EXHIBIT 2 

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UNITED STATES DISTRICT COURT 

SOUTHERN DISTRICT OF OHIO

SARA HAWES, individually, and on behalf of 

all others similarly situated,

Plaintiff,

v.

MACY’S WEST STORES, INC.,

Defendant.

Case No. 1:17-cv-00754

Judge Timothy S. Black

DECLARATION OF SEAN IYER

I, Sean Iyer, declare and state as follows:

1. I am over the age of 18 and under no disability. I have personal knowledge of the 

matters set forth herein, unless otherwise specifically indicated.

2. My name is Sean Iyer. I am an Executive Vice President at Compass Lexecon, a 

leading global economic consultancy. Previously, I was a partner at Cornerstone Research. 

3. In 1995, I received a B.S. in economics from Presidency College at the University 

of Calcutta. In 2001, I received an M.A. in economics from the Warrington College of Business, 

at the University of Florida.

4. I have extensive experience in consumer fraud, product liability litigation, and FTC 

deceptive advertising matters. My work in these areas includes class certification defense, theories 

of liability and damages analysis. 

5. I have been retained by counsel for the Defendant to provide economic analysis of 

issues related to damages in this case, particularly with regard to class certification. 

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2

6. In connection with my work in this case, I generated a written report dated April 

30, 2021. A true and accurate copy of that report, including all exhibits are attached to this 

Declaration as Exhibit A. 

Pursuant to 28 U.S.C. § 1746, I declare under penalty of perjury that the foregoing is true 

and correct. 

Executed on April 30, 2021.

Sean Iyer

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EXHIBIT A

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IN THE UNITED STATES DISTRICT COURT

SOUTHERN DISTRICT OF OHIO

SARA HAWES, individually, and on behalf 

of all others similarly situated,

Plaintiff,

v.

MACY’S WEST STORES, INC.,

Defendant.

Case No. 1:17-cv-00754

Judge Timothy S. Black

EXPERT REPORT OF SEAN IYER

Executive Vice President, Compass Lexecon

April 30, 2021

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I. Overview

A. Qualifications and Experience 

1. I am an Executive Vice President at Compass Lexecon, a leading global economic 

consultancy. Previously, I was a partner at Cornerstone Research and headed that 

firm’s national intellectual property practice. I have led or currently lead 

engagements in high-stakes litigation in antitrust and competition, intellectual 

property, consumer fraud and product liability, and other complex commercial 

disputes.

2. I have a Bachelor’s and a Master’s degree in economics. My undergraduate training 

was in economics, mathematics and statistics. My graduate training focused on 

applied microeconomics and econometrics. Since 2002, I have been a practicing 

economic consultant and have broad expertise in applied economics, pricing, 

damages, survey methodology, and market research methods. I currently serve as 

Vice Chair of the American Bar Association’s Antitrust Division IV Specialized 

Intellectual Property Section. 

3. Since 2002, I have worked as a testifying or consulting expert on damages and 

damages-related issues on dozens of matters and my engagements have included 

several of the most-watched matters of recent times. I also have extensive 

experience and expertise in designing and conducting market research, including 

surveys, in both litigation and in my consulting work. Over the course of my career, 

I have reviewed, analysed, or conducted over a hundred surveys across a wide 

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variety of industries and applications. I have reviewed hundreds of scholarly 

articles, trade publications, and white papers on survey methodology and practice. 

Outside of my work in litigation, I have conducted surveys on behalf of corporate 

clients and for the Licensing Executives Society. I have authored or co-authored 

articles on survey design and contributed a peer-reviewed, scholarly chapter on 

conjoint surveys in the Handbook of Marketing Analytics. 

4. I have spoken at several conferences and in numerous law firms on topics relating 

to damages and market research methods, including surveys. I have been an invited 

guest lecturer at New York University (M.B.A. and undergraduate) and at the 

Wharton School, University of Pennsylvania (M.B.A.) on the use of surveys in 

litigation applications. 

5. I have served as an expert witness on both damages and survey methodology and 

practice and have testified in intellectual property cases in U.S. Federal District 

Courts, at the U.S. International Trade Commission, before the American 

Arbitration Association, and the Delhi (India) High Court. A copy of my curriculum 

vitae, which includes a list of publications and presentations pertaining to survey 

methodology and practice as well as a list of cases in which I have testified as an 

expert, is attached as Exhibit 1 to this report. 

B. Assignment

6. I have been retained by Brooks Pierce, counsel for Macy’s West Stores, Inc. to 

evaluate and opine on the Expert Report of Mr. Stefan Boedeker (“Boedeker

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Report”)1 and to opine on the viability of Plaintiff’s theory for obtaining class-wide 

damages.

C. Materials Considered 

7. I have relied upon materials from the sources listed in Exhibit 2 to this report. 

8. My findings are based on materials currently available to me. I may revise or 

supplement my opinions pursuant to testimony provided by experts or fact 

witnesses or if additional materials relevant to my assignment are produced by the 

parties. 

D. Compensation

9. I am being compensated at a rate of $800 per hour for my work on this assignment, 

with reimbursement for expenses as incurred. I have been assisted by an analyst at 

Compass Lexecon in my work. My compensation does not depend in any way on 

the substance of my opinions or on the outcome of this matter. 

II. Summary Evaluation and Opinion Regarding the Boedeker 

Report

10.Mr. Boedeker was retained to determine if it is possible to use an economic loss 

model to quantify the damages suffered by the Plaintiffs and the Class as a result of 

the misleading and/or false representations or statements pertaining to the thread 

1 Expert Report of Stefan Boedeker, February 1, 2021.

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count on the labels of the products.2 Additionally, he was asked by counsel for 

Plaintiffs to (i) explain and outline an empirical study to quantify the economic loss 

to consumers, by assessing consumers’ changes in choices and preferences if the 

misrepresentations were disclosed at the point of purchase, and (ii) explain and 

outline a statistical methodology to calculate class-wide damages utilizing 

transactional purchase data that enable the valuation of the attributes that a 

product is comprised of.3 The empirical study he proposes in the Boedeker Report 

is called conjoint analysis. The statistical methodology that Mr. Boedeker claims he 

will use to calculate class-wide damages utilizing transactional purchase data is 

called hedonic pricing.

4

11. Notably, Mr. Boedeker has not actually done any empirical analysis. He has not 

actually conducted a conjoint analysis-based survey, only said that he intends to do 

one. Nor has he actually analyzed any transactional data. In fact, he does not offer 

a model that is specific to this matter. Instead, he recites a summary of general 

approaches to hedonic pricing along with a superficial and incomplete explanation 

of what explanatory variables he may use. Thus, in the absence of any empirical 

analysis, it is unscientific and speculative for Mr. Boedeker to contend that his two 

approaches can be used with actual data in the instant matter to arrive at reliable 

ways to calculate economic loss, if any, to consumers. 

2 Boedeker Report, ¶11. 3 Boedeker Report, ¶11. 4 Boedeker Report, ¶57.

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12.Any claim that conjoint analysis and hedonic pricing are well known approaches 

and therefore applicable in the instant matter is a red herring and irrelevant. What 

is important is the specific application of each model to the context at hand to see 

if the predictions of the models work. That is the essence of the scientific method.

5

The scientific method involves making hypotheses, deriving predictions from them 

as logical consequences, and then carrying out experiments or empirical 

observations based on those predictions. Hypotheses are then tested by conducting 

experiments or studies. In the Boedeker report, Mr. Boedeker has only made 

hypotheses. He has not done any empirical work or even presented an actual model

to test predictions. Therefore, at a basic level, his approach is unscientific and 

speculative, and cannot be used to calculate economic loss, if any, to consumers. 

13.The remainder of this report is organized as follows. In Section III, I evaluate and 

opine on Mr. Boedeker’s conjoint approach proposal. In Section IV, I evaluate and 

opine on his hedonic pricing proposal. 

