Kelly Lear Nordby, Ph.D.[1]
Introduction
On November 14, 2022, the U.S. Supreme Court declined StarKist Company’s petition to review the Court of Appeals for the Ninth Circuit’s en banc opinion upholding certification of three subclasses of tuna purchasers in Olean Wholesale Grocery Cooperative, Inc. v. Bumble Bee Foods LLC, 31 F.4th 651, No. 19-56514 (9th Cir. Apr. 8, 2022) (“Olean”).[2] These decisions received much attention because defendants argued that almost a third (28%) of the direct purchaser plaintiffs (DPPs) class was not injured by the alleged price-fixing.[3],[4] The main issue addressed on appeal was whether the plaintiffs’ regression model, along with other expert evidence, was capable of showing classwide antitrust impact.[5]
Using a regression model that pooled data from all three defendants, the DPPs’ expert found that DPPs were overcharged, on average, by 10.28 percent.[6] This article discusses the Ninth Circuit’s analysis of the DPPs’ and defendants’ experts’ regression models, focusing on the approaches used to estimate the number of potentially uninjured class members.[7] I identify three findings and discuss key takeaways of the decision for economic analyses of the predominance requirement under Federal Rule of Civil Procedure 23(b)(3) for damages classes within the Ninth Circuit and possibly beyond.
First, the Ninth Circuit rejected any categorical argument that pooled regression models are inherently unreliable. Second, citing the Supreme Court’s decision in Tyson Foods, Inc. v. Bouaphakeo (“Tyson”),[8] the court found that a regression model can be relied upon to establish classwide liability if each class member could rely on the model “to show antitrust impact of any amount,”[9] if he or she had brought an individual action. Third, the court found that the district court did not err or abuse its discretion in concluding DPPs’ expert’s pooled regression model, along with other evidence, was “capable” of showing antitrust impact on a classwide basis.[10] In reaching this conclusion, the district court accepted DPPs’ expert’s argument that putative class members with insufficient data can rely on the pooled regression model’s results for other “similarly situated plaintiffs.”
In view of these findings, one can expect plaintiffs’ experts to continue to proffer pooled models as evidence of classwide impact. In such cases, it is important to test econometrically whether the evidence suggests the pooled model’s common overcharge estimate holds for all or nearly all putative class members. Additionally, when confronted with data limitations, experts may consider whether putative class members with sufficient data are representative of those with insufficient data. If the evidence suggests that the putative class members with limited data differ from those used to estimate the model in ways that would likely have affected prices, then one cannot simply assume the pooled model’s results are unbiased and hold for all putative class members.
Uninjured Plaintiffs and the “Fear” of “Monstrously Oversized Classes”
In its petition, StarKist asked the Supreme Court to address two important questions, specifically:
“[w]hether, and in what circumstances, the presence of uninjured class members precludes the certification of a class under Federal Rule of Civil Procedure 23(b)(3)”
and
“[w]hether, and in what circumstances, a plaintiff may rely on representative evidence such as averaging assumptions to establish classwide proof of injury to satisfy Rule 23’s requirements.”[11]
Several organizations also filed Amicus Curiae briefs urging the Supreme Court to review the en banc court’s decision and clarify Rule 23’s requirements.[12] The Supreme Court declined.
The prospect of allowing certification of classes that potentially contain large numbers of uninjured members is concerning because, as the dissent noted, class action cases rarely proceed to trial.[13] In Olean, dissenting Judge Lee, joined by Judge Kleinfeld, “fear[ed] that [the] decision will unleash a tidal wave of monstrously oversized classes designed to pressure and extract settlements,” with implications that extend to class actions beyond price-fixing.[14]
“Pooled” Regression Models and Potentially Uninjured Plaintiffs
When experts rely on regression models that “pool” (i.e., gather together)[15] sets of data for putative class members to examine the common question of whether there is evidence of antitrust impact in the form of higher prices paid by all or nearly all class members, criticisms related to the use of “averaging assumptions” and the presence of potentially uninjured plaintiffs are intertwined.
