Abstract
Economists in the U.S. Federal Trade Commission’s Bureau of Economics perform economic analysis in support of the Commission’s dual missions to protect consumers and competition by preventing anticompetitive, deceptive, and unfair business practices through law enforcement, advocacy, and education. This article first presents summaries of analyses that FTC economists performed to estimate the consumer harm from two different types of deception that involved misleading information about lease terms and suppression of negative product reviews. The essay next turns to economic analyses of mergers: We first consider the vertical issues that arose in a semiconductor merger; and then we provide a discussion of how complementarity between hospitals may affect the analysis of hospital mergers.
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Notes
For instance, the FTC announced in January of 2021 a study of physician group and healthcare facility acquisitions. The Commission voted to use compulsory process to acquire data from six major health insurers to study the effects of physician group and healthcare facility consolidation that occurred from 2015 through 2020; see https://www.ftc.gov/news-events/news/press-releases/2021/01/ftc-study-impact-physician-group-healthcare-facility-mergers.
Copies of the papers that were presented along with a video of the conference are available at https://www.ftc.gov/news-events/events/2021/11/fourteenth-annual-federal-trade-commission-microeconomics-conference.
Details are available at: https://www.ftc.gov/news-events/events/2022/11/fifteenth-annual-federal-trade-commission-microeconomics-conference. Should public health conditions dictate, this conference may need to be held virtually.
See https://www.ftc.gov/legal-library/browse/cases-proceedings/182-3127-progressive-leasing for details on the case.
Financially constrained customers necessarily faced a lease cost that was significantly greater than the retail price. Because of that, it is likely that their largest injury stems from the decision to take the lease because they did not understand the lease cost, when they would not have leased from Progressive if they had known the true lease cost. Consumers who would not have taken a Progressive lease if they had been aware of the true cost could have forgone purchasing the good altogether or used an alternative financing option if a less expensive one was available to them. We estimated that injury in a different manner which we do not describe here.
We could not determine whether one of these consumers would have used the 90-day option or would have forgone the lease financing altogether. Because both of these alternatives were significantly cheaper than an EBO, in practice the difference between the EBO and 90-day cost captured the majority of injury. We also calculated and reported to the Commission an injury estimate that included the 90-day fee.
A few consumers took multiple Progressive leases, but these consumers were less likely to be injured–they may have been satisfied by the first Progressive experience, hence were less likely to have been misled–so in analyzing injury we restricted the data to one-time consumers of Progressive.
A probit model yielded similar results.
Raval (2020) analyzed both complaint data and victim lists from fraud cases, and found considerable variation in the ratio of complaints per victim across cases.
See FTC press releases: “FTC Approves Final Consent Agreement with Sunday Riley Modern Skincare, LLC,” November 2020; “FTC Puts Hundreds of Businesses on Notice about Fake Reviews and Other Misleading Endorsements,” October 2021; “Fashion Nova will Pay $4.2 Million as part of Settlement of FTC Allegations it Blocked Negative Reviews of Products,” January 2022.
Existing research largely relies on partnerships with online platforms as well as tedious manual data collection. Luca and Zervas (2016) partnered with Yelp to study fake reviews that were identified by Yelp’s machine learning algorithm. He et al. (2022) collected data manually from Facebook groups that facilitate sales of fake Amazon reviews.
See Wood and Stone (2018).
See FTC press release “FTC Finalizes Order with Fashion Nova Over Allegations it Blocked Negative Reviews” March 2022.
We also tried different cut offs for high- and low-rated products, and the results remained similar.
Unfortunately, by including average star ratings instead of individual reviews, the data prevented us from identifying products with only four stars or above reviews, which would have been relatively unaffected by the review posting.
Implicit in this exposition of the analysis is an assumption that the release of reviews did not materially impact the number of consumers considering purchases at this retailer.
This phrase refers to the Friday immediately after Thanksgiving in November, when retailers have often accumulated enough operating surpluses so as to have covered their annual fixed costs and are thenceforth operating “in the black”. It is also a traditional day for special low-price limited-time “sales”.
