EXHIBIT 1

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UNITED STATES DISTRICT COURT

EASTERN DISTRICT OF NEW YORK

No. 1:15-cv-02995-MKB-RER

No. 1:16-cv-01153-MKB-RER

No. 2:17-cv-00093-MKB-RER

DECLARATION OF STEFAN BOEDEKER

IN SUPPORT OF PLAINTIFF’S MOTION FOR CLASS CERTIFICATION

JENNIFER HASEMANN and DEBBIE HOTH, 

individually and on behalf of all others similarly 

situated,

Plaintiffs,

v.

GERBER PRODUCTS COMPANY,

Defendant.

JERREMY GREENE and CETARIA 

WILKERSON, individually and on behalf of all 

others similarly situated,

Plaintiffs,

v.

GERBER PRODUCTS COMPANY,

Defendant.

WENDY MANEMEIT, individually and on behalf 

of all others similarly situated,

Plaintiff,

v.

GERBER PRODUCTS COMPANY,

Defendant.

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Table of Contents

1 Introduction ............................................................................................................................. 1

1.1 Qualifications................................................................................................................... 1

1.2 Case Background.............................................................................................................. 2

1.3 Assignment....................................................................................................................... 3

1.4 Materials Considered........................................................................................................ 4

1.5 Structure of the Report ..................................................................................................... 4

2 The Market for Formula Products........................................................................................... 5

3 Theoretical Framework of Economic Loss ............................................................................. 6

3.1 Demand and Supply in a Competitive Market................................................................. 6

3.1.1 Willingness-to-Pay.................................................................................................... 6

3.1.2 Willingness-to-Accept .............................................................................................. 8

3.1.3 Market Equilibrium................................................................................................... 9

3.2 Shifting Demand Curves and Changes in Equilibrium Price......................................... 11

3.3 A Model of Economic Loss ........................................................................................... 14

3.4 Consideration of the Supply Side in the Economic Damages Model ............................ 17

3.5 Approaches to Estimating the Value of Individual Attributes in Composite Products.. 17

4 Methodology of the Empirical Study .................................................................................... 18

4.1 Conjoint Analysis – Methodology ................................................................................. 18

4.2 Statistical Estimation Techniques Applied in Conjoint Analysis .................................. 21

5 Empirical Study ..................................................................................................................... 23

5.4 Pre-Test Study................................................................................................................ 26

5.5 Choice Based Conjoint Study ........................................................................................ 27

6 Economic Loss Calculation ................................................................................................... 31

6.1 Four-Step Estimation Process ........................................................................................ 31

6.2 Market Simulations in Conjoint Analyses ..................................................................... 33

6.3 Quantification of Economic Loss Based on Conjoint Analysis Supported by Market 

Simulations.................................................................................................................... 34

7 Summary and Conclusion...................................................................................................... 37

8 References ............................................................................................................................. 39

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Table of Figures

Figure 1: Willingness-to-Pay and Demand..................................................................................... 8

Figure 2: Willingness-to-Accept and Supply.................................................................................. 9

Figure 3: Supply & Demand......................................................................................................... 10

Figure 4: Shift in the Demand Curve and the Effect of the Equilibrium Price............................. 12

Figure 5: Consumer Welfare for Product With and Without a False/Misleading Claim.............. 13

Figure 6: Difference in the Manufacturers’ Welfare between Producing the Product with and 

Without the False/Misleading Claim ............................................................................................ 14

Figure 7: Compensation Required to Make Consumers Whole After Purchasing Product with 

False/Misleading Claim ................................................................................................................ 16

Figure 8: Example of a CBC Choice Menu .................................................................................. 30

Figure 9: Example of a CBC Choice Menu .................................................................................. 31

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1 Introduction

1.1 Qualifications

1. I am a Statistician and an Economist. I received a Bachelor of Science degree in Statistics and 

a Bachelors of Arts degree in Business Administration from the University of 

Dortmund/Germany in 1988. I received a Master of Science degree in Statistics from the 

University of Dortmund/Germany in 1988, and I received a Masters of Arts degree in 

Economics from the University of California, San Diego in 1992. I also completed Ph.D. 

requirements (except dissertation) in Economics at the University of California, San Diego. 

2. I am currently employed as a Managing Director at the Berkeley Research Group (“BRG”) in 

one of its Los Angeles area offices at 550 South Hope Street, Suite 2150, Los Angeles, CA, 

90071. Prior to joining BRG, I was a Partner at Resolution Economics. I also held Managing 

Director positions at Alvarez & Marsal, Navigant Consulting, and LECG. I also held partnerlevel positions at Deloitte & Touche LLP, PricewaterhouseCoopers LLP, and Arthur 

Andersen LLP. At the three latter firms, I was responsible for the Economic and Statistical 

Consulting group on the West Coast. Before moving to the United States to attend graduate 

school, I worked as a statistician for the German Government for three years, from 1986 to 

1989.

3. For over 25 years, my work has focused on the application of economic, statistical, and 

financial models to a variety of areas, such as providing solutions to business problems, 

supporting complex litigation in a consulting and expert witness role, and conducting

economic impact studies in a large variety of industries including, but not limited to,

healthcare, retail, grocery manufacturing, technology, entertainment, manufacturing, 

automotive, energy and utilities, hospitality, and federal, state, and local government agencies.

4. I have extensive experience designing and conducting surveys and empirical studies as well 

as statistically analyzing results from surveys and empirical studies in both the litigation 

context as a consultant and/or designated expert and the non-litigation context as a statistical 

or economic consultant. I have issued numerous expert and rebuttal reports dealing with 

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surveys and statistical sampling related issues. I have been deposed on numerous occasions, 

and have also testified in court regarding survey and statistical sampling-related issues.

5. I am not an expert on the manufacturing of infant formula products. I do not have an opinion 

one way or the other about the allegations in this case. Instead, I have relied on my experience 

and expertise and have purely applied statistical methodologies based on the assumptions 

provided herein as to the alleged false statements at issue in this litigation outlined in the 

Complaint and per the instructions of plaintiffs’ counsel.

6. All the facts and circumstances set forth in this report are known to me personally and I am 

prepared to testify to them if called upon to do so. My curriculum vitae which includes matters 

in which I have testified is attached to this report as Exhibit A. BRG is being compensated for 

its work on this matter based on an agreed upon hourly billing rate schedule. My hourly billing 

rate for professional services related to this case is $650 and the billing rates of BRG staff 

supporting me on this engagement range from $150 to $550. BRG’s payment in this matter is 

not contingent upon my opinions or the outcome of this litigation.

1.2 Case Background

7. It is my understanding that the defendant Gerber Products Co. d/b/a Nestle Nutrition, Nestle 

Infant Nutrition, and Nestle Nutrition North America (“Gerber”) is alleged to have 

deceptively and misleadingly marketed “Good Start, a line of infant formula made with wheyprotein concentrate”1 with respect to its claim that the product “is the first and only formula 

whose consumption reduces the risk of infants developing allergies” and that “Good Start is 

the first and only formula that the United States Food and Drug Administration (“FDA”) 

endorses to reduce the risk of developing certain allergies, such as dermatitis.”2

8. It is further my understanding that there is no credible scientific evidence to substantiate the 

qualified health claim made by Gerber that consumption of Good Start can reduce the risk of 

developing allergies.

 

1 Class-Action Complaint, 1/6/17, paragraph 2.

2 Class-Action Complaint, 1/6/17, paragraph 3.

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9. It is further my understanding that Plaintiffs allege the “reduce the risk of developing 

allergies” claim is an express claim that appeared on the packaging of Gerber Good Start 

products.

10. It is further my understanding that Plaintiffs allege that Gerber’s claim is literally false. As 

such, it is my understanding that Plaintiffs allege that they have overpaid for a product that 

does not provide the promised benefits.3

11. It is my further understanding that Plaintiffs allege that some of Gerber’s claims were 

misleading and implied (among other things) that the FDA’s qualified health claim was in 

fact a blanket endorsement of Good Start, generally, or that Good Start could unqualifiedly 

reduce the risk of atopic dermatitis.4

1.3 Assignment

12. I have been retained by counsel for Plaintiffs to opine on the feasibility of developing an 

economic loss model to quantify the damages, if any, suffered by the proposed class that are 

attributable to the purchase of a product that was not as presented and advertised to the 

consumers. If the proposed class is certified by the Court, my assignment will be to develop 

and perform an empirical study to assess the value that customers who purchase infant formula 

products place on the claims at issue here, including that a product can reduce the risk of 

developing allergies, generally, and that the FDA endorsed a formula when, in fact, it did not.5

13. I would then use the results of the empirical study and other data to develop an 

econometric/statistical model to quantify and estimate class-wide damages to purchasers of 

Gerber Good Start products with the alleged misstatements due to not receiving benefits and 

features that they paid for and that they were led to believe the Gerber Good Start products

possessed.

14. One could argue that the entirety of the actual purchase price of a Gerber Good Start product 

could be fully included in an economic loss model because the purchasers of a product with a 

 

3 Class-Action Complaint, 1/6/17, paragraph 13.

4 Class-Action Complaint, 1/6/17, paragraphs 56–58.

5 Class-Action Complaint, 1/6/17, paragraph 55.

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false claim did not receive what was advertised and what they intended to purchase. In my 

analysis, I consider that consumers may still have obtained some value from the purchase of

the Gerber Good Start products at issue in this litigation even though they did not provide the 

advertised benefits, such as reducing the risk of developing allergies, generally. This implies 

that the economic loss to the purchaser may have been less than the entirety of the purchase 

price of the product. However, it cannot be ruled out that the economic loss is equal to or even 

greater than the purchase price. This concept is discussed in more detail in Section 6.3.

1.4 Materials Considered

15. In forming my opinions for this report, I have considered the following materials:

a. Class Action-Complaint, dated January 6, 2017.

b. Plaintiffs’ Statement of Facts in Support of Their Motion for Class Certification

16. In addition, I have considered all materials cited in the text and in the footnotes to this report.

1.5 Structure of the Report

17. The remainder of this report is structured as follows: 

a. Section 2 gives a brief overview of the formula market.

b. Section 3 gives an overview of the theoretical framework of the economic loss 

model based on supply, demand, equilibrium price setting mechanisms, and the 

relationship between consumers’ willingness-to-pay for a product or features of a 

product and market prices.

c. Section 4 discusses the methodology of the empirical study that I would perform

for this matter. In Section 4, I also introduce Choice Based Conjoint Analysis as a 

tool to quantify the impact of changing market conditions on consumers’ choices 

and willingness-to-pay.

d. Section 5 contains a detailed description of the empirical study that I would perform 

to quantify the impact of changing market conditions on consumers’ preference and 

choices and if and how changes in demand may affect equilibrium prices.

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e. Section 6 discusses how I would apply advanced statistical estimation techniques 

to obtain economic losses suffered by members of the proposed class, if any, based 

on the results from the conjoint study.

f. Section 7 concludes that it is possible to reliably quantify class-wide economic 

losses based on the proposed methodology and the results from an actual properly 

designed and implemented conjoint study.

2 The Market for Formula Products

18. Infant formula is a synthetic product that closely mimics the physical and nutritional 

properties of breast milk. It provides an alternative method for mothers to feed their infants 

and is typically designed for babies and infants under one year of age. It is made by blending 

fats, oils, proteins, sugars, minerals, salts, and trace elements together in an attempt to mimic

the characteristics of breast milk. 

19. There are three categories of formula: milk-based formulas, soy-based formulas, and 

specialized (non-milk and non-soy-based) formulas. The three categories are designed to fit 

an infant’s needs depending on whether the infant has sensitivity or an allergic reaction to 

milk or soy-based products. Formulas are also available in three different forms: powder, 

liquid concentrate, and ready-to-feed. Powder and liquid concentrate are the cheapest options 

available and ready-to-feed is the most expensive. Ready-to-feed bottles eliminates the 

potential for contamination because no mixing is required prior to feeding the infant.

20. Although many health organizations recommend breast feeding as the preferred way of 

feeding infants, the market for infant formula has grown in the past few decades. This is due 

to the progress that has been made in the composition of the formula, safety standards that 

have been developed, and the increased participation of women in the workforce. Globally, 

the infant formula market reached $26.5 billion in 2017 and is expected to grow at a compound 

annual growth rate of 9.5%.6In 2014, North American consumers accounted for 42% of sales, 

European consumers accounted for 24%, Latin American consumers accounted for 12%, 

 

6 https://www.prnewswire.com/news-releases/infant-formula-market-to-surpass-a-valuation-of-more-than-us-66-bnby-2027-673897923.html

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African and Middle Eastern consumers accounted for 11%, and Asian consumers accounted 

for 10% of sales. Developing markets are the fastest growing markets for infant formula. Latin 

America grew 46.8% in 2014 and Africa and the Middle East grew at 13.4%. North America 

grew 0.9% and Europe grew 5.6%.7

21. The infant formula market is highly consolidated. The U.S. market is dominated by four main 

competitors: Mead Johnson, Abbott, Gerber, and private label. The biggest drivers of growth 

for infant formula are brand name, nutrition, and safety. In developed markets, price is also 

an important purchase consideration.

3 Theoretical Framework of Economic Loss

22. In this section, I use a generic example to describe in basic economic terms how prices are set 

for products and how it can be tested if damages exist, and if they do exist, how to quantify 

them and how to determine the appropriate class-wide compensation. 

3.1 Demand and Supply in a Competitive Market

3.1.1 Willingness-to-Pay

23. In economic theory, willingness-to-pay is derived from “Utility.” Utility describes a 

consumer’s preferences and it is a measure of the value or usefulness of a good or service to 

that consumer.8 Therefore, utility does not have a unit of measurement. The empirical study 

developed in this report allows the researcher to associate a monetary value to a consumer’s 

utility.