III. Evaluation and Opinions Regarding Mr. Boedeker’s Proposed 

Conjoint Approach

14.Setting aside the fact that Mr. Boedeker has not done any empirical work or 

collected any data to test any predictions with respect to economic loss, I now 

evaluate and opine on Mr. Boedeker’s proposed conjoint approach. 

5 See, for example, Karl R. Popper, The Logic of Scientific Discovery, Routledge, 2nd Ed. (2002). 

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15.Generally speaking, conjoint analysis is a method for quantifying consumer 

preferences. The premise is that a product or service can be decomposed into its 

attributes (e.g., number of minutes included in a phone plan) that each has 

different levels (1,000 minutes, unlimited minutes, etc.). The output of a conjoint 

analysis is an estimation of how much each survey respondent values each level of 

each attribute. These preferences are known as “partworths,” since they capture 

how much each part of the product is worth to the respondent.6

16.To understand how Mr. Boedeker proposes to use conjoint analysis, it is useful to 

summarize the “economic framework” outlined in Section 3 in the Boedeker 

Report. In essence, Mr. Boedeker claims he can estimate the economic losses 

incurred by consumers due to a failure to accurately disclose the thread count in 

certain Macy’s bedding products by comparing market outcomes in two purely 

“hypothetical” states of the world. In his proposed analysis, he refers to one 

hypothetical state of the world as the “Actual-World,” and the second he refers to 

as the “But-for-World.”7 The hypothetical premise in his actual-world assumes 

consumers bought the sheets without having the knowledge that certain thread 

counts were allegedly misrepresented whereas in the but-for-world, he assumes 

that consumers would be fully informed at the point of purchase of the alleged 

misrepresentation.

6 Paul E. Green and Vithala R. Rao, “Conjoint Measurement for quantifying judgmental data,” Journal of 

Marketing Research, pp. 355-363 (1971). 7 Boedeker Report, ¶22. In the foregoing discussion, I do not capitalize the terms. 

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17.Mr. Boedeker discusses his proposed conjoint analysis approach in Section 4.2 of the 

Boedeker Report. Specifically, he states that conjoint analysis can determine the 

difference in value (measured in dollars or as a percentage of the purchase price in 

the actual-world) that customers place on products without misrepresentations 

and without omissions compared to an otherwise identical product with 

misrepresentations and with omissions.8

18.Mr. Boedeker’s proposed conjoint driven approach to calculating economic loss has 

a serious economic flaw at its heart. His assumption that Macy’s would sell the same 

number of sheets in the but-for world despite commanding a lower price for each 

sheet at the same marginal cost per sale is tantamount to assuming a forced sale 

for Macy’s. As I show below, this means that Mr. Boedeker proposes to ignore the 

fact that Macy’s willingness to sell would undoubtedly be different in the but-forworld in which Boedeker has attempted to model consumers' willingness to pay. 

This alone renders his analysis unhelpful to establish the but-for market price 

because his proposal, if carried out, would never consider a true but-for market 

equilibrium where demand (willingness to pay) and supply (willingness to sell) 

match. 

19.Below, I describe in detail these and other flaws in Mr. Boedeker’s proposed conjoint 

approach. 

8 Boedeker Report, ¶82. 

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A. Because Mr. Boedeker Ignores But-For Supply, His Proposed 

Conjoint-Based Estimates are Not Useful to Determine Economic Loss

20. When examining loss of economic value, one must start with the concepts of supply 

and demand. As is well known from basic economics textbooks, the demand curve 

reflects the relationship between the quantity of a good demanded by consumers 

and prices, with more units of the good demanded at lower prices, all else equal. 

The inverse relationship between price and quantity demanded is known as the 

“Demand Curve.”9 The supply curve reflects the relationship between the quantity 

of a good supplied by firms and prices, with more units of the good supplied at 

higher prices, all else equal. The positive relationship between price and quantity 

supplied is known as the “Supply Curve.”10 Figure 1 shows these relationships, along 

with the competitive equilibrium outcome where market demand and market 

supply intersect.

9 Paul A. Samuelson and William Nordhaus, Economics, 19th Ed., pp.46-50. 10 Paul A. Samuelson and William Nordhaus, Economics, 19th Ed., pp.51-53.

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21.To compare market outcomes between the actual and the but-for states of the world, 

Mr. Boedeker would need to measure:

 Market supply curve in the actual-world,

 Market supply curve in the but-for-world,

 Market demand curve in the actual-world, and

 Market demand curve in the but-for-world.

22. With these four elements properly measured, one can evaluate market outcomes, 

i.e., equilibrium price and quantity. For example, holding price constant, the 

quantity demanded for a sheet with an alleged misrepresentation of thread counts 

is likely to be lower than the quantity demanded for the same product without such 

0

20

40

60

80

100

120

140

160

180

200

0 5 10 15 20 25

Price ($)

Quantity

Figure 1

Competitive 

equilibrium price

Competitive

equilibrium

quantity

Competitive equilibrium

Supply Curve

Demand Curve

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a misrepresentation. This means that the demand curve for the allegedly 

misrepresented sheet is going to be shifted to the left compared to the nonmisrepresented sheet because there is less quantity demanded at any price for the 

misrepresented sheet compared to a non-misrepresented one.

23. In contrast to the typical supply and demand diagram, in which there is an “upward 

sloping” supply curve that indicates how the quantity supplied by firms increases 

with higher prices, the market supply curve that Mr. Boedeker purports to use is 

“fixed to the quantity of sheets sold” in the actual-world. While never 

acknowledged by Mr. Boedeker, this assumption is tantamount to a very specific 

type of market supply curve—a vertical market supply curve. Such a market supply 

curve implies that at “every price level the same quantity is supplied.” There are 

two interpretations of such a market supply curve: either the nature of the product 

is such that is perfectly inelastic, an extreme assumption that rarely occurs in the 

real world, or it is an assumption unrelated to the nature of the product made by 

Mr. Boedeker. I discuss each interpretation, in turn. 

24. Examples of products that have perfectly inelastic11 market supply curves in the 

long-run are typically limited to rare art or ancient artifacts. This is because those 

items cannot be reproduced or duplicated—their supply is fixed regardless of the 

price. Importantly, Mr. Boedeker’s demand-side conjoint analysis assumes that the 

market provides various types of sheets with their components (like thread counts)

at various prices—the perfectly inelastic supply curve assumption is completely at 

11 See Fig.4-6 in Paul A. Samuelson and William Nordhaus, Economics, 19th Ed., p.73.

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odds with his demand side formulation. Sheets can be duplicated, and their supply 

can expand or contract. Generally, when demand for sheets increases (e.g., due to 

rising incomes), sellers responded by selling more sheets. Similarly, a decrease in 

demand for sheets will lead to a reduction in the quantity supplied.

25. Since it is unrealistic for sheets to be perfectly inelastic, the vertical market supply 

curve is therefore an assumption (unrelated to the nature of the product) made by 

Mr. Boedeker. For him to claim that his analysis would estimate a change in market 

equilibrium prices, he must also be assuming that the supply does not change in 

response to a reduction in demand (from the actual-world market demand curve 

to the but-for-world market demand curve). This assumption has an important 

implication for the size of the estimated economic loss. It assumes that suppliers 

will not respond to the decrease in demand (e.g. will supply the same level of output 

regardless of market price). Therefore, by definition, a reduction in demand will 

only result in a lower price. Further, this price change would be equal to the full 

(vertical) difference between the two demand curves.

