BENCHMARKING THE UPWARD PRICING PRESSURE MODEL WITH FEDERAL TRADE COMMISSION EVIDENCE. Malcolm B. Coate

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1 Journal of Competition Law & Economics, 7(4), doi: /joclec/nhr014 Advance Access publication 18 October 2011 BENCHMARKING THE UPWARD PRICING PRESSURE MODEL WITH FEDERAL TRADE COMMISSION EVIDENCE Malcolm B. Coate ABSTRACT The upward pricing pressure (UPP) model was introduced in the 2010 revision of the Merger Guidelines, although little insight was offered for how to operationalize an UPP screen. Abstracting from the potential for efficiencies, this article defines an UPP-related benchmark of 15 percent using data from a review of Federal Trade Commission merger challenge decisions. While the historical record highlights the importance of diversion ratios, the other key input into the UPP index, the margin, appears to play little role in the review process. A supplemental analysis of the case-specific evidence associated with unilateral merger review serves to confirm the benchmark results. Moreover, a detailed case study of unilateral-effects analyses identifies a number of application issues that may negate the finding of an UPP-based competitive concern. Thus, careful study should be undertaken before (1) using an UPP index as a merger screen with a benchmark well below 15 percent (diversions well below 30 percent), (2) customizing the UPP calculation for either high or low values of the margin, or (3) using an UPP index to impose a strong presumption of a competitive concern. JEL: K21; L40 I. INTRODUCTION The 2010 revision of the Horizontal Merger Guidelines introduced the upward pricing pressure (UPP) model as new methodology for unilateral-effects analysis. 1 Developed by Joseph Farrell and Carl Shapiro as a simple diagnostic test to flag horizontal mergers that are most likely to Economist, Federal Trade Commission. mcoate@ftc.gov. The analyses and conclusions set forth in this article are those of the author and do not necessarily represent the views of the Commission, any individual Commissioner, or any Commission Bureau. I would like to thank Jeffrey Fischer and Joseph Simons for helpful suggestions on previous drafts of this article. 1 U.S. DEPT. OF JUSTICE & FED. TRADE COMM N, HORIZONTAL MERGER GUIDELINES (Aug. 19, 2010), available at [hereinafter MERGER GUIDELINES]. For the 1992 Guidelines, see U.S. DEPT. OF JUSTICE & FED. TRADE COMM N, HORIZONTAL MERGER GUIDELINES, ANTITRUST TRADE AND REGULATION REPORT (No. 1559, 1992). Published by Oxford University Press on behalf of US Government 2011.

2 826 Journal of Competition Law & Economics lead to unilateral anti-competitive price effects in markets for differentiated products, the UPP methodology requires some type of benchmark to differentiate high values of the UPP index from low values of the index prior to application. 2 To the extent that UPP analysis represents historical policy, this benchmark statistic is implicit in the enforcement record. On the other hand, if UPP analysis would modify the status quo, a review of past decisions will identify the potential changes and allow analysts to decide if UPP should be used to operationalize broad changes in the merger review process, or instead be reserved for special case analysis in which an exception to the historical precedent would be appropriate. Using the Federal Trade Commission s (FTC s) merger enforcement data, along with a case study of the competitive-effects analyses in a sub-sample of the relevant cases, it is possible to generate these insights. Further review of the case study evidence reveals a range of considerations that negate the presumption of a competitive concern linked to an UPP model. This article starts, in Part II, with a short overview of unilateral-effects analysis at the FTC. An UPP-related index is introduced along with three structural indices ( post-merger market share, the change in the Herfindahl, and the number of significant rivals) to aid in the evaluation of merger policy. In Part III, these four variables, coupled with entry and unilateral-effects evidence, are used to create deterministic models of the merger challenge decision. This analysis generates policy benchmarks, including a level of 15 percent for a simple UPP-related screen, although the modeling structure suggests that the diversion ratio, and not the margin, drives policy. Part IV provides a more detailed discussion of the merger review process, as it summarizes the results of a case study analysis that explores the reasons why market structure did or did not determine the enforcement decision. Again, diversion ratios appear to affect policy at a benchmark value comparable to that implicit in the screening analysis. Broadening the evaluation from a screening decision to a study of the competitive process in differentiated product markets allows for an even more comprehensive analysis of those cases in which the factual evidence did not substantiate a unilateral concern. This review results in a tabulation of considerations, in Part V, that seem to negate a structural concern in unilateral merger analysis. Part VI concludes by noting that the focus on diversion ratios implicit in the 2010 Guidelines appears compatible with the historical evidence, with the diversion benchmark set around 30 percent. More research is needed to validate the importance of the margin statistic in the merger review process. 2 Joseph Farrell & Carl Shapiro, Antitrust Evaluation of Horizontal Mergers: An Economic Alternative to Market Definition, 10 B.E. J. THEORETICAL ECON. 34 (2010).

