EXHIBIT 2 

Case 5:17-cv-00551-LHK Document 333-3 Filed 12/12/19 Page 1 of 7
UNITED STATES DISTRICT COURT

NORTHERN DISTRICT OF CALIFORNIA

SAN JOSE DIVISION

CHRISTINA GRACE and KEN POTTER, )

Individually and on Behalf of All Others, )

Similarly Situated, )

)

Plaintiffs, )

)

v. ) Case No. 5:17-cv-00551-LHK

)

APPLE, INC., )

)

Defendant. )

____________________________________)

Expert Report of Professor Justine S. Hastings, Ph.D.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

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is the period before the Break became publicly known and could be incorporated into customers’

willingness to pay in the resale market. The “damages period” is the period after the Break 

occurred, potentially affecting the value of the phones in the used market. The “damages” period 

can be compared to the “benchmark” period to estimate how prices changed as a result of the 

harmful act, holding other demand and supply factors constant.

41. As an economist, I specify a model that relates the price of the product in question 

to demand and supply factors that affect price based on economic principles and facts about the 

market. This regression model captures the effect of the harmful act on price by including a 

“dummy variable” – a variable that captures the difference in price during the period the harmful 

act was in effect, controlling for the influence on price of other demand and supply factors. This 

approach is called the Dummy Variable Approach of calculating damages.64 The demand and 

supply factors in the model determine what price would have been but-for the harmful act; the 

dummy variable captures the impact of the harmful act relative to the but-for price.

42. Demand factors that affect the price of a used phone include the age of the model,65

the brand of the model, and other features of the model, such as color, storage size, and condition.

66

These are the primary factors that customers choose when deciding how much they are willing to 

 64 See, e.g., Justine S. Hastings and Michael A. Williams, “What is a But-For World?,” Antitrust (2016), vol. 31, pp. 

102-108; ABA Section of Antitrust Law (2017), Proving Antitrust Damages: Legal and Economic Issues, 3rd Ed., 

American Bar Association, Ch. 6, Section F; McCrary, J. and Rubinfeld, D. (2014), “Measuring Benchmark 

Damages in Antitrust Litigation,” Journal of Econometric Methods, vol. 3, pp. 63-74; and ABA Section of Antitrust 

Law (2014), Econometrics: Legal, Practical, and Technical Issues, 2nd ed., American Bar Association, Ch. 12.

65 See Hall Deposition, at 32:5-8 (“Q. Okay. Other than the condition, does the age of the phone impact the value 

that you would provide to the customer? A. Age?·Yes.”). See also Hall Deposition, at 32:9-11 (“Q. Does the model 

of the phone impact -- impact the value? A. Yes.”)

66 Zeinfeld Deposition, at 20:19-21:4 (“Q. But would it have the impact -- would it have an impact on how -- like, 

for example, in the process that you were describing of the price being provided to Apple, would it have an impact 

on that pricing, the month-to-month pricing? A. Well, the -- the device does itself. Q. Mm-hmm. A. And what 

operating system is on the device is almost irrelevant. It’s the device that you’re giving the value on, the device, the 

memory, the color, the cosmetic.”).

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pay for a used phone, as demonstrated by the fact that data on these factors are collected by 

platforms when determining how much a used phone is worth (see Appendix III, Tables A1 and 

A2). They are also primary factors cited by industry experts deposed in this case who utilize the 

data collected from consumers as described in Tables A1 and A2 to manage used phone buy-back 

and resale programs.

67

43. Several supply factors may affect the probability that individuals sell their used 

phones. A primary factor is the release of new models, which would increase the value to current 

phone owners of selling their current phone to upgrade to the latest model.68

44. Macroeconomic factors such as measures of economic wellbeing, like GDP, may 

also affect price as they affect individuals’ decisions to make purchases, such as used or new 

phones, and may also affect individuals’ price sensitivity which in turn impacts market prices.69

Thus, these factors may affect both supply and demand as they affect the rate at which individuals 

may purchase new phone models and resell their existing phones, as well as the rate at which 

individuals purchase used phones and their price sensitivity when making a purchase.

 67 Zeinfeld Deposition, at 18:15-24. See also Hall Deposition, at 32:12-14 (“Q. Does the amount of storage, you 

know, 8 gig, 16 gig, 32 gig, impact the amount of value? A. Yes.”). “Jailbroken” is not a primary factor affecting the 

vast majority of used phone prices. (Zeinfeld Deposition, at 60:14-22. See also Plott Deposition, at 40:10-20 and 

43:1–12.) Operating system is also not a primary factor affecting the price of used phones. (Plott Deposition, at 

39:14-22. See also Zeinfeld Deposition, at 40:9 – 41:4, 56:5 – 57:16, and 72:4-7.) Hall Deposition, at 37:7-10 (“Q. 

