Equal Pay Enforcement: Minimizing the Risks

1. Equal Pay Enforcement: Minimizing the Risk Association of Corporate Counsel – Louisiana Chapter December 6, 2013 Equal Pay Enforcement: Minimizing the Risks Presented by: T. Scott Kelly Ogletree, Deakins, Nash, Smoak & Stewart, P.C. Scott.Kelly@ogletreedeakins.com 205.986.1024

2. Discussion Points • Pay Equity Enforcement Theories • Understanding the Analytical Approach (ugh… statistics) • Crafting Corrective Action • Best Practices to Avoid Litigation and Minimize Risk

3. 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

4. The Enforcement of Pay Equity • Title VII • Disparate Treatment • Individual • Systemic/Pattern and Practice • Needs proof of discriminatory motive • Direct Evidence • Circumstantial Evidence • Disparate Impact • No proof of discriminatory motive required • Employer must show business justification/alternatives • Equal Pay Act

5. 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.

6. Statistical Concepts • Understand the compensation decision-making process • Estimate the outcome expected in a neutral setting. • Compare actualand expectedcompensation levels • Is the difference statistically significant (2 standard deviations or 5%)?

7. ® Why not? Comparison of averages not sufficient

8. ® • 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”

9. ® • 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.) “Similarly Situated”: Examples

10. ® 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. “Regression Analysis”

11. ® What Does a Regression Look Like?

12. ® What Does a Regression Look Like?

13. ® What Does a Regression Look Like?

14. ® What Does a Regression Look Like?

15. ® • 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 Statistical Concepts: Review

16. ® • Understand the decision-making process • Who makes the compensation decisions? Conducting a Compensation Analysis

17. 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

18. ® • Your data will have problems, such as • Reusing Employee IDs • Date inconsistencies • Data entry inconsistencies • Extreme values • Default values • Legacy systems • Example Collecting the Data

19. ® • 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 Statistically Significant Results – Now What?

20. ® “Outlier” Review

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

22. ® • 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 Other Events That May Affect Compensation

23. ® • 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 STILL Statistically Significant Results – Now What?

24. ® • Other work history events affect current pay • Factors may vary within the company • Not just what plaintiff or regulatory agency wants Other Considerations

25. ® • 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 Starting Pay Analyses

26. ® • 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 Promotional Increase Analyses

27. ® • 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 Merit Increase Analyses

28. ® • 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? What To Do With Opposition’s Compensation Analysis?

29. 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

30. Equal Pay Enforcement: Minimizing the Risk Association of Corporate Counsel – Louisiana Chapter December 6, 2013 Equal Pay Enforcement: Minimizing the Risks Presented by: T. Scott Kelly Ogletree, Deakins, Nash, Smoak & Stewart, P.C. Scott.Kelly@ogletreedeakins.com 205.986.1024