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Paul Karlsgodt, Baker Hostetler 303.764.4013 Brian Troyer, Thompson Hine 216.566.5654 Rick Preston,

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Presentation on theme: "Paul Karlsgodt, Baker Hostetler 303.764.4013 Brian Troyer, Thompson Hine 216.566.5654 Rick Preston,"— Presentation transcript:

1 Paul Karlsgodt, Baker Hostetler 303.764.4013 Brian Troyer, Thompson Hine 216.566.5654 Rick Preston, Hitachi Consulting 303.329.8993 Statistics in Class Certification Proceedings What they’re good for, and how to discredit them 1 Copyright 2012 Paul Karlsgodt Brian Troyer Rick Preston All rights reserved

2 Agenda Part I – Introduction (~15 min.) Why is this topic important? What do we mean by “statistics”? How are statistics used in class certification? Part II – Case law on the use of statistics in class certification (~40 min.) Part III – Practical tips on presenting and challenging statistics (~20 min.) Question and Answer (~15 min.) 2

3 Part I – Introduction 3

4 Why is this topic important? Wal-Mart Stores, Inc. v. Dukes creates a more demanding standard for class certification The lower courts are starting to fill in the gaps left by the Dukes Court’s analysis—see, for example, Duran v. U.S. Bank National Association Both sides are likely to attempt to create a more well- developed factual record Statistics often provide an appealing way to illustrate how aggregate or common proof is possible. Data is more available and accessible than ever before. 4

5 Rough Justice & Big Data 5 Sources of Data Growth Email, collaboration tools, and mobile devices Machine and sensor-generated messages Digitization of business records and personal content Instrument devices Governance, privacy, and regulatory compliance requirements “Big Data: What It Is and Why You Should Care” IDC (June 2011) Hard Disc Storage Price/GB Solid State Disc Storage Price/GB Over the past decade, as storage and computing power have increased exponentially, it has become increasingly tempting to use statistical sampling as a proxy for the actual adjudication of facts in class or mass actions.

6 General Overview “Statistics is the science and art of describing data and drawing inferences from them”* *(Finkelstein and Levin, p. 1) Describes relationships, correlations, events Statistics Inferential Statistics Descriptive Statistics Descriptive Statistics Makes inferences, generalizations, estimates, predictions 6

7 Types of Class Actions in Which Statistics Are Commonly Used Employment discrimination Wage and hour Securities fraud Pollution and toxic exposure Consumer/sales and prescriptions Product failures Antitrust 7

8 Common Uses of Statistics in Law Most commonly presented to prove commonality (Rule 23(a)(2)), predominance and Superiority (Rule 23(b)(3)), and cohesiveness (Rule 23(b)(2)) As proof of a common policy or practice As proof of a common relationship between the defendant’s conduct and some injury to class members (e.g. reliance, causation, injury) As common proof of aggregate or class-wide damages, restitution Less commonly presented to prove other factors E.g., In re Initial Public Offering Securities Litig., 471 F.3d 24 (2d Cir. 2006) (numerosity). 8

9 Part II – Case Law on the Use of Statistics in Class Certification 9

10 Common Impacts:“Fraud on the Market” The Fraud on the Market Theory in Securities Litigation - Basic Inc. v. Levinson, 485 U.S. 224, 247, 108 S.Ct. 978, 991, 99 L.Ed. 194 (1988). Efficient Market - the market price of a security reflects all information known to the market. In re Burlington Coat Factory Sec. Litig., 114 F.3d 1410, 1425 (3d Cir. 1997). When the market characteristics satisfy the FOTM prerequisites, individual reliance is rebuttably presumed. Quantitative/statistical proof used to show efficient market, not market response to adverse information. Reliance is separate from the element of loss causation. Loss causation need not be proved as a condition to class certification. Erica P. John Fund v. Halliburton Corp. But it presumably must still be susceptible to common resolution (Dukes). Nevertheless, common proof of loss causation and damages also are typically based on quantitative market analysis, and plaintiffs often plead and argue loss causation facts in support of fraud on the market/reliance (to show that the market responded to adverse information). 10

11 Borrowing “Fraud on the Market” Efforts to apply these concepts from securities fraud cases in consumer class actions represent one of the most prevalent uses of statistical and quantitative analysis in class certification. Statistical and econometric analyses are typically offered to show that prices and sales of a consumer product were inflated because of fraud—fraud on the consumer market. The difference is that markets for consumer goods and services are inherently different from securities trading markets. 11