3 II. MERGER REVIEWAND STRUCTURAL PROXIES Benchmarking UPP with FTC Evidence 827 The Hart-Scott-Rodino merger review program is designed to ensure that almost all significant mergers filed in the United States undergo a review by either the Federal Trade Commission or the Antitrust Division of the Department of Justice (DOJ). For those matters raising substantial concerns, a comprehensive investigation starts with the issuance of a second request for detailed information and ends with a decision to challenge or not to challenge the merger. Building on the results of the initial investigation, the staff of the enforcement agency applies the tools in the Merger Guidelines to undertake an industry study that is able to predict whether the merger in question is likely to substantially lessen competition. The investigation entails detailed interviews with customers and rivals of the merging firms, close review of the parties internal documents, depositions of the parties senior staff if necessary, and independent studies of the competitive process. The UPP methodology represents one approach to structuring the relevant information in an attempt to address the question of whether the merger appears likely to substantially lessen competition. Historically, the staff had applied the unilateral-effects analysis structured by the 1992 Merger Guidelines. Focused basically on the unique competition between the merging firms, the analysis attempted to identify the extent to which the merging firms were regarded as the first and second choices for a significant share of customers in a relevant market. While sources of factual information were listed, the relevant economic theory was left a little vague. The 2010 Guidelines advance the UPP model as one possible line of analysis. Additional analyses needed to move from structural concern to competitive problems are also detailed in both the 1992 and 2010 Guidelines. Three structural statistics ( post-merger market share, change in the Herfindahl, and number of significant competitors) were considered potentially relevant for the competitive-effects review associated with the 1992 Guidelines. Based on its usage in the monopoly model, post-merger market share was the most obvious choice for a structural statistic. The 1992 Merger Guidelines even went as far as to set a share presumption at 35 percent when market share could be shown to reflect the likelihood of close rivalry. In each matter, more detailed analysis was required to obtain a full understanding of the pattern of competition. Gregory Werden and Luke Froeb introduced a second statistic, the change in the Herfindahl, based on its performance in a simulation that showed the change in the Herfindahl predicted competitive concerns much more closely than did the post-merger market share or the Herfindahl index. 3 Regardless of its success in simulation, the Guidelines expected 3 Gregory J. Werden & Luke M. Froeb, Simulation as an Alternative to Structural Merger Policy in Differentiated Products Industries, in THE ECONOMICS OF THE ANTITRUST PROCESS 65, 75 (Malcolm B. Coate & Andrew N. Kleit eds., Kluwer Acad 1996). The change in the

4 828 Journal of Competition Law & Economics case-specific evidence supportive of a unilateral effect prior to a merger challenge. The third statistic, the number of significant competitors, appears to have evolved over time with the investigational experience of the agencies. The basic concept involves the identification of firms that could be shown to materially constrain the market behavior of one (or both) of the merger partners. Stated more simply, significant competitors were firms thought to matter in the competitive process. The statistic was well known by the time it was used as a variable in Malcolm Coate s and Shawn Urlick s merger policy study. 4 While low values for the statistic would be suggestive of a competitive concern, deeper competitive analysis is required before a final inference should be drawn on the merger s likely competitive effect. The UPP methodology gives rise to a fourth structural proxy. To obtain a generic estimate of the index, diversion can be based on market shares, while margins and efficiencies need to be assigned to representative values (here, 0.50 for the margin and 0 for efficiencies). Without efficiencies, the UPP statistic becomes a gross upward pricing pressure index (GUPPI), with the larger of the two firm-specific indices used in the study. Farrell and Shapiro validate the basic idea by postulating that share diverts to rivals in proportion to the ratio of that rival s share to the share not controlled by the merger partner. Then, the actual diversion ratio is computed by multiplying the proportion by the market recapture rate (set to 0.8 in Farrell s and Shapiro s study) to reflect the fact that some sales are lost to competitors outside the market (or outside the set of close rivals). For example, given five premerger rivals with equal share, diversion would be 0.20 (equal to 0.20/(1 0.2) 0.8), and with the margin set to 0.5, the index becomes Like the other structural indicators, the GUPPI serves to predict the likelihood of a competitive concern, but additional evidence is required to show that a merger is likely to substantially lessen competition. All four statistics could be used to create structural screens for unilateral analysis. Herfindahl is nominally defined by subtracting the pre-merger Herfindahl from the post-merger Herfindahl. However, a little algebra shows that the change in the Herfindahl is equal to twice the product of the market shares of the merging parties. For any given post-merger share, this statistic takes on its highest value when the shares of the merging parties are equal. 4 Malcolm B. Coate & Shawn W. Ulrick, Transparency at the Federal Trade Commission: The Horizontal Merger Review Process, 73 ANTITRUST L. 531 (2006). Earlier uses of the term are readily available in court records. For the DOJ, see Complaint for Injunctive Relief, United States v. Allied Waste Indus., Inc., No. 1:99 CV (D.D.C. July 20, 1999), available at and for the FTC, see the Memorandum in Support of Plaintiff s Motion for Preliminary Injunction, Fed. Trade Comm n v. Swedish Match N. Am. Inc., No. 1:00 CV (D.D.C. June 23, 2000), available at gov/os/2000/06/swedishmatchbrief.pdf.

