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Compensation 101 for Federal Contractors - Preparing for
OFCCP Changes in 2010
May 19, 2010
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HRCI Credit
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• COMPare v2.0 has been released!• A free version of this powerful software package is
available to members of BCGi!
NEWS FLASH!
• What is COMPare?– Designed specifically to help federal contractors comply
with compensation analysis requirements from the OFCCP.
– Uses multiple regression, t-tests and specialized processes to evaluate data assumptions, identify problem areas, create reports, and even calculate dollar amounts needed to eliminate statistically significant disparitiesneeded to eliminate statistically significant disparities.
• Become a member of the BCGi online learning community (membership is free) to make sure that you receive access to this great analytical tool!
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About Our Sponsor: BCG
EEO Consulting since 1974
• Expert Witness in EEO discrimination cases
• Affirmative Action Plan software and services
• Compensation Analysis software and services
• Pre-Employment software and services
• Published: Adverse Impact and Test Validation, 2nd Ed.
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• Published: Compensation Analysis: A Practitioner’s Guide to Identifying and Addressing Compensation Disparities, 1st Ed.
• Editor & Publisher: EEO Insight an industry e-Journal
www.Biddle.com
Compensation 101 for Federal Contractors - Preparing for
OFCCP Changes in 2010
May 19, 2010
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Contact Information
• Presenters– Marife Ramos– EEO/AA Senior Consultant
• 800-999-0438 x129
– Criselda Pontilla– EEO/AA Analyst• [email protected]
• 800-999-0438 x109
• Panelist– Jim Higgins – Director of Compensation
• 800-999-0438 x 144
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Agenda
OFCCP Updates, Hot Spots and Trends
Laws and Regulations
OFCCP and Compensation
Compensation Review in an OFCCP Audit
M lti l R i
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Multiple Regression 101
Pitfalls and Data Issues
Recommendations
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OFCCP Updates, Hot Spots and Trends
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OFCCP Hot Spots
Recordkeeping
Adverse Impact in the Selection Process
ARRA AuditsARRA Audits
Rebuilding of Enforcement Capacity• Outreach to Veterans • Outreach to Individuals with Disabilities
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OFCCP Hot Spots
Scheduling Process will be Overhauled• CSAL will continue to be used• April letters already went out
Recidivism Measures• Industry Based• Organization Based
Identify and resolve all discrimination cases• Systemic• Individual complaints
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OFCCP Trends
Increase in Number of On Site AuditsAudits
Increase Scrutiny of Job Postings
Compliance Officers Doing the
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Compliance Officers Doing the “Math” on Data
Request for Extension = Notice of Violation
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Compensation: Laws and Regulations
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Laws and Regulations
Why Analyze Compensation?
Executive Order 11246Executive Order 11246• According to 41 CFR 60-2.17(b)(3), contractors must
evaluate their “compensation system(s) to determine whether there are gender-, race-, or ethnicity-based disparities.”
According to 41 CFR 60 20 5 the employer’s wage
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• According to 41 CFR 60-20.5, the employer s wage schedules must not be related to or based on the sex of the employee.
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Title VII of the 1964 Civil Rights Act
Laws and Regulations
Title VII of the 1964 Civil Rights Act
• It shall be an unlawful employment practice for an employer to fail or refuse to hire or to discharge any individual, or otherwise to discriminate against any individual with respect to his compensation . . . because of such individual's race color religion sex or national
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of such individual s race, color, religion, sex, or national origin.
Lilly Ledbetter Fair Pay Act of 2007A d Titl VII th ADEA ADA d th
Laws and Regulations
• Amends Title VII, the ADEA, ADA, and the Rehabilitation Act of 1973 to clarify discriminatory compensation decisions/practices are unlawful and the discrimination occurs each time the compensation is paid.
• Recovery is limited (2 years from filing charge)
Paycheck Fairness Act (Pending)Paycheck Fairness Act (Pending)• Only applies to differences by Gender• Geared towards class action lawsuits• Allows recovery of compensatory and punitive
damages.16
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OFCCP and Compensation
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June 16, 2006: The OFCCP released the final compensation analysis standards/guidelines.