12 Common Impacts: McLaughlin Plaintiffs alleged implicit representation that light cigarettes are healthier; sought $800 billion. Plaintiffs relied upon sixteen experts, including economists who proposed statistical and econometric analyses. Judge Weinstein certified nationwide class of light cigarette consumers under RICO, applying “price impact” theory of reliance similar to the FOTM theory. Reversed by McLaughlin v. American Co., 522 F.3d 215 (2d Cir. 2008). Individual proof was required: reliance, loss causation, injury, damages (and limitations). Market for light cigarettes is not efficient. Individual facts presented to show non-reliance by customers. Expert’s survey evidence “pure speculation.” Statistical analysis did not prove the relevant facts. Rejected “fluid recovery” approach of awarding aggregate “class” damages followed by “simplified proof of claim procedure” and cy pres. 12

13 Common Impacts: In re Neurontin Sales and Mktg. Practices Litig. Causation problem: Which off-label prescriptions were caused by allegedly fraudulent promotion? Plaintiffs relied upon econometric analysis to try to show causation of “all” off-label prescriptions. In first opinion, 244 F.R.D. 89 (D.Mass. 2007), Judge Saris gave plaintiffs opportunity to show through “statistical proof” that essentially all prescriptions in each category were caused by fraud. Second class certification motion also denied, 257 F.R.D. 315 (D. Mass. 2009): Not an efficient market. Defendant’s right to present evidence defeats predominance Closer scrutiny of expert opinions for class certification was mandated that presumed in earlier opinion. Where expert’s opinion was that less than substantially all (e.g. >99%) of prescriptions were caused by fraud, individual inquiry required. Where expert’s opinion was that substantially all prescriptions were caused by fraud, the expert analysis was flawed. 13

14 Common Impact: In re Zyprexa Judge Weinstein’s certification of off-label economic loss class under RICO reversed by the Second Circuit. UFCW Local 1776 & Participating Health & Welfare Fund v. Eli Lilly & Co., 620 F.3d 121 (2d Cir. 2010). “Excess price” analysis could not provide common proof of but-for (transactional) causation, because drug pricing is inelastic. proximate (direct) causation, because alleged chain of causation was incomplete. “Excess sales” theory could not provide common proof of causation because, e.g., it assumed away all other factors affecting prescriptions. There was individualized evidence of non-reliance. it ignored alternative prescriptions and costs, some of which could even have cost more. 14

15 Common Impacts: Rhodes Rhodes v. E.I. Du Pont de Nemours and Co., 253 F.R.D. 365 (S.D.W.V. 2008) Medical monitoring claim based on contamination of drinking water with C-8. Problems with toxicologist’s and epidemiologist’s quantitative opinions offered to establish common proof: Did not address the question of the relationship between exposure and a significantly increased risk of health problems; and Did not provide any common proof that any given individual suffered a significantly increased risk of the exposure. Preliminary and insufficient data was used. Failed to rule out other variables. Proposed remedy was a precautionary public health measure, not something that can be awarded as a tort remedy. 15

16 Common Practice/Policy: Wal-Mart Stores, Inc. v. Dukes At issue: Title VII sex discrimination claims Plaintiffs are required to prove a pattern or policy of discrimination. Ninth Circuit affirmed certification of a class of 1.5 million current and former female employees, arguing that all female employees were subject to a discriminatory policy. Dukes reaffirmed: that Rule 23 is not a mere pleading standard, but that the proponent must prove that the requirements are satisfied. that a court must conduct a “rigorous analysis.” that “[f]requently that ‘rigorous analysis’ will entail some overlap with the merits of the plaintiff’s underlying claim. That cannot be helped.” 131 S.Ct. at 2551. 16 The Broad Question: What does a “rigorous analysis” of statistical evidence looks like?

17 Dukes: Proof of Common Injury Two ways to bridge gap between the individual’s claim and the existence of a class who suffered the same injury Biased testing procedure (not at issue) Significant proof of a general policy of discrimination Plaintiffs offered a “social framework” analysis by sociologist Dr. Bielby claiming to show that Wal-Mart’s corporate culture made it vulnerable to gender bias, but he could not determine with any specificity how regularly stereotypes played a meaningful role, and could not say whether 0.5% or 95% of decisions were discriminatory. 17 “If Bielby admittedly has no answer to that question, we can safely disregard what he has to say.” 131 S.Ct. at 2554.