Does the operating system model that is on the device when the consumer provides the device to AT&T impact the 

trade-in value? A.·No.”).

68 Zeinfeld Deposition, at 18:15-24 (“Q. How is it determined how much Brightstar pays Apple for those devices? 

A. We tell Apple every month what the upcoming month’s value will be per model. Q. And what factors go into the 

determination of that price? A. It’s a endless list. It’s where the marketplace is, the time -- it’s the time of year

compared to new product being introduced. It’s how many generations old the device might be. It’s memory size. 

It’s looking at the history of what grade of product had been coming in through that source. It’s supply of inventory 

in the marketplace. And I’m sure there’s many more.”). See also Hall Deposition, at 32:15-21 (“Q. The timing of the 

resell from the customer to AT&T relative to releases of new models, would that impact the price? A. Yes. Q. How? 

A. You release a new device, such as an Apple 6 or 6S, and the 5 declines in value.”)

69 N. Gregory Mankiw (2011), Principles of Microeconomics, 6th ed., Cengage Learning, p. 70. 

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45. Specifically, I estimate the impact of the Break on the value of iPhone 4 and iPhone 

4S models in the used market by estimating a multivariate regression model where the log of used 

phone prices is the dependent (outcome) variable. I capture the impact of the Break using a dummy 

variable which equals zero before the Break and one after the Break. I control for demand variables 

including the age of the model of the phone that is for sale, a time trend to capture overall demand 

trends in the market, and dummy variables that capture the relative value of different 

manufacturers, colors, storage sizes, conditions, and carriers of phones. I control for factors that 

may affect the supply of used phones, such as events related to releases of new models of phones 

by each manufacturer. I also control for macroeconomic factors such as GDP that may affect the

demand for and supply of used phones. Dummy variables for each month of the year capture 

seasonal effects in demand and supply, and I include indicators for the weeks surrounding 

Christmas each year to account for holiday demand effects. I also control for several promotions 

run for the iPhone 4 and iPhone 4S. 

IV.C. Model Results

46. Table 1 presents results from my multivariate regression model. Column 1 presents 

the model results using data from AT&T. Column 2 presents results using data from Best Buy, 

and column 3 presents results using the EcoATM data. In each regression, I use the log of Sale 

Price as the dependent variable. The variable denoted “Impact” measures the effect of the Break 

on the prices of used iPhone 4 and 4S models relative to other phone models and controlling for 

other demand and supply factors. The coefficient on Impact is negative and highly significant in 

all three columns. 

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TABLE 1

MULTIVARIATE REGRESSION MODEL RESULTS FOR PERCENT DIMINUTION IN 

VALUE RESULTING FROM THE BREAK

(1) (2) (3)

Dependent Variable: 

Log Resale Price AT&T Best Buy ecoATM

Impact -0.122*** -0.136*** -0.267***

(0.001) (0.002) (0.003)

Log Time -0.240*** -0.033*** -0.268***

(0.001) (0.001) (0.005)

Log Model Age -0.194*** -0.030*** -0.135***

(0.001) (0.002) (0.003)

Log US GDP -22.047*** -1.618*** -28.835***

(0.038) (0.120) (0.190)

Observations 6,761,984 1,941,517 2,051,892

R-squared 0.930 0.787 0.767

Percent Diminution 

in Value -11.45% -12.67% -23.45%

Notes:

Standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10.

All regression specifications include: monthly controls, Christmas season controls, controls for 

new releases of Apple and Samsung phones, controls for promotions of iPhone 4 and iPhone 4s, 

and fixed effects for the full interaction of phone model, phone condition, phone carrier, and 

phone storage size. For Best Buy, the specification also includes controls for color of the phone. 

47. The final row translates the Impact coefficients into estimates of percent diminution 

in value. The coefficient of -0.122 in the AT&T column translates into an 11.45% reduction in the 

value of the used iPhone 4 and iPhone 4S models due to the Break; the coefficient of -0.136 in the 

Best Buy column translates into a 12.67% reduction in value; and the coefficient of -0.267 in the

third column corresponds to a 23.45% reduction in value.

48. The signs and magnitudes of other coefficients in the model make economic sense. 

Prices of a used phone model decline over time and with the model’s age; the coefficients on the 

log of time and the log of age are both negative and significant. The coefficient on log of GDP is 

negative, implying that when GDP increases, prices of used phones fall. This indicates that used 

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I declare that the foregoing is true and correct to the best of my knowledge. Executed on this 13th

day of July at _________ Providence, RI ___________________. 

________________________ 

Justine S. Hastings 

 

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