More specifically, in antitrust class actions that allege price-fixing, plaintiffs’ experts often employ multiple regression analysis where price is modeled as a function of supply and demand factors, as well as other factors that economic theory suggests influence prices, such as product, customer and market characteristics.[16] A “before-and-after” approach may be used to assess whether there is evidence of classwide impact by estimating the model using data from the period when the anticompetitive conduct allegedly occurred (the “impact period” or “class period”), as well as before and/or after the anticompetitive conduct. The period before and/or after the impact period serves as a benchmark for how prices would have been set without (“but-for”) the alleged misconduct.
An indicator variable (also known as a “dummy variable”) that is set equal to one during the impact period and zero otherwise can be included in the model to isolate and estimate the effect of the alleged conduct on prices over the alleged impact period.[17] In the simple model where a single coefficient is estimated on the impact period dummy variable using sales transaction data that is “pooled” across all putative class members, products, and/or defendants, the single coefficient represents the average effect of the anticompetitive conduct on prices during the impact period across all putative class members, products, and/or defendants, respectively.[18] If the estimated coefficient on the impact period dummy variable is statistically significant and the sign is consistent with the plaintiffs’ theory of harm, i.e., positive in a price fixing case, plaintiffs interpret the results as evidence of common impact (i.e., overcharges) for all putative class members.
Pooled regression models are often criticized by defendants and practitioners for employing “averaging assumptions”[19] that “mask individual differences”[20] and for presuming, rather than proving, common impact.[21] Thus, before assuming the single dummy variable pooled model is appropriate, experts should test econometrically whether the common overcharge estimate holds for all or nearly all putative class members.
Three approaches used commonly by experts to test estimates of average impact from pooled regression models include the following:
- Sub-regression approach: Estimate the pooled regression model using subsets of the data, such as by putative class member or by partitioning the proposed class into subclasses based on factors that economic theory predicts ex ante account for differences in prices.[22] Regressions estimated on subsets of the data are also referred to as “sub-regressions.”[23] The “sub-regression” approach results in a different overcharge estimate for each subset. In addition, this approach allows the coefficients on all other explanatory variables (“covariates”) in the regression model to vary by subset.[24] The expert can then test whether the coefficient on the impact period dummy variable for each subset is positive and statistically significant.[25] Additionally, if the coefficients on the other covariates in the sub-regressions vary widely across putative class members, then it is unlikely that a single model provides a common method of proof.[26]
- Interaction approach: Estimate the regression model using pooled data but allow the overcharge estimate to vary for each putative class member (or group of class members) by interacting the impact period dummy variable with dummy variables that identify each putative class member (or group).[27] Like the sub-regression approach, this approach results in a different overcharge estimate for each class member/group but it estimates a single regression using all the data, rather than running separate sub-regressions using subsets of the data. The expert can then test whether the coefficients on the impact-interaction dummy variables are consistent with each class member/group being injured (i.e., a statistically significant and positive).[28] Without additional interaction terms, this regression model imposes the restriction that the effects of the supply and demand factors on price were common across class members and were not affected by the alleged conduct.[29]
- Prediction approach: Use the estimated coefficients from the proffered regression model and the underlying data to obtain predicted prices without (“but-for”) the alleged anticompetitive conduct for each putative class member and then determine if the actual price charged exceeds the predicted but-for price.[30],[31] An actual price that is above the predicted but-for price is consistent with the plaintiff being overcharged. The expert may then report statistics such as the percentage of class members with at least one positive overcharge or the percentage with positive mean overcharges.[32]
With the first two approaches, estimated coefficients on the impact period dummy variables that are not statistically significant or that have a sign that contradicts the expected effect of the alleged conduct (e.g., a negative overcharge) are typically interpreted as evidence that not all class members were harmed. With the third approach, for any given putative class member, if the predicted but-for prices always exceed the actual prices paid, then the model would not support a conclusion of injury for that putative class member.
The Supreme Court’s Decision in Tyson and the Use of Statistical Evidence
The Ninth Circuit’s en banc decision relies heavily on the Supreme Court’s decision in Tyson, a wage-and-hour class action where the Court held that representative evidence (specifically averages estimated from a sample) could be used to show predominance and affirmed certification.[33] Tyson focused on the use of representative proof “to fill an evidentiary gap” created by the defendant, specifically due to defendants’ failure to keep adequate records.[34] In Tyson, the Supreme Court stated:
[W]hile petitioner, respondents, or their respective amici may urge adoption of broad and categorical rules governing the use of representative and statistical evidence in class actions, this case provides no occasion to do so. Whether a representative sample may be used to establish classwide liability will depend on the purpose for which the sample is being introduced and on the underlying cause of action.[35]
The Supreme Court further held that one way a sample could be relied upon for proving classwide liability is “by showing that each class member could have relied on that sample to establish liability if he or she had brought an individual action.”[36] As discussed below, the Ninth Circuit extended these concepts to statistical evidence (again averages) derived from regression analysis.
