Compensation 101 for Federal Contractors - Preparing for OFCCP Changes in 2010

Transcription

1 Compensation 101 for Federal Contractors - Preparing for OFCCP Changes in 2010 May 19, 2010 Please wait while others arrive, the presentation will begin momentarily. Visit BCGi Online If you enjoy this webinar, Don t forget to check out our other training i opportunities through the BCGi website. Join our online learning community by signing up (its free) and we will notify you of our upcoming free training events as well as other information of value to the HR community. 1

2 HRCI Credit BCG is an HRCI Preferred Provider CE Credits are available for attending this webinar Only those who remain with us for at least 80% of the webinar will be eligible to receive the HRCI training completion form for CE submission You will receive your HRCI certification documentation within 1 week by NEWS FLASH! COMPare v2.0 has been released! A free version of this powerful software package is available to members of BCGi! 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 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! 2

3 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, 2 nd Ed. Published: Compensation Analysis: A Practitioner s Guide to Identifying and Addressing Compensation Disparities, 1 st Ed. Editor & Publisher: EEO Insight an industry e-journal 5 Compensation 101 for Federal Contractors - Preparing for OFCCP Changes in 2010 May 19,

4 Presenters Contact Information Marife Ramos EEO/AA Senior Consultant x129 Criselda Pontilla EEO/AA Analyst x109 Panelist Jim Higgins Director of Compensation x Agenda OFCCP Updates, Hot Spots and Trends Laws and Regulations OFCCP and Compensation Compensation Review in an OFCCP Audit Multiple l Regression 101 Pitfalls and Data Issues Recommendations 8 4

5 OFCCP Updates, Hot Spots and Trends 9 OFCCP Hot Spots Recordkeeping Adverse Impact in the Selection Process ARRA Audits Rebuilding of Enforcement Capacity Outreach to Veterans Outreach to Individuals with Disabilities 10 5

6 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 11 OFCCP Trends Increase in Number of On Site Audits Increase Scrutiny of Job Postings Compliance Officers Doing the Math on Data Request for Extension = Notice of Violation 12 6

7 Compensation: Laws and Regulations 13 Laws and Regulations Why Analyze Compensation? Executive Order According to 41 CFR (b)(3), contractors must evaluate their compensation system(s) to determine whether there are gender-, race-, or ethnicity-based disparities. According to 41 CFR , the employer s wage schedules must not be related to or based on the sex of the employee. 14 7

8 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 origin. 15 Laws and Regulations Lilly Ledbetter Fair Pay Act of 2007 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) Only applies to differences by Gender Geared towards class action lawsuits Allows recovery of compensatory and punitive damages. 16 8

9 OFCCP and Compensation 17 OFCCP and Compensation June 16, 2006: The OFCCP released the final compensation analysis standards/guidelines. 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 benchmark To effectively/properly use multiple regression requires a significant understanding of the underlying statistics and associated assumptions. 18 9

10 Compensation Analysis in the Past Back Pay/Make Whole Relief Group by Pay Grade Compare Average Compensation Between Groups The focus was on simple differences in average compensation. Cohort Analysis T-Tests or DuBray Analysis 19 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 due 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

11 T-Test (In a Nutshell) Pros Highly sensitive Easy to interpret Works with small samples Can help pinpoint areas in need of further investigation T-test? Cons Too simplistic Fails to take into account that real differences may exist for legitimate reasons Results in findings of pay disparities when none exist 21 Compensation Review in an OFCCP Audit 22 11

12 OFCCP Audit: Compensation Initial Data Review Request for Additional Data Employ Multiple Regression Analysis to assess pay disparities On-Site (to gather anecdotal evidence) 23 OFCCP Audit: Compensation 1. Initial Data Review: 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! 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 24 12

13 OFCCP Audit: Compensation Type 1: OFCCP s Compensation Triggers 2/30/30/3 5/30/10/3 Test 1: Is there at least one occurrence of a two percent or greater difference in pay between groups? 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 30 negatively impacted women or minorities? 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 2: Are there at least 30 negatively impacted women or 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? 25 OFCCP Audit: Compensation Type 2: The Search for Low-Lying Fruit: 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: 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

