Systemic Discrimination Litigation: What s WORKING

Transcription

1 Systemic Discrimination Litigation: What s WORKING Presented by: Patrick M. Nooren, Ph.D. BIDDLE CONSULTING GROUP, INC. Merrily S. Archer, Esq., M.S.W. EEO LEGAL SOLUTIONS, LLC Merrily Archer EEO Legal Solutions, LLC JD/MSW Washington University in St. Louis EEOC Trial Attorney, , Denver Biglaw employment defense attorney, (e.g., Jackson Lewis, Fisher & Phillips) SuperLawyer: employment litigation defense 2012: top 10 most powerful Colorado attorneys, Denver Business Journal Enjoys embarrassing daughters with singing, tap dancing Patrick Nooren Biddle Consulting Group Ph.D. California School of Professional Psychology Alliant University California State Personnel Board: Test Validation & Construction ( ) Biddle Consulting Group, Executive Vice President (1996 Present) Secondary author: Adverse Impact and Test Validation (3rd ed.) Primary Author: Compensation Analyses: A Practitioners Guide (1st ed.) Certified (sort of) Stat Geek

2 The winning arguments in EEOC systemic litigation have been STATISTICAL, not LEGAL Early multidisciplinary intervention SAVES MONEY EEOC Dismissals Shortfalls Litigation wins Minimize legal entourages The POINT... Boring basics Brief overview of Systemic initiative (2006) Disparate impact theory EEOC-targeted practices Systemic litigation process Opportunities EEOC litigation problems EEOC v. Kaplan EEOC v. Freeman Preparing to WIN The Plan EEOC Systemic Initiative: Highlights April 4, 2006: Commissioners unanimously vote to shift enforcement strategy to systemic discrimination OFCCP adopted this model circa 2001; big monetary recoveries Systemic initiative is cornerstone of EEOC/DOL SEP Increasing benchmarks (quotas) for systemic cases over next four years EEOC/DOL money metric EEOC s Reasonable Factor Other Than Age (RFOA): March 29, 2012 EEOC final rule on RFOA under ADEA requires employers to demonstrate the extent to which it assessed any potential adverse impact of the challenged practice (i.e., RIF criterion) EEOC litigation surfaces to public view EEOC v. Kaplan University (credit scores) EEOC v. Freeman (criminal background checks)

3 FY2013: $372.1m (Another Record Year) Fewer new charges Fewer charges dismissed (inventory reduction) Fewer reasonable cause determinations More no reasonable cause determinations Fewer cases in litigation Record collections: Enforcing the law more effectively More SYSTEMIC cases Systemic Investigations: Q4 Adverse Impact 101

4 EEOC and DOL Discriminatory Hiring Barriers Any practice, procedure, or test that has a disparate/adverse impact on a protected group Credit history Educational standards Physical and functional tests Criminal background checks Anything potentially associated with age English only policies or English proficiency classifications Competitive reassignment under the ADA Termination upon exhaustion of FMLA Pay/Compensation policies Pre-employment tests Adverse Impact Analyses: A Legal Overview An unlawful employment practice based on disparate impact is established only if: A complaining party demonstrates that a respondent uses a particular employment practice that causes an adverse impact, and the respondent fails to demonstrate that the challenged practice is job-related for the position in question and consistent with business necessity, or the complaining party makes the demonstration described above with respect to an alternate employment practice, and the respondent refuses to adopt such alternative employment practice. Adverse Impact Alone Disparate Impact Discrimination Adverse Impact (i.e., significant difference in selection rates) Sufficient Job Relatedness /Validity w/o = Disparate Impact Discrimination

5 Burden 1 (Plaintiff): Selection Rate Comparisons Evaluate whether a practice, procedure or test (PPT) results in disproportionate selection rates by gender, race/ethnicity, or age group. Burden 1 (Plaintiff): Selection Rate Comparisons 2 X 2 Table Comparison Impact Ratio Analysis (IRA) - Adverse Impact Analysis Fisher Exact (mid-p)/ Chi-Square / 80% Test Hiring Analysis / RIF Analysis / Retention Analysis Took Test v Pass/Fail Men Selected (50) Women Selected (25) Men Not Selected (50) Women Not Selected (75) Men Passing Rate (50%) Women Passing Rate (25%) Results in a value indicating if the observed difference in rates is not likely due to chance (i.e., statistically significant). Adverse Impact: Hires Free Adverse Impact Standard Deviation (SD) 1.96 = Significant Statistically 80% Trending (Four-Fifths) toward significant Rule significance disparity of Thumb (i.e., likely (but not occurring there yet) by chance)