24. To explain the concept, let us assume that it is known how much benefit or utility each 

consumer in a given market derives from a product or service. The willingness-to-pay is the 

highest price a consumer is willing to pay for the product, which is based on the perceived 

utility derived from the product and the consumer’s budget. The consumer will purchase the 

 

7 Nielsen Global Baby Care Report, “Trends in the Global Baby Food and Diaper Markets.” Available at 

http://www.nielsen.com/content/dam/nielsenglobal/de/docs/Nielsen%20Global%20Baby%20Care%20Report%20-

%20August%202015.pdf.

8 Hal R. Varian, Intermediate Microeconomics, 8th Edition, 2009, Page 54.

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product if the market price of the product is lower than or equal to his willingness-to-pay, but 

he will not purchase the product if the price is higher than the willingness-to-pay. 

25. In that sense, the individual willingness-to-pay for a product differentiates the buyer from the 

non-buyer for a certain product. There is no other correlation between the individual 

willingness-to-pay and the market price. In this context, the marginal consumer is defined as 

the consumer whose willingness-to-pay equals the market price. 

26. With this knowledge, it is now possible to rank the consumers by their willingness-to-pay. As 

an illustrative example, let us assume that the consumer with the highest willingness-to-pay 

is willing to spend $33 for a brand new widget. If the price of the product were $33, this 

consumer would purchase the product but nobody else would. This consumer would also buy 

the product for any price less than $33. If there is an additional consumer with the next highest 

willingness-to-pay of $28, then this consumer and the consumer with a willingness-to-pay of 

$33 would purchase the product, and so forth. Each consumer would buy the product at a 

price that is equal to or less than their respective willingness-to-pay. If a consumer’s 

willingness-to-pay is less than the price for the product, then this consumer will not buy the 

product.

27. Based on the ranking of consumers by their willingness-to-pay, a demand curve can be 

constructed in the following way: In a diagram that depicts the amount of the willingness-topay for each individual consumer on the vertical axis and the number of consumers on the 

horizontal axis, the demand curve will begin in the top left corner at the intersection of one 

consumer and a willingness-to-pay of $33. The next data point is at the intersection of two 

consumers and a willingness-to-pay of $28, and so forth.

28. The demand curve would look like a downward facing set of stairs. For simplicity, textbooks 

typically stylize the demand curve as a smooth downward sloping line or curve. Figure 1

below illustrates this concept.

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Figure 1: Willingness-to-Pay and Demand

Source: Own analysis based on hypothetical data

3.1.2 Willingness-to-Accept

29. Following the same principle as in the example of developing the demand curve, we can also

determine the minimum price at which each manufacturer is willing to sell the product. This 

is called the willingness-to-accept, which is equal to the marginal cost to the manufacturer. 

The marginal cost is the cost the manufacturer incurred when producing the last or marginal 

unit of the product. 

30. Like the consumers on the demand side, the manufacturers can be ranked by their willingnessto-accept. In a diagram with volume on the horizontal axis and prices and willingness-to-pay 

on the vertical axis, the manufacturer with the smallest marginal costs, say $3, will be 

positioned on the left. If the price of the product were to be just above $3, only this 

manufacturer would be willing to accept the price. Assuming that the next manufacturer offers 

one unit for $6, at the price of $6 two units would be offered in the market and so on. When 

connecting all ranked values of willingness-to-accept, we get the supply curve. It typically 

slopes upwards from left to right. The supply curve would look like an upward facing set of 

stairs. For simplicity, textbooks stylize the supply curve as an upward sloping smooth line or 

curve. Figure 2 below illustrates the concept.

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Figure 2: Willingness-to-Accept and Supply

Source: Own analysis based on hypothetical data

3.1.3 Market Equilibrium

31. The market balances supply and demand. At a price of $4.50, almost all consumers in my 

example would purchase the product but the manufacturers would offer only one unit. 

Conversely, at a price of $33, only one consumer would be willing to purchase the product 

while all manufacturers would be willing to sell the product. In the generic example, the 

market clears at a price of $15. At this point, not all but most consumers and manufacturers 

will be brought together. In the graphical representation, the supply and demand curves 

intersect (Figure 3). If the price exceeds $15, more manufacturers would offer their product 

but fewer consumers would be willing to purchase the product. If the price drops below $15, 

more consumers would be willing to purchase the product but fewer manufacturers would be 

willing to sell the product. For the marginal consumer, the equilibrium price of $15 is equal 

to the willingness-to-pay; and for the marginal manufacturer, the equilibrium price of $15 is 

equal to the willingness-to-accept. 

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Figure 3: Supply & Demand

Source: Own analysis based on hypothetical data

32. The equilibrium price is not the simple average of all consumers’ willingness to pay. Rather, 

the equilibrium price depends on supply and demand. The equilibrium price is the price where

the supply curve and the demand curve intersect. Every consumer to the left of the marginal 

consumer has a willingness-to-pay that exceeds the equilibrium price, and therefore, will 

purchase the product. 

33. The difference between the willingness-to-pay and the market price can also be illustrated 

with a real-world example: In an eBay auction, I may have put my eye on an item. I put my 

upper limit for my bids at $50. This upper limit signals my willingness-to-pay. Given that 

willingness-to-pay, if I saw the same item with a “Buy it Now” price tag of $25, I would buy

it for $25. What happened in this example? Did my utility from purchasing the item suddenly 

change? Did my willingness-to-pay change? Obviously not. However, what has changed is 

that the projected amount that I would pay going through the bidding process is different than 

the price I will pay when the competing offer is presented to me. In other words, the

willingness-to-pay does not necessarily reflect the actual price that a consumer ends up paying 

for a product.

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3.2 Shifting Demand Curves and Changes in Equilibrium Price

34. Based on Lancaster’s theory of utility,

9

the utility a consumer derives from a product and,

therefore, the consumer’s willingness-to-pay for the product is aggregated from the 

willingness-to-pay for each of the product’s characteristics, parts, and features. In this case, 

the product is infant formula and the characteristics of the product include nutritional 

information, health claims, formulation (i.e., powder, liquid, or ready-to-feed10), class (i.e., 

cow-milk based, soy-based, and specialized11), and other features. 

35. A consumer’s overall willingness-to-pay for the entire infant formula product is equal to the 

weighted sum of the willingness-to-pay the consumer expresses for each individual attribute. 

Changes in the composition of the attributes may lead to a shift of the demand curve for the 

infant formula product. The change in the composition of the product’s attributes can relate 

to changes in tangible attributes such as nutritional value or health benefits. It can also relate 

to statements about the product and advertised features of the product that are used to market 

the product to the consumers. In the case where statements used for marketing purposes are 

alleged to be false and misleading, it has to be determined if and by how much the demand 

curve shifts when the consumers learn about the false and misleading statements at the point 

of purchase. If the demand curve shifts downward, some consumers may still be willing to 

buy the product but at a lower price and some consumers may no longer be willing to buy the 

product. Figure 4 below illustrates the scenario:

a. Where one or more of the claims about the product are false or misleading, and 

b. Where the consumers receive information about this false or misleading claim at 

the point of purchase.

 

9 Lancaster, Kelvin J. (1966), “A New Approach to Consumer Theory,” Journal of Political Economy 74 (2): Pages 

132–157.

10 Review of Infant Feeding: Key Features of Breast Milk and Infant Formula, Section 4.1 The Infant Formula 

Market. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4882692/.

11 Review of Infant Feeding: Key Features of Breast Milk and Infant Formula, Section 4.3 Classes of Infant Formula 

Products. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4882692/.

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Figure 4: Shift in the Demand Curve and the Effect of the Equilibrium Price

Source: Own analysis based on hypothetical data

36. In the following paragraphs, I will refer to the situation where the consumer bought the infant 

formula without having the knowledge that certain information is false and misleading as the 

“actual world” and the situation where the consumer was informed at the point of purchase of 

the false and misleading information about the infant formula as the “but-for world.”

37. If consumers are willing to pay less for the product knowing that the advertised claim was 

false in the but-for world, then it can be empirically tested how this new information impacts 

the demand for the product. If the demand curve shifts downward, then a drop in overall 

willingness-to-pay can be observed, although the drop in willingness-to-pay may vary across 

consumers. The consumers are ranked again according to their willingness-to-pay, resulting 

in the yellow dots in Figure 4, which defines the new demand curve. 

38. The new demand curve may have a different shape than the demand curve for the product 

where the consumer does not know that the claim is false. All else equal, the shift of the 

demand curve results in a new market equilibrium, where the price and the transaction volume 

are lower. This is the new market equilibrium in the but-for world. 

39. In economic theory, the net benefit to each consumer purchasing a given product is the 

difference between the willingness-to-pay and the price paid. Aggregated across all

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consumers in a market, the net benefit to all consumers is defined as consumer welfare. It is 

equal to the area under the demand curve and above the price line (red area and dark orange 

area in Figure 5). If the claim about the product is known to be false at the point of purchase,

the demand curve will shift downwards (dark grey line in Figure 5). The new consumer 

welfare after the shift in the demand curve due to the false claim is equal to the area under the 

new demand curve and above the new price line (light orange area in Figure 5). 

Figure 5: Consumer Welfare for Product With and Without a False/Misleading Claim

Source: Own analysis based on hypothetical data

40. Since the demand curve for the product with the known-to-be-false claim is below the demand 

curve for the product absent a false claim, the consumer welfare for the product with the 

known-to-be-false claim is generally smaller than the consumer welfare of the product absent 

a false claim. Therefore, the consumers will have suffered economic losses.

41. Another way of looking at the economic loss to the consumer focuses on the manufacturer. 

Generally, a manufacturer’s welfare is the difference between the willingness-to-accept and 

the price obtained in the market. Aggregated over all manufacturers, the manufacturers’ 

welfare is the area below the price line and above the supply curve. 

42. Corresponding to the consumers’ welfare, we can also derive the manufacturer’s welfare or 

gross profit. For each unit, the manufacturer’s welfare is the difference between the price 

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received and the marginal costs of producing the unit. A shift in the demand curve results in 

a change in the manufacturer’s welfare. In Figure 6 below, the difference in manufacturers’ 

welfare between the product with the false claim and the product without the false claim is 

depicted by the yellow area. Recall that in the equilibrium between supply and the demand 

for the product with the claim was that six consumers would have paid $15 (see Figure 3). In 

the market equilibrium depicted in Figure 6, for the product with the false claim, four 

consumers would have paid $11 instead of $15 when the claim was known to be false at the 

point of purchase. In addition, two consumers who purchased the product would not have 

purchased the product with the false claim in the new market equilibrium. The yellow area in 

Figure 6 depicts the additional manufacturers’ welfare obtained by not disclosing that the 

claim was false. Therefore, the manufacturer gains at the cost of the consumer.

Figure 6: Difference in the Manufacturers’ Welfare between Producing the Product with

and Without the False/Misleading Claim

Source: Own analysis based on hypothetical data

3.3 A Model of Economic Loss

43. Figure 6 shows how the false or misleading advertising increases the manufacturer’s gross 

profits at the expense of consumers. The excess profit due to the false or misleading 

advertising would be considered restitution to the plaintiff in a legal framework that is built 

on profit disgorgement (e.g., in a dispute involving intellectual property). However, in this 

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case, profit disgorgement would not compensate consumers for their economic loss. Instead,

the proper economic loss methodology in this case has to be based on the loss in utility and 

the associated shift in demand of the affected consumers.

44. To make consumers whole for the economic losses, every consumer would have to receive an 

additional payment. The payment has to be sufficiently large to vertically shift the demand 

curve such that the demand curve for the product with the known-to-be-false claim plus the 

additional compensation intersected with the supply curve in the equilibrium for the product 

if the claim were true.

45. To determine how much the demand curve would have to be shifted, we need to focus on the 

marginal consumer in the market for the product without the false claim and compare the price 

she had paid to the price she would have paid for the product with the known-to-be-false claim 

at the point of purchase.

46. The compensation to make the marginal consumer whole after purchasing the product with 

the false claim is not simply the difference between the equilibrium prices on the demand 

curve for the product without the false claim and the demand curve for the product with the 

known-to-be-false claim (Figure 7). Rather, the compensation of the marginal consumer needs 

to be equal to the difference between the price this marginal consumer would have paid for 

the product with the known-to-be-false claim and the product without the false claim.

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Figure 7: Compensation Required to Make Consumers Whole After Purchasing Product 

with False/Misleading Claim

Source: Own analysis based on hypothetical data

47. In the example illustrated in Figure 7 above, the marginal consumer paid $15 for the product 

before becoming aware of the false claim, but would have paid only $6 for the product with 

the false claim. The difference in willingness-to-pay is $9 ($15 minus $6). 

48. The correct way to measure the economic loss to the class members considers that the 

downward shift in the demand curve is comprised of two components: a) a drop in units sold 

as measured on the horizontal axis, and b) a drop in the price paid by customers who are still 

buying the product even after receiving the information that the product does not have the 

features as advertised.

49. In the illustrative example in Figure 7 above, the new market equilibrium occurs at a lower 

price ($9 instead of $15) and at a lower number of units sold (4 instead of 6). Each buyer of 

the defective product has to be made whole because they overpaid for the product. The price 

that makes everyone whole is the price at which all 6 original buyers of the product would 

buy the product again. In the illustrative example in Figure 7 above, this price can be found 

by moving down the shifted demand curve to find the price at which the original number of 

buyers would purchase the product again. In the illustrative example above, this price is $6.

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50. All else equal and depending on the shape of the supply curve and the demand curve before 

and after the disclosure of the information about the allegedly misleading claim at the point 

of purchase, the economic loss will differ.

