Innovation and the institutional design of merger control.

AuthorJennejohn, Matthew
  1. INTRODUCTION II. MAKING ENFORCEMENT DECISIONS UNDER CONDITIONS OF UNCERTAIN A. The Error Cost Framework and Ex Ante Decision Rules B. Innovation and Experimentalist Institutional Design 1. Origins-American Pragmatism and Japanese Co-Design 2. Collaborative Innovation and Generative Contracting 3. Regulatory Experimentalism III. EXPERIMENTALIST MERGER REVIEW A. Fundamentals of the Antitrust Law Relating to Mergers B. Merger Review as an Innovation Problem 1. The Static Approach to Predicting Effects on Competition 2. Dynamic Merger Analysis and Its Implications a. The Model Uncertainty Problem b. The Filter Failure Problem c. When Model Ambiguity and "Filter Failure" Combine: Mergers Review as an Innovation Problem C. The HSR Act as Infrastructure for Collaborative Innovation 1. Substantive Flexibility 2. Iterative Information Exchange a. Learning Routines in Second Requests b. Further Evidence from Antitrust Provisions in Merger Agreements 3. Generative Merger Remedies D. Summary and Diagnosis IV. TOWARDS RESTRUCTURING INNOVATION PROCESSES IN MERGER REVIEW A. Reframing the Debate in Merger Review Policy B. Steps Toward a More Experimentalist Regime in Merger Review 1. Investing in Predictability by Enforcing Information Exchange a. Front-loading Bilateral Information Exchange b. Lowering the "Substantial Compliance" Hurdle c. Create a Potent Ratchet Effect 2. Managing Ambiguity Through Contingent Relief V. CONCLUSION I.INTRODUCTION

    Since Frank Easterbrook's seminal essay, The Limits of Antitrust, (1) viewing competition policy through what is known as the error cost framework has become common, although not universal, (2) among those engaged in the antitrust enterprise. From the error cost perspective, the central question of antitrust enforcement is whether intervention in a market will promote competition to an extent sufficient to offset its costs, which include not only the expenditure of limited agency resources but also the harm to market participants when ill-conceived enforcement decisions have anticompetitive effects of their own. (3) Recent scholarship has built upon that foundation, arguing that a decision theoretic approach should be adopted in order to rationalize the agencies' analyses of a variety of conduct. (4) Decision theory posits that enforcement choices can be improved by following an elegant algorithm: the benefits and costs of a decision under consideration are valued; the probabilities of those benefits and costs occurring are calculated; the valuations of the benefits and costs are then weighted by those probabilities; and finally the probability-weighted benefits and costs are then combined to determine whether the decision will result in a likely net benefit or detriment. (5)

    The decision theoretic approach introduces the question of how antitrust institutions should be designed to minimize costly decision making errors. Here, the focus tends to be upon substantive legal rules. For example, in response to uncertainty and its effects, (6) Easterbrook and others offer crafting simple filtering or safe harbor rules as a solution: they argue that presumptively removing certain classes of conduct--which either theory or experience teaches are not anticompetitive--from scrutiny reduces the scope for erroneous enforcement decisions. (7) A per se liability rule, employed in price fixing and other situations, is the classic example of such an ex ante filtering rule. That rule economizes on agency resources and reduces the likelihood of error by automatically categorizing conduct, rather than subjecting it to Rule of Reason analysis. (8)

    The utility of those filters decreases as uncertainty increases and business practices grow more heterogeneous. (9) Accurately applying decision theory is at times easier said than done. Particularly in markets characterized by high rates of technological innovation, determining whether intervention is justified presents a dilemma. Rapid technological change unsettles standard models of economic behavior, ambiguating the conceptual roadmaps agencies use in substantive antitrust analysis. That ambiguity increases the risk that targeted interventions miss their mark, causing more harm than good. Furthermore, the complexity of those markets counsels for discrete, surgical interventions, by which an enforcement agency can minimize the risk of ongoing entanglement. In the merger context, for example, the federal antitrust agencies (10) prefer definitive enforcement actions that reset the structure of a market, rather than directly regulating market participants' behavior over time. 11 In short, the federal agencies are caught between a rock and a hard place: they desire crisp, definitive solutions but crafting them under conditions of significant uncertainty is difficult.