18 Dukes: Plaintiff’s Proof of the Existence of a Common Policy Plaintiffs attempted to show through statistical and anecdotal evidence a “common mode” of exercising discretion. Dr. Drogin (statistician) compared, by region, the number of women promoted with the percentage of women in pool of hourly workers. Dr. Bendich (labor economist) compared work- force data of Wal-Mart and competitors, concluding that Wal-Mart promoted lower percentage of women. 18 The only allegedly discriminatory general policy identified was that Wal-Mart gave supervisors discretion.

19 Dukes: Court Finds No Proof of the Existence of a Common Policy These statistical analyses failed to show that the existence of a general policy or practice of discrimination was a question common to all class members. First, there was a mismatch between the statistical method and conclusion - regional and national disparities failed to provide a basis to infer a “uniform, store-by-store disparity” and thus a company-wide policy. Second, even assuming a disparity in each store from regional or national data, “[m]erely showing that Wal-Mart’s policy of discretion has produced an overall sex-based disparity does not suffice.” 131 S.Ct. at 2555-56. Note the dissent’s charge that the majority misunderstood the methods used. 19 There were inferential gaps between plaintiffs’ statistical analyses and their conclusions.

20 Dukes: Rejection of Trial by Formula The Court also rejected class certification based on “Trial by Formula.” 131 S.Ct. at 2561. A sample set of class members’ claims would be tried. The percentage of valid claims and the average backpay award to determine a “class recovery” to be distributed without further individual proceedings. “We disapprove that novel project.” This scheme would deprive Wal-Mart of the right to litigate defenses to individual claims, and would violate the Rules Enabling Act. This holding is similar to the one in McLaughlin. Is it a class if some members would win and some would lose? Would the losers recover a share of the award? 20 Trial by formula... “We disapprove of that novel project”

21 Dukes in Summary Does not change the landscape regarding statistics and class certification but confirms necessity of rigorous scrutiny. Gives a strong hint in favor of Daubert, but does not answer the question. The Court examined the statistical analyses and found inferential gaps between the policy that statistics were claimed to show and what they actually showed. Court evaluated the merits/substance of the statistics. Illustrates and confirms inherent limitations of statistical and aggregate proof. Confirms that, validity of statistics aside, conceptual gaps are critical. Even if statistics showed the claimed pattern, that pattern would not establish commonality. Whether any individual decision was discriminatory would still require individual proof. Keep in mind that the issue was whether statistical evidence could be used as representative proof on behalf of all women at once, not whether it could be used at all by individual plaintiffs. 21

22 Statistical Concepts in Dukes 22 Descriptive statistics Average salary male>female 2001 Why the average? What is the distribution? Why 2001? Inferential statistics Promotion analysis controls for feeder job, store, and move year. Valid given Bielby’s assertion that relocation across stores “creates a greater burden for women”? 1 Break-out sub-set for further analysis Was it responsible for the overall differences ? What is left in the set of observations for, “Wal-Mart, not Sam’s Club?” 1 Class Cert p. 25 22

23 Statistical Concepts in Dukes (cont’d) 23 Recall that regression analysis is used to describe the relationship between phenomena Plaintiffs in Dukes... Tried to predict salary using job held, store where person worked, promotions/transfers, full-/part-time, salaried/hourly Outcome: Using gender in the equation made it a better predictor of observed salary. So gender was in fact significant. Earnings between men and women are disparate But it was not determined to be caused by an active policy to discriminate against women. So the difference is not “impact” Just because there is a difference, doesn’t make it actionable 23

24 Visualizing The Issues 24 Is there a common “answer” for all class members—i.e. did the same set of circumstances apply to each class member; “Yes” in Halliburton; “No” in Dukes Is there perhaps some other explanation (other than gender)? Root Cause (Ishikawa) Diagram Class Definition “’[A]ll women [w]ho have been or may be subjected to Wal-Mart’s challenged pay and management track promotions policies and practices.” Personal Traits Employment Status Management Behaviors Policies & Procedures Gender Personal decisions Age Family Situation Full-/part-time Tenure Role Previous job Performance Discretion Mobility Workload Expectations What Else? Paraphrasing: While disparity may exist, the underlying root causes are likely to be different among class members Region Dept Store 24