5 Benchmarking UPP with FTC Evidence 829 The Merger Guidelines detail three other considerations (entry, general competitive factors, and evidence of merger-related effects) that are relevant in all merger investigations. 5 The importance of entry analysis is obvious, because a merger cannot be anticompetitive if the adverse effect of the merger is quickly offset by new entry into the market. Analysts have addressed this issue by considering evidence on entry impediments to be a necessary condition for a competitive concern. General competitive factors comprise the considerations implicit in the competitive-effects section of the 1992 Guidelines (now in separate unilateral effects and coordinate interaction sections in the 2010 Guidelines). Basically, all other market structure issues, along with observed market behavior (conduct) fall into this category. The breadth of the category makes it virtually impossible to summarize outside a case study. (I discuss some case study inferences below in Parts VI and V.) Finally, merger-related effects evidence serves as a useful test of any analytical model of the merger review process. 6 Confirming a theoretical concern with effects evidence serves to substantiate the challenge prediction and thus could be used to create a more sophisticated model of the merger enforcement process. III. DETERMANISTIC MODELS OF MERGER POLICY A deterministic model can be considered a policy screen when it links a structural variable (for example, market share) with a policy outcome (for example, merger challenge). The analytical problem involves defining a benchmark statistic to separate the screened matters into two classifications one for matters likely to be challenged and the other for matters likely to be allowed to proceed. A good screening variable/benchmark combination is able to successfully separate the data into the two classifications, such that both potential outcomes are predicted well. Thus, screening requires both a theoretically based screening variable and an empirically based benchmark parameter. More complicated deterministic screening models can use multiple variables to supplement the initial structural screen. Again, the same separation problem exists, but if it can be addressed with a benchmark, a reasonable policy model could be defined as long as the multiple variables could be easily identified in the investigation. 5 Particular investigations may require other lines of analysis. For example, if the financial health of one of the merging parties is at issue, a failing firm/division review would be required. Alternatively, if the merging parties claim that the transaction would materially reduce costs, that factor would also need to be evaluated. 6 For statistical evidence suggestive of the impact of effects evidence on the merger challenge decision, see Malcolm B. Coate, Alive and Kicking: Collusion Theory in Merger Analysis at the Federal Trade Commission, 4 COMPETITION POL Y INT L 3 (2008); Malcolm B. Coate, Unilateral Effects Under the Guidelines: Models, Merits, and Merger Policy (2008, revised 2011), available at For an overview of the use of effects evidence in merger review, see Malcolm B. Coate, The Use of Natural Experiments in Merger Analysis (2011), available at

6 830 Journal of Competition Law & Economics After a brief overview of the data, deterministic structural screening models will be presented for four structural indices: rivals, post-merger share, change in the Herfindahl, and a GUPPI statistic. A benchmark will be established for each index. By reviewing the prediction success of each model, it will be possible to generate some merger policy insights. The prediction success of the GUPPI will be of particular interest, as will be the benchmark statistic. 7 A. Data for the Analysis of Merger Challenges To define a deterministic merger challenge model based on a structural proxy, ease of entry, and effects evidence, it is necessary to have historical data for the values of the screening variables as well as information on the outcomes of merger investigations. An enhanced version of the data, summarized in the FTC s Horizontal Merger Investigation Data (Data), provides the core information. 8 Additional review extended the information in Tables 6.1 and 6.2 of the FTC report back to the beginning of fiscal year 1993 and forward to mid-2010, for a total sample of 317 markets, of which 184 were found to involve relevant unilateral-effects issues and thus formed the core of the sample. 9 Minor adjustments are made in the raw data to define a more consistent sample for analysis. Following the procedure in Coate and Ulrick, Bureau of Economics (BE) information is substituted for Bureau of Competition (BC) data when necessary to standardize the review on a literal interpretation of the Guidelines. 10 Moreover, the market share 7 Willig recognizes the ability of the GUPPI to compare competitive concerns across markets given a benchmark statistic. He notes that the DOJ considers a 5-percent GUPPI to fall within a safe harbor, but is unable to cite a statistic associated with a structural presumption. See Robert Willig, Unilateral Competitive Effects of Mergers: Upward Pricing Pressure, Product Quality, and Other Extensions, 39 REV. INDUS. ORG. 19, n.18 (2011). Assuming that a five-percent price increase causes concern and a 50-percent pass-through is relevant, the benchmark GUPPI would be 10 percent. 8 For the most recent data, see FED. TRADE COMM N, HORIZONTAL MERGER INVESTIGATION DATA, FISCAL YEARS (2008), available at hsrmergerdata.pdf. This information has been used in a series of papers addressing merger analysis. For a summary, see Malcolm B. Coate, Transparency at the Federal Trade Commission: Generalities and Innovations in Merger Analysis, 12 COMPETITION POL Y INT L ANTITRUST CHRON. 1 (2009). 9 Because the Horizontal Merger Investigation Data release contains information from specific FTC files, along with some industry specific summaries, data releases must be spaced out over time to ensure that details on specific investigations are not released. However, updated data can be used in tabulations, because the tabulations do not release any case-specific information. 10 Malcolm B. Coate & Shawn W. Ulrick, Do Court Decisions Drive the Federal Trade Commission Merger Enforcement Policy on Merger Settlements?, 34 REV. INDUS. ORG. 99 (2009). Given that the Merger Guidelines are only guidelines, neither the staff of the Bureau of Competition nor the Bureau of Economics feels constrained to apply the specific standards in absolutely every case. However, to obtain the most comparable results, a research design decision was