3.4 Consideration of the Supply Side in the Economic Damages Model

51. The supply curve in the damages model is identical for the actual world (no disclosure) and 

the but-for world (the same infant formula product is now offered with full disclosure of the 

false and misleading claims by the salesperson at the point of purchase). The units of the 

product that were sold with the false claim are identical. Therefore, the shift in the attribute 

level has no impact on the marginal costs of the supplier and therefore the supply curve. 

Consequently, only the changes in the demand curve are relevant for the damage assessment.

52. Because the infant formula products supplied in the actual world and the but-for world are 

identical and the marginal costs in the actual and the but-for world do not change, the only 

relevant question is if the additional information of the disclosure (e.g., that the claim that the 

product can reduce the risk of developing allergies is deceptive and/or misleading) at the point 

of purchase makes the Gerber Good Start products actually sold inferior to the Gerber Good 

Start products as advertised in the eyes of the consumer. If the consumer perception changes 

such that the Gerber Good Start product that is not as advertised is viewed as inferior, the 

demand curve will shift downwards. This implies that for an upward sloped supply curve for 

the product, the downward shift of the demand curve is associated with a drop in the 

equilibrium price.

3.5 Approaches to Estimating the Value of Individual Attributes in 

Composite Products

53. In general, there are two different types of approaches to estimate the values of the individual

characteristics, parts, and features that together form a composite product when there is no 

direct market for the individual characteristics, parts, and features (also known as attributes; 

price is also considered an attribute of a product) themselves: 

a. Revealed Preference based, and

b. Stated Preference based.

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54. Revealed Preference based approaches observe actual purchases by consumers or published 

prices and infer from that information the decomposition of the overall price of the composite 

product into its constituent attributes. This is most often accomplished by using hedonic 

pricing models where the actual transaction prices of the composite product with varying 

attributes is regressed on the specifications of the composite product. The regression 

coefficients are then interpreted as the implicit market prices of each attribute.

55. Stated Preference based approaches involve asking individuals directly or indirectly how 

much they value a particular product. This is done by investigating how much they would be 

willing to pay for a particular attribute/feature in a composite product. In this context, Conjoint 

Analysis is an approach exploring respondents’ preferences over multiple sets of choices, 

which produces rich data sets and numerous data points from which to estimate the value of 

the attribute/feature of interest. Conjoint Analysis is conducted in a survey setting where 

demographic, socio-economic, and general decision-making processes and preference 

information about the product in question collected and integrated into the estimation process. 

The particular strength of Conjoint Analysis is the fact that the stated preferences are derived 

from indirect questions, thereby avoiding the pitfalls of strategic responses in direct 

questioning.

56. In my opinion, Conjoint Analysis is an appropriate approach in this case to estimate the values 

of the individual attributes and features in question to assess the extent to which the allegedly 

false claims at issue (including that Gerber Good Start reduces the risk of developing 

allergies), resulted in a loss of utility to the consumers, and thus created economic losses to 

the members of the proposed class.

4 Methodology of the Empirical Study

4.1 Conjoint Analysis – Methodology

57. Conjoint analysis enjoys wide use in market research and is discussed in depth in the market 

research literature.12 Over 14,000 commercial applications of Conjoint Analysis are estimated 

 

12 See, for example: Rao, Vithala, Applied Conjoint Analysis, Springer-Verlag, 2014.

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to take place each year.13 Vithala Rao’s book, Applied Conjoint Analysis, gives numerous 

examples of the widespread use of Conjoint Analysis including, but not limited to, several 

high-profile applications by large corporations and large public agencies such as (i) Microsoft 

for pricing newly released software products, (ii) Proctor & Gamble for consumer-goods 

pricing and new product development, (iii) Marriott Corporation for the development of the 

Courtyard hotel brand, and (iv) T-Mobile for developing optimal cellular plans. Conjoint 

Analysis was also integral to the development of the EZPass electronic toll collection system 

by regional transit agencies in New York and New Jersey in the 1990s.14

58. The general idea behind Conjoint Analysis is that consumers’ preferences for a particular 

product are driven by features or descriptions of features embodied in that product. Conjoint 

Analysis is a set of econometric and statistical techniques that have been developed to study 

consumers’ decision-making processes, determining trade-offs between products, features, 

and price, as well as quantifying consumers’ gains and/or losses of utility when choosing 

between different alternatives. By simulating real world and/or hypothetical choices between 

product features and prices under different levels of information, Conjoint Analysis is ideally 

suited to model the impact of different choice scenarios on a consumer’s utility function.

59. The data required for a Conjoint Analysis are collected in the setting of a survey where survey 

participants are shown product profiles with different levels of each attribute. The survey 

participants are consumers who currently are or recently have been in the market for the 

product of interest – in this case, infant formula products. After reviewing a set of choice 

menus of product attributes and their levels, survey participants are then asked to indicate 

their preferences for those profiles. The product profiles include choice options for different 

price points for each set of features on the choice menu.

60. After the completion of the survey, the Conjoint Analysis uses data from the survey on the 

attribute levels of the product profiles shown, and the resulting preferences or choices of 

respondents, to decompose the respondents’ preferences for a product into the partial 

contribution of these attribute levels (“part-worths”) to overall product utility using 

 

13 Orme, Bryan K, Getting Started with Conjoint Analysis: Strategies for Pricing Research, 2nd ed., Madison: 

Research Publishers, 2005.

14 Rao, Vithala, Applied Conjoint Analysis, Springer-Verlag, 2014, Chapters 6.4 and 6.5. 

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appropriate statistical methods. The statistical models used in my analysis – Mixed Logit 

Models and Hierarchical Bayesian Estimation – will be discussed in more detail in Section 6 

“Economic Loss Model.” These statistical estimation techniques quantify the part-worths for 

feature levels such that the resulting estimated part-worths best predict respondents’ 

preferences or choices from the survey. 

61. The price reduction needed to compensate for the loss of a feature, or the additional price 

customers would pay for the inclusion of a feature can then be calculated and a variety of 

choice situations and trade-offs between choices can be modeled and their outcomes can be 

precisely quantified. The precision, and thus the reliability, of the resulting estimations 

depends on the number of survey participants. The more respondents take part in the survey, 

the more precise the resulting predictions are.

62. For this assignment, I can apply a form of Conjoint Analysis known as Choice-Based Conjoint 

Analysis (“CBC”). In CBC, study participants are shown sets of product profiles (called 

“choice sets” or “choice menus”), and are asked to choose the profile that they would prefer 

to purchase if the choice menu offered would describe the only products that were available

to them. CBC survey methods closely mimic real-world purchase processes.15 Conjoint 

Analysis allows for the prediction of the probability that a respondent will choose any product 

profile that is described by the part-worths and can do so for any competitive set of products.16

Based on the estimations, it is also possible to simulate how choice shares would change in a 

market based on a change in overall price. CBC enables us to determine the difference in 

value (measured in dollars) that customers place on an infant formula product that claims—

for example—it “reduces the risk of developing allergies” compared to an otherwise identical 

infant formula product that does not make this claim. 

 

15 Orme, Bryan K, Getting Started with Conjoint Analysis: Strategies for Pricing Research, 2nd ed., Madison: 

Research Publishers, 2005.

16 Allenby, Greg M & Peter E Rossi, “Hierarchical Bayes Models,” in Grover, Rajiv & Marco Vriens, eds., The 

Handbook of Marketing Research, Thousand Oaks: Sage Publications, Inc., 2006.

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4.2 Statistical Estimation Techniques Applied in Conjoint Analysis

63. The underlying econometric and statistical estimation techniques of the Conjoint Analysis are 

based on Mixed Logit models and Hierarchical Bayesian Estimation techniques, which are 

widely employed in economics and marketing research to analyze preferences over a discrete 

set of choices.17

64. Mixed Logit models use the idea that each consumer assigns a utility to each choice, and this 

utility measures the attractiveness of each choice. These utility values are correlated with the 

attributes of the actual choice (for example, adding an indicator that a product is organic to an 

otherwise identical infant formula product or including the claim that the product reduces the 

risk of developing allergies to an otherwise identical infant formula product where it is known 

to the consumer that the claim is not true) and the price associated with that choice. The 

utilities are also correlated with observable characteristics of the consumers making the choice 

(such as their age or income).

65. The utility of each product consists of two components – a deterministic component and a 

random component. The deterministic component can be modeled by observable factors such 

as socio-economic and demographic characteristics of the consumers, product features, and 

market conditions. In general terms, the random component summarizes all the unobservable 

factors in the individual consumer’s choice process. In Mixed Logit models, the random 

component is expressed through a logistic distribution function. Together with the observable 

factors, this distribution function is used to predict the probability that a particular choice is 

made.18

66. Once shown a menu of choices of different levels of attributes and different price alternatives, 

the consumer then chooses the one choice in the menu that yields the highest utility from that 

 

17 Underlying the Mixed Logit is a model of random utility. Berkeley economics professor Daniel McFadden 

developed the random utility model in the 1970s while working as a consultant on the design of the Bay Area Rapid 

Transit (BART) system in California. This work won McFadden the Nobel Prize in 2000. See Hal Varian, 

Intermediate Microeconomics, 8th Ed, 2009, Page. 68.

18 See, for example: Rao, Vithala, Applied Conjoint Analysis, Springer-Verlag, 2014, Chapter 4, for a detailed 

discussion of the use of mixed multinomial logit models in choice based conjoint studies.

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menu of choices.19 Observing consumers’ choices from various choice menus enables one to 

estimate the relative value consumers place on one attribute over another. 

67. Price is included as an attribute, which allows for the estimation of the value of an attribute 

relative to price – that is, the dollar value of the willingness-to-pay for that attribute. In fact, 

the willingness-to-pay for an attribute is the negative ratio of the attribute’s coefficient to the 

price coefficient in the underlying choice model.20

68. Bayesian statistics is a subset of statistics where the underlying model parameters are assumed 

to be random variables rather than fixed quantities. Bayesian modelling is based on assigning 

prior probability distributions to any unknown parameters. In this case, the unknown 

parameters to be estimated are the part-worths of the attributes of a composite product derived 

from the choice sets in the conjoint analysis. These parameters are estimated by a technique 

referred to in the literature as Hierarchical Bayesian Estimation.21

69. In Hierarchical Bayes Estimation (“HBE”), the parameter estimates are derived in a two-step 

hierarchical approach. At the higher level, the individual consumers’ part-worths are assumed 

to follow a specified distribution (like multivariate normal distribution or log-normal 

distribution). At the lower level, it is assumed that the individual consumers’ choice 

probabilities can be described by a model, such as a Mixed Logit model. Initial estimates of 

part-worth are estimated for each study respondent to use as a starting point. New estimates 

are updated using an iterative process called “Gibbs Sampling” and “Metropolis Hastings 

Algorithms.”22 This process is typically repeated thousands of times whereby in each iteration, 

an estimate is made for each parameter, conditional on current estimates of the others. After 

many iterations, this process converges to the correct estimates for each of the parameters.

 

19 See Figure 8 for an example of what a choice menu might look like where a respondent is presented a menu with 

four choices of combinations of features and the choice of “no purchase.”

20 Train, Kenneth E., “Discrete Choice Methods with Simulations,” Cambridge University Press; 2nd edition, 2009. 

Chapter 12 gives a detailed derivation of the Bayesian approach applied in this report.

21 See, for example: Rao, Vithala, Applied Conjoint Analysis, Springer-Verlag, 2014, Chapter 4.11, for a detailed 

discussion of the use of Hierarchical Bayesian Estimation in choice based conjoint studies.

22 Rao, Vithala, Applied Conjoint Analysis, Springer-Verlag, 2014, Page. 168.

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70. The HBE method combines random effect specifications at the aggregate level to account for 

variation across individuals and specific modelling of choice probabilities at the individual 

level. With market simulations, the performance of competing alternatives can be evaluated.

5 Empirical Study

71. The empirical study can be conducted as a consumer survey, including a pre-test/pilot study,

and a Choice Based Conjoint (“CBC”) study. In this section, I first discuss in general terms 

consumer survey methodology and then continue to describe the components of the empirical 

study and how they would be implemented.

72. To conduct the empirical study, I will commission a survey vendor to program and host the 

questionnaires of my design to analyze the preferences and choices of consumers who have 

purchased infant formula products within a set time period.

73. The survey vendor specializes in internet panel surveys. In my experience, internet-based 

surveys can provide reliable results and can have some advantages over other recruiting 

methodologies. Over the last decade, internet surveys have increasingly gained popularity and 

acceptance, including in litigation.

74. Current research suggests that the increased use of internet surveys has great advantages over 

other traditional methods. For instance, studies have found that computer data collection 

yields higher concurrent validity, with less chances of participants framing answers to attempt 

to please the questioner, and less random measurement error when compared to mall intercept 

studies and telephone surveys. Internet surveys also allow for broad geographic reach to areas 

where surveying via mall intercept or other face-to-face methods would not be feasible.23

Well-executed internet survey research is regularly accepted by courts.24

 

23 See “Reference Guide on Survey Research,” S.S. Diamond, Reference Manual on Scientific Evidence, Third 

Edition, Federal Judicial Center, 2011, Page 401. Additionally, online surveys have advantages in terms of 

efficiency and cost.

24 “Why Online Surveys Can Be a Smart Choice in Intellectual Property Litigation,” B. Isaacson et al., IPL 

Newsletter (ABA Section of Intellectual Property Law) Vol. 26, No. 3, 2008.

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75. Moreover, internet surveys have become a fixture in the corporate world. According to the 

Global Research Business Network, internet surveys now account for more than a quarter of 

global market and social research revenues. In many of the world’s top research markets, 

internet surveys are now the primary means of research.25

76. The efficacy of internet studies is often furthered by survey market research firms that operate 

large internet panels. These firms employ trained professionals who program, administer, and 

quality control the surveys to increase the quality of the results. 