OFCCP and Compensation
Guidelines for Self Evaluation with Compensation Practices for Compliance with Non Discrimination Requirements (the “Guidelines”)
• Provides suggested techniques• Voluntary• Chose Multiple Regression as the analytical
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• Chose Multiple Regression as the analytical benchmark• To effectively/properly use multiple regression
requires a significant understanding of the underlying statistics and associated assumptions.
…
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Group by Pay Grade •The focus was
Compensation Analysis in the Past
Compare Average
Compensation Between Groups
Back Pay/Make
Whole Relief
on simple differences in average compensation.
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T-Tests or “DuBray” Analysis
Cohort Analysis
T-Test (In a Nutshell)
Group 1(Avg. $)
Group 2(Avg. $) = Difference
Is the difference in the Average Salaries statistically significant?
How it works:• The difference is made up of purely random differences that are
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p p ydue purely to chance AND POSSIBLY inappropriate differences that are due to gender or minority status.
• T-Tests essentially “filter out” the random differences so that, if any real differences between groups exist.
• If there are “real” differences between groups, the difference is said to be “statistically significant.”
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T-Test (In a Nutshell)
T-test?
Pros• Highly sensitive• Easy to interpret• Works with small
samples
Cons• Too simplistic• Fails to take into
account that real differences may exist
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samples• Can help pinpoint
areas in need of further investigation
yfor legitimate reasons
• Results in findings of pay disparities when none exist
Compensation Review in an OFCCP Audit
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OFCCP Audit: Compensation
Initial Data Review
Request for Additional Data
Employ Multiple Regression Analysis to assess pay disparities
On-Site (to gather anecdotal evidence)
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1. Initial Data Review:
• Type 1: OFCCP Red Flag Analyses
OFCCP Audit: Compensation
Type 1: OFCCP Red Flag Analyses
– GOAL: Identify signs of potential systemic compensation disparities.
– Unofficial (i.e., not official OFCCP policy) . . . but OFCCP has used it!
• 2-30-30-3
• 5-30-10-3
• Initially created using the data given in response to Item 11 of the itemized listing
– OFCCP claims they don’t use this any more but it is still a useful “rule of thumb”
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Type 1: OFCCP’s “Compensation Triggers”2/30/30/3 5/30/10/3
OFCCP Audit: Compensation
• Test 1: Is there at least one occurrence of a two percent or greater difference in pay between groups?
• Test 2: Are there at least 30negatively impacted women or minorities?
• Test 3: Are at least 30% of women
• Test 1: Is there at least one occurrence of a five percent or greater difference in pay between groups?
• Test 2: Are there at least 30negatively impacted women or minorities?
• Test 3: Are at least 10% of women
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• Test 3: Are at least 30% of women or minorities negatively impacted?
• Test 4: Are the women or minorities being negatively impacted at a rate that is at least three times the impact of the men or non-minorities?
• Test 3: Are at least 10% of women or minorities negatively impacted?
• Test 4: Are the women or minorities being negatively impacted at a rate that is at least three times the impact of the men or non-minorities?
Type 2: The Search for Low-Lying Fruit:• Red-flag analysis often misses legitimate problem areas
OFCCP Audit: Compensation
Red flag analysis often misses legitimate problem areas.
• At the end of the day, compensation enforcement is based on individual SSEGs and/or job titles . . . so . . . look for individual job titles with large differences in average salaries between men/women or whites/minorities.
• The degree of “Low-Hanging Fruitedness (sic)” is based on:
Si f diff i l (bi diff b d)– Size of difference in average salary (big differences are bad)
– Number of employees (big numbers are bad here too)
Important note: Large differences in average salaries between groups can mean a legitimate issue . . . or an incorrect grouping of employees.
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2. Request for Additional Data:– If the OFCCP finds a reason to investigate further they will
OFCCP Audit: Compensation
request additional data. This data typically includes:
OFCCP 12-Factor Data Request
Employee ID Current base salary
Gender PT/FT
Race/Ethnicity Exempt/Non-Exempt
Time in Company Job Title
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Time in Job Grade or Salary Band
Date of birth Location
• Important note: BCG recommends employers submit the necessary data/fields to explain differences in salary regardless of whether they are requested.