25 Human Nature & Variable Complexity 25 Tension & Vested Interest Consideration of many variables can lead to: Class re-definition Sub-classing Removal of damages categories Class de-certification Objections: “Let’s keep it simple” “It’s too complicated” “It’s not manageable” Few VariablesMany Variables Consideration of just a few variables can lead to: Agreement on priorities, focus Expedited timeframes Objections: “Yes, but we’re not considering...” “We seem to be in denial of how many moving pieces there are... “ “This is too simplistic” “Point of Discomfort” Start of Analysis “Return to Sanity”

26 Post-Dukes Cases – Effect on Class Certification In re Wells Fargo Residential Mortgage Lending Discrimination Litig., slip op., No. 3:08-md-01931-MMC (Sept. 6, 2011). Denied certification of claims under Fair Housing Act and Equal Credit Opportunity Act. Found regression analysis allegedly showing disparate impact of discretionary policy insufficient. Daubert motion denied for purposes of decision. But see McReynolds v. Merrill Lynch, Pierce, Fenner & Smith, Inc., No. 11-3639 (7th Cir., Feb. 24, 2012) (Posner, J.). Disparate impact Distinguished Dukes on the ground that an affirmative policy was being alleged to create a disparate impact on a protected class. Contemplates that a single, common body of evidence would be used to prove or disprove that the policy had a discriminatory impact. 26

27 Post-Dukes Cases – Trial By Formula Violates Due Process Duran v. U.S. Bank National Association, No. A125557 & A126827 (Cal. App., Feb. 6, 2012). Same expert (Dr. Drogin) as in Dukes. Court used Drogin’s analysis as a model but came up with its own simplified analysis. Court applied “statistical” analysis to estimate the number of employees within the class that had been misclassified for overtime pay purposes. Court held: Methodology violated due process because it denied defendant opportunity to provide relevant evidence and individualized defenses relating to classification of each employee. Methodology was flawed because sample was arbitrary. Sampling would have been improper even if used to calculate damages due to the high margin of error. 27

28 Post-Dukes Cases – Trial By Formula Does Not Satisfy FRCP 23 In re Facebook, Inc. PPC Advertising Litigation, No. C 09-3043 PJH, slip op. (N.D. Cal. Apr. 13, 2012). Allegation that Facebook breached “cost-per-click” agreements with advertisers by charging for “invalid” clicks. Plaintiffs proposed that their experts could create a methodology that would distinguish between valid and invalid clicks. Court rejected this argument, finding that “there is no way to conduct this type of highly specialized and individualized analysis for each of the thousands of advertisers in the proposed class.” 28

29 Part III – Practical tips on presenting and challenging statistics 29

30 Summary of how statistics are used to support class certification The existence of a common practice A relationship between the defendant’s conduct and some injury to class members The total damages or other impact caused by a practice The percentage of people impacted by a practice. Given a set of characteristics, the probability that a person was impacted by a practice. Common reliance Truly common reliance, e.g. “fraud on the market” Reliance by “most” of the class 30

31 Challenging Statistics Daubert challenges The expert is not qualified The statistical model is not sound The methodology is flawed The underlying data is unreliable What is the applicable Daubert standard on class certification? Other challenges The expert opinion is not relevant to any issue to be decided at trial. The opinion does not show that an issue is susceptible to common, class-wide proof. 31

32 General Considerations Does the statistical evidence satisfy Daubert? Even if it satisfies Daubert standards, does it hold up to rigorous analysis? Does it satisfy the proponent’s burden of proof (resolution of conflicting opinions) that Rule 23’s requirements are satisfied? Does it show what it purports to show? Are there inferential gaps in the analysis itself? Does it leave data or factors unaccounted for? Is circular reasoning involved, or does it purport to prove what it actually assumes? Are there conceptual gaps between the data or conclusions and the true requirements of Rule 23? Does it show that the answer to a question necessarily is the same for all class members, or does it merely generalize that the answer might be the same for some of them? Does it show that all class members were similarly affected, or only that each one might have been? Is it consistent with governing substantive law? Is there a conceptual gap between the evidence and the proof requirements of substantive law? 32

33 Common Fact Patterns to Watch Out For Single policy or practice (Dukes) Does a single policy exist? (McLaughlin) Is there a way to prove a causal link between the policy and some alleged harm? Can the causal link be resolved by reference to common, classwide evidence. Mass reliance/common impact—ask whether legal theory is such that individual reliance is not required (if so, still have to consider the separate question of causation) Reliance question can be both proved and resolved by reference to common evidence. 33