77. A frequent point of criticism of internet surveys is the fact that they typically do not conform 

with the requirement for statistical random samples which states that for every individual in 

the target population, the selection probability must be a known number greater than zero and, 

therefore, no inference can be drawn about the precision and/or margin of error of the study. 

78. However, advanced statistical methods can be applied to compute model-based confidence 

intervals for well-designed and well-balanced non-probability samples. In 2016, the American 

Association of Public Opinion Research (“AAPOR”) issued a guidance paper on “Reporting 

Precision for Nonprobability Samples”26 which details approaches and reporting guidelines 

when precision calculations are performed for non-probability samples.

79. In summary, properly designed and well-executed internet surveys have increasingly gained 

acceptance and can be used to draw valid statistical inferences about the target population.

80. As described above, the empirical study includes a CBC module which is designed to quantify 

the value that consumers assign to common infant formula attributes/features. The survey 

vendor will administer the survey and the empirical study via an online panel. The survey 

vendor will follow accepted standards regarding:

a. Survey panelist recruiting;

b. Strategic partnerships with other market research firms;

c. Use of advanced software and technology;

 

25 http://fortune.com/2015/09/16/online-survey-companies-law-firms/. 

26 AAPOR Guidance on Reporting Precision for Nonprobability Samples -

https://www.aapor.org/getattachment/Education-Resources/ForResearchers/AAPOR_Guidance_Nonprob_Precision_042216.pdf.aspx.

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d. Use of proprietary survey completion time tracker;

e. High quality filtering system to track respondent information and respondent 

behavior to deliver the highest quality sample;

f. Best practices of quality control - including removal of sign-ups who provide 

inconsistent demographic information, GeoIP lookups at time of registration and, 

most importantly, periodic use of mailed survey awards for U.S. panelists to 

verify street addresses;

g. Data tabulation and recording; and, 

h. Survey participation validation.

81. As is standard practice for surveys used in litigation proceedings, the surveys are conducted 

in a “double-blind” fashion27– that is, neither the staff at the survey vendor nor the 

respondents are aware of the survey sponsor or the ultimate intention of the survey. 

Additionally, the data collection and initial tabulation are automated and concurrent with 

answering the online questionnaire.

82. To ensure that the data generated by the survey are of the highest quality, the survey vendor

will implement additional quality control measures:

a. Respondents are required to enter their gender and age at the outset of the 

survey. If these data conflict with the respondent information on file with the 

survey vendor, the respondent is excluded.

b. Respondents who indicate that they do not understand or are unwilling to 

adhere to the survey instructions are also screened out of the survey.

c. The survey vendor sends an individual link to the online survey by email. This 

link contains an embedded identification number to ensure that only invited 

respondents can answer the survey and that each respondent can only complete 

the survey once, and that only one member per household can complete the 

survey.

d. The survey also includes a control measure to evaluate the extent to which 

 

27 Diamond, Shari, S. (2012) “Reference Guide on Survey Research,” Reference Manual on Scientific Evidence, 

Committee on the Development of the Third Edition of the Reference Manual on Scientific Evidence; Federal 

Judicial Center; National Research Council, Pages 410-411.

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respondents were involved in completing the survey. As a control, the survey 

vendor relies on survey administration measures, which include review of each 

respondent's survey completion time, review of text field responses, straightline testing, and other filtering techniques that filter automated responses and 

result in superior data as well as higher quality feedback.

5.4 Pre-Test Study

83. The first component of my empirical study is a Pre-Test Study, which is a ‘small’ scale version 

or trial run done in preparation for the major study.28,29 One of the many benefits of 

administering a pre-test study is that it might give important insights of what might happen in 

the main study. It may also be a reliable mechanism to narrow down the list of attributes. 

84. In this context, “attributes” refers to characteristics of the infant formula products (such as 

formulation, class, nutrition information, health benefits, price, etc.). Each attribute has at 

least two levels – where level refers to the values that an attribute can take – e.g., the attribute 

“formulation” could have three levels (powder, liquid, ready-to-feed) while the attribute price 

could have six levels, which means that there are six different prices in the conjoint study. 

85. Survey research literature gives the following reasons for conducting pilot studies:30

a. Developing and testing adequacy of research instruments,

b. Assessing the feasibility of a (full scale) study,

c. Establishing whether the sampling frame and techniques are effective,

d. Identifying logistical problems which might occur using proposed methods,

e. Estimating variability in outcomes to help determine sample size, and

f. Collecting preliminary data.

 

28 Polit, D.F., Beck, C.T., Hungler, B.P. (2001) Essentials of Nursing Research: Methods, Appraisal, and Utilisation 

(5th ed.). Philadelphia: Lippincott.

29 Baker, T. L. (1994), Doing Social Research (2nd ed.), New York: McGraw Hill Inc.

30 De Vaus, D.A. (1993), Surveys in Social Research (3rd ed.), London: UCL Press.

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86. As part of the Pre-Test Study, approximately 400 respondents will complete a survey in which 

they are asked about the importance of product features of infant formula products. 

Respondents will only be included in the survey if they meet the following criteria:

a. Respondent is 18 years or older;

b. Respondent resides in the United States;

c. Respondent has purchased an infant formula product in the past six years;

d. Respondent is not working in market research.

5.5 Choice Based Conjoint Study

87. The second component of my empirical study is comprised of the Choice Based Conjoint 

analysis based on a survey of 2,000 consumers. I will divide the 2,000 respondents into a test 

group of approximately 1,400 purchasers of Gerber Good Start products and a control group 

of approximately 600 purchasers of products from competing brands like Enfamil and 

Similac. 

88. Using the survey vendor’s access to large online consumer panels, I am able to target a 

demographically diverse group of respondents who have been recent purchasers of Gerber 

and competing brands. When recruiting survey participants, the survey vendor will employ a 

method of balancing the survey participants based on demographics and socio-economic 

factors such as gender, age, income, and geographical region.

89. The survey vendor will apply the same filter criteria as in the Pre-Test Study. Respondents 

will only be included in the survey if they meet the following criteria:

a. Respondent is 18 years or older;

b. Respondent resides in the United States;

c. Respondent has purchased an infant formula product in the past six years;

d. Respondent is not working in market research.

90. The survey vendor will be able to replicate the demographics of the Pre-Test Survey with 

respect to gender, age, income, and geographical region.

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91. After qualifying for the survey, the participants will respond to questions designed to elicit 

their thoughts about infant formula products. Further, they will be asked which brand they 

bought most recently, and which attributes are important to them when making purchasing 

decisions (e.g., price, brand, etc.).

92. Neither the respondents nor the survey vendor will have any information on the context of the 

study or who commissioned it, and they will not be informed that the data will be used in 

litigation, or more specifically, in a lawsuit against Gerber.

93. The next section of the survey will be comprised of the actual CBC exercise itself. During 

this exercise, respondents will view a discrete number of choice sets, each containing a 

combination of discrete number of attributes, plus a price. Below is an example of potential 

attributes and their respective levels:

a. Formulation – the levels for this attribute are:

i. Powder

ii. Liquid

iii. Ready-to-feed

b. Class – the levels for this attribute are:

i. Cow-milk based

ii. Soy-based

iii. Specialized

c. Claim regarding reduction in the risk of developing allergies – the levels for this 

attribute are:

i. Reduces the risk of developing allergies

ii. No such claim is made

d. Organic – the levels for this attribute are:

i. Organic

ii. Not organic

e. Price – the levels for this attribute are:

i. $14.00

ii. $19.25

iii. $24.50

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iv. $30.00

v. $34.75

vi. $40.00

94. Three considerations determined the boundary of this price range: 

a. Generally, the price range should cover realistic prices for the product. For 

example, a price of $1 would not be realistic as the typical retail price is far higher. 

Similarly, a price of $1,000 would also not be realistic.

b. Prices can be higher or lower than the prices of currently offered products as we 

test product attribute combinations that might not yet be available in the market.

c. In the case that we test the impact of false and misleading advertising, we determine 

the price for a product without the advertised claim, which is currently not available 

in the market. Hence, in order to estimate a demand curve for the product without 

the advertised claim, we need to include prices both below and above the price 

points common in the market.

In my opinion, the price range from $14 to $40 complies with these considerations.

95. The CBC employed in the survey will randomly assign choices from all possible choices31

with equal likelihood and with uniform frequency of each level of each attribute and each pair 

of attribute/level permutations. That is, the CBC design is balanced and orthogonal. Balanced 

and orthogonal surveys are commonly employed in CBC.32 The importance of an orthogonal 

and balanced design lies in the fact that designs of this type are 100% efficient. Efficiency 

implies that the resulting estimations have the smallest mean squared error out of all possible 

designs.

33 The mean squared error measures the level of variation and, as such, the precision 

of the resulting estimates. The smaller the mean squared error of an estimate the more precise 

 

31 There are 3 levels for the formulation attribute, 3 levels for the class attribute, 2 levels for the allergy claim 

attribute, 2 levels for the organic attribute, and 6 levels for the price. This yields 3×3×2×2×6 (or 216) different 

possibilities of combining the different levels of the attributes in the study.

32 Bakken, David & Curtis L Frazier, “Conjoint Analysis: Understanding Consumer Decision Making,” in Grover, 

Rajiv & Marco Vriens, eds., The Handbook of Marketing Research, Thousand Oaks: Sage Publications, Inc., 2006, 

Chapter 15.

33 The mean squared error (MSE) is calculated as the average of the squared distances between the estimator and 

what is estimated, or the “errors.” Efficient designs are ones that minimize the MSE. 

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it is. As such, efficiency of a design is a measure of the information content of a design. 

Therefore, more efficient designs imply more reliable results.

34

96. Each choice set will consist of five choices: four with various combinations of product 

attributes and prices as described above and a fifth “opt-out” choice, indicating dissatisfaction 

with each of the first four choices.

35 After each survey choice, respondents will be asked to 

whether they would have purchased the selected product or not. The following Figure 8 and 

Figure 9 below show examples of choice menus with two different ways of incorporating the 

no-purchase option.

36 Each participant will respond to 12 such screens. Each screen shows 

randomly selected levels for each attribute. Therefore, Figure 8 does not necessarily show all 

levels for each attribute.

Figure 8: Example of a CBC Choice Menu

 

34 The standard error is the standard deviation of the sampling distribution of a statistic. A smaller standard error 

implies a smaller margin of error, which results in a tighter confidence interval around an estimate.

35 See Orme (2005) for an exposition of the advantages of including the opt-out option.

36 Each of the 2,000 participants in the CBC will respond to 12 screens with five choices yielding a total of 

2,000×12×5=120,000 data points for the statistical analysis.

Option 1 Option 2 Option 3 Option 4 Option 5

Formulation Powder Liquid Powder Ready-to-Feed

Class Milk-based Soy-based Specialized Soy-based

Health Claim Reduces the risk of 

developing allergies - -

Reduces the risk of 

developing allergies

Organic No Yes Yes No

Price $24.50 $14.00 $19.25 $34.75

Which option would 

you prefer? ○ ○ ○ ○ ○

Please indicate which of the options you would select.

None of these.

I would shop 

somewhere else for 

better options.

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Figure 9: Example of a CBC Choice Menu

97. It is a known phenomenon that choices presented earlier in a list of choices in a questionnaire 

are disproportionately likely to be selected.37 This phenomenon is known as order bias. To 

avoid order bias in my study, attributes will be shown in a different order, chosen at random, 

to each respondent – except for price, which is always shown last.

6 Economic Loss Calculation

6.1 Four-Step Estimation Process

98. A purchaser of a Gerber Good Start product that contains a claim that it reduces the risk of 

developing allergies (for example), a claim which is alleged to be false, actually paid for the 

product expecting to receive a product where the advertised claim was true but in fact received

an inferior product. As described above, I will design a choice based conjoint study to assess 

if giving the consumer the information at the point of purchase that the claim of reducing the 

risk of developing allergies is false will lead to a downward shift of the demand curve.

 

37 Krosnick, Jon and Duane Alwin, “An evaluation of a cognitive theory of response order effects in survey 

measurement,” Oxford Journals Social Sciences Public Opinion Quarterly Volume 51, Issue 2, Pages. 201-219.

Option 1 Option 2 Option 3 Option 4

Formulation Powder Liquid Powder Ready-to-Feed

Class Milk-based Soy-based Specialized Soy-based

Health Claim Reduces the risk of 

developing allergies - -

Reduces the risk of 

developing allergies

Organic No Yes Yes No

Price $24.50 $14.00 $19.25 $34.75

Which option would 

you prefer? ○ ○ ○ ○

Please indicate which of the options you would select.

Would you purchase this option? ○ Yes ○ No

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99. I will apply the following four-step estimation process to determine the economic loss 

associated with purchasing a product where the claim made turns out to be false:

a. Step 1: Based on the results from the CBC analysis, compute individual utilities for 

each respondent for each attribute and each level in the study.

b. Step 2: Construct the demand curves for the product in the actual world and the butfor world.

c. Step 3: Quantify the drop in consumer demand and the corresponding economic 

loss that the purchasers experienced because they were unaware at the point of 

purchase that the claim is false.

d. Step 4: Conduct market simulations to assess the drop in demand for a large variety 

of product combinations.

100. In Step 1, I utilize a software program called Sawtooth to compute utilities for the attributes 

and the levels for each attribute in the study. The Sawtooth software applies the Hierarchical 

Bayesian Estimation technique explained above to compute individual utilities for each 

respondent and aggregate utilities for all levels and attributes in the study. The Sawtooth 

software allows the researcher to specify the models in different ways. One important feature 

is a constraint that specifies the model in a fashion that higher prices are always associated 

with a lower utility.