26. However, if Mr. Boedeker had recognized the basic fact that suppliers respond to 

the reduction in demand by reducing output (i.e., exhibit an upward sloping supply 

curve), the new market outcome would be both lower prices and output, but the 

implied price decline would not be as large as would be the case if there was no 

output response. Intuitively, when suppliers respond to declining demand by 

decreasing output, this decrease in production reduces their costs and these savings 

are passed on to consumers, which then mitigates the decline in demand and the 

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corresponding change in market price. Therefore, when the role of supply is 

properly recognized, the decrease in price is smaller than under Mr. Boedeker’s 

assumption of no change in supply. Figure 2 illustrates this, where the change in 

market price (the smaller green bracket) is smaller than the “Boedeker Loss 

Measure” (the larger green bracket).

27. Therefore, even if Mr. Boedeker’s conjoint analysis can yield a reliable estimate of 

the change in demand due to the disclosure of a product defect by ignoring the 

potential for supply to respond to a shift in market demand Mr. Boedeker’s 

assumption ends up overestimating the size of economic damages from his model 

by calculating the largest possible price decline due to his hypothetical “measured” 

0

50

100

150

200

250

300

350

400

0 5 10 15 20 25 30 35 40 45

Willingness-To-Pay, Willingness-To-Accept, Price ($)

Quantity

Figure 2

Shifted Demand Demand Curve with Misrepresentation

Curve

Demand Curve 

with Disclosure

Change in 

Market Price

Boedeker Loss Measure

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demand shift (Mr. Boedeker’s “vertical distance ∆ between points A and B is equal 

to the compensation required to make all purchasers whole.”12)

28. Mr. Boedeker claims that it is appropriate to treat supply as fixed because “the 

shape of the supply curve(s) in the Actual-World and in the But-For-World is 

irrelevant for the quantification of economic damages because the focus is to 

determine a price in the But-For-World for the units sold in the Actual-World.”13 

But this assertion merely reflects Mr. Boedeker’s views about how damages should 

be calculated. It is not based on how markets work and how prices and output are 

determined in reality. Simply put, there is no economic basis for this extreme 

assumption. Indeed, Mr. Boedeker’s argument that quantity should be considered 

to be fixed for damages purpose establishes that he has not calculated a market 

price because the economic loss that he proposes is not premised on a market price 

under actual supply and demand conditions, which would conform to fundamental 

economic principles.

29. To assume, as Mr. Boedeker does, that Macy’s would sell the same number of sheets 

in the but-for world despite commanding a lower price for each sheet at the same 

marginal cost per sale is to assume a forced sale by Macy’s. Put differently, although 

Mr. Boedeker's hypothetical constant supply curve admits that Macy’s would 

ordinarily be willing to sell a smaller quantity of sheets at the lower price consumers 

12 Boedeker Report, ¶37, shown by the vertical distance between the blue and orange lines in his Figure 1. 13 Boedeker Report, ¶52.

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would then be willing to pay, Mr. Boedeker simply assumes that Macy’s would be 

willing to sell the same quantity anyway, notwithstanding the lower price.

30. Thus, Mr. Boedeker’s analysis does not estimate the change in market price 

resulting from failure to disclose product defects. Instead, his analysis proposes to 

estimate the reduction in price necessary to generate the same level of expected 

sales for a hypothetical product with and without a disclosed mischaracterization 

of thread count. This “discount” approach bears no economic loss relationship to 

any particular putative class member.

14

B. The Claimed Mechanics of Mr. Boedeker’s Conjoint Approach Relies 

Upon a Statistical Methodology that Makes it Particularly Unsuitable to 

Determine if There is Common Impact Among Individual Members of the 

Putative Class

31.The claimed mechanics of his proposed conjoint approach15 rely upon a statistical 

methodology that makes it particularly unsuitable to determine whether there is 

common impact among individual members of the putative class because 

individual respondent-level utilities for attribute levels in his conjoint study would 

be derived from utilities from all other respondents. I explain why below. 

32. Mr. Boedeker summarily describes the mechanics of his proposed approach to 

derive “partworths” in paragraphs 88-96 in the Boedeker Report. The methodology 

14 Mr. Boedeker mentions this “discount” in Boedeker Report, ¶116. 15 Boedeker Report, ¶¶92-94. The methodology is known as Hierarchical Bayes Estimation with Gibbs 

Sampling using the Metropolitan Hastings Algorithm. Attributes are the sheet characteristics that Mr. 

Boedeker may include in his study, e.g., thread counts. Attribute levels are the specific values given to 

thread counts, e.g., 200 or 400. The above-named methodology is a specific technique to derive utilities 

(also known as “partworths”) for each attribute level in a conjoint study. 

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is known as Hierarchical Bayes Estimation with Gibbs Sampling using the 

Metropolitan Hastings Algorithm. In his study, Mr. Boedeker purports to use this 

methodology to then calculate the value of an attribute level. Recall that attributes 

are sheet characteristics that Mr. Boedeker may include in his study, e.g., thread 

counts. Attribute levels are the specific numerical value for the thread counts, e.g., 

200 or 400. The above-named methodology is a specific technique to derive 

individual-level utilities (known as “partworths,” as described above) for each 

attribute level in a conjoint study. 

33. The difficulty with using this approach is that any individual-level partworths do 

not depend only on that individual respondent’s data—it is a function of all other 

respondents’ data. Thus, if respondent Sue entered Mr. Boedeker’s survey and gave 

certain answer choices, then Sue’s estimated “partworths” would depend on 

respondent Jack’s answer choices and every other respondent’s answer choices. The 

below language from a Sawtooth Software research paper—Sawtooth Software is 

the very software that Mr. Boedeker claims he will use in his proposed conjoint 

analysis—makes this clear while describing how the Metropolis Hastings algorithm

works:

If a respondent’s choices are fitted well, his estimated b [partworth] depends 

mostly on his own data and is influenced less by the population distribution. 

But if his choices are poorly fitted, then his estimated b depends more on the 

population distribution, and is influenced less by his data. In this way, HB 

[Hierarchical Bayes] makes use of every respondent’s data in 

producing estimates for each individual.

16

16 Richard M. Johnson, “Understanding HB: An Intuitive Approach,” Sawtooth Software Research Paper 

Series, (2000) at p. 10. Emphasis added. 

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Therefore, Mr. Boedeker chooses a methodology that assumes away differences at 

the individual level. By construction, estimates at the individual level are 

suppressed because they are always a function of every other individual’s estimates. 

Thus, respondent Sue may not care to pay more for higher thread levels, but Mr. 

Boedeker’s methodology would suppress that information because her “partworth” 

for thread counts would depend on Respondent Jack’s partworth and every other 

respondents’ parthworth for thread counts. This makes his claimed conjoint 

approach unsuitable to determine whether there is common impact among 

members of the putative class. 

C. Mr. Boedeker’s Conjoint-Derived Estimate Would Mask Individual 

Differences in Willingness to Pay

34. Setting aside the fact that individual respondent-level utilities for attribute levels 

in his conjoint study would be derived from utilities from all other respondents, I 

now discuss why Mr. Boedeker’s proposed assumptions with regard to disavowing 

individual level data and relying on aggregates to measure economic loss to all 

members of the putative class is flawed. Specifically, he disavows individual-level 

information because, in his saying, “it is well-established that individual level partworth estimates of conjoint models are not reliable at the individual level and that 

results should only be considered at the aggregate level,”17 and that “we are not 

interested in the probability of each individual survey participant purchasing a 

17 Boedeker Report, ¶116.

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particular attribute combination.”18 He claims that his method “can be used to 

calculate class-wide damages in the merits phase of this case by multiplying the 

percentage of economic loss per unit as established above with the sales of the 

product during the class period.”19 Therefore, his method hinges on calculating an 

average value across all respondents and ascribing that value to each putative class 

member. 

35. In reality, Mr. Boedeker’s proposed estimate (an average) would mask substantial 

differences in economic losses across individuals, and would not provide an 

appropriate basis for compensating all victims of misrepresentation. Contrary to 

Mr. Boedeker’s claims, his proposed estimate cannot be interpreted to reflect a 

single value appropriate for all consumers who purchased a misrepresented 

product.