7 Benchmarking UPP with FTC Evidence 831 data behind the structural indices are used to compute the post-merger share and the UPP-based variables. Entry analysis combines the Bureau of Competition reports on timeliness, likelihood, and sufficiency considerations reported in the Data with comparable information from the Bureau of Economics. 11 A consensus impediment finding is considered made when at least two out of the six possible affirmative findings on entry impediments with respect to the timeliness, likelihood, and sufficiency issues are made in the staff files. Finally, the effects evidence variable combines the BC findings of validated customer concerns and hot documents (internal documents that predict the merger will cause a reduction in the level of competition) from the Data with comparable information from the BE memo, along with evidence of natural experiments found by either the economists or lawyers. Here, an affirmative finding of effects evidence is considered made if the staff identified validated customer complaints, hot documents, or natural experiments. 12 Table 1 lists definitions, ranges, and the average values for the four structural indices. Three structural proxies (significant rivals, post-merger market share, and change in the Herfindahl) were usually lifted directly from the staff analyses and reflect information that was readily available to the FTC at the time of the merger challenge decision. 13 The fourth proxy, an UPP-based GUPPI statistic is a pro-forma number, generated from a sharebased estimate for diversion, a margin set to 0.5, and an efficiency variable set to zero. It ranges from virtually 0 to 0.4 (an index associated with a merger to pure monopoly). 14 All four structural indices are correlated. 15 made to apply the Guideline s methodology whenever the two analyses materially differed. Changes were made in only a few special case situations. 11 When the BE stipulated to the BC analysis, the BC results were considered to represent the BE findings. 12 In contrast to the entry analysis where the staff always makes a finding, effects evidence is not addressed in a number of cases. An unreported analysis integrated procompetitive-effects evidence into the model, but found that the results did not improve. Only a few matters were affected when this evidence was used to reject a challenge that would otherwise be made. 13 In a few matters, the data are pro-forma calculations based on the information in the file. For example, the number-equivalent assumption of equal shares was imposed when the staff discussion suggested it was appropriate. 14 Five other UPP-related measures were considered. One, introduced by Coate in 2011, weighted the firm level GUPPI by market share and then took the higher of the two values, whereas another added the two share-weighted GUPPIs together. Three other statistics replaced the GUPPI with the UPP statistic (with a ten-percent allowance for efficiencies), introduced in Farrell & Shapiro, supra note 2. All generated almost identical results, although the benchmark statistic was customized to the case-specific data. For discussion of the various UPP indices, see Malcolm B. Coate, The Enhanced UPP Screen, Merging Markets into the UPP Methodology, WORLD COMPETITION L. & ECON REV. (forthcoming) (also available at 15 The GUPPI statistic showed a very high correlation with post-merger market share (0.984), but much lower correlation with rivals ( 0.607) and change in the Herfindahl (0.448). The

8 Table 1. Overview of the means of the structural variables Variable Definition Range Mean Enforce Mean Closed Significant Rivals Number of pre-merger significant rivals in the market affected 2/ (3.47 ) 4.51 (4.72) by merger Post-Merger Share Post-merger share of the firm involved in the merger (in 11/ (64.2 ) 55.9 (53.5) percentage terms) Change in HHI Change in the Herfindahl Index caused by merger. Also defined 57/ (1647 ) 1325 (1137) as twice the product of the shares of merging parties GUPPI Product of the share-based diversion and margin (set at.50), 0/ (.2234 ).1892 (.1792) with no efficiency adjustment in the screen Cases Number of matters reviewed 145 (59) 39 (36) 832 Journal of Competition Law & Economics Notes: The results in parentheses exclude the two-to-one mergers. Indicates that the sample mean of the enforced cases is significantly different from the mean of closed cases.