3. Employing Multiple Regression Analysis:
OFCCP Audit: Compensation
C iCompensation
SSEG
Job Related Variables
Multiple Regression
“Ingredients”
“Tool”
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Multiple Regression
Potential Problem Area(s)
Anecdotal Evidence
Tool
“Product” (i.e., results)
“Additional Ingredient”
Discrimination
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What is a Similarly Situated Employee Group (SSEG)?
OFCCP Audit: Compensation
Similar Work Similar Level of Responsibility
SSEG
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Similar Qualifications
Similar Skill Levels
Important note: BCG recommends employers create realistic/appropriate SSEGs without consideration of required minimum sample size (i.e., “they are what they are”).
Multiple Regression 101
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Multiple Regression Defined
Multiple Regression
– Allows an analyst to determine whether differences in compensation are due to “legitimate job-related factors” or “some other factors”—especially those that that appear to be directly related to gender or ethnicityethnicity.
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Explaining Compensation: Before Controlling for Legitimate Factors
r = 35
Multiple Regression 101
Compensation
(100%)Employee
Gender
r .35
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Percent of compensation explained by gender (12%)
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Why Multiple Regression?
Multiple Regression 101
Differences in Compensation
Experience
Performance
Gender
Multiple Regression
Allows an analyst to determine whether differences in compensation are due to “legitimate job-related factors” or “some other p
Tenure
Education
Job Market Factors
factors or some other factors”—especially those that that appear to be directly related to gender or ethnicity.
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Multiple Regression and Correlation
Correlation – describes the degree of relationshipbetween two variables.
Salary Time with Company
Gender
Salary 1 .35 .63
Time with Company .35 1 .20
Gender .63 .20 1
Sample correlation matrix/table:
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3434
Correlation coefficient (r) between salary and time with company = .35
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Correlation Coefficient (r = .35)
Multiple Regression and Correlation
Think--- “Co-Relation”io
nio
n
35
Co
mp
en
sat
Co
mp
en
sat
Time with CompanyTime with Company
35
Multiple Regression and Correlation
ion
ion
Co
mp
en
sat
Co
mp
en
sat
Time with CompanyTime with Company
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• Always between -1.00 and +1.00• Closer to + or – 1.00: stronger the
l i hi
• Always between -1.00 and +1.00• Closer to + or – 1.00: stronger the
l i hiR
The Correlation Coefficient
Multiple Regression and Correlation
relationship• Closer to 0.00: weaker the relationships• 0.00: “no relationship”
relationship• Closer to 0.00: weaker the relationships• 0.00: “no relationship”
Range
• Negative numbers: “as one variable goes up the other variable goes down”
• Positive Numbers: “as one variable goes up the other variable goes up”
• Negative numbers: “as one variable goes up the other variable goes down”
• Positive Numbers: “as one variable goes up the other variable goes up”
Direction
• If you square the correlation coefficient, the number you get tells you “the percent of one variable that is accounted for by the other variable.”
• If you square the correlation coefficient, the number you get tells you “the percent of one variable that is accounted for by the other variable.”
Coefficient of Determination
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Multiple Regression and Correlation
en
sati
on
en
sati
on
Regression/PredictionRegression/PredictionLineLine
YY
Co
mp
eC
om
pe
Time with CompanyTime with CompanyXX
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How Does Multiple Regression Help Identify Potential Pay Discrimination?
Model Showing No Discrimination
Multiple Regression 101
Compensation Tenure
Education Experience
• All variables together become the basis for a prediction “model” known as a regression model.
Th i d l
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Performance
Gender
• The regression model predicts a certain percentage of what makes up an employee’s compensation.••R = .67R = .67
••RR22 = 45%= 45%
How Does Multiple Regression Help Identify Potential Pay Discrimination?
Model Showing Discrimination
Multiple Regression 101
Compensation Tenure
Education Experience
• Q: So how does regression help to identify discrimination in pay?
• A: If the prediction d l b
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PerformanceGender
model becomes significantly better afterincluding the protected variable.
••RR22 = 45%= 45%••without without gendergender
••RR22 = 51%= 51%••With With
gendergender
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A Word About Potentially“Tainted Variables”
Multiple Regression 101
TaintedTainted UntaintedUntainted
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Questions for Contractors before using multiple regression:
D t C id ti G i M th d (S l )
Multiple Regression 101
Data Considerations
• Do I have the data needed for this type of analysis (i.e. time in company, previous experience, performance appraisal, etc.)?