36. Mr. Boedeker proposed to estimate the discount that would equate aggregate sales 

of the misrepresented product with those sales that would have occurred had the 

misrepresentation been disclosed. However, this single “discount” figure bears no 

necessary relationship to the economic loss to any particular class member because 

misrepresentation of a product can have varying impacts across different 

consumers. By proposing an average number, Mr. Boedeker’s analysis fails to 

address these differences across consumers. Rather, his proposed estimate of the 

discount required to equate sales in the actual and but-for worlds would have little,

18 Boedeker Report, ¶119. 19 Boedeker Report, ¶154.

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if any, relationship to the harm suffered by any of the individual consumers due to 

misrepresentation.

37. To better understand why, it is necessary to more fully understand the mechanics 

of Mr. Boedeker’s calculation. My description of his calculation here relates to a 

particular configuration of a hypothetical sheet package and a particular thread 

count scenario. My discussion here assumes that Mr. Boedeker has generated 

reliable estimates of demand curves both for the actual and but-for worlds, which, 

of course, he has not done. I use that assumption here for the sole purpose of 

demonstrating the flaws in economic logic that underlie Mr. Boedeker's proposal

that he will use one numerical figure, and that provides the appropriate 

compensation for all consumers affected by the alleged thread count 

misrepresentations.

38. Mr. Boedeker’s proposed calculation is based on the following steps:

 The responses to Mr. Boedeker’s conjoint survey questions would be used 

to develop statistical estimates of each respondent’s willingness to pay for 

attributes of a hypothetical sheet, including their willingness to pay to 

avoid the allegedly misrepresented thread counts. This information, in 

turn, would be used to develop respondent-specific estimates of the 

probability that each respondent will purchase a particular hypothetical 

sheet at various prices.

 Mr. Boedeker proposes to use the respondent-specific estimates of 

willingness to pay for attributes of the hypothetical sheet to estimate the 

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probability that each respondent will purchase such sheets without a 

misrepresented thread count at each price point used in the analysis. The 

exercise would then be repeated to determine the probability that each 

respondent would purchase the same sheet with a disclosed 

misrepresentation scenario.

 Mr. Boedeker would then aggregate (i.e., averages) the estimated 

purchase probabilities across all respondents for the given sheet. Based 

on these average probabilities, he would first construct a demand curve 

for the no-misrepresentation sheet, which reflects the average probability 

that respondents will purchase the sheet at different prices. Following the 

same approach, Mr. Boedeker would then construct the demand curve for 

the same package that now includes the misrepresentation using the 

estimate of each respondent’s value for the misrepresentation.

 With the two estimated aggregate demand curves, Mr. Boedeker can then 

calculate the discount required to generate the same level of aggregate 

sales of the misrepresented sheet to that he has proposes to estimate for 

the same sheet without a misrepresentation.

 As this description indicates, the analysis is based entirely on average 

probabilities calculated across all survey respondents. As such, the 

calculation fails to reflect differences in respondents’ willingness to pay 

to avoid misrepresentations and, as a result, it would fail to provide an 

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21

estimate of the appropriate compensation for any respondent who 

differed from that average:

o Some individuals place little value on the alleged thread count 

misrepresentation at issue. For example, this could be because of 

shopping convenience (they were already at Macy’s) or a strong 

inclination toward a sheet attribute such as color. Thus, disclosure 

of misrepresentation has little effect on their willingness to pay for 

(and probability of purchasing) the hypothetical sheet at issue. 

Payment of the discount that equates the average probabilities of 

purchasing the sheet with and without the thread count 

misrepresentation more than fully compensate these claimants, 

including those estimated to have no loss at all based on Mr. 

Boedeker’s framework.

o Some individuals place great value on the alleged thread count 

misrepresentation at issue and disclosure of the misrepresentation

thus has a large negative effect on their willingness to pay for (and 

probability of purchasing) the hypothetical sheet at issue. Payment 

of Mr. Boedeker’s calculated discount would fail to fully 

compensate these individuals.

o Some individuals would have been willing to purchase the 

hypothetical sheet with alleged thread count misrepresentation at 

issue at the discounted price but would not have been likely to 

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22

purchase the misrepresented package at the higher price. Again, 

compensation based on the discount that equates sales of the 

product with and without misrepresentation has little relationship 

to the harm suffered by any of these types of individuals. 

39. Further, the demand and supply framework I discussed above is based on analyzing 

the behavior of a “marginal consumer.” In economics, market demand is 

determined by consumers’ willingness to pay for that product. A consumer will 

purchase a good if the price is below that consumer’s willingness to pay (all else 

equal).20 The marginal consumer is defined as the person for whom the market 

price is equal to that consumer’s marginal willingness to pay. Mr. Boedeker’s 

proposed analysis, however, is based on survey respondents’ willingness to 

purchase a hypothetical sheet package with his chosen features. These sheet 

packages, which Mr. Boedeker has not yet identified, are, therefore, not sold in the 

marketplace and as a result, there is no market price for them and, concomitantly, 

no marginal consumer exists in his proposed approach. Instead, Mr. Boedeker 

proposes to use a range of prices for a wide array of hypothetical sheet packages 

and arbitrarily use an average of the partworth estimates as the basis of his 

economic loss calculation.

40. Thus, none of Mr. Boedeker’s economic loss calculations rely on any individual 

consumer, marginal or otherwise. Instead, it is based on the average probability 

20 This is because willingness to pay is the highest price that a buyer is willing to pay for an extra unit of a 

good. Daron Acemoglu et al, Microeconomics, Global Edition. Harlow: Pearson, 2016, p.97.

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23

that sample respondents would purchase a given hypothetical sheet at a specified 

price. 

41.Because it is based on averages, Mr. Boedeker’s proposed analysis cannot establish 

that members of the proposed class suffered a common impact as the result of 

defendants’ actions. The use of an average, by construction, collapses all variation 

in the underlying distribution of respondent data and thus cannot be used to 

identify whether individual respondents suffer similar impacts. That is, the 

existence of an average impact does not imply that the individuals that comprise 

that average faced a common impact.

D. In the Absence of a Market-Tested Set of Sheet Attributes, Mr. 

Boedeker’s Proposed Survey-Based Conjoint Analysis is Set Up To Fail

42. Dr. Vithala Rao, whom Boedeker cites in the Boedeker Report, explains that proper 

attribute selection for a conjoint study must be done by referencing “information 

available from a previous consumer survey;” “[e]xternal sources such as Consumer 

Reports,” and the “list of attributes used in their evaluations of the product 

category;” or conducting a “primary study among a small sample of consumers.”21

Mr. Boedeker has done none of these things. Indeed, at this stage, Mr. Boedeker is 

undecided about which specific attributes he will use for his conjoint study.22

43. Importantly, he has not tied thread count to needs that consumers may have with 

respect to sheet choice. These needs could be various: trustworthiness of the brand 

21 Vithala Rao, Applied Conjoint Analysis, p.43. 22 Boedeker Deposition, pp. 70:22-71:17; 95:11-96:3; 112:19-22; 190:2-6.

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24

or indeed the store where they are sold, the touch and feel of the sheets (luxurious 

or sateen, for example), type of material used, resistance to bleaching, whether they 

are wrinkle-resistant, their color, and so on.

23

44. Mr. Boedeker has not done any of the steps recommended by Dr. Rao, he does 

not—and cannot—know the set of salient attributes important to consumers; 

whether higher thread counts are desirable because they connote higher quality; 

whether and how thread count is related to “touch and feel” sheet attributes like 

softness or plushness; whether thread count is related to comfort characteristics 

like breathability; or indeed whether thread count is even salient for consumers 

when purchasing sheets. 