9 Benchmarking UPP with FTC Evidence 833 As noted in Table 1, the sample contains 89 two-to-one mergers. These mergers identified (by construction) only two significant rivals, involved very large post-merger market shares (some at 100 percent while others fell short of pure monopoly due to fringe rivalry), generally led to very large increases in the Herfindahl (only a few increases were less than 2,000 points) and were associated with very high GUPPIs. While the presentation includes these cases in the analysis, results are also computed and footnoted for a smaller sample that excludes these matters. B. Modeling the Unilateral Merger Enforcement Process Table 2 summarizes the average challenge rates associated with variation in the structural variables, first for the structural data and then for the adjusted data that accounts for ease of entry and effects evidence. Two adjustments are undertaken to reflect the impact of the entry and evidence variables. First, all matters in which the staff analysis fails to sufficiently establish entry impediments are predicted to close, regardless of the structural index. Second, all matters with structural statistics suggestive of closing are recharacterized as merger challenges when (1) entry impediments exist and (2) the effects evidence variable indicates a competitive concern. As observed in Table 2, these adjustments allow the deterministic model to predict more outcomes successfully. Implicit in this discussion are empirically-based cut-off parameters that separate the case-specific statistics into a set of values likely to be associated with closing and another set of values likely to be linked to merger challenges. Cut-off parameters are rounded up or down to create simple break points. The count of the number of significant competitors is evaluated first, with the basic information in columns (1) and (2) of Table 2. The analysis reports that roughly half the sample involves two-to-one mergers and, not surprisingly, these transactions almost always lead to merger challenges. Slightly more than a quarter of the sample involves three-to-two mergers; these transactions are challenged at a 78.4-percent rate. Four-to-three transactions appear to define the marginal challenge, because enforcement action is unlikely whenever five or more pre-merger rivals exist. Instead of a gradual decline in the challenge probability with the increase in the rival s variable, the data supports a sharp drop-off in the challenge probability as the number of pre-merger rivals increases beyond four. A deterministic model that assumes a merger challenge whenever four or fewer pre-merger rivals exist (and closing otherwise) successfully predicts the FTC s decision in 87.0 percent of the investigations. rivals variable was correlated with both post-merger share ( 0.623) and change in the Herfindahl (0.501).

10 834 Journal of Competition Law & Economics Table 2. Merger challenge rates by market structure, (1) (2) (3) (4) Significant Rivals Raw Sample Challenge Rate Adj. Sample Challenge Rate 2 to % % 3 to % % 4 to % % 5 to % 6 0.0% Over % % Entry Issues NA % Proof of Concern NA % Post-Merger Share Raw Sample Challenge Rate Adj. Sample Challenge Rate 90 to % % 80 to % % 70 to % % 60 to % % 45 to % % Under % % Entry Issues NA % Proof of Concern NA % Change in HHI Raw Sample Challenge Rate Adj. Sample Challenge Rate 3000 or over % % 2000 to % % 1500 to % % 1000 to % % 800 to % % Under % % Entry Issues NA % Proof of Concern NA % GUPPI model Raw Sample Challenge Rate Adj. Sample Challenge Rate 0.35 and above % % 0.30 to % % 0.25 to % % 0.20 to % 20 90% 0.15 to % 20 75% Under % % Entry Issues NA 24 25% Proof of Concern NA % Note: NA indicates not applicable. The more complex deterministic model starts by integrating the entry index into the rivals model, predicting a merger challenge whenever the count of pre-merger rivals is four or less and the entry indicator variable equals one and closing otherwise. The second adjustment (labeled proof of concern in Table 2) triggers an enforcement recommendation in matters with both relatively low values for the structural proxy (five or more premerger rivals) and entry impediments whenever the staff identifies effects

