Compensation Analyses: Understanding the Process as well as the Mechanics

1. Compensation Analyses: Understanding the Processas well as the Mechanics June 14, 2005

2. Contact Information Dan Biddle, Ph.D. & Patrick Nooren, Ph.D. Dan@biddle.com Patrick@biddle.com 800-999-0438 (toll free) www.biddle.com Current Address Biddle Consulting Group, Inc. 2868 Prospect Park Drive, Suite 110 Rancho Cordova, CA 95670 After August 1, 2005 Biddle Consulting Group, Inc. 193 Blue Ravine Rd, Ste 270 Folsom, CA 95630

3. Agenda • Compensation Analyses and the OFCCP: The Foundation • Understanding Compensation Analyses: The Process • Understanding Regression Analyses: The Mechanics

4. Compensation Analyses and the OFCCP: The Foundation

5. Compensation Analyses and the OFCCP: The Foundation • The OFCCP’s authority to investigate compensation systems comes from primarily two (2) sources: • Executive 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 schedules must not be related to or based on the sex of the employee. • 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 origin.

6. Compensation Analyses and the OFCCP: The Foundation • Compensation analyses have always been required by the OFCCP . . . what was lacking was a single unified approach to compensation audits and a concise set of guidelines regarding how to properly perform the analyses. • 2003/2004 – OFCCP hired Michael Sinclair, Ph.D., Director of Statistical Analysis, as well as regional statisticians in an effort to upgrade their technical competence. • November 16, 2004: OFCCP released proposed “guidance” regarding standards for identifying systemiccompensation discrimination . . . The goal of which being a “definitive interpretation” of policy for contractors to follow.

7. OFCCP’s Recently Released Guidance on Compensation Analyses There are six (6) criteria to the proposed guidelines: • The analyses are performed on similarly situated employee groupings (SSEGs) • The employer must make a reasonable attempt to create SSEGs that contain large enough sample sizes for meaningful analyses • The employer must analyze the SSEGs annually • The employer must investigate and remedy any statistically significant compensation disparities • The employer must create and retain all information used to explain and justify SSEGs, compensation differences, results of non-statistical analyses, and • The employer must make all documents available to the OFCCP in the event of audit.

8. OFCCP’s Recently Released Guidance on Compensation Analyses • Criteria one (1): Similarly situated employee groups (SSEGs) • Compensation analyses must be completed by SSEGs consisting of employees that: • Perform similar work • Have similar responsibility levels • Occupy positions involving similar qualifications and skills • Criteria two (2): SSEGs should be large enough for meaningful analysis • In general, SSEGs should contain at least 30 employees and five or more incumbents who are members of either of the following pairs: male/female or minority/non-minority • Where the statistical analyses do not encompass at least 80% of the employer’s workforce, the OFCCP will carefully scrutinize the SSEGs, statistical analyses, and related non-statistical analyses.

9. OFCCP’s Recently Released Guidance on Compensation Analyses • Criteria three (3): Annual statistical analysis • Employers with 250 or more employees must perform annual regression analyses. Generally speaking, regression analyses identify those factors that significantly contribute to differences in pay. For example: • Education, relevant experience with previous employers (years), seniority, performance ratings, productivity metrics, shift, gender, race • Ideally, the protected variable (e.g., sex, race), does not contribute significantly to the differences in compensation • The OFCCP will investigate each particular case to ensure that none of the included variables are themselves discriminatory (example: performance appraisal scores)

10. OFCCP’s Recently Released Guidance on Compensation Analyses • Criteria four (4): Investigation and remedy of significant disparities • If a protected variable (e.g., gender/race) significantly contributes to the differences in compensation the employer must adequately determine whether the difference can be explained by other, non-discriminatory factors (perhaps using a non-statistical method). If not, the employer must provide appropriate remedies. • Remedies may have to be made for current and prior disparities. • OFCCP uses a two-year window for back-pay and interest corrections. • Criteria five (5): Create and retain supporting documentation • Criteria six (6): Make documentation available to the OFCCP during a compliance review

11. OFCCP’s Recently Released Guidance on Compensation Analyses • OFCCP Compensation Audits: • Compensation audits will consist of two primary components: • Did the contractor properly analyze their workforce? • Were statistically significant differences identified? If so, were they remedied.