45. Most of the sheets purchased at issue are sold in clear plastic bags with an 

unsecured zipper, which means that a customer on the sales floor can open the 

sheets and feel them.24 The hand feel or how a sheet feels when touched is very 

important in selling sheets.25 But when asked about touch and feel, Mr. Boedeker

had this to say:

It's also a variable that is hard to measure. It's what would be the at (sic) level 

for a touch and feel variable because everything feels and has a different 

touch from consumer[s] across the spectrum.26

Because touch and feel is so subjective, it is entirely possible, even likely, that for 

at least some consumers a higher thread count is associated with better touch and 

23 Declaration of Danielle Swift, ¶18, April 30, 2021. Ms. Swift is employed with Macy’s Merchandising 

Corporation as the Director of Buying for Sheets. 24 Declaration of Danielle Swift, ¶12, April 30, 2021. 25 Declaration of Danielle Swift, ¶12, April 30, 2021. 26 Deposition of Stefan Boedeker, March 1, 2021, p.147: 19-23. 

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25

feel. Thus, it is not sufficient for Mr. Boedeker to include thread count as one of the 

attributes in his proposed conjoint study, calculate a positive “partworth” for it and 

claim that this shows that consumers care in a specific, mathematical way about 

thread count. Those positive partworths may be spurious because thread count 

may be related to other sheet attributes such as touch and feel or something else.

46. Because Mr. Boedeker has not identified let alone market tested the actual set of 

attributes he will use for his conjoint study, we must take as an article of faith that 

Mr. Boedeker will be able to isolate from all other sheet attributes the thread count 

attribute, so that thread count is independent. This is a critical failing in Mr. 

Boedeker’s purported conjoint approach. Put simply, he cannot know if his conjoint 

attribute selection with thread count as an independent attribute is even possible. 

In other words, as stated by the author of a paper that Mr. Boedeker cites:

Attributes should be independent. This is especially important for partialprofile conjoint studies such as ACA and partial-profile CBC studies. With 

partial-profile or hybrid conjoint (ACA involves both), attributes that have 

overlap in meaning can get “double counted,” resulting in too much inferred 

influence on product choice. It is therefore important to economize; 

including attributes with overlapping meanings is wasteful.27

Thus, it is speculative for Mr. Boedeker to assert that he will use his conjoint 

approach to reliably derive economic loss. 

27 Bryan Orme, “Formulating Attributes and Levels in Conjoint Analysis,” Sawtooth Software Research Paper 

Series, p. 1. Mr. Boedeker’s claimed design is a partial-profile CBC design. 

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26

E. Mr. Boedeker Does Not Take Account of Hypothetical Bias in His 

Proposed Survey

47. That survey-based estimates overstate actual willingness-to-pay has been 

recognized for decades.

28 Several experiments have shown that participants in 

hypothetical surveys have significantly higher willingness-to-pay than participants 

in auctions with real money on the line.29 Conjoint analysis is also susceptible to 

hypothetical bias.30 A review of the relevant literature concludes “that, on average, 

CBCA [choice-based conjoint analysis] grossly overestimates the true WTP of 

consumers.”31

48. On the practical side, in a book for practitioners on how to run conjoint analysis, 

the authors include an entire chapter on distortions in conjoint surveys resulting 

from hypothetical bias.32 The authors outline various methods that practitioners 

can use to mitigate hypothetical bias, such as ensuring that the conjoint presents 

realistic choice scenarios, and by incorporating revealed preference data. Mr. 

Boedeker ignores all of these recommendations about how to mitigate hypothetical 

bias in his proposed conjoint study. Setting aside all the other flaws in his 

28 Kenneth Arrow et al “Report of the NOAA Panel on Contingent Valuation,” (1993). 29 John List, “Do Explicit Warnings Eliminate the Hypothetical Bias in Elicitation Procedures? Evidence 

from Field Auctions for Sportscards,” American Economic Review 91, no.5, 1498-1507. 30 For example, see Min Ding et al, ”Incentive-aligned conjoint analysis,” Journal of Marketing Research, 42, 

pp. 67–82 (2005); Min Ding, “An incentive aligned mechanism for conjoint analysis,” Journal of Marketing 

Research, 44, pp. 214–223 (2007); Christina Sichtmann et al, “Estimating Willingness-to-pay with Choicebased Conjoint Analysis – Can Consumer Characteristics Explain Variations in Accuracy?” British Journal of 

Management, 22: 628-645 (2011). 31 Christina Sichtmann et al, “Estimating Willingness-to-pay with Choice-based Conjoint Analysis – Can 

Consumer Characteristics Explain Variations in Accuracy?” British Journal of Management, 22: 628-645 

(2011).

32 Bryan K. Orme and Keith Chrzan, “Chapter 2: Reducing Hypothetical Bias,” in Becoming an Expert in 

Conjoint Analysis: Choice Modeling for Pros, Sawtooth Software: Orem, UT (2017).

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27

methodology, this would render unreliable any estimates he derives from his 

conjoint survey to determine economic loss.

IV. Evaluation and Opinions Regarding Mr. Boedeker’s Proposed 

Hedonic Regression 

49. Setting aside the fact that Mr. Boedeker has not done any empirical work or even 

presented an actual model to test predictions, I now evaluate and opine on Mr. 

Boedeker’s proposed hedonic regression approach. Mr. Boedeker’s treatment of the 

hedonic pricing approach in Section 4.1 of the Boedeker Report is cursory. He 

provides little detail on how a hedonic pricing approach would actually work in the 

instant matter. Instead, he posits—in the most general terms—exemplary 

relationships (“In general terms, and without controlling for other attributes that 

could influence price, the higher the thread count, the higher prices are”)33 and a 

regression equation (“I will consider a regression equation where Price is a function 

of Thread Count and certain attributes, like, for example, Weave, Size, Origin”).34

50. Thus, Mr. Boedeker has not proposed a model that would apply to the instant case, 

just a very general one that is not specified. In the context of regression, model 

specification consists of selecting an appropriate functional form for the model and 

choosing which independent variables to include or exclude from a regression 

equation.35 Relatedly, Mr. Boedeker does not even know how many regressions 

33 Boedeker Report, ¶74. Emphasis added. 34 Boedeker Report, ¶77. Emphasis added. 35 Michael P. Allen, Understanding Regression Analysis, Ch.35, Springer (2004). 

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 31 of 43 PAGEID #: 1971
28

(none of which he has specified) he will need to run since he has not sufficiently 

analyzed the data.36

51.Therefore, the few paragraphs Mr. Boedeker spends on hedonic prices are too vague 

to be of any real use in determining whether he can reliably compute economic loss 

for putative class members. Nevertheless, I explain below why this vague discussion 

of the hedonic approach creates real issues of reliability that he has not addressed. 

52. The starting point of this approach is the hedonic hypothesis: Each good is 

considered a bundle of characteristics, and its price depends solely on these 

characteristics.37 Mr. Boedeker’s approach appears to consist of linearly regressing 

the price of sheets on their characteristics.

38 A regression is a formal means of 

expressing the two essential ingredients of a statistical relationship: a tendency of 

the response variable (here, price) to vary with the predictor variable (here, all 

attributes of sheets that matter to consumers) in a systematic fashion; and, a 

scattering of points around the curve of the statistical relationship.39 After this step, 

one considers the estimated coefficients’ implicit prices of the characteristics. The 

next econometric step would be to conduct tests to assess the impact of each 

characteristic on the price of sheets.

40

36 Deposition of Stefan Boedeker, March 1, 2021, p.71:15-72:3. 37 Kelvin Lancaster, “A new approach to consumer theory,” The Journal of Political Economy, 74(2):132–57, 

1966. Sherwin Rosen, “Hedonic prices and implicit markets: Product differentiation in pure competition,”

The Journal of Political Economy, 82(1):34–55, 1974. 38 Boedeker Report, ¶77. 39 Michael H. Kutner et al, Applied Liner Statistical Models, 5th Ed., p.5. 40 Mr. Boedeker appears to suggest he will conduct such testing. Deposition of Stefan Boedeker, March 1, 

2021, p.151: 7-14.