Key Takeaways for Economic Analyses of the Predominance Requirement
The Ninth Circuit’s en banc majority’s decision in Olean has at least three key takeaways for regression models used to assess classwide antitrust impact.
1. “[A]ny categorical argument that a pooled regression model cannot control for…individualized differences among class members must be rejected.”[37]
The en banc court rejected any argument that pooled regression models “involve improper ‘averaging assumptions’ and therefore are inherently unreliable,”[38] noting that Tyson rejected “any categorical exclusion of representative or statistical evidence.”[39] Additionally, the court stated that, “any categorical argument that a pooled regression model cannot control for variables relating to the individualized differences among class members must be rejected,” and that, in antitrust cases, “regression models have been widely accepted as a generally reliable econometric technique to control for the effects of the differences among class members and isolate the impact of the alleged antitrust violations on the prices paid by class members.”[40]
In the wake of this decision, one can expect plaintiffs’ experts to continue to proffer pooled models as evidence of classwide impact. As always, it will be important to examine whether the model correctly measures and controls for individualized differences and other factors that affect prices.[41] For instance, it may be that certain explanatory variables in the model capture observable differences believed to influence prices that vary by putative class member (or groups of putative class members), such as customer type or geographic location.[42] Using prices that account for promotional credits, discounts and rebates would also enable one to account for individual customers’ bargaining power.
However, a pooled model that specifies a single overcharge estimate does not control for possible differences in the effects of the alleged price-fixing across putative class members over the impact period. As discussed above, the sub-regression and interaction approaches allow one to test econometrically whether the single coefficient dummy variable model applies to all putative class members.
In Olean, both sides’ experts proffered alternative models to test whether the data suggest the single overcharge estimate is valid for all or nearly all putative class members. Defendants’ expert revised the DPPs’ expert’s regression model to allow the overcharge estimate to vary by putative class member and reported that, of the 604 direct purchasers, 169 direct purchasers (28 percent) could not rely on the model to show a positive, statistically significant overcharge.[43] In addition, the DPPs’ expert allowed the overcharge estimate to vary by customer type and found large, statistically significant overcharges for every customer type.[44] The description of the evidence suggests both experts used the interaction approach (#2) described above to examine whether overcharges differ by putative class member or class member type, respectively, although the sub-regression approach (#1) could achieve the same objective.[45]
Economists recognize that the distribution of such individual- or group-specific overcharge estimates can inform the question of classwide impact.[46] If the distribution of the plaintiff-specific overcharge estimates has a large variance, such that the estimates differ in magnitude and/or sign, then there may still be a substantial number of uninjured plaintiffs.[47]
When certifying the class, the district court credited the DPPs’ expert, who asserted that the defendants’ expert’s model could not provide any result for 61 direct purchasers (approximately 10 percent of the putative class) because they did not make any purchases during the benchmark period, and that many other purchasers had too few transactions to generate statistically significant results using any regression model.[48] Thus, the Ninth Circuit concluded that, “[c]ontrary to the dissent’s claim… [defendants’ expert] did not show that 28% of the class potentially suffered no injury.”[49]
2. A regression model can be relied upon for proving classwide liability if each class member could have relied on the model to establish liability in an individual action.
The Ninth Circuit noted that Tyson established “the rule” that plaintiffs could satisfy their Rule 23(b)(3) requirement if “‘each class member could have relied on [the plaintiffs’ evidence] to establish liability if he or she had brought an individual action,’ and the evidence ‘could have sustained a reasonable jury finding’ on the merits of a common question.”[50] In Olean, the en banc court extended the Tyson “rule” to expert evidence derived from regression models.
