Examining Class Certification Issues with Statstics

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Examining Class Certification Issues in Employment Discrimination Litigation Using Statistical Analysis presented by Stephanie R. Thomas, Ph.D. sthomas@thomasecon.com Thomas Econometrics Silver Lake Executive Campus 41 University Drive, Suite 400 Newtown PA 18940 215-642-0072 www.thomasecon.com

Agenda : 

Agenda Requirements for Class Certification Basics of Statistical Inference Statistical Analysis Commonality Typicality Class Certification Analysis Versus Merits Analysis Importance of Expert Statistical Testimony Randall v. Rolls Royce Dukes v. WalMart

Requirements for Class Certification : 

Requirements for Class Certification Rule 23(a) and (b) of the Federal Rules of Civil Procedure govern class certification Rule 23(a) sets out four requirements, all of which must be met: Numerosity: The class is so numerous that joinder of class members is impractical; Commonality: There are questions of law or fact common to the class; Typicality: The claims or defenses of the class representatives are typical of those of the class; Adequacy: The class representatives will fairly and adequately protect the interests of the class.

Requirements for Class Certification : 

Requirements for Class Certification Rule 23(b) sets out three requirements, only one of which must be met: Prosecution of separate actions risks either inconsistent adjudications which would establish incompatible standards of conduct for the defendant or would as a practical matter be dispositive of the interests of others; Defendants have acted or refused to act on grounds generally applicable to the class; There are common questions of law that predominate over any individual class member’s questions and that a class action is superior to other methods of adjudication

Requirements for Class Certification : 

Requirements for Class Certification Statistical analysis can be used to address commonality and typicality requirements of Rule 23(a)

The Basics of Statistical Inference : 

The Basics of Statistical Inference Three steps to every comparative statistical analysis: Determine the expected outcome from the challenged process for the protected group, assuming no discrimination Compare the expected and actual outcomes to calculate a disparity measure Assess the statistical significance of the disparity

The Basics of Statistical Inference : 

The Basics of Statistical Inference 1. Determine the expected outcome from the challenged process for the protected group, assuming no discrimination Probability theory Flip a coin 10 times: we expect 5 heads out of 10 flips because the probability of getting a head on any given flip is 50% 10 flips X 50% chance of heads per flip = 5 expected heads

The Basics of Statistical Inference : 

The Basics of Statistical Inference 2. Compare the expected and actual outcomes to calculate a disparity measure I flip the coin 10 times, and I get 10 heads (the actual outcome) We expected 5 heads The actual outcome was 10 heads The disparity measure is the difference between expected and actual: 5 expected – 10 actual = surplus of 5 heads

The Basics of Statistical Inference : 

The Basics of Statistical Inference 3. Assess the statistical significance of the disparity Statistical significance refers to the likelihood that the difference between expected and actual occurred due to simple chance Coin flip example: is a surplus of 5 heads in 10 flips enough to conclude that chance is not the cause?

Probability Distribution : 

Probability Distribution

The Basics of Statistical Inference : 

The Basics of Statistical Inference Deviations from “expected” results can occur due to chance factors 10 heads is expected to occur once in 1,000 games Is the observed outcome a sufficiently ‘rare’ result to conclude that chance is not the likely explanation? Where do we draw the line? How many actual heads above the expected 5 heads is “too many”?

The Basics of Statistical Inference : 

The Basics of Statistical Inference Hazelwood School District v US (1977): a disparity of at least “2 or 3” standard deviations is “statistically significant”… shifts the burden to the employer

The Basics of Statistical Inference : 

The Basics of Statistical Inference The probability of 10 heads in 10 flips of a fair coin is 0.001, or 1 in 1,000, or 3.10 units of standard deviation This satisfies the Hazelwood threshold of “2 or 3 units” or more of standard deviation The difference between “actual” and “expected” is statistically significant You would infer that chance is not the likely explanation for the observed disparity

The Basics of Statistical Inference : 

The Basics of Statistical Inference No valid adverse inference can or should be drawn if the disparity is not statistically significant A disparity that is not statistically significant is statistically equivalent to zero

Basics of Statistical Inference : 