6 Life Cycle of EEOC Systemic Case: Administrative Process EEOC Lawsuit Prerequisites Charge Commissioner s Charge or ADEA Directed Investigation Investigation No time limits, broad subpoena authority Database construction, document production Most expensive phase Determination Conciliation Minimizing shortfalls Posturing for litigation Errors in EEOC s analysis or reasoning Iron Curtain of Confidentiality Government deliberative process privilege Life Cycle of EEOC Systemic Case: Litigation Process Five (5) phases of litigation Pleading Fight about jurisdiction, venue, proper parties, EEOC prerequisites, legal/factual sufficiency of Complaint Discovery Written questions/answers, document production, depositions Biggest expense: e.g., time, discovery fights Motions Practice ER moves for dismissal before trial (summary judgment) Question of law and/or no genuine dispute of material fact If done properly, most SJ motions successful in federal court Saves money, reduces risk of jury trial Trial: Ask any old rattlesnake - perfect marriage of expense and risk Appeal: Court of Appeals, petition for certiorari EEOC v. Kaplan University (Credit) Summary Judgment, 1/28/13 EEOC argued that use of credit reports in hiring process for financial aid advisors had adverse impact on black applicants (applicants were disqualified for default on student loans) EEOC failed to satisfy prima facie element of adverse impact, failed to provide statistically reliable evidence of discrimination (i.e., they failed the initial burden)

7 EEOC v. Kaplan University (Credit) So what happened? No race information was requested by Kaplan during hiring EEOC subpoenaed records from the Department of Motor Vehicles in 38 states and the District of Columbia 14 states provided race 28 states only provided driver s license photos EEOC expert enlisted a panel of race raters to identify race based on driver s license photos, addresses, and names Based on results of visual best guess race estimate, EEOC expert determined the existence of adverse impact EEOC v. Kaplan University (Credit) So what happened? (cont.) EEOC s expert analyses were thrown-out Decision in Daubert v Merrell Dow Pharmaceuticals establishes a framework for experts to challenge one another s techniques and/or theories: EEOC expert was involved in a rating panel Raters were provided names EEOC expert used only a sample of all data but sample was not representative Georgia residents 10.1% in overall data 19.6% in the analysis dataset Wisconsin residents 7.3% in overall data 0.6% in the analysis dataset Race rater process was ultimately not supported under Daubert EEOC v. Freeman (Criminal Background/Credit Checks) Dismissed on summary judgment on 8/9/13 EEOC argued that use of criminal background and credit information had disparate impact on black applicants Defense Daubert motion challenging EEOC experts analysis, followed by MSJ days later So what happened? Numerous coding errors in database (employer produced accurate information during investigation) Had access to 58K applicant sample, but analyzed only 2K applicant scenarios (i.e., data was cherry-picked ) EEOC statistical analysis was ultimately not given credibility by the courts

8 What We ve Learned... The EEOC (and OFCCP) will back off WEAK statistical showings, even in the administrative phase Statistical v. practical significance All data must be cleaned and analyzed before sharing with a governmental agency Sloppy mistakes, worst case scenarios: as in Freeman, ability to show that an accurate dataset was provided to the EEOC Early involvement of statistical/validation experts helps Scare away systemic investigations, lawsuits DATA-DRIVEN STRATEGY Frame conciliation discussions by minimizing shortfalls Strengthen subsequent settlement and litigation positions Statistical, not purely legal, arguments are carrying the day in litigation Cleaning Data : Analyses/Data Must Reflect Reality Some examples of data cleaning include (but are not limited to), removing those who: Actively withdrew (e.g., no longer interested, accepted another position, moved away, etc.) Passively withdrew (e.g., does not respond to repeated attempts to contact, no show to test/interview, etc.) Were not willing (based on salary, location/commute, shift, etc.) Were not considered for a specific position Applied after last hire of at-issue time period Didn t adhere to the strict application process Applied more than once to the same position (should just be one record) Duplicate records of applicants hired elsewhere within your organization Did not meet the Basic Qualifications Preparing to WIN: What Works Evaluate the quality of your HRIS data Learn how to use Biddle s free online adverse impact, selection rate comparison calculator at Use the right tool for the job Multi-disciplinary collaboration is more cost effective than an entourage of lawyers

9 Your Takeaways Audit NOW, especially if you use a hot criterion Statistical arguments often carry the day Adverse impact/test validation experts are essential in an EEOC systemic investigation Often retained too late in the process Stay Tuned... March s FREE webinar, Age Discrimination: Generations at Work March 19, 2014 at 12:00 p.m. (MST) SEEN: Small Employer Education Network on LinkedIn Provides free compliance resources to small/midsized employers, changes the public perception of employers Latest Blogs on Why Hire PEOPLE? UPDATE: EEOC Litigation/Enforcement Statistics Belie Common Mediator Pressure Tactics SEEN Channel on YouTube! Additional Questions/Issues Find us on LinkedIn! Merrily Archer (303) (direct) (303) (cell) archerm@eeolegalsolutions.com Patrick Nooren, Ph.D. (916) x 111 (Direct) pnooren@biddle.com