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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.

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equal pay enforcement minimizing the risks

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

discussion points
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
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 equity1
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
risk factors for pay claims
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
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%)?
comparison of averages not sufficient

®

Why not?

Comparison of averages not sufficient

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”

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.)

“Similarly Situated”: Examples

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.

“Regression Analysis”

what does a regression look like

®

What Does a Regression Look Like?

what does a regression look like1

®

What Does a Regression Look Like?

what does a regression look like2

®

What Does a Regression Look Like?

what does a regression look like3

®

What Does a Regression Look Like?

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

Statistical Concepts: Review

conducting a compensation analysis

®

  • Understand the decision-making process
  • Who makes the compensation decisions?

Conducting a Compensation Analysis

conducting a compensation analysis1
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 as
    • Reusing Employee IDs
    • Date inconsistencies
    • Data entry inconsistencies
    • Extreme values
    • Default values
    • Legacy systems
  • Example

Collecting the Data

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

Statistically Significant Results – Now What?

outlier review

®

“Outlier” Review

outlier review after reviewing data and identifying outliers

®

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

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

Other Events That May Affect Compensation

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

STILL Statistically Significant Results – Now What?

other considerations

®

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

Other Considerations

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

Starting Pay Analyses

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

Promotional Increase Analyses

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

Merit Increase Analyses

what to do with opposition s compensation a nalysis

®

  • 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?

best practices
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 risks1

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