More specifically, in Olean, the defendants argued that the pooled model could not sustain liability in individual proceedings because individual plaintiffs pursuing their own claims showed overcharges both above and below the average (10.28 percent).[51] The en banc court rejected the defendants’ argument, noting that, “[f]or purposes of determining whether each member of the DPP class can rely on the model to prove antitrust impact,” the “question is whether each member of the class can rely on [plaintiffs’ expert’s] model to show antitrust impact of any amount.”[52] The court recognized that,
“[w]hile individualized differences among the overcharges imposed on each purchaser may require a court to determine damages on an individualized basis…, such a task would not undermine the regression model’s ability to provide evidence of common impact. Accordingly, we reject the Tuna Suppliers’ argument that the regression model could not sustain liability in individual proceedings.”[53]
When rebutting defendants’ expert’s criticisms, DPPs’ expert examined the pooled regression model’s predictions for each member of the class (i.e., approach #3 above) and found that 94.5 percent of purchasers made at least one purchase at an inflated price.[54] The en banc court stated that this “again provided further evidence that the conspiracy had a common impact on all or nearly all the members of the DPP class.”[55] However, defendants’ expert claimed the calculation was “misleading” because it was premised on the 10.28 percent average overcharge estimate.[56] In addition, DPPs’ expert examined the predicted prices using the defendants’ expert’s revised model and found positive overcharges for 94 percent of customers with any result.[57] Although these results could be interpreted as suggesting that as much as 6 percent of the DPP class was uninjured, the court did not address this issue because defendants did not argue that this result precludes certification.[58] Other courts have found that 5 or 6 percent “constitutes the outer limits of a de minimis number” of uninjured plaintiffs who may be included in a certified class.[59]
Thus, when determining whether a proposed model is capable of showing common impact for all class members, going forward, courts may place greater weight on analyses of but-for prices and overcharges predicted by regression models for individual class members (approach #3) over analytical approaches that provide individual-specific overcharge estimates (approaches #1 or #2 above) when the latter regression models are vulnerable to small sample size criticisms.
3. Putative class members with limited data can rely on the regression model’s results for “similarly situated” plaintiffs.
In Olean, the en banc court disagreed with the defendants’ and the dissent’s contention that the district court failed to resolve a dispute among the parties as to whether 28 percent of the putative class were not injured.[60] The en banc majority held that the defendants and the dissent “mischaracterize[d] the import” of defendants’ expert’s findings,[61] and concluded that defendants’ expert “did not make a factual finding that 28 percent” of the DPP class were uninjured.[62] Rather, the en banc majority found that the district court “resolved [the] methodological dispute…in favor of [plaintiffs’ expert] by crediting [plaintiffs’ expert’s] rebuttal that even class members with limited transactions during the class period can rely on the pooled regression model as evidence of impact on similarly situated class members.”[63] The en banc court concluded that “the district court determined that [plaintiffs’ expert’s] pooled regression model was capable of showing that the DPP class members suffered antitrust impact on a class-wide basis.”[64]
This finding that putative class members with limited data (i.e., few transactions) can rely on the pooled model’s results as evidence of impact is noteworthy because it raises some of the same questions as the presence of potentially uninjured plaintiffs. For example, what is the maximum percentage of putative class members for which the proposed model cannot yield any result (or any statistically significant result) and still be deemed capable of showing classwide impact? Here, the court permitted at least 10 percent (i.e., those with no benchmark period transaction) and perhaps as high as 28 percent (i.e., those who could not rely on the model to show a positive, statistically significant overcharge).[65]
Additionally, the en banc majority remarked that the defendants’ reference to DPPs’ expert’s regression model as “representative evidence” was “imprecise” because
“representative evidence generally refers to a sample that represents the class as a whole…. By contrast, a regression model analyzes available data to determine the degree to which a known variable, such as collusion, affected an unknown variable, such as price, while eliminating the effect of other variables.”[66]
While it is true regression analysis controls for the effects of the explanatory variables in the model, this statement overlooks that regression coefficients are also estimates of population parameters that measure the linear relationships between the dependent variable and the covariates in the model.[67] Thus, like sample estimates, regression coefficients can be affected by the underlying data if the data are incomplete in a way that is not random.