101. This so-called monotonicity constraint refers to a property of the utility estimates. Without 

the monotonicity constraint, the utility estimates may yield higher numerical values for levels 

that seem to be lower in utility for some individuals, and thus seemingly indicate “illogical” 

consumer choices.

102. However, this behavior can be explained by the fact that the “rational economic consumer” is 

only a postulate or an assumption in theoretical economics while in the “real” world not all 

variables affecting consumer choices can be measured, and as a consequence, consumers often 

do exhibit seemingly irrational behavior. For example, I may stop at a gas station that charges 

$0.20 more per gallon because I am already running late on my way to work and this particular 

gas station is the one I can get to easily without detour. While it may seem irrational to pay 

more for gas, the convenience factor in this example cannot be measured and quantified.

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103. The Sawtooth software allows the researcher to “smoothen” the utility estimates in a way that 

higher price levels for a specific attribute combination are always associated with a lower 

utility value. This feature ensures that “illogical” consumer choices are ruled out when a 

monotonicity constraint is built into the model. When using a monotonicity constraint, the 

demand curves are smoother, and therefore, the resulting market simulations have fewer 

extreme data points making the damages estimates on average lower.

104. In Step 2, the utility estimates are applied to construct the demand curve for the product when 

the advertised claim is believed to be true by the consumer at the point of purchase. Further, 

I construct the demand curves for the other levels for an attribute of interest.

105. In Step 3, I quantify the drop in consumer demand and the new equilibrium price. Based on 

the degree of the downward shift of the demand curve, I can then calculate economic damages 

to the members of the putative class based on their purchase of the product with the false 

claim.

106. In Step 4, I conduct market simulations to assess if economic damages exist in a wide variety 

of market conditions.

6.2 Market Simulations in Conjoint Analyses

107. To assess the robustness of the willingness-to-pay estimation under a variety of market 

conditions, I perform a comprehensive market simulation study using the individual utilities 

that I have estimated from the conjoint study using the Hierarchical Bayesian Estimation 

methodology. In my market simulations, I use the attributes and levels defined in the conjoint 

study. 

108. The Hierarchical Bayesian Estimation of the parameters of the underlying Mixed Logit choice 

model yields estimates of individual part-worth utilities for each respondent for each level of 

all attributes in the conjoint study. 

109. Market simulations are an important tool to convert the part-worths from the conjoint study 

into monetary measures reflecting consumer preferences and choices. In general, different 

permutations of product attributes and levels of those product attributes are applied in a market 

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simulation to assess the respondents’ choice probabilities for different combinations of 

product attributes and the resulting economic loss.

110. In general, conjoint studies lead to a set of utilities or part-worths that quantify respondents’ 

preferences for each level of each attribute. These utilities can be analyzed to assess how the 

respondents react to changes in the product attributes at different price points.

111. The market simulation consolidates the preferences and choices for all respondents which 

enables us to answer questions about preference and likelihood of choice when attributes and 

levels of product attributes are changed.

112. By using the individual part-worths, it is possible to determine the demand curve for any 

specific combination of product attributes and their levels for different price points. When the 

question needs to be answered if and how the change in the level of a particular attribute 

changes the demand curve then two demand curves can be calculated – the first one for a 

specific set of levels and attributes and a given price and the second one where the product 

attributes and price are identical but one level of one attribute is different. The measured shift 

in the demand curve, if any, can then be attributed to the changed level. Based on the change 

in demand curves, if any was found, it is then possible to determine the change in price that 

would be necessary to reach the same demand for the product where a level in one of the 

attributes was changed.

6.3 Quantification of Economic Loss Based on Conjoint Analysis Supported 

by Market Simulations

113. The estimated willingness-to-pay using this method is derived from the utility that 

respondents gain from purchasing a Gerber Good Start product that (for example) claims to 

reduce the risk of developing allergies compared to obtaining an otherwise identical Gerber 

Good Start product that does not make that claim. To be clear, this value is not an average 

value that would be different for all class members – rather, it is the equilibrium price

calculated based on consumers’ responses to varying choice menus in the Conjoint Analysis

designed to derive one numerical figure to value the claim. The interpretation of this figure is 

the amount consumers paid when purchasing a Gerber Good Start product making a particular 

claim compared to an otherwise identical Gerber Good Start product without such a claim.

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114. The conjoint analysis allows the researcher to estimate a demand curve for a specific 

combination of attributes. Based on the monotonic property of the price utilities, interpolation 

between the price-market share combinations allows the researcher to estimate the demand 

curve for market shares from 0 to 100 and every price between a discrete range. A change in 

the level of one or more of the attributes would result in a different demand curve.

115. Ceteris paribus, when we assume the but-for world where the consumer is told about the 

falsity of a particular claim, the levels of the attributes change, and the Gerber Good Start 

product becomes less attractive to consumers and the demand curve shifts downward.

116. In economic theory, a negative price is associated with bads – the opposite of goods.38 In 

simple terms, the consumption of a bad is associated with negative utility for the consumer, 

which will be reflected in the consumer’s willingness-to-pay. A simple example will illustrate 

the concept of an economic bad in context of price and willingness to pay: Let’s assume that 

a vendor operates a stand at mile marker 13 during a marathon race where she sells bottles of 

water for $5 and that this vendor is the only one selling water bottles at mile marker 13. The 

bottles of water at the price of $5 constitute the supply of water bottles at that particular 

location. All runners who at that moment in time would assign a subjective value greater than 

or equal to $5 to the bottle of water will become purchasers while all runners who at that 

moment in time would assign a subjective value smaller than $5 to the bottle of water will not 

purchase the bottle of water. Therefore, the subjective value itself does not matter and does 

not need to be measured accurately. The fact that one runner may assign a subjective value of 

$100 is completely irrelevant because this runner will pay the asking price of $5. 

117. Let’s now assume that the bottles sold by the vendor really contain clear vinegar and the 

vendor would tell the runners that the label on the bottle indicating that it is water in the bottles 

is wrong because the bottles really contain vinegar. It can safely be assumed that in the 

situation of a marathon race vinegar is a less desirable liquid and the demand curve for vinegar 

would be downward shifted from the demand curve for water.

 

38 Hal R. Varian, Intermediate Microeconomics, 8th Edition, 2009, Page 41.

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118. Every runner who paid $5 for the bottle labeled as “water” which actually contained vinegar 

suffered an economic loss. However, the loss is not calculated as the difference between the 

assigned subjective value of the water bottle and the assigned subjective value of the bottle 

filled with vinegar. Much rather, the economic loss is the difference between the price paid 

(i.e., $5) for the bottle and the price that would have been paid for a bottle of vinegar in this 

situation.

119. While vinegar may be perceived as an economic good with a positive utility associated with 

its consumption, it is entirely possible that there would not be any demand for a bottle of 

vinegar during a marathon race and vinegar would turn from an economic good into an 

economic bad. Therefore, the true market value of a bottle of vinegar is irrelevant if the 

consumers have been led to believe that they are purchasing water. 

120. In cases where consumers view a product as an economic bad (like a bottle of vinegar during 

a marathon race), the economic loss suffered can be equal to or larger than the purchase price. 

Therefore, the argument that large values for the economic loss would render the damages 

model unreliable is simply incorrect and unfounded. We all know the colloquial expression 

“You would have to pay me money to buy that” – this expression has a theoretical foundation 

in economic theory and a technique like choice based conjoint analysis can be utilized to

quantify the economic loss.

121. As shown in theory in Figure 7, in order to make consumers whole after they had been harmed 

by false advertising, we need to shift the demand curve in the but-for world vertically into the 

actual demand curve.

122. The market simulation results will show the market share for a Gerber Good Start product that 

is advertised (for example) with the claim that it reduces the risk of developing allergies and 

sold at a set price in the actual world. The conjoint analysis will indicate whether the same 

Gerber Good Start product but without the claim would require a much lower price to achieve 

the same market share. If the conjoint analysis shows this, this result would clearly indicate 

that the removal of the attribute represented by the claim will have turned the product into a 

bad for the marginal consumer.

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123. In my market simulations, I will have varied the levels of attributes and prices, resulting in a

discrete number of possible combinations. For each attribute combination, I will compute two 

demand curves to assess if a drop in the demand curve results in an economic loss for a set of 

price points.

124. The consumption of a bad is undesirable and it creates negative utility for consumers. 

Therefore, more consumption as measured in higher market share of a product that is a bad 

will further decrease the negative utility and can thus increase the economic loss beyond the 

actual price of the product.

7 Summary and Conclusion

125. It is possible to develop a theoretical model that shows how the demand for a product changes 

when attributes and levels of attributes for that product change. To quantify the change in 

demand when the attributes of the product change (e.g., when claims about the product are 

false and misleading), it is possible to design a choice based conjoint study.

126. I will apply the well-established scientific methodology of Mixed Logit modeling and 

Hierarchical Bayesian Estimation to analyze the data from the efficiently designed choice 

based Conjoint Analysis. The results from the conjoint analysis can be relied upon to draw 

inferences about the value of claims to customers at the point of purchase and how such value 

will change when the claims are revealed to be false at the point of purchase.

127. The conjoint analysis shows what respondents would have paid for a product with none of the 

attributes falsely claimed. This value can be used to determine if consumers suffered an 

economic loss and, if so, to calculate the economic damages if the Court decides that the claim 

at issue was false and misleading.

128. Lastly, I conclude that the method proposed and described in this report can be used to expand 

the results of the conjoint study to a complete model to calculate class-wide damages in the 

merits phase of this case by multiplying the economic loss per unit as established above with 

the number of units purchased by class members during the class period. In addition, the 

model proposed in this declaration to compute class-wide economic losses can be expanded 

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in the merits phase of this case to incorporate additional aspects if the Court deems this 

necessary. 

129. The analysis and opinions contained in this declaration are based on information available as 

of the date of this declaration. I reserve the right to supplement or amend this declaration in 

the event additional information becomes available.

130. I declare under penalty of perjury under the laws of the United States that the foregoing is true 

and correct. Executed this 9

th day of March, 2018, at Los Angeles, CA.

___________________________________

Stefan Boedeker

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DECLARATION OF STEFAN BOEDEKER

8 References

Allenby, G., & Rossi, P. (2006). Hierarchical Bayes models: a practitioners guide The Handbook 

of Marketing Research (pp. 607-670).

Bakken, D., & Frazier, C. L. (2006). Conjoint analysis: Understanding consumer decision 

making The Handbook of Marketing Research (pp. 607-670).

Diamond, S. S. (2011). Reference guide on survey research (3rd ed. Vol. 221).

Isaacson, B., Hibbard, J. D., & Swain, S. D. (2007). Why Online Consumer Surveys Can be a 

Smart Choice in Intellectual Property Cases. IPL Newsl., 26, 12. 

Kuhfeld, W. F. (2006). Construction of efficient designs for discrete-choice-experiments The 

handbook of marketing research, Uses, misuses, and future advances, Sage Publ., 

Thousand Oaks, Calif (pp. 312-363).

Lancaster, K. J. (1966). A new approach to consumer theory. Journal of political economy, 

74(2), 132-157. 

Orme, B. K. (2005). Getting started with conjoint analysis: strategies for product design and 

pricing research (2nd ed.): Research Publishers.

Rao, V. R. (2014). Applied conjoint analysis: Springer Science & Business Media.

Train, K. E. (2009). Discrete choice methods with simulation: Cambridge university press.

Varian, H. R. (2010). Intermediate Microeconomics: a Modern Approach (8th ed.): WW Norton 

& Company New York.

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Exhibit 1

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Managing Director

Berkeley Research Group

550 South Hope Street

Suite #2150

Los Angeles, CA 90071

Tel: (310) 499-4924

Cell: (213) 705-1324

Email: sboedeker@thinkbrg.com

Education

• BS in Statistics, 

University of Dortmund, Germany

• BA in Business Administration

University of Dortmund, Germany

• MS in Statistics

University of Dortmund, Germany

• MA in Economics

University of California, San Diego

• Met Ph.D. requirements except 

dissertation in Economics, 

University of California, San Diego

Professional Associations

• Member of the American Economic 

Association (AEA)

• Member of the American Statistical 

Association (ASA)

• Member of the Econometric Society

• Member of the Mathematical 

Association of America (MAA)

• Member of the American Association 

for Public Opinion Research 

(AAPOR)

• Member of the Insights Association 

(FKA MRA)

• In 2001 Stefan was a member of an 

AICPA task force dealing with 

Corporate Integrity Agreements 

(CIA). Stefan was responsible for 

issues related to statistical 

methodology utilized in CIA’s.

STEFAN BOEDEKER

Background

Stefan is a Managing Director at Berkeley Research Group where he focuses 

on the application of economic, statistical, and financial models to a variety of 

areas such as solutions to business issues, complex litigation cases, and 

economic impact studies. He has extensive experience applying economic and 

statistical theories and methodologies to a wide variety of cases where But-forscenarios have to be developed based on probabilistic methods and where 

statistical predictive modeling has to be applied to assess liability and damages.

Stefan has applied these techniques in business disputes, single-plaintiff cases, 

multi-plaintiff cases, and class action proceedings in the areas of class 

certification, liability assessment, developing damages scenarios, and post 

settlement or judgment distributions. 

Professional and Business Experience

Representative Engagements

Litigation

» In a class action alleging misleading advertising practices, Stefan 

performed statistical analyses in the class certification stage.

» For a major healthcare provider involved in a dispute with a potential 

class of more than 3,000 other providers over allegedly excessive 

outlier payments Stefan performed economic and statistical analyses. 

Ultimately, class certification was denied in that case.

» In a class action alleging discriminatory allocation of public funds by a 

large metropolitan transportation authority, Stefan performed statistical 

analyses of transportation data. 

» In a multi-plaintiff case against a state authority on improper funding 

of special education programs, Stefan performed statistical analyses of 

funding related ledger data.

