percent
Change
Reason
For
Change
$55,000
Difference
Pre-hire
$50,000
-$5,000
Hire Year
$55,000
10%
New Hire
$60,500
10%
New Hire
-$5,500
Year 2
$56,650
3%
Merit
$62,315
3%
Merit
-$5,665
Year 3
$61,182
8%
Promo
$67,300
8%
Promo
-$6,118
Year 4
$61,182
0%
No Budget
$74,030
10%
Chair
-$12,848
Year 5
$61,182
0%
No Budget
$71,809
-3%
Removed
-$10,627
Year 6
$62,406
2%
Avg Perf
$74,682
4%
Excel Perf
-$12,276
Year 7
$62,406
0%
No Budget
$78,416
5%
Match Offer
-$16,010
Year 8
$63,654
2%
Merit
$84,700
10%
New Hire
-$21,046
71
Tips to Ease the Pain of Compensation Analysis
Avoid generic job titles
difficult to form analysis groupings (SSEGs)
Avoid broad pay bands
difficult to distinguish level of responsibility
o
obtain market salary rates for jobs
Collect and maintain complete and accurate data
avoid periodic IT system purges, including data for terminated
upload historical data in conversion to new IT system
relevant prior experience
Proxy variables
-
poor measures for some groups
72
A Brief Introduction to
Compensation Analysis Methods
a´ la OFCCP
73
OFCCP Uses Paragraph 11 Data to
Conduct Preliminary Pay Analysis
[P]rovide compensation data (wages, salaries,
commissions and bonuses) by
•salary range
•rate
“Pay Division”
•grade or level
showing the total number of employees by
race and gender
and
total compensation by race and gender.
Present these data in a manner that is most
consistent with your current compensation
system (i.e., contractor can provide pay data
using other pay divisions).
74
Recent Methods OFCCP Has Used to Analyze Pay
Preliminary Analysis Using Item 11 Data
75
OFCCP’s Three Step (“Prong” or “Trigger”) Analysis
Step 1:
Identify “pay divisions” with pay differences
that are ≥ 2% (or ≥ 5%)
Step 2:
Compute the percent of women in pay divisions in
which women are paid less by at least 2% (5%)
Compute the percent of men in pay divisions in
which men are paid less by at least 2% (5%)
If 30%+ (10%+) of women are paid at least 2%
(5%) less than men proceed to step 3
Further investigation if the percent of affected women
is at least 3 times the percent of affected men
76
Hypothetical Example – For Illustrative Purposes Only
Example of OFCCP’s Three Step Preliminary Analysis
Step 1:
Pay
Division
Average
Male
Salary
Number
of Men
Average
Female
Salary
1
$28,000
20
$30,000
25
$2,000
7.1%
2
$42,000
20
$38,000
30
-$4,000
-10.5%
3
$56,000
40
$51,000
20
-$5,000
-9.8%
4
$65,000
40
$64,000
20
-$1,000
-1.6%
5
$75,000
10
$75,000
5
0
Total
250
Number
Dollar
Percentage
of Women Difference Difference
100
Step 2:
Percent of Women Affected = 50/100 = 50% > 30% (10%)
Percent of Men Affected
= 20/250 = 8%
Step 3:
Percent of Affected Women = 50%
Percent of Affected Men
8%
Further Investigation?
---
= 6.25
> 3.00
Yes
77
Recent Methods OFCCP Has Used to Analyze Pay
Preliminary Analysis Using Item 11 Data
Further Investigation – “12 Factor” or “Mini” Regression
78
Further Investigation? YES
OFCCP Data Request - “12 Factors”
Employee ID
Gender
Race/Ethnicity
Annual Salary
Job Title (and Job Group)
Part/Full-time Status
Exempt/Nonexempt Status
Grade Level or Salary Band
Years with Company (Date of Hire)
Years in Current Job (Job or Grade Entry Date)
Location
Date of Birth or Date of Last Degree (Years of Prior Experience)
Any Other Factor Relevant to Compensation (Policies)
79
Using “12 Factor” Data, OFCCP:
Constructs Similarly Situated Employee Groups
(SSEGs) or Job Families
Estimates Regression Equations
•identifies significant differences
Attempt Conciliation – Back Pay and Benefits Demand
or
Request Additional Data
80