11 Benchmarking UPP with FTC Evidence 835 evidence for a competitive concern. This revised model (benchmarked as noted above) correctly predicts 95.2 percent of the challenged matters and 79.5 percent of the closed matters, with an overall success rate of 91.8 percent. 16 The last two columns of Table 2 link the adjusted challenge probabilities to market structure. If the merger involves at most a four-to-three transaction in a market affected by impediments to entry, then the challenge probability remains above 90 percent. For the markets with four or more post-merger rivals (five-to-four or more mergers), along with strong entry findings but no effects evidence suggestive of a competitive problem, the likelihood of challenge almost collapses to zero. Not surprisingly, lack of clear entry evidence is compatible with closing. On the other hand, solid effects evidence leads to a merger challenge in four of five situations in which a simple structural analysis tends to suggest that no challenge is required. A post-merger-market-share model generates slightly less accurate predictions. Table 2 presents high challenge rates as long as the post-merger market share remains above 60 percent. The critical share of 45 percent appears to be the appropriate benchmark point to split the data, given that mergers with post-acquisition shares below 45 percent are challenged at a 31.8-percent rate, while mergers with shares between 45 percent and 60 percent face challenge at a 62.5-percent rate. 17 Overall, this model predicts the outcome of 83.2 percent of the cases correctly, a rate that increases to 88.6 percent when evidence of entry is required for a challenge prediction (given post-merger share at or above 45 percent), and evidence of an anticompetitive concern is considered sufficient for a challenge in matters with a post-merger share below 45 percent (whenever impediments to entry exist). 18 Again, the last two columns of Table 2 present the adjusted challenge rates. As long as the post-merger share remains above 70 percent (accompanied with strong findings on impediments to entry), the probability of a challenge remains above 90 percent. Lower post-merger shares in the range of 45 to 70 percent are challenged at rates of 71.4 to 85.7 percent. Structures with post-merger shares under 45 percent (with entry 16 Deleting the 89 mergers to monopoly from the sample allowed the same deterministic model to correctly predict 77.9 percent of the decisions based on the number of rivals and 89.4 percent when both the entry and evidence variables are used to supplement the structural model. 17 This result suggests that the 35-percent share presumption in the 1992 Guidelines was easily rebutted for shares under 60 percent. 18 Deleting the two-to-one mergers from the sample and retabulating the data shows 71.6 percent of the policy decisions are correctly predicted by the share screen (benchmarked at 45-percent share of the market). Integrating ease of entry and adding effects evidence to the model raises the result to 83.2 percent.

12 836 Journal of Competition Law & Economics impediments, but not effects evidence) are challenged at only a 20-percent rate. Finally, effects evidence matters as long as entry impediments exist, with five of six low-share matters challenged. The change-in-herfindahl model performs noticeably worse than the rivals model. Reviewing the data suggests that a change of 800 points defines a reasonable benchmark for a structural model, but this model can only predict 78.8 percent of the challenges (a rate significantly lower than that of the rivals model, with a t-test of 2.08). This result is not surprising, because column (2) of Table 2 shows that the change in Herfindahl has little predictive power when the statistic is below Adjusting the analysis for entry impediments and adding consideration of evidence for the matters with a change in the Herfindahl below 800 points (when the required evidence on entry exists) raises the success rate to 85.8 percent (which is still significantly below the value for the rivals model, with a t-statistic of 1.82). 19 Predictions for the adjusted model contained in Table 2 show that the challenge rates appear more accurate once the entry and evidence adjustments are made, but the success is not able to match that of the significant-rivals model. The diversion-based GUPPI test performs much like the post-merger-share model. High values of the GUPPI are associated with merger challenges, while low values suggest that the investigation will close. A GUPPI benchmark of 15 percent (here, a diversion ratio of 30 percent) serves to separate the sample of cases into those likely to be challenged and those likely to be closed. Overall, the GUPPI model is able to correctly forecast 82.1 percent of the policy decisions. Requiring evidence of entry impediments to underpin an enforcement decision and generalizing the model to lead to a competitive concern whenever evidence of anticompetitive effects is found contemporaneous with entry impediments in low GUPPI markets increases the prediction success to 88.6 percent. 20 Table 2 presents the impact of these adjustments. As long as the GUPPI remains above 20 percent (here, diversion ratio of 40 percent), the mergers are challenged at a rate of 90 percent or more, when evidence of barriers to entry exists. The prediction probability for the marginal case (with a GUPPI between 15 and 20 percent) changes very little. As noted above, a low value for the structural index or a lack of entry impediments makes a merger challenge unlikely. 19 Using the sample of 95 studies that do not apply a monopoly/dominant firm model generates a prediction rate of 66.3 percent using only the change in the Herfindahl (benchmarked at 800 points), and 81.1 percent once the predictions are adjusted for both entry and effects evidence. These predictions are significantly less than the numbers for the rivals model. 20 Dropping the mergers to monopoly generates prediction rates ranging from 68.4 percent for a pure structural model to 83.2 percent for the more complex entry-evidence model (again with the same benchmark).