» In a class action alleging improper practices of charges for gym 

memberships, Stefan performed statistical analyses in the class 

certification analysis. Based on the analysis, the ultimately certified 

class was significant smaller than initially defined. In this case, Stefan 

also developed statistical models to assess damages.

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» In a class action alleging losses to consumers due to faulty window regulators in automobiles, Stefan 

utilized statistical models to assess economic damages.

» In a class action against a large financial institution alleging fee overcharges for personal trust accounts, 

Stefan utilized statistical analyses to segment the account holders and ultimately reduce the size of the 

class.

» In a class action case where a provider of a used car evaluation model was ordered by the court to test 

if their model did not significantly undervalued cars, Stefan performed statistical analyses.

» In a class action case over fee overcharges in the payment process of car insurance, Stefan developed a 

distribution model of repayments to class members after a settlement had been reached.

» In a class action of home owners over alleged diminution of property values due to proximity to a plume 

of contaminated soil, Stefan performed statistical analysis to assist counsel in a motion against class 

certification.

» In a natural resource damage class action case, Stefan provided econometric analysis of property value 

loss due to proximity to a solid waste site utilizing hedonic regression models.

» For a class action case involving potential damage from a landfill in a state park, Stefan analyzed data 

about travel, tourism and park attendance. Stefan specified and estimated linear regression models and 

time series models to predict park attendance.

» In a class action case involving alleged diminution of property values due to ground-water 

contamination, Stefan specified and estimated hedonic regression models to show that other factors 

than the contamination contributed significantly to the loss in property value.

» In a class action against a large financial institution alleging non-payment of coupon payments for 

bearer bonds Stefan designed and administered large-scale databases to reconstruct accounting records 

of a large financial institution’s Corporate Trust Department. He developed statistical models to analyze 

bondholders’ presentment behavior of Bearer bonds.

» In a class action dispute between the Department of Interior and individual Native Americans over 

mismanagement of individual trust accounts, Stefan performed a statistical analysis of an electronic 

database with approximately 60 million records in order to draw a statistically valid sample of accounts 

for further analysis.

» In a trademark infringement case of video equipment, Stefan calculated damages based on the 

defendant's unjust enrichment utilizing statistical time trend models.

» For a shareholder derivative action against a leading publicly-traded health care provider, employed an 

econometric approach to quantify potential damages per share due to alleged section 10b-5 violations 

and other claims. For the same matter, developed a multi-trader model to estimate the number of shares 

potentially damaged. 

» In a dispute between a major health care provider and private payor groups, Stefan developed statistical 

stratified sampling models to assess exposure across different contract types.

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» For a large financial institution’s personal trust department involved in a consumer class action, Stefan 

designed a random sample to estimate the potential exposure due to fee overcharges.

» For a computer equipment leasing company involved in an employee class action, Stefan utilized 

statistical models to estimate exposure due to alleged forfeiture of unpaid vacation time in a class action 

of former and current employees.

» For a limousine company involved in a wage and hour class action, Stefan developed a statistical 

sampling based exposure model to quantify the impact of alleged unpaid overtime and missed meal 

breaks.

» In several cases involving 12 hour shift workers at hospitals Stefan performed rebuttal analyses of 

plaintiff’s damages computations.

» For a large electronic retail chain Stefan calculated exposure based on the failure of paying overtime 

for store managers.

» For a major department store Stefan performed a statistical analysis of manager surveys where he found 

significant differences in the managers’ allocation of time across department and stores. Ultimately, 

due to these differences a class was not certified.

» For a large sporting goods retail chain Stefan assisted in defining the size of the potential class and in 

estimating the potential exposure which led to a favorable, early settlement of the case.

» For a women’s shoes retail chain Stefan designed and statistically analyzed an observational study to 

quantify the amount of time spent on exempt versus non-exempt tasks.

» For a video rental store chain Stefan developed sampling algorithms based on in-store security cameras 

to analyze time spent by assistant managers on exempt versus non-exempt activities.

» For a large fast food chain Stefan directed a team collecting employee work information from restaurant 

locations in order to monitor and gain compliance in response to litigation

» For a large mass merchandiser Stefan developed a document and data reconciliation tool and he 

developed a statistical sampling mechanism to proof compliance with a court ordered document 

retention procedures in the course of a wage and hour litigation.

» Stefan worked with a Fortune 500 bank in a class action suit to review the claims of managers that were 

misclassified and should have been paid overtime. To compute damages, Stefan reviewed the overtime 

records of employees in this position prior to a job classification change and, in the absence of overtime 

data after the job classification change, Stefan reviewed sign in and sign out times of the office building. 

» For a long-term care provider Stefan used data from timesheets, payroll, and other scheduling records 

to create comprehensive reports showing potential exposure for each of the claimed areas: timely wage 

payment, overtime wage payment, adequate daily meal and rest break periods, and travel time 

compensation.

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» For a maternity clothing store chain Stefan performed analyses related to exempt/non-exempt status 

issues for managers and assistant managers. Stefan also conducted a break time analysis for all 

employees. 

» For a commercial flooring contractor Stefan assessed the job duties and responsibilities of a group of 

supervisors. During the engagement, the scope of work expanded to include an analysis of 

misclassification and back-pay exposure for additional groups of employees.

» For a software developer Stefan analyzed how department and project specific characteristics impacted 

the work flow and the correlation of that impact to certain exemptions.

» For a large meatpacker Stefan conducted a time and motion study to properly assess the duration of 

certain separately compensated activities to rebut allegations of violation of minimum wage laws.

» For a public university housing department Stefan conducted an extensive time and motion study to 

identify the tasks (and associated time range to perform each task) related to processing a contract 

cancellation. 

» For a large drugstore chain Stefan used in-store cameras for the smaller stores and actual in-store 

observations for the larger stores to conduct a time motion study and quantify the time spent by assistant 

managers on certain pre-defined tasks. 

» For a large public storage company Stefan conducted a detailed time and motion study to determine the 

cost of collection and administration of late payments. Using both self-logging and independent review 

techniques, Stefan defined each step in the late payment process, calculated the cost to the company for 

such activities, and compared this cost to the late fees under dispute. 

» For a large retail store chain Stefan performed statistical analyses of regularly conducted employee 

activity surveys.

» For a mass merchandiser, Stefan conducted an observational study of activities of all individuals 

classified as managers to show significant differences in daily activities.

» For a department store, Stefan conducted an in-store observational study of managers and assistance 

managers to assess the percentage of time spent on managerial tasks.

» For a state ferry system in the Pacific Northwest, Stefan conducted an observational study of engine 

room personnel during shift changes to quantify potentially unpaid time worked.

» For a large retail chain Stefan conducted an extensive analysis of the company’s compliance with break 

time rules and regulations and also the employees’ usage and potential abuse of break time.

» For a large mass merchandise retailer Stefan compiled a comprehensive database of punch clock data, 

payroll data, point of sales data, hardcopy information about manual edits of time entries, store security 

system data, etc. to analyze allegations of inserting breaks, deleting time and forcing employees to work 

after they clocked out. 

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» For a large electronic retail chain Stefan analyzed time card data, point of sales data and other store 

specific attributes to quantify potentially missed meal and rest breaks.

» In a gender discrimination case involving a client in the food processing industry, Stefan analyzed the

impact of the implementation of an Affirmative Action Plan on the allegedly discriminatory 

employment practices.

» In a class action case alleging age discrimination for a vegetable seed company, Stefan performed 

rebuttal work of the plaintiff’s expert’s liability and damages analysis.

» In a class action case alleging age discrimination for a major aerospace company, Stefan performed 

statistical analyses to rebut allegations of age discrimination.

» In a class action race discrimination suit against the Alabama Department of Transportation, Stefan 

developed statistical regression models and tests to analyze the alleged discrimination.

» In a class action gender discrimination case against a large real estate brokerage firm, Stefan provided 

deposition testimony to class certification issues. 

» In a gender discrimination case against a temporary employment agency, Stefan performed econometric 

analyses to disprove salary discrimination against two former female employees. Stefan addressed 

plaintiffs’ expert’s damages calculations and developed alternative scenarios.

» For a large meat processing plant, Stefan performed statistical analyses of employment data to address 

allegations of discriminatory hiring practices.

» For a leading publicly-traded developer of enterprise management software, Stefan employed a 

statistical approach to demonstrate the diversity of investment styles among proposed lead plaintiffs for 

a securities class action lawsuit alleging section 10b-5 violations and other claims. For the same matter, 

Stefan employed an econometric approach to estimate potential damages for each lead plaintiff. 

» For a leading publicly-traded developer of enterprise management software, Stefan employed an 

econometric time-series model to analyze allegations of insider trading and the timing of certain stock 

transactions relative to information available to officers in the company.

» For a shareholder derivative action against a leading publicly-traded health care provider, employed an 

econometric approach to quantify potential damages per share due to alleged section 10b-5 violations 

and other claims. For the same matter, developed a multi-trader model to estimate the number of shares 

potentially damaged. 

» For a publicly-traded manufacturer of office supplies, developed a Black-Scholes application and 

utilized a binomial distribution probability methodology to evaluate the appropriateness of the size of 

a loan loss reserve related to a loan collateralized by the assets of an employee stock purchase plan. 

» For a large software developer, Stefan performed statistical modeling to assist in a securities class action 

litigation involving allegations of improper revenue recognition, reserve allocations, financial 

statement disclosures and other accounting irregularities

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» For a failed computer hardware company in defense of a 10b-5 securities litigation action, Stefan 

performed statistical analyses of accounting transactions, inventory and receivable reserves and the 

auditor’s work papers in its evaluation of the allegations. 

» In several Rule 10b(5) class actions, Stefan used the event study approach to calculate the value line of 

a security. In these cases Stefan applied complex and advanced one, two, and multi-trader models.

Non-Litigation

» For large grocery store chains, Stefan analyzed the effectiveness of a frequent shopper card program 

utilizing data mining techniques. He also analyzed customer data to facilitate the introduction of oneto-one marketing tools.

» For a grocery store chain, Stefan utilized econometric elasticity models to recommend pricing strategies 

for in-store promotions.

» For a grocery store chain, Stefan developed customer segmentation models to design segment specific 

marketing campaigns.

» For the American Film Marketing Association, Stefan performed an economic impact study of the 

influence of the independent film producers and distributors on the U.S. economy in general, and the 

California economy in particular.

» For a large entertainment client, Stefan developed statistical models to predict the return of video 

cassettes and DVDs.

» For several clients in the retail industry, Stefan developed statistical models to estimate the liability of 

unredeemed gift certificates.

» For a client in the restaurant business, Stefan developed statistical models to quantify the dollar amount 

of outstanding unredeemed gift certificates.

» For a major hotel chain, Stefan developed statistical models to forecast the redemption of frequent 

traveler program points for tax purposes.

» For a high profile e-commerce company, Stefan’s team produced an interactive business decision tool 

to forecast company growth and profitability. The interactive model allows the client, through the 

choice of a few fundamental inputs, to measure the simultaneous impact on all cost and revenue 

dimensions of the company, including real estate and equity participation.

» For the Nevada Resort Association, Stefan quantified the economic impact of the gaming industry with 

special emphasis on the accelerated population growth in greater Las Vegas.

» For the Los Angeles Unified School District, Stefan performed an economic study about the impact of 

different recycling programs.

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» For the Los Angeles County Department of Health Services, Stefan conducted a time and motion study 

to determine the time required to complete specific Medi-Cal eligibility and provider forms.

» For the Arizona Tax Research Association, Stefan developed economic models to quantify the revenue 

impact of a proposed change of taxation in the construction sector in Arizona.

» For a hotel property management company, Stefan analyzed customer data, and used data mining 

methods to develop predictive models for customer acquisition, retention, and attrition.

» For a project analyzing the extent of competition in the market segments of a pipeline company, Stefan 

estimated regression and Tobit-models to determine optimal bidding behavior for gas storage demand. 

He prepared testimony given in filings before the Federal Energy Regulatory Commission (FERC).

» For a hotel property management company, Stefan developed a demand driven yield management 

system.

» For a company providing self-storage space, Stefan developed a demand driven price-setting strategy 

utilizing own- and cross-price elasticity regression models.

» For a high-tech start-up with a unique service offering of new products, Stefan recommended productpricing scenarios.

» For a large international conglomerate, Stefan developed customized data mining techniques for the 

implementation within a customer knowledge management system.

» For a large law firm, Stefan performed a comprehensive statistical analysis of Los Angeles Superior 

Court jury verdicts over the last decade. The project tested the hypothesis of systematic bias in particular 

courthouses with respect to plaintiff-win probability, length of trial, length of deliberation, and dollar 

amounts awarded.

Depositions & Testimony

Depositions

1. MRO Communications, Inc vs. American Telephone and Telegraph Company, United States District 

Court District of Nevada, Case. No. -5-95-903-PMP, Deposition Testimony, September 26, 1996

2. Yolanda Aiello Harris, individually and on behalf of all others similarly situated; Jennifer Hopkins, 

individually and on behalf of others similarly situated; Shannon L. Bradley, individually and on 

behalf of others similarly situated, Plaintiffs, vs. CB Richard Ellis, Inc., a California corporation; CB 

Commercial INC., a California corporation; Defendants, Superior Court of California, County of San 

Diego, Case No. GIC 745044, Deposition Testimony, January 5, 2001.

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3. State of Tennessee, ex rel., Douglas Sizemore, Petitioner vs. Xantus Healthplan of Tennessee, Inc., 

Chancery Court of Davidson County, Tennessee at Nashville, Case No 99-917-II, Deposition 

Testimony, October 11, 2001.