    The focus on substantive filtering rules also overlooks more process-focused institutional design questions. Decision theory is a way of understanding the problem of deploying resources to interpret data, an issue that, in part, invokes procedural questions of how information flows should be structured between the parties involved in the enforcement process. Addressing such processual issues may require particular attention in some enforcement areas, such as merger review, where a more idiosyncratic regulatory regime has supplanted traditional litigation institutions in certain respects.

    In response, this Article focuses on another tool, complementary to filtering rules and safe harbors, for reducing error costs under conditions of uncertainty: structuring a learning process so as to better manage, or perhaps even resolve, uncertainty over a compressed period of time. (12) I explore the applicability of such a process to the antitrust law of mergers, a highly active area of antitrust enforcement that has undergone extensive institutional change over the past generation. I consider the possibility that a novel institutional response to the problem of harnessing uncertainty in merger control is in the process of unfolding. (13) This new institutional arrangement does not promise to clarify ex ante ambiguity, a difficult prospect given the current state of the art in industrial organization research. Rather, contemporary merger review follows a pattern found in other domains characterized by high uncertainty, such as inter-firm technology collaborations in private industry or environmental regulation in the public sector. Aspects of current merger review practice can be understood as an attempt to create a disciplined framework for agencies and merging parties to jointly explore uncertain decision landscapes, coupling that learning process with adaptive remedies that create greater ex post decision flexibility. In short, the approach introduces novel back-end mechanisms, which structure the investigative process and expand the room for remedial maneuver, in situations where ex ante accuracy is elusive. The result is a more reliable, deliberative process with respect to both merger analysis and remedy design that has the promise of providing some of the stability necessary for both more accurate agency decision making and business planning.

    I undertake this analysis because the uncertainty problem in enforcement decision making is one of the most pressing issues in contemporary antitrust policy. (14) As an example, consider what is at stake in the context of mergers & acquisitions (M&A). Antitrust investigations of proposed transactions often come at a significant price to merging parties. Lawyers' and economists' fees for defending a transaction are substantial, but they are dwarfed by the cost of delay in swift-moving M&A markets, where, per the old adage, time is the enemy of the deal. (15) Those delay costs are compounded when the antitrust process interferes with other parts of the institutional constellation regulating the market for corporate control. For example, lengthy, indeterminate antitrust investigations can give merging parties opportunities for strategic behavior, as in noteworthy recent M&A cases such as Hexion v. Huntsman and In re Dollar Thrifty Shareholder Litigation. (16) Furthermore, it is not clear that antitrust intervention consistently produces offsetting benefits. Each year, the federal agencies require merging parties to undertake billions of dollars' worth of divestitures to remedy alleged anticompetitive effects. (17) But studies of whether those divestiture requirements are effective in preventing harm to competition are inconclusive. (18)

    In a subtler but equally significant respect, the problem extends beyond the small number of transactions that undergo a full antitrust investigation. The uncertainty inherent in the modern antitrust analysis of mergers undermines the M&A market by hamstringing merging parties' ability to assess antitrust risk ex ante. As Daniel Crane has noted, "[merger] analysis has become far more nuanced and technical--and therefore less predictable. Lawyers can no longer offer their clients clean predictions in many potentially close cases." (19) As a result, many potentially pro-competitive deals die on the drawing board. (20)

    My analysis begins in Part II by reviewing, first, the error cost framework and subsequent research applying decision theory to antitrust enforcement. I then turn to recent scholarship on both public regulation and private ordering, which recasts decision making as a provisional or "experimental" process, whereby participants explore and test possibilities collaboratively in an effort to construct a solution to an uncertain problem. (21) That process is supported through information sharing routines, by which participants regularly disclose their learning to one another, and through a disciplining institution, which "ratchets up" the participants' investment in the joint learning process as disputes arise. (22) In both the public and private sector, these institutions for collaborative learning are used...

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