More specifically, a non-random sample will only yield unbiased coefficients if the model is (i) the same for all subsets of the population (i.e., those included and those excluded from the data) and (ii) the regression data exhibit variation in the covariates that is comparable to what is observed in the population, including encompassing observations for which there is insufficient data to run the regression.[68] However, the first assumption is exactly what plaintiffs’ analysis aims to prove.[69]
Experts therefore may seek to examine whether class members with insufficient data differ from those with sufficient data in order to assess whether the evidence indicates they are, in fact, “similarly situated.” In other words, does the evidence suggest the putative class members with sufficient data are representative of those with insufficient data? If the evidence suggests this assumption does not hold, then it may not be appropriate to assume the pooled model’s results for a subset of plaintiffs hold for all putative class members when evaluating the question of common impact. Defendants may therefore seek to show that exclusion of certain putative class members from the regression biases the results.[70]
Conclusion
In sum, Olean has at least three key takeaways for expert regression analysis of the predominance requirement in antitrust class actions within the Ninth Circuit and possibly beyond. First, it suggests pooled models will continue to be proffered as common evidence of impact and that plaintiffs’ experts may assert that class members with limited data can rely on the pooled model’s results as evidence of impact. Before relying on a pooled model’s single overcharge estimate, experts should test econometrically whether the estimate holds for all or nearly all putative class members. Second, going forward, courts may place greater weight on analyses that examine a regression model’s predictions of the impact on individual putative class members, rather than estimates of impact from regression analyses based on limited data for individual plaintiffs (or subsets of plaintiffs). Third, when determining whether individual plaintiffs with limited transaction data can rely on the regression model to prove impact, courts may consider whether the facts and data indicate these class members differ systematically from those with sufficient data, in order to assess whether the evidence suggests they are “similarly situated.” If the evidence suggests plaintiffs with limited data differ from those used to estimate the regression in ways that may have insulated them from the alleged conduct, one cannot merely assume the regression model is capable of reliably demonstrating antitrust impact for all putative class members.
©2023. Published in Antitrust Committee Articles, December 19, 2023, by the American Bar Association. Reproduced with permission, all rights reserved. This information or any portion thereof may not be copied or disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association or the copyright holder.
[1] The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC., its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.
[2] StarKist Co., et al., Petitioners, v. Olean Wholesale Grocery Cooperative, Inc., On Behalf of Itself and All Others Similarly Situated, et al., Supreme Court of the United States, No. 22-131, Opinion, November 14, 2022.
[3] See, e.g., Nate Raymond, “U.S. Supreme Court rejects StarKist’s tuna price-fixing class action appeal,” Reuters, November 14, 2022, last accessed on November 18, 2022, available at https://www.reuters.com/legal/us-supreme-court-rejects-starkists-tuna-price-fixing-class-action-appeal-2022-11-14/; StarKist Co.; Dongwon Industries Co., Ltd., Petitioners, v. Olean Wholesale Grocery Cooperative, Inc., et al., Respondents, Petition for a Writ of Certiorari, August 8, 2022, at 2.
[4] In 2017 and 2018, Bumble Bee, StarKist, and three tuna industry executives pleaded guilty to the conspiracy in a parallel U.S. Department of Justice investigation. Olean, at 662.
[5] Olean, at 661-62, 670. In addition to a regression model, DPPs’ expert also put forth a “pricing correlation test” to evaluate if quantitative evidence supported the theory that the defendants’ collusive behavior affected prices on a classwide basis. Id. at 671. The correlation test was not the main focus of the en banc court’s review.
[6] Variables in the model included product characteristics, supplier costs, customer type, and factors related to consumer preferences and demand such as disposable income, geography, and seasonality. Olean, at 671.
[7] For a summary of other criticisms of DPPs’ expert’s model raised by defendants, see Olean, at 674.
[8] See, e.g., Olean, at 667-68 (citing Tyson Foods, Inc. v. Bouaphakeo, 577 U.S. 450, 453, 455, 456-57, 459-60 (2016)).
[9] Id. at 679.
[10] Id. at 676.
[11] StarKist Co.; Dongwon Industries Co., Ltd., Petitioners, v. Olean Wholesale Grocery Cooperative, Inc., et al., Respondents, Petition for a Writ of Certiorari, August 8, 2022.