34 Common Fact Patterns to Watch Out For (cont’d) Winners and losers Some class members are actually better off as a result of the alleged practice. Subclasses may cure this problem, but problem might be in identifying who goes in which category. Trial by formula Statistics used to estimate percentage of class members to whom the defendant may be liable This violates due process according to Duran. Statistics used to aggregate and apportion damages No, according to dictum in Dukes, but some courts may be more welcoming of this argument. 34

35 Tools for challenging statistical evidence Assumption vs. conclusion – Does the analysis prove a fact to be true, or does it assume the fact is true? Underlying data – Where does it come from? Is it complete? Is it being interpreted correctly? Methodology – Is it peer reviewed? Has it been discredited? Relevance – Does the analysis address the right issue? Sample size – Is it big enough to be predictive? Error rate – How accurate are the predictions? Other logical fallacies Is the analysis circular? Are variables ignored? 35

36 Tips for Dealing With Experts 36 Draw Inferences (optional) Analyze Collect Data Collect Data Major Strategic Considerations How collected? Trusted source? Is the method / measurement process reliable (consistent with repetition)? Valid? Recorded properly? Categories appropriate? What is the non-response rate (survey)? Why? Can the results be generalized? How are charts/graphs presented? What method is used to select the units (or scale)? Do analyses reach different opinions? What variables were left out? Did the expert answer the right question? How do I estimate whatever is missing? Ask, “What is missing? Who would know?” 36

37 Common Statistical Flaws Illusory commonality When (even reliable) statistics only purport to answer a question for X or X% of a class, or show that X or X% of a proposed class is affected, commonality does not exist (indeed, is disproved). Discrimination (Dukes) Consumer fraud (Zyprexa, Neurontin) Breach of contract (e.g., timeliness of payment) Overlooked factors and intervening causes. Alternative drugs might be more expensive for some. Some people smoke lights for flavor or because they are “cool.” Circularity/Assumed Reliance When an econometric analysis purportedly shows that causation can be proved on a class-wide basis through a “price effect,” the analysis may assume reliance or causation rather than prove them. Erroneous assumptions All off-label marketing is fraudulent (legal/factual error). Third-party payors have similar rates of reimbursement for off-label prescriptions (factual error). All class members were unaware the drug was unapproved (factual error). 37

38 Confidence in Confidence 38 Question for the Courts: At what point do we get to an acceptable level of common proof?

39 Future Issues Minority Report technology Is there a point at which “trial by formula” becomes acceptable? Will an increase in societal complexity require shortcuts in class and other aggregate litigation? The “Most Class Members” Problem Example – Proof that 80% of class members were harmed means that 20% were NOT harmed. What if the proof is that nearly all class members were harmed? Is 90% enough? What about 99%? Confidence and Error Rate Don’t confuse this with the issue of the percentage of class members injured. What confidence level and error rate will become acceptable? 39

40 For Further Study David H. Kaye & David A. Freedman, Reference Guide on Statistics, Reference Manual on Scientific Evidence 2d Ed. (Federal Judicial Center 1981) ($file/sciman02.pdf)$file/sciman02.pdf Robert Ambrogi, Statistics Surge as Evidence in Trials, IMS Newsletter, BullsEye: August 2009, ( surge-as-evidence-in-trials-081409.asp) surge-as-evidence-in-trials-081409.asp Edward K. Cheng, A Practical Solution to the Reference Class Problem, 109 Colum. L. Rev. 2081 (2009) ( Denise Martin, Stephanie Plancich, and Mary Elizabeth Stern, Class Certification in Wage and Hour Litigation: What Can We Learn from Statistics? (Nera Economic Consulting 2009) ( Dukes, plaintiff’s Expert Dr. Richard Drogin’s Statistical Report ( Dukes, class certification ( Michael O. Finkelstein and Bruce Levin, Statistics for Lawyers: Second Edition (Springer, 2001) Finkelstein, Michael O., Basic Concepts of Probability and Statistics in the Law (Springer, 2009) Olive Jean Dunn and Virginia A. Clark, Applied Statistics: Analysis of Variance and Regression, Second Edition (John Wiley & Sons, 1987) 40

41 Thank You Topics covered Increasing importance of statistics & growth of data Basic statistical concepts and use in litigation Case studies Practical tips Questions? 41

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