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29

53. This approach presents considerable drawbacks. First, when multiple tests are 

performed, the final confidence level in the model as a whole does not correspond 

to the nominal level of each test. Moreover, the tests are typically not independent 

of each other, thus requiring involved statistical techniques to compute the final 

level of the multiple tests. Also, with several sheet characteristics, is difficult to 

choose which variables to test without involving subjective judgments of the 

variables’ importance. These are important considerations before selecting the 

hedonic regression method. Mr. Boedeker does not address any of these.

54. These issues are worsened by the presence of multicollinearity among the 

characteristics.41 Multicollinearity refers to a situation in which more than two 

explanatory variables in a multiple regression model are linearly related.

42 This is 

certainly a possibility in the instant case whereby, for example, fabric type and 

thread count may display multicollinearity, either because they are related or 

because they are both a proxy for a “hidden” characteristic such as touch and feel 

that matters to consumers. 

55. When multicollinearity is present, the variance of the estimated coefficients is 

usually high, increasing the likelihood that the estimates are not statistically 

significant.43 (When estimates are not statistically significant, one cannot rule out 

chance “explaining” the relationship posited, as opposed to some underlying 

41 Otis W. Gilley and R. Kelley Pace, “Improving Hedonic Estimation with an Inequality Restricted Estimator,” 

The Review of Economics and Statistics, Vol. 77, No. 4 (Nov., 1995), pp. 609-621. 42 Michael H. Kutner et al, Applied Liner Statistical Models, 5th Ed., Ch.7.6. 43 Michael H. Kutner et al, Applied Liner Statistical Models, 5th Ed., Ch.7.6.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 33 of 43 PAGEID #: 1973
30

relationship.) When multicollinearity is present, excluding a not significant 

variable in the selected model may result in model misspecification, and lead to 

erroneous conclusions regarding the set of characteristics affecting the price. 

Moreover, the presence of multicollinear characteristics negatively affects the 

prediction accuracy of hedonic linear models. 

56. Mr. Boedeker’s has not undertaken any analysis to specify his model or test for 

multicollinearity.44 This omission is important since Mr. Boedeker has no external 

model that explains how sheet characteristics relate to price (i.e., he has no topdown approach) and because there is strong reason to believe (in a bottom-up 

approach that selects all potential explanatory variables) that multicollinearity is a 

distinct possibility. Thus his contention that hedonic regression would be a reliable 

method to arrive at economic loss is speculative because he has no specific model 

to offer. 

_____________________________

April 30, 2021, Bethesda, MD

44 Deposition of Stefan Boedeker, March 1, 2021, pp.148:-149:3.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 34 of 43 PAGEID #: 1974
Exhibit 1

CURRICULUM VITAE

Sean Iyer

OFFICES

Washington, D.C.

555 12th St. NW

Suite 501

Washington, DC 20004

202-753-5273 direct

New York, N.Y.

156 West 56th Street

19th Floor

New York, NY 10019

212-782-3570 direct

siyer@compasslexecon.com

SUMMARY

Sean is an Executive Vice President with Compass Lexecon and works in high-stakes litigation in 

antitrust and competition, intellectual property, consumer fraud and product liability, and other 

complex commercial disputes. Prior to joining Compass Lexecon, he headed Cornerstone Research’s 

national intellectual property practice and developed that practice. He has broad subject matter 

expertise in applied economics, pricing, damages, survey methodology, and market research methods.

His antitrust experience covers analysis of class certification and merits in monopolization, price-fixing, 

and exclusive-dealing claims matters and in cases involving brand-generic settlements, and allegations 

of sham litigation. As a consultant, Sean has particular expertise in online advertising, social media, and 

privacy. He has worked on several large matters that stand at the interface between marketing, high 

technology, and the law. He also consults on competition issues arising from the assertion of standardessential patents (SEPs) and fair, reasonable, and non-discriminatory (FRAND) commitments and 

terms. 

In intellectual property, Sean has worked on many of the most-watched disputes involving the world’s 

leading technology companies. His experience includes dozens of patent and other intellectual property 

cases in U.S. federal courts, matters before the American Arbitration Association, in the International 

Trade Commission (ITC), and international venues. In life sciences, his work includes numerous patent 

damages matters involving pharmaceuticals, biologics, and medical devices. He is experienced in 

ANDA and BPCIA matters. His work includes several trade secrets misappropriation and Lanham Act 

matters. He has analyzed business-to-business (b2b) marketing, vendor practices, hiring and employee 

mobility issues, and intellectual property-ownership agreements for joint ventures. His copyright 

experience includes rate-setting proceedings at the Copyright Royalty Board. He has also worked on 

several trademark and trade dress cases that involved branding, confusion, and harm-to-brand issues.

Sean has extensive experience in consumer fraud, product liability litigation, and FTC deceptive 

advertising matters. His work in these areas includes class certification defense, theories of liability and 

damages analysis. He has also analyzed marketing and consumer behavior aspects of what the 

“reasonable consumer” would expect and market studies to assess reliance and materiality. 

Sean is a Vice Chair in the ABA’s Antitrust Division IV Specialized IP Section Antitrust / Interface 

Committee.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 35 of 43 PAGEID #: 1975
Exhibit 1

2

PROFESSIONAL EXPERIENCE

• 2017 – Executive Vice President, Compass Lexecon

• 2002 – 2017, Vice President, Cornerstone Research

• 2000, Analyst, Freddie Mac

TRIAL TESTIMONY 

Rovi Guides, Inc. and TiVo Solutions, Inc. v. Videotron G.P. and Videotron Ltd., Federal Court of Canada, 

Court File No. T-921-17. (Survey design and analysis.)

Certain Digital Video Receivers, Broadband Gateways, and Related Hardware and Software Components,

U.S. International Trade Commission No. 337-TA-1158. (Survey design and analysis.)

Space Race, LLC v. Funrise, Inc., American Arbitration Association, Case No. 01-18-0002-0041. 

(Damages.)

The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. v. Norwell, Inc., 

American Arbitration Association, Case No. 16 193 Y 0066712. (Damages.)

DEPOSITION TESTIMONY 

Certain Laproscopic Surgical Staplers, Reload Cartridges, and Components Thereof, U.S. International 

Trade Commission No. 337-TA-1167. (Analysis of Domestic Industry.)

Certain Digital Video Receivers, Broadband Gateways, and Related Hardware and Software Components, 

U.S. International Trade Commission No. 337-TA-1158. (Survey design and analysis.)

Barry v. DePuy Synthes Companies et al., U.S. Eastern District of Pennsylvania, Case No. 2:17-cv-03003. 

(Survey design and analysis.)

Velocity Patent, LLC v. FCA US LLC, U.S. Northern District of Illinois, Case No. 13-cv-8419. (Survey 

design and analysis.)

Telefonaktiebolaget LM Ericsson vs. Lava International Ltd., in the High Court of Delhi at New Delhi, 

Ordinary Original Civil Jurisdiction, CS (OS) 764/2015. (Damages.)

EXPERT REPORTS 

Certain Laproscopic Surgical Staplers, Reload Cartridges, and Components Thereof, U.S. International 

Trade Commission No. 337-TA-1167. (Analysis of Domestic Industry.)

Rovi Guides, Inc. and TiVo Solutions, Inc. v. Videotron G.P. and Videotron Ltd., Federal Court of Canada, 

Court File No. T-921-17 (Opening and Reply reports). (Survey design and analysis.)

Certain Digital Video Receivers, Broadband Gateways, and Related Hardware and Software Components, 

U.S. International Trade Commission No. 337-TA-1158. (Survey design and analysis.)

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 36 of 43 PAGEID #: 1976
Exhibit 1

3

Barry v. DePuy Synthes Companies et al., U.S. Eastern District of Pennsylvania, Case No. 2:17-cv-03003. 

(Survey design and analysis.)