Basics of Statistical Inference Statistical significance is a function of: The size of the disparity The number of things being studied For example, consider the following:

Statistical Significance and Sample Size : 

Statistical Significance and Sample Size

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Assume a proposed class action in which the named plaintiffs allege they were not promoted to management because of their gender

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Typicality: are the named plaintiffs’ allegations typical of the proposed class members? an overall disparity in promotions can be used to argue typicality of the named plaintiffs’ allegations among the proposed class members absence of disparity in promotions can be used to argue the lack of typicality

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Commonality: is there a pattern or practice common amongst the proposed class? similar outcomes across different comparison groups can be used to argue commonality different outcomes across different comparison groups can be used to argue the lack of commonality

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Study the challenged process(es) across meaningful subsets Decision-maker, location, functional unit, etc. Determine whether there is a common pattern across subsets

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Consider the following example: A group of female plaintiffs allege a pattern and practice of discrimination based on gender Allege that males were hired to “Job A” (a “better” job leading to management positions) while females were hired to “Job B” (a “worse” job not leading to management positions)

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A This analysis addresses typicality This analysis does not address commonality If the hypothesis of a pattern and practice of gender discrimination is correct, we would expect to see the same hiring disparity across different locations, decision makers, etc.

Job A : 

A Job A This analysis addresses commonality In this case, a common explanation not unique to each location likely exists to explain the disparities Class certification is likely appropriate

Statistical Analysisin Class Certification : 

Statistical Analysisin Class Certification Consider the following example: A group of female plaintiffs allege that they were denied promotions because of their gender

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Assume that these promotion decisions are occurring within five different divisions of the organization, and that each division has a different decision-maker

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Slide 29: 

No Commonality

Class Certification Versus Merits : 

Class Certification Versus Merits Eisen v. Carlisle & Jacqueline In its 1974 decision, United States Supreme Court held that judges should not conduct a preliminary inquiry into the merits of a suit as part of the decision whether to certify a class

Class Certification Versus Merits : 

Class Certification Versus Merits Plaintiffs submit expert opinions to show that the contested issues can be decided on the basis of common proofs that would apply to all class members Defendants offer their own expert testimony to show that trial of the claims would instead depend on individual proofs specific to each plaintiff’s case

Class Certification Versus Merits : 

Class Certification Versus Merits This expert testimony bears directly on the commonality requirements for class certification It also concerns the merits of the case Dueling statistical experts Randall et al. v. Rolls Royce Corporation (No. 06-cv-860-SEB-JMS)

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce Sally Randall and Rona Pepmeier – high level female managers Claimed: Paid less than male counterparts Passed over for promotions Witnessed male executives putting down women Plaintiffs seeking class certification

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce In March 2010, Hon. Sarah Evans Barker of the US District Court for the Southern District of Indiana denied certification of a putative gender discrimination class claim.

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce Judge Barker’s decision: thorough analysis of competing statistical evidence before the court Decision highlights the significance of powerful expert reports and testimony in class action litigation

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce “If there is a dispute as to the value or applicability or efficacy of either side’s expert statistical analysis, the way in which that dispute is resolved impacts both the underlying systemic discrimination claim and the determination of whether a viable class action exists.”

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce Commonality requirement of Rule 23(a) is a relatively low hurdle She concluded hurdle not met because plaintiffs’ expert’s statistical analysis was not convincing Defendant’s expert’s statistical analysis “convincingly demonstrated” no common gender effect across putative class

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce Typicality requirement of Rule 23(a) Conclusions of Defendant’s expert “substantially more convincing” than “less substantially complete analysis” of Plaintiffs’ expert.