Grouping Method (Samples)
• Job Title/Job Code• Pay Grade/Salary Band• SSEG• Job Group
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• Consider different groups might use different factors.
• Is the data in electronic format?
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Questions for Contractors before using multiple regression (continued):
Multiple Regression 101
How should I consider splitting my employees? Samples:
• Location• AAP
Li f b i
Have I considered sample size?
• When should I use multiple regression?
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• Lines of business• Exemption Status
regression?• 30 and 5
• When should I do a cohort analysis?
Pitfalls and Issues to Consider
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Pitfalls and Issues to Consider
The dog chasing its tail: Making compensation changes to one group can affect others (for better g g p (or worse)
– Rectifying problem areas for women may create problem areas for minorities
– Rectifying problems in one SSEG may create problems for a department, location, manager, etc.
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• “Statistical Power” issues
R i l b “d t i t i ”
Pitfalls and Issues to Consider
• Regression analyses can be very “data intensive”
• Missing variables
• Missing data within a variable (regression analyses typically require all data for all records)
• Be sure to analyze your explanatory variables for inequities between groups (e.g., performance appraisal scores)
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• Be sure to evaluate and include an adequate timeframe for your data (e.g., performance appraisal scores, productivity metrics etc )
Pitfalls and Issues to Consider
productivity metrics, etc.)
• Flip-flops in disparities (against men/whites in some circumstances and women/minorities in others) may mean your organization does not systematically discriminate . . . this strategy has been used to undermine class claims of discrimination.
• Statistics are cold and must be supported by anecdotal evidence
• Personnel files/records (i.e., cohort analysis)
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Recommendations
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Recommendations
• All correspondence, analyses, and results should be covered under attorney-client privilege.
Perform a preliminary “trigger test” analyses on all compensation • Perform a preliminary trigger test analyses on all compensation data prior to submitting plan to OFCCP.
• Identify the factors/variables that affect compensation (may be different by location, department, etc.). Rank-order the list.
• Establish company-wide data collection/retention protocols for those compensation factors (beginning with the top of the list).
• Perform yearly, proactive regression analyses by job title and pay grade or SSEG (Important: Be mindful of “low lying fruit”).
• First perform “preliminary” regression analyses by including only 2-3 primary variables that are readily available.
• Include additional relevant explanatory variables, if necessary.49
• Evaluate all statistically significant differences using a non-statistical cohort analysis (i.e., file-by-file comparison).
Recommendations
comparison). – In most cases, the OFCCP will only issue a Notice of Violation
where there is both statistical and anecdotal evidence of discrimination.
• If proper regression analyses and a file-by-file cohort review fail to identify justifiable reasons for compensation disparities, calculate the amount needed to eliminate statistical significance and consider making changes – (Important: Beware of the dog chasing it’s tail – perhaps conduct
“what if” analyses).
• GET HELP!!!50
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Additional Resources
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Additional Resources
• Interpreting Nondiscrimination Requirements of Executive Order 11246 with Respect to Systemic Compensation DiscriminationCompensation Discrimination
– http://www.dol.gov/esa/regs/fedreg/notices/2006005458.htm
– This document outlines the OFCCP’s definitive compensation analysis strategy and guidelines.
• Voluntary Guidelines for Self-Evaluation of Compensation Practices for Compliance with Nondiscrimination Requirements of Executive Order Nondiscrimination Requirements of Executive Order 11246 with Respect to Systemic Compensation Discrimination
– http://www.dol.gov/esa/regs/fedreg/notices/2006005457.htm
– This document outlines a recommended compensation analysis structure similar to that used by the OFCCP (and to be used by contractors who wish to request the compliance coordination incentive).
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• Miscellaneous articles and flowcharts on regression and compensation
biddl / ti
Additional Resources
www.biddle.com/compensation
• Adverse Impact and Test Validation: A Practitioners Guide, 2nd Ed. by Dan Biddle, Ph.D.
• Miscellaneous resources• Miscellaneous resourceshttp://www.bcginstitute.org/resources_articles_books.php
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Questions
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