12. Understanding Compensation Analyses: The Process

13. Understanding Compensation Analyses: The Process • Unlike adverse impact analyses where data is entered and results are interpreted, compensation analyses are an infinitely dynamic, iterative process. • Are problem areas legitimate or an artifact of: • non-similarly situated employees being compared together • all relevant explanatory variables not being included • Compensation Analysis Process - Flowchart • It is recommended that employers ALWAYS perform an employee-level “cohort” analyses prior to making compensation adjustments . . . the explanation may not be found inside a regression analysis.

14. Understanding Compensation Analyses: The Mechanics

15. Multiple Regression: what it is what it isn’t! Multiple Regression is: Prediction based on multiple factorsMR is not: causation!

16. Multiple Regression: what it is and is not MR is: an “exact” mathematical science that is easily “persuaded” by data “NUANSES”MR is not: a looking glass…

17. Multiple Regression Mechanics Consider a Customer Service Rep position with 100 employees who are interviewed one at a time . . . • How long have you worked here? (Time in Job) • What your last job review rating? (Job Performance) • How many relevant years of experience did you have before coming to work here? (Outside Experience) • What is your education level? (Education) • What is your race? (Race) • What is your gender? (Gender)

18. Multiple Regression Mechanics Can we predict pay levels for Customer Service Reps after interviewing 10 employees? 20? 30? 50? After interviewing “enough” employees, we can begin “accurately predicting pay” and certain levels of job qualification variables will begin to be worth dollars, for example: • Time in Job: Each year of tenure is worth for $112 • Job Performance: Each rating level is worth $350 • Outside Experience: Each year of outside experience is worth $500 • Education: Each year of education is worth $329 • Race: Minority status counts for $-450 • Gender: Gender status counts for $0

19. Multiple Regression Mechanics  Predictor Coefficients

20. Multiple Regression Mechanics

21. Multiple Regression Mechanics

22. Multiple Regression Mechanics

23. Multiple Regression Mechanics

24. Multiple Regression Mechanics • The Mechanics of Multiple Regression: • MR is both an art and a science • There is no “one right way” • The inclusion of one variable changes the way other variables load into the overall prediction equation

25. Multiple Regression Mechanics • Here is one method… • Load in all “fair predictors” into the first prediction block (a set of variables) using “enter” method • Add “protected variable” (race/gender) as a dummy-coded variable into Block 2 • See if there is a “significant F change” after entering the second block • This process allows testing whether race/gender was a significant factor “after controlling for other allowable factors” (such as experience and education)

26. Compensation Analysis Pitfalls and Issues to Consider

27. Compensation Analysis Pitfalls and Issues to Consider • The dog chasing it’s tail: Changes to one group can affect others (for better 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.

28. Compensation Analysis Pitfalls and Issues to Consider • Regression analyses can be very “data intensive” • Missing data can easily undermine your analyses • Missing variables • Missing data within a variable (regression analyses typically require all data for all records) • Be sure to analyze your explanatory variables for discrimination (e.g., performance appraisal scores)

29. Compensation Analysis Pitfalls and Issues to Consider • Be sure to evaluate and include an adequate timeframe for your data (e.g., performance appraisal scores, 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 • Interviews • Personnel files/records (i.e., cohort analysis)

30. Recommendations

31. Recommendations • All correspondence, analyses, and results should be covered under attorney-client privilege • Identify the factors/variables that affect compensation (may be different by location, department, manager, etc.) • Establish company-wide data collection/retention protocols for those compensation factors • Perform yearly, proactive regression analyses by SSEG (if over 250 employees) • First perform “preliminary” regression analyses by including only 2-3 primary variables • Include additional explanatory variables if necessary to justify differences

32. Recommendations • Evaluate all statistically significant differences using a non-statistical cohort analysis (i.e., file-by-file comparison). In most cases, the OFCCP will only issue a Notice of Violation where there is both statistical and anecdotal evidence of discrimination. • If regression analyses and a file-by-file cohort review fail to identify the reasons for compensation disparities, calculate the amount needed to eliminate statistical significance and begin cutting checks. • GET HELP!!!

33. Additional Training • Upcoming in-depth webinars ($199) • Upcoming full-day compensation/regression seminars: • San Francisco, CA • Chicago, IL • Dates to be determined Visit www.biddle.com for more details.

34. Links to Relevant Documents • Proposed Compensation Analysis Guidance • http://www.dol.gov/esa/regs/fedreg/proposed/2004025401.htm • http://www.dol.gov/esa/regs/fedreg/proposed/2004025402.htm • Uniform Guidelines on Employee Selection Procedures • www.uniformguidelines.com • OFCCP Advance Warning Letter • http://www.dol.gov/esa/ofccp/pdf/FCSS_04.pdf

35. Questions