14 OFCCP Audit: Compensation 2. Request for Additional Data: If the OFCCP finds a reason to investigate further they will 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 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. 27 OFCCP Audit: Compensation 3. Employing Multiple Regression Analysis: Compensation SSEG Ingredients Job Related Variables Multiple Regression Tool Potential Problem Area(s) Anecdotal Evidence Discrimination Product (i.e., results) Additional Ingredient 28 14

15 OFCCP Audit: Compensation What is a Similarly Situated Employee Group (SSEG)? Similar Work Similar Level of Responsibility SSEG 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 ). 29 Multiple Regression

16 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 ethnicity. 31 Multiple Regression 101 Explaining Compensation: Before Controlling for Legitimate Factors r =.35 Compensation (100%) Employee Gender Percent of compensation explained by gender (12%) 32 16

17 Multiple Regression 101 Why Multiple Regression? 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 ethnicity. Job Market Factors Experience Gender Differences in Compensation Education Performance Tenure 33 Multiple Regression and Correlation Correlation describes the degree of relationship between two variables. Sample correlation matrix/table: Salary Time with Company Gender Salary Time with Company Gender Correlation coefficient (r) between salary and time with company =

18 Multiple Regression and Correlation Think--- Co-Relation Correlation Coefficient (r =.35) Compensat ion Time with Company 35 Multiple Regression and Correlation Compensation Time with Company 36 18

19 Multiple Regression and Correlation The Correlation Coefficient Range Always between and Closer to + or 1.00: stronger the relationship Closer to 0.00: weaker the relationships 0.00: no relationship Direction Negative numbers: as one variable goes up the other variable goes down Positive Numbers: as one variable goes up the other variable goes up Coefficient of Determination 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. 37 Multiple Regression and Correlation Compe ensation Y Time with Company X Regression/Prediction Line 38 19

20 Multiple Regression 101 How Does Multiple Regression Help Identify Potential Pay Discrimination? Model Showing No Discrimination Gender Education Experience Compensation Tenure R R =.67 R 2 = 45% Performance All variables together become the basis for a prediction model known as a regression model. The regression model predicts a certain percentage of what makes up an employee s compensation. 39 Multiple Regression 101 How Does Multiple Regression Help Identify Potential Pay Discrimination? Model Showing Discrimination Education Experience Q: So how does regression help to identify discrimination in pay? R 2 = 45% without gender R 2 = 51% With gender Compensation Tenure Gender Performance A: If the prediction model becomes significantly better after including the protected variable

21 Multiple Regression 101 A Word About Potentially Tainted Variables Tainted Untainted 41 Multiple Regression 101 Questions for Contractors before using multiple regression: Data Considerations Grouping Method (Samples) Do I have the data needed for this type of analysis (i.e. time in company, previous experience, performance appraisal, etc.)? Consider different groups might use different factors. Is the data in electronic format? Job Title/Job Code Pay Grade/Salary Band SSEG Job Group 42 21

22 Multiple Regression 101 Questions for Contractors before using multiple regression (continued): How should I consider splitting my employees? Samples: Location AAP Lines of business Exemption Status Have I considered sample size? When should I use multiple regression? 30 and 5 When should I do a cohort analysis? 43 Pitfalls and Issues to Consider 44 22

23 Pitfalls and Issues to Consider The dog chasing its tail: Making compensation 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. 45 Pitfalls and Issues to Consider Statistical Power issues Regression analyses can be very data intensive i 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) 46 23

24 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 Personnel files/records (i.e., cohort analysis) 47 Recommendations 48 24

25 Recommendations All correspondence, analyses, and results should be covered under attorney-client privilege. 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 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 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 25

26 Additional Resources 51 Additional Resources Interpreting Nondiscrimination Requirements of Executive Order with Respect to Systemic Compensation Discrimination 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 with Respect to Systemic Compensation Discrimination 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)

27 Additional Resources Miscellaneous articles and flowcharts on regression and compensation Adverse Impact and Test Validation: A Practitioners Guide, 2 nd Ed. by Dan Biddle, Ph.D. Miscellaneous resources _books.php 53 Questions 54 27