Presentation on theme: "EEOC v. Morgan Stanley U.S. District Court, SDNY(2004) Judge Ellis."— Presentation transcript:
EEOC v. Morgan Stanley U.S. District Court, SDNY(2004) Judge Ellis
Background 9/2001: EEOC sued Morgan Stanley (MS) for employment discrimination in violation of Title 7 of the Civil Rights Act of 1964. Alleged discrimination against Allison Schieffelin and close to 100 others in the Institutional Equity Division (IED). Also said, when Shieffelin complained, MS retailed by firing her. ISSUE: Did MS pay and promote similarly situated male employees at higher rates?
Spoiler! Case ended up settling right before opening statements. DETAILED consent decree –MS maintained it didn’t violate Title 7 –Names kept confidential –Stipulated to everything: attorneys’ fees, policies, etc. MS settled for $54 million –$12 mil for Allison –$2 mil spent by MS for diversity initiatives –$40 mil to be distributed amongst class of 67 women
FRE 702 Brush up –Gate-keeping function of court. –Admits only reliable and relevant evidence –Daubert factors –Any expert knowledge, not just scientific –Focus on methods, not conclusion –Rejection is the exception, not the rule
FRE 702 Motions Motions from three parties (Allison, MS, and EEOC) Three types of evidence: Statistics, Social Science, & Miscellaneous (i.e. damages) Court separated by evidence type and then analyzed each expert
Statistical Experts: Farrell Bloch (EEOC) EEOC says: –Found disparities in pay/promotion by analyzing MS personnel database –Experience & education are good proxies for performance & productivity –Missing variables aren’t quantifiable Morgan Stanley says: –Didn’t control for “non- discriminatory factors” –Included foreigners –Grouped together employees performing different job functions –Improper pooling of pay/promotion decision- makers
Judge says: In Jury can analyze how probative, whether Bloch’s objective variables are reasonable substitutes Raise criticisms on cross-examination No comprehensive, common database of personnel decisions The variables identified by Wecker are subjective, so no quantification is possible.
Wecker (MS) to counter Bloch Morgan Stanley says: –Bloch made wrong inference (disparity due to gender) from data Failed to consider job function & performance Failed to find meaningful substitute measures EEOC says: –He didn’t do own full analysis—easier to poke holes –Used two small sample groups within IED. No weight because size too small and info given during interview with MS managing director.
Wilde (MS) to counter Bloch Morgan Stanley says: –Shows Bloch’s statistical methods to estimate damages were unreliable & irrelevant –Analyzes Bloch’s data to say women were paid equal to or more than men EEOC says: –He didn’t do his own empirical studies –Criticisms are merely speculatory –Improper opinion on legal issue
Judge says: In Wecker and Wilde’s analysis of insufficiencies are significant, own statistical analysis is not necessary EEOC’s issues impact probative weight, not admissibility Not legal conclusions
Social Science Experts: William Bielby (EEOC) Background: –Ph.D. in Sociology –President of American Sociological Association, 2002-03 –Prof at U Penn –Prior Cases: Betty Dukes v. Walmart Cremin v. Merrill Lynch Martens v. Smith Barney (’00 & ’03)
Bielby’s Testimony: Explains “factors that create and minimize workplace gender bias, including how gender stereotypes affect personnel decisions, organizational policies & practices that create barriers to career advancement of women, and the kind of policies and procedures that effectively minimize gender bias, particularly in male-dominated work environments.” Summarizes social science research Applies to this case through “social framework analysis” Finds male-dominated environment at IED, policies that don’t detect & minimize gender discrimination, & arbitrary/subjective procedures for pay/promotion
Morgan Stanley’s Objections Lacks first-hand knowledge b/c testimony based on depositions No background in social psychology, human resources, or personnel policies No studies of IED Unreliable methodology –No science –Omits inconsistent literature –“Social framework analysis” isn’t accepted Male-dominated culture is irrelevant b/c can’t prove intentional discrimination Misapplies burden of proof by saying that gender discrimination is inevitable in IED without appropriate safeguards (which aren’t there)
Judge Says: Yes & No Acceptable methodology Critiques go towards weight Can say testify to gender stereotypes, how they may have affected decisions at MS, and whether policies/practices relating to gender bias might affect employees’ utilization of an equal opportunity program Can’t say male-dominated environment –Too theoretical to say male-dominated environment results in discrimination –Jury would expect discrimination think MS would have to prove otherwise
MS Experts to Counter Bielby Barbara Gutek (accepted): –Bielby’s opinions are unscientific –No consensus in social science research regarding best policies/practices to handle discrimination –Inappropriately relies on her work Ira T. Kay (denied): –Compensation consultant with Ph.D. in Labor Economics –Tries to challenge Bielby’s “arbitrary” designation Doesn’t use the qualitative factors –Says that pay/promotion system is the same in MS as in other wall street firms and why they adopt such policies
MORE MS Social Science Experts June O’Neill (Accepted & Denied): –Not all pay discrepancies between men & women = gender discrimination –Presents study regarding compensation based on IED in North America showing no statistically significant differences in HIRING based on gender –Ct says hiring part is irrelevant, but choice testimony if fine Christopher Winship (Accepted & Denied): –“Social Science methodologist” –Bielby’s invalid scientific methodology Use of social science literature Flaws in analysis of IED and Schieffelin –Court says he is qualified but has to leave out his conclusions about pay/promotion procedures b/c not a human resource specialist
Major Problem with Bielby: the Causal Story How can he prove discrimination? –Difficulty in finding link between environment and the actual decisions being made Difficulty of getting into someone’s head –How to remove other factors (i.e. not promoting b/c afraid of mommy track, male clients don’t trust women) Which factors to remove? mommy track reasonable? –Subjective nature of evaluation—hard to quantify things like leadership, teamwork, management ability, integrity, product knowledge, & contribution to profitability
The Big Picture Gender discrimination exists whether a study can prove it or not Justice requires that the legal system right this wrong –Not doing so perpetuates discrimination How can we prove this discrimination type? Don’t want to get rid of causal link Some alternatives: –Potential outcome analysis v. regression –A control—perhaps businesses outside industry known for being progressive model, i.e. Costco. But how comparable? Same issues facing management in different industry?