[12] StarKist Co., Dongwon Industries Co., Ltd., et al., Petitioners, v. Olean Wholesale Grocery Cooperative, Inc., et al., Respondents, No. 22-131, Brief of the Chamber of Commerce of the United States of America, the Pharmaceutical Research and Manufacturers Association of America, and the Software & Information Industry Association as Amicus Curiae in Support of the Petition, September 9, 2022; Brief of the Computer & Communications Industry Association as Amicus Curiae in Support of Petitioner, September 9, 2022; Brief of Washington Legal Foundation as Amicus Curiae in Support of Petitioners, September 8, 2022.
[13] Olean, at 685.
[14] Id. at 692.
[15] In statistics, “pooling” is “the practice of gathering together small sets of data that are assumed to have the same value of a characteristic (e.g., a mean) and using the combined larger set (the ‘pool’) to obtain a more precise estimate of that characteristic.” Stanley N. Deming, “Statistics in the Laboratory: Pooling,” American Laboratory, October 24, 2018, available at https://americanlaboratory.com/353521-Statistics-in-the-Laboratory-Pooling/.
[16] This type of regression equation is commonly known as a “reduced-form” model. See, e.g., Jonathan B. Baker and Daniel L. Rubinfeld. “Empirical Methods in Antitrust Litigation: Review and Critique,” American Law and Economics Review, 1999, 1(1), 386-435, at 391-398.
[17] See, e.g., Justin McCrary and Daniel L. Rubinfeld. “Measuring Benchmark Damages in Antitrust Litigation,” Journal of Econometric Methods, 2014, 3(1), 63-74, at 63.
[18] American Bar Association, Antitrust Law Section. Econometrics: Legal, Practical, and Technical Issues, 2nd ed., 2014, at 356-57.
[19] Olean, at 677 (“According to the Tuna Suppliers, [plaintiffs’ expert’s] evidence is not a permissible method of proving class-wide liability because the regression model uses ‘averaging assumptions,’ meaning that the model assumes that all DPPs were overcharged by the same uniform percentage (10.28 percent). These averaging assumptions, according to the Tuna Suppliers, ‘paper over’ individualized differences among class members.”).
[20] See, e.g., In re Processed Egg Products Antitrust Litig., 312 F.R.D. 171, 186 (E.D. Pa. 2015) (“Defendants criticize [expert’s] use of averages, because averages ‘by their very nature mask individual differences between purchasers and preclude the ability to determine any impact on any of the plaintiffs.’” (citation omitted)); In re Pre-Filled Propane Tank Antitrust Litigation, 14-02567-MD-W-GAF, 2021 WL 5632089, at 8 (W.D. Mo. Nov. 9, 2021) (“Ferrellgas argues that, even accepting [Plaintiffs’ expert’s] regression model, which pools all data across all customers to estimate an ‘average’ overcharge, the model masks the pricing variation that exists in the market and therefore is a methodology that is designed to assume, rather than to assess, the ‘common impact’ that Plaintiffs have the burden of showing at class certification.”).
[21] Laila Haider, John H. Johnson, and Gregory K. Leonard, “Turning Daubert on Its Head: Efforts to Banish Hypothesis Testing in Antitrust Class Actions,” Antitrust, Spring 2016, 30(2), 53-59, at fn. 22 (collecting the literature that discusses this failure of the single dummy variable overcharge regression model).
[22] See, e.g., John H. Johnson and Gregory K. Leonard. “Economics and the Rigorous Analysis of Class Certification in Antitrust Cases,” Journal of Competition Law and Economics, 2007, 3(3), 341-356, at 350-51; Bret M. Dickey and Daniel L. Rubinfeld, “Antitrust Class Certification: Towards an Economic Framework,” NYU Annual Survey of American Law, March 2011, 66, 459-486, at 463-65 (discussing subclasses that account for individual differences such as the volume of product purchased or the geographical location (or market) in which the product was purchased); and Laila Haider, John H. Johnson, and Gregory K. Leonard, “Turning Daubert on Its Head: Efforts to Banish Hypothesis Testing in Antitrust Class Actions,” Antitrust, Spring 2016, 30(2), 53-59, at 57 (discussing partitioning customers by size, geography, supplier, customer, or end use).
[23] For example, the magistrate judge in In re Air Cargo Shipping Servs. Antitrust Litigation referred to regressions estimated “using only the data for specific subsets of the class” as “sub-regressions.” In re Air Cargo Shipping Servs. Antitrust Litig., Master File No. 06-MD-1175 (JG)(VVP), at 28 (E.D.N.Y. Oct. 15, 2014).
