Paul Karlsgodt, Baker Hostetler [email protected]
303.764.4013 Brian Troyer, Thompson Hine
[email protected] 216.566.5654 Rick Preston, Hitachi
Consulting [email protected] 303.329.8993 Statistics
in Class Certification Proceedings What theyre good for, and how to
discredit them 1 Copyright 2012 Paul Karlsgodt Brian Troyer Rick
Preston All rights reserved
Slide 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
Slide 3
Part I Introduction 3
Slide 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 Courts
analysissee, 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
Slide 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.
Slide 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
Slide 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
Slide 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 defendants 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
Slide 9
Part II Case Law on the Use of Statistics in Class
Certification 9
Slide 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
Slide 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 fraudfraud on the consumer market.
The difference is that markets for consumer goods and services are
inherently different from securities trading markets. 11
Slide 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. Experts 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
Slide 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. Defendants
right to present evidence defeats predominance Closer scrutiny of
expert opinions for class certification was mandated that presumed
in earlier opinion. Where experts opinion was that less than
substantially all (e.g. >99%) of prescriptions were caused by
fraud, individual inquiry required. Where experts opinion was that
substantially all prescriptions were caused by fraud, the expert
analysis was flawed. 13
Slide 14
Common Impact: In re Zyprexa Judge Weinsteins 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
Slide 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
toxicologists and epidemiologists 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
Slide 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 plaintiffs 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?
Slide 17
Dukes: Proof of Common Injury Two ways to bridge gap between
the individuals 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-Marts 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.
Slide 18
Dukes: Plaintiffs 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.
Slide 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-Marts policy of discretion has
produced an overall sex-based disparity does not suffice. 131 S.Ct.
at 2555-56. Note the dissents charge that the majority
misunderstood the methods used. 19 There were inferential gaps
between plaintiffs statistical analyses and their conclusions.
Slide 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
Slide 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
Slide 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 Bielbys
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 Sams Club? 1 Class Cert p. 25
22
Slide 23
Statistical Concepts in Dukes (contd) 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, doesnt make it actionable 23
Slide 24
Visualizing The Issues 24 Is there a common answer for all
class membersi.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-Marts 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
Slide 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: Lets keep it simple Its too
complicated Its not manageable Few VariablesMany Variables
Consideration of just a few variables can lead to: Agreement on
priorities, focus Expedited timeframes Objections: Yes, but were
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
Slide 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
Slide 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 Drogins 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
Slide 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
Slide 29
Part III Practical tips on presenting and challenging
statistics 29
Slide 30
Summary of how statistics are used to support class
certification The existence of a common practice A relationship
between the defendants 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
Slide 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
Slide 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 proponents burden of proof
(resolution of conflicting opinions) that Rule 23s 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
Slide 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 impactask 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
Slide 34
Common Fact Patterns to Watch Out For (contd) 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
Slide 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
Slide 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
Slide 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
Slide 38
Confidence in Confidence 38 Question for the Courts: At what
point do we get to an acceptable level of common proof?
Slide 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 Dont confuse this
with the issue of the percentage of class members injured. What
confidence level and error rate will become acceptable? 39
Slide 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)
(http://www.fjc.gov/public/pdf.nsf/lookup/sciman02.pdf/$file/sciman02.pdf)http://www.fjc.gov/public/pdf.nsf/lookup/sciman02.pdf/$file/sciman02.pdf
Robert Ambrogi, Statistics Surge as Evidence in Trials, IMS
Newsletter, BullsEye: August 2009,
(http://www.ims-expertservices.com/newsletters/aug/statistics-
surge-as-evidence-in-trials-081409.asp)http://www.ims-expertservices.com/newsletters/aug/statistics-
surge-as-evidence-in-trials-081409.asp Edward K. Cheng, A Practical
Solution to the Reference Class Problem, 109 Colum. L. Rev. 2081
(2009)
(http://www.columbialawreview.org/assets/pdfs/109/8/Cheng.pdf)http://www.columbialawreview.org/assets/pdfs/109/8/Cheng.pdf
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)
(http://www.nera.com/extImage/PUB_Wage_Hour_Litigation_1109_final.pdf)http://www.nera.com/extImage/PUB_Wage_Hour_Litigation_1109_final.pdf
Dukes, plaintiffs Expert Dr. Richard Drogins Statistical Report
(http://www.walmartclass.com/all_reports.html)http://www.walmartclass.com/all_reports.html
Dukes, class certification
(http://www.walmartclass.com/staticdata/walmartclass/classcert.pdf)http://www.walmartclass.com/staticdata/walmartclass/classcert.pdf
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
Slide 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