The complaint, http://www.ftc.gov/system/files/documents/cases/d09404_part_3_complaint_public_version.pdf, also alleged a reduction in innovation through two channels: the sharing of competitively sensitive information between NVIDIA and its rivals; and a change in the future path of innovation of Arm to favor technologies that do not compete with NVIDIA.
See e.g. Pittman (2021).
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404-answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404-answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Merger simulation could shed light on the equilibrium interaction of both effects (Das Varma and De Stefano, 2020).
Note that this assumes that Arm incurs no marginal costs to license its IP. Otherwise, Arm’s margin would be smaller than the 1–2% that it charged in royalties. This would make foreclosure more likely.
According to the FTC complaint, “Arm’s licensing model is based on upfront license fees and royalties.” Therefore, one could argue that some of Arm’s upfront license fees could be pro-rated to the marginal unit of IP. In this case, Arm’s margin would be higher, which would make foreclosure less likely.
See FTC Complaint at Sect. 2.
See FTC Complaint at 65 and 85.
Without vertical integration, it is in Arm’s interest to provide continuing support to all firms, because, among other things, the relatively low margin is likely similar for all licensees.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404-answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404-answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Note that the parties did not articulate specific types of investment that would be undertaken in their response to the complaint. https://www.ftc.gov/system/files/documents/cases/d09404-answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404_-_answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404_-_answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
The applicability of Israel and O’Brien’s (2021) model to the NVIDIA/Arm merger depends, in part, on how well the details of that industry match the assumptions of their model.
Answer and Defenses of Respondents to FTC Complaint https://www.ftc.gov/system/files/documents/cases/d09404_-_answer_and_defenses_of_respondentsv_nvidia_corporationsoftbank_group_corp._and_arm_ltd.pdf.
Substitution from the perspective of insurers and substitution from the perspectives of patients are related, but distinct, concepts in bargaining theory. As will be discussed below, the more that patients view two hospitals as substitutes at the point of service, the more likely it is that the hospitals will be substitutes from the perspective of insurers in bilateral bargaining. The distinction between substitution for insurers in bilateral bargaining and substitution for patients at the point of service may be important. For example, it could be the case two hospitals are complements from the perspective of an insurer in bilateral bargaining and yet the hospitals compete directly for patients at the point of service.
Throughout this discussion, we focus on the prices that are negotiated between providers and insurers, as opposed to the prices that insurers charge to consumers and employers.
The literature often refers to the ‘concavity’, in the case of substitutes, or ‘convexity’, in the case of complements, of the insurer’s profits in the providers A and B to describe these conditions.
This definition of complementarity and the convexity definition in the previous paragraph are equivalent if the insurer’s profit if it excluded both A and B is zero.
Note that neither A nor B is a ‘must-have’ provider in the literal sense since the insurer would earn a positive profit if it excluded either A or B while including the other. However, the merged provider AB would be a ‘must-have’ in that the insurer would earn zero profit if it excluded both A and B.
To connect this example with the notion of convexity, note that the value-added of A is only 30 if B is not in network but 80 if B is in network. Similarly, the value-added of B is only 20 if A is not in network but 70 if A is in network. Hence, the presence of A or B in the network increases the value-added of the other.
As a simple example of bargaining substitutes, suppose that the insurer would earn 100 if it had both A and B in network. Further suppose that the insurer would earn 90 if it had A but not B in network, and 80 if it had B but not A in network. In this situation, A and B are substitutes from the perspective of the insurer since 100 < 90 + 80. To connect this with the notion of concavity, note that the value-added of A is 90 if B is not in network but only 20 if B is in network. Similarly, the value-added of B is 80 if A is not in network, but only 10 if A is in network. Hence, the presence of A or B in the network decreases the value-added of the other.
Commonly, the value of α is assumed to be one-half. Under this assumption, the provider and the insurer evenly (50–50) split the joint benefit of reaching an agreement.