Seven Networks, LLC v. ZTE USA, Inc. and ZTE Corporation., U.S. Northern District of Texas, Case No. 

3:17-cv-1495. (Survey design and analysis.)

Space Race, LLC v. Funrise, Inc., American Arbitration Association, Case No. 01-18-0002-0041. 

(Damages.)

Velocity Patent, LLC v. FCA US LLC, U.S. Northern District of Illinois, Case No. 13-cv-8419. (Survey 

design and analysis.)

Telefonaktiebolaget LM Ericsson vs. Lava International Ltd., in the High Court of Delhi at New Delhi, 

Ordinary Original Civil Jurisdiction, CS (OS) 764/2015. (Damages.)

The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. v. Norwell, Inc., 

American Arbitration Association, Case No. 16 193 Y 0066712. (Damages.)

CONSULTING ENGAGEMENTS (representative, not exhaustive)

Arizona

Bard Peripheral Vascular, Inc., et al. v. W.L. Gore & Associates, Inc. (2:2003-cv-00597)

California

Autodesk, Inc. v. Dassault Systemes Solidworks Corporation (No: 3:08-CV-04397-WHA)

Boston Scientific Corporation v. Cordis Corporation (3:02-cv-00790-SI)

In Re: Chase Bank USA, N.A. "Check Loan" Contract Litigation (retained on behalf of defendants)

(3:2009-md-02032)

SPH America, LLC v. Acer, Inc., et al. (09-cv-02535 CAB (KSC))

Apple Inc. v. Samsung Electronics Co. Ltd. et al (5:2011-cv-01846)

Apple Inc., a California corporation v. Samsung Electronics Co., Ltd. et al. (12-cv-00630)

Delaware 

Siemens Medical Solutions USA Inc. v. Saint-Gobain Ceramics & Plastics Inc. (1:2007-cv-00190)

Finjan Inc. v. McAfee Inc. et al (retained by defendants) (1:10-cv-00593)

IMS v. Symphony Health Solutions, Source Healthcare Analytics and Impactrx (1:13-cv-02071-

GMS) Parallel Networks Licensing, LLC v. IBM Corporation (1:13-cv-02072-SLR)

IBM Corporation v. Priceline, Kayak Software Group, and OpenTable (1:15-cv-00137-LPS-CJB)

Roquette Freres, S.A. v. Solazyme, Inc. (1:14-cv-01442-SLR)

SNMP Research, Inc., and SNMP Research International, Inc., v. Avaya Inc. (1:13-cv-00204-RGSA)

Adidas AG v. Under Armour Inc., and MapMyFitness Inc. (1:14-cv-00130-GMS) 

Florida

Angela Sanchez-Knutson et al. v. Ford Motor Company (0:14-cv-61344-WPD)

Grasso, et al. v. Electrolux Home Products, Inc. (8:16-cv-00911-CEH-TGW)

Illinois

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 37 of 43 PAGEID #: 1977
Exhibit 1

4

Apple, Inc., and Next Software Inc. v. Motorola, Inc. and Motorola Mobility, Inc. (1:11-cv-08540)

Velocity Patent, LLC v. FCA US LLC, U.S. District Court for the N.D. Illinois, (13-cv-8419)

Massachusetts

Janssen Biotech, Inc., and New York University v. Celltrion Healthcare Co., LTD., et al (1:15-cv10698)

Celltrion Healthcare Co., Ltd., and Celltrion, Inc., v. Janssen Biotech, Inc. (0:14-11613)

EMC v. Pure Storage (1:13-cv-12789)

Desktop Metal, Inc. v. Markforged, Inc. and Matiu Parangi, No. 1:18-cv-10524-WGY

New Jersey

Teva Women's Health Inc., v. Lupin, Ltd., et al (2:2010-cv-00080)

Teva Women's Health, Inc., v. Mylan Inc., et al. (2:2010-cv-01234)

New York

In Re Iridium Operating LLC et al (1:1999-bk-45005) (retained by defendants)

Harvey A. Lapin, et al. v. Goldman Sachs Group, Inc., et al. (1:2004-cv-02236)

The Research Foundation of State University of New York v. Nektar Therapeutics, Inc. (1:2009-cv01292)

Enzo Biochem, Inc. and Enzo Life Sciences, Inc., v. PerkinElmer, Inc. and PerkinElmer Life 

Sciences, Inc. (03-cv-3817)

Roche Diagnostics GMBH et al v. Enzo Biochemical, Inc. et al (04-cv-4046-RJS)

The Dial Corporation, et al. v. News Corporation, et al. (1:13-cv-06802-WHP)

Pennsylvania

Federal Trade Commission v. Cephalon, Inc., et al. (08-cv-2141)

Vista Health Plan, Inc., et al. v. Cephalon, Inc., et al. (06-cv-1833)

In re Methyl Methacrylate (MMA) Antitrust Litigation - Direct Purchaser Action File (retained by 

defendants) (2:2006-md-01768)

Apotex, Inc. v. Cephalon, Inc., et al. (06-cv-2768)

Ferring Pharmaceuticals et al. v. Shire U.S. Inc., et al. (2008-00941)

King Drug Company of Florence, et al. v. Cephalon, Inc. et al. (06-cv-1797)

Vista Health Plan, Inc., et al. v. Cephalon, Inc. et al. (06-cv-1833)

Apotex, Inc. v. Cephalon, Inc. et al. (06-cv-2768)

Texas

Rochester Medical Corporation v. C.R. Bard, Inc. et al (5:2004-cv-00060)

Tessera, Inc. v. Micron Technology Inc., et al.(2:2005-cv-00094)

Bright Response LLC v. Google Inc., et al (2:2007-cv-00371) (retained by Yahoo)

PA Advisors v. Yahoo, Inc. et al. (2:2007-cv-00480)

Barry v. Medtronic (1:14-cv-00104-RC)

Mobilemedia Ideas LLC v. HTC Corporation and HTC America, Inc. (2: 10-cv-112-T JW)

Ericsson Inc., et al. v. TCL Communication Technology (2:15-cv-11)

Smartflash v. Apple (6:13-cv-447)

Virginia

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 38 of 43 PAGEID #: 1978
Exhibit 1

5

Amdocs (Israel) Limited v. Openet Telecom, Inc. et al (1:2010-cv-00910)

Coinstar, Inc. v. Coinxchange, LLC (3:2006-cv-00299)

TecSec, Incorporated v. International Business Machines Corporation et al (1:10-cv-00115)

UCB, Inc. v. Yeda Research and Development Co. LTD (1:14-cv-01038-LMB-TCB)

Level 3 Communications, LLC v. Limelight Networks Inc. (2:07CV589 (WDK/FBS))

Rolls-Royce plc v. United Technologies Corporation (1:10-cv-00457-LMB-JFA)

TomTom, Inc., v. AOT Systems GmbH (1:12-cv-528 (E.D. VA.))

Washington

Microsoft Corporation v. Motorola Inc., et al (2:2010-cv-01823)

FTC v. Amazon, Inc. (2:14-cv-01038)

International Trade Commission

Certain Flash Memory and Products Containing Same, No. 337-TA-685.

Certain Electronic Devices, Including Wireless Communication Devices, Portable Music and 

Certain Wireless Communication Devices, Portable Music And Data Processing Devices, 

Computers and Components Thereof, No. 337-TA-745.

Data Processing Devices, and Tablet Computers, No. 337-TA-794.

Certain Wireless Communication Devices, Portable Music and Data Processing Devices, 

Computers, and Components Thereof, No. 337-TA-856.

Integrated Circuits with Voltage Regulators and Products Containing Same, No. 337-TA-1024.

Certain Digital Video Receivers and Related Hardware and Software Components, No. 337-TA-1103.

100- to 150-Seat Large Civil Aircraft from Canada, Inv. Numbers 701-TA-578 and 731-TA-1368.

