Equal Pay Enforcement: Minimizing the Risks

Equal Pay Enforcement: Minimizing the Risks

Presented by:

T. Scott KellyOgletree, Deakins, Nash, Smoak & Stewart, [email protected] 205.986.1024

Equal Pay Enforcement: Minimizing the Risk

Association of Corporate Counsel – Louisiana ChapterDecember 6, 2013

mailto:[email protected]


Association of Corporate Counsel – Louisiana Chapter - December 6, 2013

Discussion Points Pay Equity Enforcement Theories

Understanding the Analytical Approach (ugh… statistics)

Crafting Corrective Action

Best Practices to Avoid Litigation and Minimize Risk


The Enforcement of Pay Equity Title VII of the Civil Rights Act (“discrete

acts”) Ledbetter Fair Pay Act (“pay decisions”) Equal Pay Act (“equal work”) (Paycheck Fairness Act) Dodd-Frank Offices of Minority and Women

Inclusion EEOC and OFCCP National Equal Pay Enforcement Task Force


The Enforcement of Pay Equity Title VII

– Disparate Treatment• Individual• Systemic/Pattern and Practice• Needs proof of discriminatory motive

Direct EvidenceCircumstantial Evidence

– Disparate Impact• No proof of discriminatory motive required• Employer must show business

justification/alternatives Equal Pay Act


Risk Factors for Pay Claims Lack of meaningful standards, guidelines, or

guidance Lack of management training Exercise of discretion Subjective decision-making Failure to document pay decisions and

bonuses Failure to communicate criteria and basis

for pay decisions and bonuses Favoritism – others are better paid, get

more overtime, preferential shifts, etc.


Statistical Concepts Understand the compensation decision-

making process Estimate the outcome expected in a

neutral setting. Compare actual and expected

compensation levels Is the difference statistically significant

(2 standard deviations or 5%)?


Comparison of averages not sufficient

®

Why not?


“Similarly Situated”

®

Similar paths to current position Perform similar work (job content) Similar skills/qualifications Similar level of responsibility Other pertinent factors (e.g., full-

time status, “permanent”)


“Similarly Situated”: Examples

®

Job family Pay grade Company experience (time in grade,

time in job, tenure) Education Prior relevant experience Performance Organizational unit (e.g., division,

department, etc.)


“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.


What Does a Regression Look Like?

®

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Market Salary


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Market Salary

Males Females


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Market Salary

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Statistical Concepts: Review

®

Plaintiffs and regulatory agencies sometimes use whatever procedures they want

Straight averages are not sufficient Proper regressions allow for comparisons

between similarly-situated employees Note the difference between actual and

expected compensation levels Need to understand decision-making

process


Conducting a Compensation Analysis

®

Understand the decision-making process

Who makes the compensation decisions?


Conducting a Compensation Analysis What factors affect compensation? Some examples:

• Job code/title• Job group (or SSEG)• Pay grade• Race, gender, etc.• Original date of hire• Date entered job• Date entered grade• Department• Division• Location• Performance rating• Education/training• Measure of market pay


Collecting the Data

®

Your data will have problems, such aso Reusing Employee IDso Date inconsistencieso Data entry inconsistencieso Extreme valueso Default valueso Legacy systems

Example


Statistically Significant Results – Now What?

®

Problems with underlying data files Important factors not in the model Legitimate pay disparities Use regression to focus on hot spots Identify people who have the largest

influence on the outcome (“outliers”) Contractor and counsel review outliers

and explain their compensation level


“Outlier” Review

®

Grade Job Family #Females #Males Pay

Difference#Std Dev R Square Stat Sig?

Overall 789 1574 $ (1,339.71) -2.59 93% *

E07 Engineers 9 93 $ (12,255.21)

-3.25 55% *


“Outlier” Review (After Reviewing Data and Identifying Outliers)

®

Grade Job Family

#Females

#Males Pay Difference

#Std Dev

R Square Stat Sig?

Overall 788 1574 $ (1,210.97)

-1.80 93%

E07 Engineers

8 93 $ (2,705.59)

-0.47 62%


Other Events That May Affect Compensation

®

Break in service Change in career interests Education/training attained after hire Joined via acquisition Demotions Alternative work arrangement → part-

time Move to location with different cost of

living


STILL Statistically Significant Results – Now What?

®

By now, we should have an understanding of the cause of the problem. In the meantime,

Use regressions to isolate the focus on

individuals or smaller groups of individuals to limit exposure. Not a company-wide problem.

• Review of outliers• “Sensitivity” tests


Other Considerations

®

Other work history events affect current pay

Factors may vary within the company

Not just what plaintiff or regulatory agency wants


Starting Pay Analyses

®

Everyone hired in the past “X” years

Analyze starting pay levels by hire year

Control for factors related to• Job hired into• Qualifications prior to joining company


Promotional Increase Analyses

®

Everyone promoted in the past “X” years

Analyze promotional increase amounts by year

Control for factors related to • Job at time of promotion• Performance, other relevant factors


Merit Increase Analyses

®

Everyone given a merit raise in the past “X” years

Analyze merit increase amounts by year

Control for factors related to • Job at time of increase• Performance, other relevant factors


What To Do With Opposition’s Compensation Analysis?

®

Read it very carefully Is their analysis consistent with

reality? Attempt to replicate their results Conduct sensitivity tests Anything else that doesn’t look

right?


Best Practices Review/revise compensation policies, job descriptions, and

training programs Do a self-audit under privilege by appropriate professional

Know where you stand before employees or their counsel complain

Monitor starting pay, current pay, merit increases, promotional pay

Review raises: consistent with evaluations? Look at pay policies: are they sufficient?

Understand the factors behind pay Understand why disparities in pay exist Keep good data Fix unexplained disparities (but don’t assume discrimination!) Analyze data files before producing to opposition or

regulators


Equal Pay Enforcement: Minimizing the Risks

Presented by:

T. Scott KellyOgletree, Deakins, Nash, Smoak & Stewart, [email protected] 205.986.1024

Equal Pay Enforcement: Minimizing the Risk

Association of Corporate Counsel – Louisiana ChapterDecember 6, 2013



Equal Pay Enforcement: Minimizing the Risks

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