4. Howard Wright, Inc., a California corporation doing business as AppleOne Employment Services, 

Plaintiffs, vs. Olsen Staffing Services, Inc., a Delaware Corporation, Dagney Smith, an individual, 

Vicky Riechers, an individual, and Linda Shiftman, an individual, Defendants, Superior Court of the 

State of California for the County of Los Angeles, Case No. BC 200657, Deposition Testimony, 

December 7, 2001.

5. Sacred Heart Medical Center, et al., Plaintiffs, -vs- Department of Social and Health Services, and 

Dennis Braddock, the Secretary of the Department of Social and Health Services, Defendants, 

Superior Court of the State of Washington in and for the County of Thurston, No. 00-2-01898-1, 

Deposition Testimony, January 23, 2003.

6. Patrick Bjorkquist individually and on behalf of all others similarly situated, Plaintiff, vs. Farmers 

Insurance Company of Washington, Defendant, in the Superior Court of the State of Washington for 

King County, Case No.: 02-2-11684-1 SEA, Deposition Testimony, November 3, 2003.

7. Diversified Property, a general partnership, Dora Saikhon Family Trust, and Nancy Saikhon Borrelli, 

an individual, Plaintiffs vs. Manufacturers Life Insurance (U.S.A.), a Michigan corporation, 

erroneously sued as Manufacturers Life Insurance Company, Inc., Defendants in the Superior Court 

of California, County of San Diego, Case No.: GIC 815128, Deposition Testimony on July 21, 2004.

8. Alan Powers, Plaintiff, vs. Laramar Group et al., Defendants in the United States District Court, 

Northern District of California, No. C-02-3755 SBA, Deposition Testimony on August 27, 2004.

9. Group Anesthesia Services, A Medical Group, Inc., Claimant, vs. American Medical Partners of 

North Carolina, Inc., etc., et al., Respondents, JAMS Arbitration, Reference No. 1100040919, 

Deposition Testimony on February 9, 2005.

10. Group Anesthesia Services, A Medical Group, Inc., Claimant, vs. American Medical Partners of 

North Carolina, Inc., etc., et al., Respondents, JAMS Arbitration, Reference No. 1100040919, 

Deposition Testimony on March 11, 2005.

11. Fujitsu v. Cirrus Logic et al., United States District Court, Northern District of California, San Jose 

Division, Case No. 02CV01627. Deposition Testimony on April 21 and 22, 2005.

12. Goldman et al. v. RadioShack Corporation, United States District Court, Eastern District of 

Pennsylvania, Case No. 03 CV 0032, Deposition Testimony on May 18, 2005.

13. Perez et al. v. RadioShack Corporation, United States District Court, Northern District of Illinois, 

Eastern Division, Case No. 02-CV-7884, Deposition Testimony on December 13, 2005.

14. United States of America ex rel. A. Scott Pogue v. American Healthcorp Inc., Diabetes Treatment 

Centers of America Inc., et al., United States District Court, Middle District of Tennessee at 

Nashville, Civil No. 3-94-0515, Deposition Testimony on May 12, 2006.

15. School Districts’ Alliance v. State of Washington, United States District Court, Eastern District of 

Thurston, Case No. 04-2-02000-7, Deposition Testimony on July 20, 2006.

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16. Boca Raton Community Hospital, Inc., a Florida not-for-profit corporation d/b/a Boca Raton 

Community Hospital, on behalf of itself and on behalf of Class of all others similarly situated v. Tenet 

Healthcare Corp., a Nevada Corporation, United States District Court, Southern District of Florida, 

Miami Division, Case No. 05-80183-CIV-SEITZ/MCALILEY, Deposition Testimony on July 25, 

2006.

17. Boca Raton Community Hospital, Inc., a Florida not-for-profit corporation d/b/a Boca Raton 

Community Hospital, on behalf of itself and on behalf of Class of all others similarly situated v. Tenet 

Healthcare Corp., a Nevada Corporation, United States District Court, Southern District of Florida, 

Miami Division, Case No. 05-80183-CIV-SEITZ/MCALILEY, Deposition Testimony on October 

13, 2006.

18. Louise Ogborn v. McDonald’s Corporation et al., Commonwealth of Kentucky 55th Judicial District, 

Bullitt County Circuit Court, Case No. 04-CI-00769, Deposition Testimony on October 19, 2006.

19. Elise Davis v. Kohl’s Department Stores, Inc. consolidated with Rosie Grindstaff v. Kohl’s 

Department Stores, Inc., Superior Court of the State of California for County of Los Angeles Central 

District, Case No. BC 327426 (lead case) consolidated with Case No. BC 341954, Deposition 

Testimony on April 25, 2007.

20. Norman Utley, et al., v. MCI, Inc., MCI Worldcom Communications, Inc., and MCI Network 

Services, Inc., formerly known as MCI Worldcom Network Services, Inc., United States District 

Court, Northern District of Texas, Dallas Division, Civil Action No. 3:05 - CV- 0046 - K, Deposition 

Testimony on May 30, 2007.

21. Ramon Moreno and Ernesto Morailo, on behalf of themselves and all others similarly situated v. 

Guerrero Mexican Food Products Inc., a division of Gruma Corporation; and Gruma Corporation, a 

Nevada Corporation, United States District Court, Central District of California, Case No. CV05-

773RSWL(PLAx), Deposition Testimony on August 10, 2007.

22. Darensburg et al. v. Metropolitan Transportation Commission, U.S. District Court, Northern District 

of California, Case No. C-05-1597-EDL, Deposition Testimony on March 18, 2008.

23. In Re: King Pharmaceuticals, INC, Derivative Litigation, Lead Case No: BOO19077(M), The 

Chancery Court, Sullivan County at Bristol, Tennessee, Deposition Testimony on April 4, 2008.

24. P. Ansley et al. v. Lewis Homes of California, a California General Partnership, et al., Superior Court 

of the State of California, For the County of Solano, Case No. FCS02445, Deposition Testimony on 

April 10, 2008.

25. Personnel Plus v. Ashish Wahi et al., Superior Court of the State of California, County of Orange, 

Case No. 07CC08363, Deposition Testimony on August 13, 2008.

26. First Capitol Consulting Inc. v. LVX, Inc. et al., Superior Court of the State of California for the 

County of Los Angeles, Case No. BC378202, Deposition Testimony on October 27, 2008.

27. R. Molina et al. v. Lexmark International, Inc., Superior Court of the State of California for the 

County of Los Angeles, Case No. BC339177, Deposition Testimony on November 19, 2008.

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28. In re National Century Financial Enterprises, Inc. Investment Litigation, No. 2:03-MD-1565-JLGMRA (S.D.Ohio), Deposition Testimony on January 22, 2009.

29. New York City Employees’ Retirement System, et al. v. Bank One, N.A., et al., Case No. 03-cv09973 (LAK) (S.D.N.Y.), Deposition Testimony on January 22, 2009.

30. Dole Fresh Fruit International, Ltd, Hyundai Precision America, Inc., JAMS Arbitration, ADRS Case 

#05-1138-RTA, Deposition Testimony on December 21, 2009.

31. D. Berry, L. Hedges et al. v. Volkswagen of America, Inc. In The Circuit Court of Jackson County, 

Missouri, at Independence, No. 0516-CV01171 Division 2, Deposition Testimony on February 18, 

2010.

32. D. Aberle et al. v. Davidson Builders, Inc., et al., Superior Court of the State of California, County 

of Orange, Case No.: 37-2008-00083718-CU-CD-CTL, Deposition Testimony on March 24, 2010.

33. Urga, et al. v. Redlands Community Hospital, Superior Court of the State of California, County of 

San Bernardino, Case No. SCVSS 123769, Deposition Testimony on May 17, 2010.

34. Oberschlake, et al v. St. Joseph Hospital of Orange, et al, Superior Court of the State of California, 

County of Orange, Case No. 05CC00301, Deposition Testimony on August 12, 2010.

35. J. Morrison v. The Vons Companies, Inc., Superior Court of State of California, County of San Diego, 

Case No. 37-2009-00081026-CU-BT-CTL, Deposition Testimony on December 7, 2010

36. R. Pate, et al. v. Children’s Hospital of Orange County, Superior Court of California, County of 

Orange, Case No. 05CC00303, Deposition Testimony on April 13, 2011.

37. M. St. Croix, et al. v. Cedar Fair, L.P., et al., Superior Court of California, County of Orange, Case 

No. 30-2008-0214500, Deposition Testimony on August 22, 2011.

38. Steven Domalewski, a minor v. Hillerich and Bradsby Co., et al., Superior Court of New Jersey, 

Passaic County, Docket No.: PAS-L-2119-08, Deposition Testimony on January 5, 2012.

39. Cathleen McDonough, et al., v. Horizon Blue Cross/Blue Shield of New Jersey, United States District 

Court, District of New Jersey, Civil Action No. 09-cv-00571-(SRC) (PC), Deposition Testimony on 

January 10, 2012.

40. Daniel Ordonez, et al., v. Radio Shack, United States District Court, Central District of California, 

Case No. CV 10-07060 CAS (JCGx), Deposition Testimony on October 24, 2012.

41. Ameritox, Ltd., v. Millennium Laboratories, Inc., United States District Court, Middle District of 

Florida, Case No. 8:11-cv-00775-SCB-TBM, Deposition Testimony on December 20, 2013.

42. United States of America, ex rel. Glenda Martin v. Life Care Centers of America, Inc., United States 

District Court Eastern District of Tennessee at Chattanooga, Civ. Action No. 1:08-CV-251, 

Deposition Testimony on January 15, 2014.

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43. United States of America, ex rel. Tammie Taylor v. Life Care Centers of America, Inc., United States 

District Court Eastern District of Tennessee at Chattanooga, Civ. Action No. 1:12-CV-64, Deposition 

Testimony on January 15, 2014.

44. Darren Smith, et al., v. Panera Bread Company, Superior Court of California, County of San Diego, 

Case No. 37-201-00084077 CU-BT-CTL, Deposition Testimony on April 30, 2014.

45. Joseph Hummel et al., v. Castle Principles, LLC et al., Superior Court of California, County of Santa 

Clara, Case No. 112CV223170, Deposition Testimony on June 19, 2014.

46. Sherman Way Oil, Inc. (Bijan Pouldar), American Pacific Enterprises Group (Sherwin Louie), 

Bahman Kohanteb, Hamid Kalhor , Claimants, Vs. Circle K Stores, Inc., Respondent, Alternative 

Dispute Resolution Case No’s 13-7103-DSC through 13-7106-DSC, Deposition Testimony on 

September 25, 2014.

47. In re: ExxonMobil Oil Corporation, et al., Southern California Bulk Sale Litigation, Case No. CV12-

04689-PA (VBKx), Deposition Testimony on September 25, 2014.

48. Oracle Wage and Hour Cases, Raghunandam Matam et al., v. Oracle Corporation, Superior Court of 

California, County of Alameda, No. RG-09480164, Deposition Testimony, October 21, 2014.

49. G. Taylor et al. v. Shippers Transport Express, Inc., et al., United States District Court, Central 

District of California, Case No.: CV13-02092-BRO (PLAx), Deposition Testimony on October 24, 

2014.

50. Denise Mays et al. v. Children’s Hospital of Los Angeles, Superior Court of California, County of 

Los Angeles, Case No. BC477830, Deposition Testimony on March 17, 2015.

51. Direct General Insurance Company v. Indian Harbor Insurance Company et al., United States District 

Court, Southern District of Florida, Miami Division, Case No. 14-20050-CIV-Cooke/Torres, 

Deposition Testimony on March 27, 2015.

52. Dennis Dickman v. Gerdau Reinforcing Steel, et al., Superior Court of California, County of San 

Bernardino, Case No. CIV-DS-1406231, Deposition Testimony on July 7, 2015.

53. Fred Devries, et al. v. Morgan Stanley & Co. LLC, et al., United States District Court, Southern 

District of Florida, Case No. 9:12-cv-81223-KAM, Deposition Testimony on July 31, 2015.

54. Dennis Dickman v. Gerdau Reinforcing Steel, et al., Superior Court of California, County of San 

Bernardino, Case No. CIV-DS-1406231, Deposition Testimony on September 11, 2015

55. Leah Davis, and Amy Krajec, et al. v. St. Jude Hospital, Superior Court of California, County of 

Orange, Case No. 30-2012-00602596-CU-OE-CXC, Deposition Testimony on January 19, 2016.

56. In re MyFord Touch Consumer Litigation, Whalen, et al. vs. Ford Motor Company, United States 

District Court Northern District of California San Francisco Division, Case No. 13-cv-3072-EMC, 

Deposition Testimony on February 23, 2016.

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57. United States of America, ex rel. Glenda Martin v. Life Care Centers of America, Inc., United States 

District court Eastern District of Tennessee at Chattanooga, Civ. Action No. 1:08-CV-251 & United 

States of America, ex rel. Tammie Taylor v. Life Care Centers of America, Inc., United States District 

court Eastern District of Tennessee at Chattanooga, Civ. Action No. 1:12-CV-64, Deposition 

Testimony on March 4, 2016.

58. The United States of America and the State of Florida ex rel. Angela Ruckh v. CMC II LLC, United 

States District court for the Middle District of Florida Tampa Division, Civil Action No. 8:11 CV 

1303 SDM-TBM, Deposition Testimony on March 16, 2016.

59. Bertha Sanchez, et al. v. St. Mary Medical Center, et al., Superior Court of the State of California for 

the County of San Bernardino, Case No. CIVDS 1304898, Deposition Testimony on July 13, 2016.

60. Christian Juarez, et al v. Dignity Health, a California corporation, et al., Superior Court of the State 

of California, County of Los Angeles, Central Civil West District, Case No. BC550950, Deposition 

Testimony on August 15, 2016.