[24] Haider et al. note, “[i] t has become standard in modern empirical economics to recognize and, where possible, account for, possible heterogeneity across economic agents (e.g., customers and suppliers) in their responses to changes in economic factors.” Laila Haider, John H. Johnson, and Gregory K. Leonard, “Turning Daubert on Its Head: Efforts to Banish Hypothesis Testing in Antitrust Class Actions,” Antitrust, Spring 2016, 30(2), 53-59, at 55 (citation omitted).
[25] See, e.g., In re Processed Egg Products Antitrust Litig., 312 F.R.D. 171, 188 (E.D. Pa. 2015) (“Defendants argue that [plaintiffs’ expert’s] model is flawed because their [defendants’] expert…ran [plaintiffs’ expert’s] regression on various subsets of the data and found inconsistent results.”); In re Pre-Filled Propane Tank Antitrust Litigation, 14-02567-MD-W-GAF, 2021 WL 5632089, at 8 (W.D. Mo. 2021) (“disaggregation demonstrate that significant proportions (over 30%) of the at-issue class sales did not exhibit an overcharge….”); In re Plastics Additives, 03-CV-2038, 2010 WL 3431837, at *16 (E.D. Pa. Aug. 31, 2010) (running individual regressions finding “no evidence of a statistically significant increase in prices as a result of the alleged conduct” for 81 of 115 customers that purchased the products in both the conspiracy and the post-conspiracy periods and had “sufficient observations.”).
[26] See, e.g., Laila Haider, John H. Johnson, and Gregory K. Leonard, “Turning Daubert on Its Head: Efforts to Banish Hypothesis Testing in Antitrust Class Actions,” Antitrust, Spring 2016, 30(2), 53-59, at 55 (“If the set of explanatory variables or the effects of the explanatory variables differ across customer-supplier combinations for a proposed class of customers, in general there is not a single common regression model that applies to all.”)
[27] The collusive period dummy variable can be "interacted” with indicator variables that identify each class member (or group) by multiplying the collusive period dummy variable by the dummy variables that identify each class member (or group).
[28] It is also possible that the anticompetitive conduct affects the coefficients on other explanatory variables in the model. This can be tested by interacting the supply and/or demand variables with the impact period dummy variable. See, e.g., Justin McCrary and Daniel L. Rubinfeld. “Measuring Benchmark Damages in Antitrust Litigation,” Journal of Econometric Methods, 2014, 3(1), 63-74, at 64.
[29] If the effects of certain supply and/or demand factors are expected to differ by putative class member(s), the class member specific indicator variables can be interacted with these explanatory variables to relax this restriction.
[30] With a dummy variable model, one method for calculating the predicted but-for price is to use the actual values of the explanatory variables and the estimated coefficients, setting the indicator variable for the collusive period to zero. The upper limit of a 95% confidence interval around the predicted value may then be used as the but-for price. See, e.g., Michael O. Finkelstein and Hans Levenbach. “Regression Estimates of Damage in Price-Fixing Cases,” Law and Contemporary Problems, 1983, 46(4), 145-169, at 164.
[31] Some researchers refer to this approach as a “two-step” approach. See, e.g., Jamie McClave Baldwin and James T. McClave, Infotech Consulting. “Clarifying Common Misconceptions About the Two-Step Econometric Method for Establishing Common Impact,” ABA Antitrust Law Section, Spring Meeting, March 2023, at 2-3.
[32] Id. at 3.
[33] See, e.g., Olean, at 665 (citing Tyson Foods, Inc. v. Bouaphakeo, 577 U.S. 454-55, 458-59 (2016)).
[34] Tyson, at 450 and 456.
[35] Tyson, at 459-60.
[36] Tyson, at 455.
[37] Olean, at 677.
[38] Id. at 677.
[39] Ibid. (citing Tyson, at 459-460).
[40] Ibid. (citations omitted).
[41] For example, in Olean, defendants criticized plaintiffs’ expert for using a cost index instead of defendants’ actual cost data. Olean at 674.
[42] In Olean, plaintiffs’ expert’s model controlled for customer type. Id. at 671.