13 Benchmarking UPP with FTC Evidence 837 Another analysis customizes the GUPPI predictions for rough estimates of the industry-specific margins. Margins were raised to 0.9 for informationrelated markets and 0.8 for drugs and medical devices, and lowered to 0.2 for retailing and oil-related markets to obtain a feeling for how industryspecific margins would change the results. 21 The predictive ability of the model fell, with the pure structural model correctly noting 78.3 percent of the actual decisions, and improving to 84.8 percent when both entry and effects evidence are considered. These results are all significantly below those associated with the significant-rivals model (with t-statistics of 2.2 and 2.1, respectively). Further revision in the analysis to replace share-based divisions with actual diversions is not possible, because the files do not contain comprehensive diversion information. Overall, it appears that the significant-rivals index gives rise to the best structural screen for historical merger enforcement. In addition to generating the most accurate policy predictions, either as a stand-alone index or in combination with information on entry and effects evidence, the count of the number of significant rivals is probably the easiest index to assemble in a merger investigation. It is only necessary to identify instances of substantial competition, instead of the rough contours of the market needed to define post-merger share, the change in the Herfindahl, or the share-based GUPPI. Thus, merger analysts are likely to continue defining markets and counting rivals in the early stages of merger review. The share-based GUPPI model, with fixed margins, basically duplicates the success of the simple post-merger-market-share model and predicts almost as well as the rivals model. The benchmark GUPPI statistic of 15 percent is suggestive of a historical policy that requires substantial upward pressure on price prior to challenging a merger. Moreover, this GUPPI benchmark generates a critical diversion ratio of 30 percent, a statistic that implies mergers will tend to be challenged only in the most concentrated markets. An attempt to create a more realistic UPP model by allowing the margin to vary by industry failed, as the adjusted GUPPI screen generated relatively poor predictions. Thus, it seems reasonable to conclude that the broad 21 A total of 41 margins were increased and a total of 31 margins were reduced from their assumed value of 0.5. Simply changing the margin on all cases and recomputing the GUPPI statistic does not materially change the model, because the benchmark used to split the data into challenge and close predictions would also change, leaving the predictions basically the same. In the variable margin simulation, the benchmark GUPPI is reduced from 15 percent to 10 percent to create the screen. However, the results of the simulation change little if the 15-percent GUPPI is retained (success rates of 76.6 percent and 85.3 percent, respectively). Thus, the choice of the benchmark does not cause the lower prediction success rates. Note that once margins are allowed to vary, the critical diversion ratio is no longer twice the GUPPI cut-off, because the GUPPI is now the product of the margin and screen. The lowest diversion level in a matter predicted to be challenged is 21 percent, although many matters are not challenged at higher diversions given a margin of 0.2.

14 838 Journal of Competition Law & Economics application of a proposed UPP analysis would result in policy changes for both very low-margin and very high-margin markets. Analysts will want to think about this change before accepting the full UPP structure as a general screen for merger policy. 22 While the aggregate success rate of the adjusted significant-rivals model is relatively good, at 91.8 percent, it is useful to study how merger analysis moves beyond the issues of structure, entry considerations, and evidence to define the comprehensive unilateral-effects policy that defines much of merger review in the United States. Using the rival count to organize the presentation, this issue is addressed in the next section. IV. COMPETITIVE-EFFECTS ANALYSIS IN UNILATERAL MERGER CASES As I noted in Part II, merger analysis is influenced by a wide range of structural and behavioral considerations. Thus, some additional analysis beyond the simple statistics that I applied in Part III is required to more fully understand merger policy at the FTC. Confidentiality considerations preclude case-specific discussion, but some generalizations can be drawn for the samples of two-to-one, three-to-two, four-to-three, and five-or-more significant-rival merger analyses. Of particular interest is the ability to relate the results of the case-specific review to diversions and margins. Not surprisingly, virtually all the 89 two-to-one investigations ended in challenges. Once the market is defined, the identification of the merging parties as the only two significant rivals almost mandates a challenge. Issues associated with the three closed investigations include a minimal loss in competition (that is, the merged rivals had relatively low diversions to each other), buyer power (especially when the power buyer expects to benefit from efficiencies), a weak entry impediment argument, fringe expansion, financial distress, and other special case circumstances. In all three cases, multiple issues undermine the monopoly theory of concern. The three-to-two matters are potentially more interesting, with 40 matters resulting in challenges and 11 in closed investigations. In contrast to the default finding of head-to-head unilateral competition almost guaranteed by a two-to-one merger, roughly one-half of the challenged duopolies exhibit claims of head-to-head rivalry and thus establish a clear unilateral concern. 23 In the other challenged cases, the lack of head-to-head rivalry is not offset by stronger performance or entry evidence or by weaker customer 22 Baumann and Godek highlight the difficulty of measuring margins and note average variable cost is often a poor proxy for the marginal cost needed to compute the margin. See Michael G. Baumann & Paul E. Godek, Margin of Error: The Flawed Paradigm in the New Merger Guidelines, 3 COMPETITION POL Y INT L ANTITRUST CHRON. 1 (2011). 23 Head-to-head competition is an internal FTC term of art used to describe the situation in which the merged parties are thought to be the closest competitors and thus exhibit the highest diversion rates.