Note that the merged provider AB could negotiate more than either A or B could on its own. That is, α100 > α70 and α100 > α80. Hence, AB has more bargaining leverage than either A or B has separately. But that is not the relevant comparison. Rather, the relevant comparison is whether AB has more or less bargaining leverage than the bargaining leverage of A plus the bargaining leverage of B.
The assumption that the proportion α is not altered by the merger is crucial to this result. Lewis and Pflum (2015, 2017) explore theoretical and empirical evidence that mergers increase the proportion α that determines the negotiated payment to the merging providers. Such an increase in the proportion α would make it less likely that a merger of bargaining complements would reduce prices.
In the simulations in Balan and Brand (forthcoming), mergers that lead to price decreases always reduce the combined profits of the merging hospital systems.
In discussing the difference between substitution from the perspective of patients and substitution from the perspective of insurers in ProMedica, the Sixth Circuit noted: “ProMedica also argues that MCOs, rather than patients, are the relevant consumers here, and that the Commission therefore erred by ‘assess[ing] substitutability from the patients’ perspective.’ But this is an argument about semantics. MCOs assemble networks based primarily upon patients’ preferences, not their own; and thus the extent to which an MCO regards ProMedica and St. Luke’s as close substitutes depends upon the extent to which the MCO’s members do.”.
In extreme cases, the recapture rates may be close to one pre-merger, so any merger-induced increase would be small.
Peters (2014) considers the example of a merger between a hospital and physician group. Joint contracting post-merger could increase the bargaining leverage of the physician groups and the hospital even though they are not substitutes at the point of service. This is because more consumers would switch to another insurer if both the hospital and the physician group are excluded from the insurer’s provider network than if either the hospital or physician group (but not both) is excluded.
For an example of a merger in which the defendants made a powerful buyer argument in a bargaining setting see, Federal Trade Commission and State of North Dakota v. Sanford Health, Sanford Bismarck and Mid Dakota Clinic, P.C., IN THE UNITED STATES DISTRICT COURT FOR THE DISTRICT OF NORTH DAKOTA, MEMORANDUM OF DECISION, FINDINGS OF FACT, CONCLUSIONS OF LAW, AND ORDER, 12/15/2017 (https://www.ftc.gov/system/files/documents/cases/1710019_sanfordpiorder.pdf).
As defined in Garmon (2017), Balan and Brand (forthcoming), and elsewhere, $$GUPP{I}_{A}={Diversion}_{AB}\times Margi{n}_{B}\times \frac{{Price}_{B}}{Pric{e}_{A}}$$. The pre-merger bargaining leverage of B is measured in MarginB.
Fully insured health insurance plans are subject to margin regulation that requires they pay out at least 80-85% of their premiums as claims for clinical services and/or quality improvement. See, https://www.cms.gov/CCIIO/Programs-and-Initiatives/Health-Insurance-Market-Reforms/Medical-Loss-Ratio.
Saltzman (2019) found higher elasticities on the Affordable Care Act (ACA) exchanges in two states.
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Acknowledgements
We thank HA, MC, BE, JG, AL, RP, DR, DS, and AT: for helpful comments. The views that are expressed in this article are those of the authors, and do not necessarily reflect those of the Federal Trade Commission or any of the individual Commissioners. Taragin was an FTC employee when this article was drafted. The analysis and conclusions set forth are those of the authors, and do not indicate concurrence by other members of the Federal Reserve’s research staff or its Board of Governors.
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Michael Vita, Keith Brand, Miriam Larson-Koester, Nathan Petek, and Charles Taragin are the authors of the text discussing antitrust enforcement. William Violette and Daniel H. Wood are the authors of the text discussing consumer protection enforcement.
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Vita, M., Brand, K., Larson-Koester, M. et al. Economics at the FTC: Estimating Harm from Deception and Analyzing Mergers. Rev Ind Organ 61, 405–438 (2022). https://doi.org/10.1007/s11151-022-09883-w
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DOI: https://doi.org/10.1007/s11151-022-09883-w