Other Venues

Federal Trade Commission v. Warner Chilcott Holdings Company III, Ltd. et al (1:2005-cv-02179, 

District of the District of Columbia)

Sprint Communications Company LP v. Vonage Holdings Corp., et al (2:2005-cv-02433, District of 

Kansas)

Southeast Missouri Hospital, et al. v. C.R. Bard, Tyco International, et al. (1:07CV0031 TCM, 

Eastern District of Missouri Southeastern Division)

Boston Scientific Corporation et al v. Cordis Corporation (0:2009-cv-03445, District of 

Minnesota)

Duramed Pharmaceuticals, Inc., v. Watson Laboratories, Inc. et al (3:2008-cv-00116, District of 

Nevada)

Volumetrics Medical Imaging, LLC. v. GE Healthcare Ltd., GE Medical Systems, Inc., GE Medical 

Systems, LLC., GE Medical Systems Kretztechnik GmbH & Co OHG, Toshiba America Medical 

Systems, Inc., Medison America, Inc., and Siemens Medical Solutions USA, Inc. (1:05CV00955-

NCT-RAE, Middle District of North Carolina Greensboro Division)

ADIDAS America, Inc., et al. v. Skechers USA, Inc. (3:15-cv-1741, District of Oregon, 

Portland Division)

Apple, Inc. and Next Software Inc., v. Motorola, Inc. and Motorola Mobility, Inc. (10-CV-662 

(BBC), Western District of Wisconsin)

Cabell Huntington Hospital, Inc. proposed acquisition of St. Mary’s Medical Center, Inc. (Docket 

No. 9366, Before the Federal Trade Commission) (retained by merging parties)

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6

EDUCATION

• 2001, M.A. in Economics, Warrington College of Business, University of Florida

• 1995, B.Sc. (Honors, First Class) in Economics, Presidency College, University of Calcutta

PUBLICATIONS 

“Conjoint Analysis in Litigation,” in Handbook of Marketing Analytics, Natalie Mizik and Dominique M. 

Hanssens, eds., 2018, Edward Elgar.

“Privacy Fixing and Predatory Privacy: The Intersection of Big Data, Privacy Policies, and Antitrust,” 

September 2017, CPI Antitrust Chronicle (with Ben Dryden).

“Consumer Surveys and Other Market Based Methodologies in Patent Damages,” ABA Intellectual 

Property Litigation, Vol.26, Issue 1, 2014.

“Fair Is Foul and Foul Is Fair: A Review of Topics that are Brewing in FRAND Litigation,” ABA Antitrust 

Section Intellectual Property Committee Spring Newsletter, 2013.

“It was the Best of Times it was the Worst of Times: Patent Damages in the Wake of Uniloc v.

Microsoft,” ABA Intellectual Property Section Litigation Spring Newsletter, 2012.

“U.S./Canadian Licensing in 2007-08: Survey Results,” Les Nouvelles, Journal of the Licensing

Executives Society International, March 2009 (with Ajay Agrawal, Iain Cockburn, and Louis Berneman)

“Recent Tying Law Cases,” ABA Antitrust Sherman Act Section 1 Newsletter, Spring 2008.

“Antitrust Damages in Exclusionary Practice Cases,” ABA Antitrust Economics Committee Newsletter, 

ABA Section of Antitrust Law, Vol. 7, No. 2, Fall 2007.

“Borough of Lansdale v. PP&L, Inc.,” ABA Antitrust Sherman Act Section 1 Newsletter, Fall 2006/Winter 

2007.

REPRESENTATIVE PRESENTATIONS

“The Humira Decision and Reverse Payments After Actavis,” ABA Antitrust Section IP Committee, 

March 4, 2021

“Essential Patents and the Agencies - Incentives to Standardize,” ABA Antitrust Section IP Committee, 

March 6, 2018.

“Leveraging Surveys in Patent Litigation: Demonstrating Consumer Perception, Avoiding Errors that

Impact Damages,” Stafford Publishing Webinar, July 10, 2014.

“Federal Circuit Decisions in 2014: Patent Infringement Reasonable Royalty Damages Strategies,”

Knowledge Congress Webinar, July 8, 2014.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 40 of 43 PAGEID #: 1980
Exhibit 1

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“Patent Reform and Implications for Damages,” Licensing Executives Society Washington Chapter 

Meeting, September 8, 2009.

PROFESSIONAL MEMBERSHIPS

American Economic Association, American Bar Association (currently Vice Chair in the ABA’s Antitrust 

Division IV Specialized IP Section Antitrust / Interface Committee), American Statistical Association.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 41 of 43 PAGEID #: 1981
Exhibit 2

1

Materials Relied Upon

Third Amended Class Action Complaint, Hawes et al. v. Macy’s, Inc., United States 

District Court for the Southern District of Ohio, Case No. 1:17-cv-00754, August 12, 2019.

Expert Report of Stefan Boedeker, February 1, 2021.

Deposition of Stefan Boedeker, March 1, 2021.

Declaration of Danielle Swift, April 30, 2021.

MACYS_0000884-901.

MACYS_0000902-914.xlsx

MACYS_374-391 combined item spreadsheet.xlsx

Karl R. Popper, The Logic of Scientific Discovery, Routledge, 2nd Ed. (2002). 

Paul E. Green and Vithala R. Rao, “Conjoint Measurement for quantifying judgmental 

data,” Journal of Marketing Research, pp. 355-363 (1971). 

Paul A. Samuelson and William Nordhaus, Economics, 19th Ed.

Richard M. Johnson, “Understanding HB: An Intuitive Approach,” Sawtooth Software 

Research Paper Series, (2000). 

Daron Acemoglu et al, Microeconomics, Global Edition. Harlow: Pearson, 2016, p.97.

Vithala Rao, Applied Conjoint Analysis, p.43. 

Bryan Orme, “Formulating Attributes and Levels in Conjoint Analysis,” Sawtooth Software 

Research Paper Series. 

Kenneth Arrow et al “Report of the NOAA Panel on Contingent Valuation,” (1993).

John List, “Do Explicit Warnings Eliminate the Hypothetical Bias in Elicitation 

Procedures? Evidence from Field Auctions for Sportscards,” American Economic Review

91, no.5, 1498-1507.

Min Ding et al,” Incentive-aligned conjoint analysis,” Journal of Marketing Research, 42, 

pp. 67–82 (2005)

Min Ding, “An incentive aligned mechanism for conjoint analysis,” Journal of Marketing 

Research, 44, pp. 214–223 (2007)

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 42 of 43 PAGEID #: 1982
Exhibit 2

2

Christina Sichtmann et al, “Estimating Willingness-to-pay with Choice-based Conjoint 

Analysis – Can Consumer Characteristics Explain Variations in Accuracy?” British Journal 

of Management, 22: 628-645 (2011).

Bryan K. Orme and Keith Chrzan, “Chapter 2: Reducing Hypothetical Bias,” in Becoming 

an Expert in Conjoint Analysis: Choice Modeling for Pros, Sawtooth Software: Orem, UT 

(2017).

Michael P. Allen, Understanding Regression Analysis, Ch.35, Springer (2004). 

Kelvin Lancaster, “A new approach to consumer theory,” The Journal of Political Economy, 

74(2):132–57, 1966. 

Sherwin Rosen, “Hedonic prices and implicit markets: Product differentiation in pure 

competition,” The Journal of Political Economy, 82(1):34–55, 1974. 

Michael H. Kutner et al, Applied Liner Statistical Models, 5th Ed. 

Otis W. Gilley and R. Kelley Pace, “Improving Hedonic Estimation with an Inequality 

Restricted Estimator,” The Review of Economics and Statistics, Vol. 77, No. 4 (Nov 1995), pp. 

609-621.

Case: 1:17-cv-00754-DRC Doc #: 100-2 Filed: 05/18/21 Page: 43 of 43 PAGEID #: 1983