61. In Re Dial Complete Marketing and Sales Practices Litigation, United States District Court, District 

of New Hampshire, Case No. 11-md-2263-SM (MDL Docket No. 2263), Deposition Testimony on 

August 30, 2016.

62. In Re: Myford Touch Consumer Litigation, United States District Court, Northern District of 

California, San Francisco Division, Case No. 13-cv-3072-EMC, Deposition Testimony on September 

16, 2016.

63. United Healthcare Insurance Company v. Lincare Inc., Case Improvement Plus of Texas Insurance 

Company: Care Improvement Plus South Central Insurance Company: Care Improvement Plus of 

Maryland, Inc. v. Lincare Inc., In An Arbitration Before the American Arbitration Association, Case 

No. 01-15-0003-4095, Deposition Testimony on December 21, 2016.

64. The Moses H. Cone Memorial Hospital Operating Corporation d/b/a Cone Health v. Springfield 

Service Corporation d/b/a SPI Healthcare, United States District Court for the Middle District of 

North Carolina, Civil Action No. 1:13-cv-651, Deposition Testimony on January 17, 2017.

65. The People of the State of California, acting by and through Orange County District Attorney Tony 

Rackauckas v. General Motors LLC, Superior Court of the State of California in and for the County 

of Orange Complex Litigation Division, Case No. 30-2014-00731038-CU-BT-CX, Deposition 

Testimony on April 20 and 21, 2017.

66. In Re: Emerson Electric Co. Wet/Dry Vac Marketing And Sales Litigation, United States District 

Court for the Eastern District of Missouri, MDL No. 2382, Civil Action No. 4:12-md-2382-HEA, 

Deposition Testimony on May 17, 2017.

67. The People of the State of California, acting by and through Orange County District Attorney Tony 

Rackauckas v. General Motors LLC, Superior Court of the State of California in and for the County 

of Orange Complex Litigation Division, Case No. 30-2014-00731038-CU-BT-CX, Rebuttal 

Deposition Testimony on June 13, 2017.

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68. Clayton Dezan, et al. v. Dignity Health, a California Corporation; Community Hospital of San 

Bernardino, et al, Superior Court of The State of California for the County of San Bernardino, Case 

No. CIVDS1516658, Deposition Testimony on August 22, 2017.

69. Millennium Health, LLC v. Blue Shield of California, Counterclaim, Blue Shields of California v. 

Millennium Health, LLC, American Arbitration Association, Case No. 01-15-0005-5926, Deposition 

Testimony on August 24, 2017.

70. Matthew Townsend, et al. v. Monster Beverage Corporation and Monster Energy Company, United 

States District Court Central District of California, Case No. 5:12-cv-02188 VAP (KKx), Deposition 

Testimony on September 20, 2017.

71. Welltower Inc., v. Scott M. Brinker, In the Court of Common Pleas Lucas County, Ohio, Case No. 

CI-17-2692, Deposition Testimony on October 4th, 2017.

72. In Re Seagate Technology LLC Litigation, United States District Court, Northern District of 

California San Jose Division, Case No. 5:16-cv00523-RMW, Deposition Testimony on December 

12th, 2017.

73. Joanne Hart and Sandra Bueno v. BHH, LLC d/b/a Bell + Howell and Van Hauser LLC, United States 

District Court for the Southern District of New York, Case No. 1:15-CV-04804-WHP, Deposition 

Testimony on January 26th, 2018.

74. Thomas Davidson, et al v. Apple Inc., United States District Court Northern District of California

San Jose Division, Case No. 5:16-cv-04942-LHK, Deposition Testimony on January 29, 2018.

75. In Re: General Motors, LLC Ignition Switch Litigation, United States District Court Southern District 

Of New York, Case No. 14-MD-2543 (JMF), Deposition Testimony on February 6th and 7th, 2018.

Testimony

1. State of Tennessee, ex rel., Douglas Sizemore, Petitioner vs. Xantus Healthplan of Tennessee, Inc., 

Chancery Court of Davidson County, Tennessee at Nashville, Case No 99-917-II, Trial Testimony, 

October 16, 2001.

2. State of Tennessee, ex rel., Douglas Sizemore, Petitioner vs. Xantus Healthplan of Tennessee, Inc., 

Chancery Court of Davidson County, Tennessee at Nashville, Case No 99-917-II, Rebuttal 

Testimony, October 26, 2001.

3. Howard Wright, Inc., a California corporation doing business as AppleOne Employment Services, 

Plaintiffs, vs. Olsen Staffing Services, Inc., a Delaware Corporation, Dagney Smith, an individual, 

Vicky Riechers, an individual, and Linda Shiftman, an individual, Defendants, Superior Court of the 

State of California for the County of Los Angeles, Case No. BC 200657, Trial Testimony, March 4, 

2002.

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4. Columbia/HCA Healthcare Corporation - Billing Practices Litigation, United States District Court, 

Middle District of Tennessee, Nashville Division, Case No. 3-98-MDL-1227 on June 28, 2002.

5. Sacred Heart Medical Center, et al., Plaintiffs v. Department of Social and Health Services, and 

Dennis Braddock, the Secretary of the Department of Social and Health Services, Defendants, 

Superior Court of the State of Washington in and for the County of Thurston, No. 00-2-01898-1, 

Testimony in Liability Trial, April 14, 2003.

6. Diversified Property, a general partnership, Dora Saikhon Family Trust, and Nancy Saikhon Borrelli, 

an individual, Plaintiffs v. Manufacturers Life Insurance (U.S.A.), a Michigan corporation, 

erroneously sued as Manufacturers Life Insurance Company, Inc., Defendants in the Superior Court 

of California, County of San Diego, Case No.: GIC 815128, Trial Testimony on October 25, 2004.

7. Bridgestone/Firestone North American Tire v. Sompo Japan Ins. Co. of America, United States 

District Court for the Middle District of Tennessee Nashville Division Civil Action NO. 3-02-1117, 

March 7, 2005

8. Group Anesthesia Services, A Medical Group, Inc., Claimant, vs. American Medical Partners of 

North Carolina, Inc., etc., et al., Respondents, JAMS Arbitration, Reference No. 1100040919, 

Arbitration Testimony on March 23, 2005.

9. Goldman et al. v. RadioShack Corporation, United States District Court, Eastern District of 

Pennsylvania, Case No. 03 CV 0032, Testimony in Liability Trial, on June 28 and 29, 2005.

10. Goldman et al. v. RadioShack Corporation, United States District Court, Eastern District of 

Pennsylvania, Case No. 03 CV 0032, Rebuttal Testimony in Liability Trial, on July 5, 2005.

11. Mauna Loa Vacation Ownership LLP v. Accelerated Assets, LLP. United States District Court, 

District of Arizona, Case No. CIV 03-0846 PCT DGC. Trial Testimony, on February 22, 2006.

12. School Districts’ Alliance v. State of Washington, United States District Court, Eastern District of 

Thurston, Case No. 04-2-02000-7, Trial Testimony on November 13, 2006.

13. In the Matter of Premier Medical Group, PC, Appellant – Department of Health and Human Services, 

Office of Medicare Hearings and Appeals, Southern Field Office, ALJ Appeal No. 1-221579701, 

Medicare Appeal No. 1-18761858, Provider No. 3706654, AR No. 9406352171039, Judge Zaring 

Robertson, US Administrative Law Judge, Testimony on April 1, 2008.

14. Darensburg et al. v. Metropolitan Transportation Commission, U.S. District Court, Northern District 

of California, Case No. C-05-1597-EDL, Trial Testimony on October 9, 2008.

15. R. Molina et al. v. Lexmark International, Inc., Superior Court of the State of California for the 

County of Los Angeles, Case No. BC339177, Trial Testimony on October 22 and 26, 2009.

16. Dole Fresh Fruit International, Ltd, Hyundai Precision America, Inc., ADRS Case #05-1138-RTA, 

Trial Testimony on February 19, 2010.

17. In the matter of University of Tennessee Cancer Institute, ALJ Appeal No. 1-446 575 318, Office of 

Medicare Hearings & Appeals, Judge Z. Robertson, US Administrative Law Judge, Testimony on 

April 20, 2010.

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18. Urga, et al. v. Redlands Community Hospital, Superior Court of the State of California, County of 

San Bernardino, Case No. SCVSS 123769, Trial Testimony on July 20, 2010.

19. Marine Engineers’ Beneficial Association v. Department of Transportation, Ferries Division Federal 

Mediation & Conciliation Service Cause No. 110105-52404-6 AGO Matter No. 10499471, July 19, 

2011.

20. Richard Robinson v. County of Los Angeles, et. al., United States District Court of California, Central 

District, Case No. CV06-2409 GAF (VBKx), Trial Testimony on December 1, 2011.

21. In the matter of American Home Patient, ALJ Hearing, Appeal No. 1-982137828, Office of Medicare 

Hearings & Appeals, Miami Office Southern Field Division, Testimony on October 29, 2012.

22. In the matter of American Home Patient, ALJ Hearing, Appeal No. 1-924297238, Office of Medicare 

Hearings & Appeals, Irvine Office Western Field Division, Hearing Testimony on February 28, 

2013.

23. TaylorMade Golf Company Challenge to Callaway Golf Company’s Final Response, National 

Advertising Division, New York, Testimony on March 13, 2013.

24. United States of America, ex rel. Tammie Taylor v. Life Care Centers of America, Inc., United States 

District Court Eastern District of Tennessee at Chattanooga, Civ. Action No. 1:12-CV-64, Testimony 

on May 13, 2014.

25. United States of America v. Houshang Pavehzadeh, United States District Court for the Central 

District of California, No. CR 13-0320-R, Testimony on May 19, 2014.

26. Sherman Way Oil, Inc. (Bijan Pouldar), American Pacific Enterprises Group (Sherwin Louie), 

Bahman Kohanteb, Hamid Kalhor , Claimants, Vs. Circle K Stores, Inc., Respondent, Alternative 

Dispute Resolution Case No’s 13-7103-DSC through 13-7106-DSC, Arbitration Testimony on 

October 10, 2014.

27. Heidi’s Children Dental Center (DC14-0813-204-LM) vs. Denti-Cal, Testimony at Administrative 

Law Judge Hearing, Judge Lewis Munoz, in Los Angeles on November 5, 2014.

28. AdvanceMed Audit of Altercare of Wadsworth, Medicare Appeal, Medicare Appeal No. 1-

912446681, Bertha Sanchez, et al. v. St. Mary Medical Center, et al., Superior Court of the State of 

California for the County of San Bernardino, Case No. CIVDS 1304898, Certification Hearing 

Testimony on October 21, 2016.

29. Michael Bozsik v. Livingston International Inc., Ontario Superior Court of Justice, Court File No. 

5270/14, Cross Examination Testimony on May 12, 2016.

30. Bertha Sanchez, et al. v. St. Mary Medical Center, et al., Superior Court of the State of California for 

the County of San Bernardino, Case No. CIVDS 1304898, Certification Hearing Testimony on 

October 21, 2016.

31. In Re Dial complete Marketing and Sales Practice Litigation, United States District Court, District of 

New Hampshire, Case No. 11-md-2263-SM (MDL Docket No. 2263), Hearing Testimony on 

November 16, 2016.

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32. United Healthcare Insurance Company v. Lincare Inc., Case Improvement Plus of Texas Insurance 

Company: Care Improvement Plus South Central Insurance Company: Care Improvement Plus of 

Maryland, Inc. v. Lincare Inc., In An Arbitration Before the American Arbitration Association, Case 

No. 01-15-0003-4095, Arbitration Testimony on February 6, 2017.

33. The United States of America and The State of Florida ex rel. Angela Ruckh v. CMC II, LLC, United 

States District Court for the Middle District of Florida Tampa Division, Civil Action No. 8:11 CV 

1303 SDM-TBM, Trial Testimony on February 8, 2017.

34. Federal Government of Germany v. A Consortium of Publicly Traded Companies in an arbitration 

under the laws of Germany, Arbitration Testimony on March 21 and 22, 2017.

35. In Re Determination of Royalty Rates and Terms for Transmission of Sound Recordings by Satellite 

Radio and “Preexisting” Subscription Services (SDARS III), United States Copyright Royalty Judges 

The Library of Congress Washington, D.C., Docket No. 16-CRB-0001-SR/PSSR (2018-2022), Trial 

Testimony on May 9, 2017.

36. ZPIC Audit Appeal of Providence Health System Southern California, Office of Medicare Hearings 

and Appeals, OMHA Appeal Number 1-1823418684, Hearing Testimony on October 16, 2017.

Publications

Boedeker, Stefan and Goetz Trenkler (2001) - "A Comparison of the Ridge and Iteration Estimator" 

- in: Econometric Studies: A Festschrift in Honour of Joachim Frohn (ed. by Ralph Friedmann, Lothar 

Knueppel, and Helmut Luetkepohl), New Brunswick

Professional and Business History

» Berkeley Research Group, 2010 - Present, Managing Director

» Resolution Economics, 2008 - 2010, Partner

» Alvarez & Marsal, 2007 - 2008, Managing Director

» LECG LLC, 2005 - 2007, Director 

» Navigant Consulting Inc., 2004 -2005, Managing Director in Litigation and Investigation Practice

» Deloitte & Touche LLP, 2003 - 2004, Leader of the Economic and Statistical Consulting Practice in 

the West Region

» PricewaterhouseCoopers LLP, 2002 - 2003, Leader of the Litigation Consulting Group in Los 

Angeles, Leader of the Economic and Statistical Consulting Practice in the West Region

» Andersen LLP, 1992 - 2002, Partner (since 2000), last position held: Director of Economic and 

Statistical Consulting practice in the Pacific Region

» University of California, San Diego, 1989 - 1991, Teaching Assistant, Department of Economics

» German Government, 1986 - 1989, Economic Research Assistant

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