[43] Olean, at 673. See also In re Packaged Seafood Products Antitrust Litigation, 332 F.R.D. 308, at 323-324 (2019).
[44] Id. at 672 and n.17.
[45] Id. at 672 and n.17 (“[Plaintiffs’ expert] changed the model to evaluate the overcharge based on customer types [Retail, Club, Special Market, Food Service, Mass Merchandise, Discount, and e-Commerce].”) and 673 (“[Defendants’ expert] changed the model to evaluate overcharge based on each individual customer.”). See also In re Packaged Seafood Products Antitrust Litigation, 332 F.R.D. 308, at 323 (2019) (“[Defendants’ expert] uses the same variables and all of the 1.5 million data observations that [Plaintiffs’ expert’s] model includes but allows the overcharge coefficient to vary for each Class member.”) (citations omitted).
[46] See, e.g., Bret M. Dickey and Daniel L. Rubinfeld, “Antitrust Class Certification: Towards an Economic Framework,” NYU Annual Survey of American Law, March 2011, 66, 459-486, at 463.
[47] See, e.g., American Bar Association, Antitrust Law Section. Econometrics: Legal, Practical, and Technical Issues, 2nd ed., 2014, at 358 (“estimated effects that vary widely or are nonsensical would suggest that the alleged misconduct did not result in common impact for all members of the proposed class.”); and Bret M. Dickey and Daniel L. Rubinfeld, “Antitrust Class Certification: Towards an Economic Framework,” NYU Annual Survey of American Law, March 2011, 66, 459-486, at 463 (“It is important to note that even if there is a measurable average adverse effect of the conspiracy, if one relies on the estimation of a single regression, the possibility that a substantial number of putative class members will not have been injured cannot be ruled out. The central empirical issue surrounds the distribution of the [individual-specific overcharge estimates].”).
[48] Olean, at 674-75 and n.21.
[49] Olean, at 673, n.21.
[50] Olean, at 667 (citing Tyson, at 455).
[51] Olean, at 679.
[52] Ibid. (emphasis added).
[53] Id. at 679 (citing Tyson, at 455).
[54] Id. at 672.
[55] Ibid.
[56] Id. at 673.
[57] In re Packaged Seafood Products Antitrust Litigation, 332 F.R.D. 308 (2019), at 324.
[58] Id. at n.18.
[59] In re: Rail Freight Fuel Surcharge Antitrust Litigation, MDL No. 1869, 934 F.3d 619, at 625 (D.C. Cir. 2019) (citing Rail Freight II, 292 F. Supp. 3d at 137-138).
[60] Id. at 680.
[61] Ibid.
[62] Ibid. (emphasis added).
[63] Olean, at 680-81. See also In re Packaged Seafood Products Antitrust Litigation, 332 F.R.D. 308 (2019), 324 (“Without the results of the regression model, Defendants state that the Class members without sufficient data to produce results will have to prove their cases using evidence not common to the Class. But these Class members would still be able to point to the same econometric model as it pertains to similarly situated Class members as proof. This, along with the record evidence, guilty pleas, and market characteristics, shows that all Class members will still use common evidence and that common questions will continue to predominate over the case.”) (citations omitted).
[64] Id. at 680-81.
[65] The court also noted that, there was no factual dispute that the defendants engaged in a price-fixing scheme that affected the entire industry. Olean, at 681.
[66] Olean, at n.24 (citing Tyson at 454-55, emphasis added).
[67] Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 5th ed., 2013, South-Western, at 31 and 45.
[68] See, e.g., Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 5th ed., 2013, South-Western, at 324-26.
[69] This predicament wherein plaintiffs often do not have access to all the individual data needed to demonstrate that such data are not necessary to prove injury to all class members has been coined the “common proof paradox.” John H. Johnson and Gregory K. Leonard. “Economics and the Rigorous Analysis of Class Certification in Antitrust Cases,” Journal of Competition Law and Economics, 2007, 3(3), 341-356, at 344.
[70] See, e.g., John H. Johnson and Gregory K. Leonard. “Economics and the Rigorous Analysis of Class Certification in Antitrust Cases,” Journal of Competition Law and Economics, 2007, 3(3), 341-356, at 352-53 (illustrating how excluding a group of putative class members from a regression model can induce misspecification of the model and biased coefficient estimates, including a biased overcharge estimate).