The Importance of Statistics:Randall v. Rolls Royce : 

The Importance of Statistics:Randall v. Rolls Royce Significance of careful attention to expert reports and testimony is evident Benefits of close collaboration between counsel and statistical experts Statistical models should track actual decision-making processes at issue, and that those analyses are aligned with relevant legal inquires under Rule 23

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart On April 26, 2010, the U.S. Court of Appeals for the Ninth Circuit affirmed in part, and reversed in part, the class certification order in Dukes v. Wal-MartStores, Inc. Gender discrimination pay and promotion class action encompassing approximately 1.5 million employees This ruling addresses several cutting-edge class action issues

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Rule 23 Class Certification Standards Clarified: district courts required to undertake “rigorous analysis” of legal or factual issues necessary to determine that each requirement of Rule 23 is met, not merely alleged, even if those legal or factual issues also go to the merits of the claim

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart The practical result of Dukes is that any attack on the statistical presentation of a plaintiffs’ expert during the class-certification stage must focus on commonality issues under Rule 23(a)(2), rather than on merits-based arguments

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Impact of Expert Testimony on Rule 23 Process: court rejected Wal-Mart’s contention that the expert testimony offered by plaintiffs should be stricken under Daubert, held that Wal-Mart’s objections went to the persuasiveness of the testimony, not its admissibility under the expert testimony standard

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart The ruling did not decide whether Daubert has the same application for expert testimony offered at the class certification stage as it does for testimony offered at trial. Defendants can argue that plaintiffs’ expert presentations are flawed and lack a theoretical basis to qualify as sufficient for class certification requirements

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart However, defendants should avoid arguments over which competing model is the more persuasive Defendants should consider the proper type of analysis in terms of the proper theoretical model for challenging commonality e.g. nationwide aggregated statistics versus regional statistics

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Affirmed district court’s certification of class of female employees who were employed by Wal-Mart when lawsuit was filed in 2001 with respect to their claim of injunctive relief, declaratory relief, and back pay under Rule 23(b)(2)

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Reversed and remanded class certification of employees’ claims for punitive damages, instructing district court to consider whether to certify class under newly euclidated standards of Rule 23(b)(2) or 23(b)(3)

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Reversed and remanded claims of putative class members who no longer worked for Wal-Mart when the complaint was filed in 2001, instructing district court to consider whether to certify an additional class or classes under Rule 23(b)(3)

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart Affirmed district court’s decision not to certify promotion claims brought by class members who lacked objective evidence of their interest in promotion

The Importance of Statistics:Dukes v. Wal-Mart : 

The Importance of Statistics:Dukes v. Wal-Mart In remanding claims of employees not employed by Wal-Mart when complaint was filed, class size was reduced from 1.5 million to 500,000 In remanding punitive damages certification, Ninth Cir. may have significantly reduced Wal-Mart’s potential liability, but district court may certify additional class(es) for punitive damages for 1 million putative class members

Conclusions : 

Conclusions Statistical analysis can be used to address commonality and typicality requirements of Rule 23(a) Three steps to every comparative statistical analysis: Determine expected outcome Compare expected and actual outcome Assess statistical significance of disparity

Conclusions : 

Conclusions Statistical significance refers to the likelihood that the difference between expected and actual outcomes occurred due to simple chance Hazelwood School District v US (1977): a disparity of at least “2 or 3” standard deviations is “statistically significant”… shifts the burden to the employer

Conclusions : 

Conclusions Typicality Overall disparity can be used to argue typicality Absence of disparity can be used to argue lack of typicality Commonality Similar outcomes across different comparison groups can be used to argue commonality Different outcomes across different comparison groups can be used to argue lack of commonality

Conclusions : 

Conclusions Randall v. Rolls Royce Thorough analysis of competing statistical evidence Decision highlights the significance of powerful expert reports and testimony in class action litigation

Conclusions : 

Conclusions Dukes v. Wal-Mart Ruling addressed several cutting-edge class action issues Requires “rigorous analysis” of legal or factual issues to determine each requirement of Rule 23 is met, even if those issues also go to merits Allows for “statistical dueling” but limits issues Focus on commonality issues, not merits-based issues

Conclusions : 

Conclusions Statistical analysis plays a key role in addressing the typicality and commonality requirements of class certification under Rule 23

Slide 57: 

Examining Class Certification Issues in Employment Discrimination Litigation Using Statistical Analysis presented by Stephanie R. Thomas, Ph.D. sthomas@thomasecon.com Thomas Econometrics Silver Lake Executive Campus 41 University Drive, Suite 400 Newtown PA 18940 215-642-0072 www.thomasecon.com