15 Benchmarking UPP with FTC Evidence 839 sophistication or procompetitive-effects evidence. Thus, for the challenged three-to-two mergers, it appears clear that the staff does not insist that the merging parties exhibit the highest diversion ratio in the market. (Under the 1992 Guidelines, substantial head-to-head competition clearly caused a concern, while the implications of sales losses to more distant rivals were less clear. In contrast, the 2010 Guidelines have focused more generally on market-specific evaluations of diversion to clearly justify broader enforcement.) For the 11 closed matters, only three of the merged firms are considered to be head-to-head rivals. Ten of the eleven closed investigations fail to identify a unilateral effect, and seven of the eleven matters fail to establish entry impediments (only one matter closed primarily due to ease of entry). For two of the matters, the unilateral effect focuses on capacity limitations that could not be substantiated, and thus the investigations closed. Focusing on the eight remaining differentiated products mergers, the parties in one matter are distant rivals (which implies low diversion), and in another, competition with the leading firm sets the tone for the market (which also suggests low diversion). A third matter exhibits high diversion, but only in a very small niche in the market, and innovation competition offsets the effect of the diversion model in a fourth market. In four other matters, repositioning is also considered easy (although in three of the cases, this finding paralleled ease of entry). Overall, the staff systematically applies unilateral analysis to identify concerns in a broad range of three-to-two mergers, but rejects the theory when the evidence implies supracompetitive pricing is not likely. Four-to-three matters include 14 merger challenges and five closed investigations. In contrast to the three-to-two matters, all but one of the 14 challenges involve findings of head-to-head competition (the other challenge contained a detailed study showing direct rivalry). Thus, if three entities remain post-merger, the staff seems to prefer to have strong head-to-head evidence to support the inference of substantial diversion. Back of-the-envelope calculations suggest that this need for head-to-head competition implies that diversions over 35 percent are generally sufficient for a competitive concern, while diversions around 20 percent are not. 24 Thus, in a challenged transaction, it seems reasonable to claim that a diversion ratio 24 The intuition is developed by comparing the record on three-to-two mergers with the record on four-to-three investigations. With three post-merger rivals (four pre-merger rivals) and allowing for 20 percent to 25 percent of the product diverted outside the market, the claim of head-to-head competition (taken here to mean the merged firms exhibit the largest diversion) supports a diversion ratio of 35 to 40 percent. This leaves 35 to 40 percent to be split up among the two other rivals. In effect, non-head-to-head rivals would usually exhibit diversion ratios in the 20 percent range. Since these cases are not in the challenged sample, diversions around 20 percent are likely to be considered too small. In contrast, take the fact that three-to-two mergers are regularly challenged, regardless of the head-to-head status of the merging parties. Again, leave 20 to 25 percent diversion outside the market, a merger partner would divert 75 to 80 percent of its lost sales to the two rivals. Assuming the leading

16 840 Journal of Competition Law & Economics of 25 to 30 percent is a reasonable lower bound for the diversion to the merger partner. Entry evidence in these challenged matters is clear, but performance evidence exists for only 57 percent of the sample (for the three-to-two matters, performance evidence was found for 75 percent of the sample). This finding also suggests that structural analysis is relied on more heavily to justify the competitive concern in the post-merger triopoly sample. Three of the five closed investigations exhibit findings of head-to-head competition, but, as discussed below, other evidence rebuts the unilateral competitive concern. The five closed merger analyses identify only one situation of entry impediments, and, in that matter, the facts show that the rivals were not close competitors. In the four easy entry cases, one has sufficient evidence for a unilateral concern, while innovation competition, buyer power, and repositioning considerations reinforce the decision not to challenge the merger in the remaining three cases. Finally, turning to the 25 markets with four or more post-merger rivals leads to an analysis of the question of why challenges occurred in five of the 25 markets. Reviewing the files shows that the closeness of competition was the overriding reason for the challenge. In all five challenged matters, some evidence suggests that the merging firms are relatively close competitors (with high diversion) for a material share of their sales. Ease of entry supports the closing decision in six of 20 matters (innovation competition and trivial niche effects were also noted in those files). Another matter involves a relatively homogeneous good, with multiple rivals holding an ability to expand output, and thus no concern existed. For the thirteen closed matters with findings of differentiated products and entry impediments, a careful competitive analysis was required to justify closing the investigation. Identified problems that precluded the staff from alleging a unilateral concern included repositioning (five cases), distant rivals (three cases), very close rivals not involved in the merger (three cases), innovation competition, and buyer power. As a bottom line, the review of the files clearly shows that the staff undertook unilateral-effects analyses that move well beyond a count of significant rivals, a study of entry, and a review of the relevant effects evidence. In addition to the trump implied by the ease of entry, a wide range of complications precluded the staff from challenging specific mergers under a unilateral-effects theory. On the other hand, in the few cases where the evidence showed that rivals were close competitors and the offsetting considerations detailed above did not apply, mergers were challenged in markets with four or more post-merger rivals. rival might capture 40 to 45 percent of the diversion, this leaves diversion ratios of 30 to 35 percent as sufficient to trigger a challenge in a duopoly.

17 Benchmarking UPP with FTC Evidence 841 Rationalizing merger challenge decisions with representative diversion statistics suggests a diversion challenge benchmark of around 25 to 30 percent. Thus, it seems reasonable to conclude that the diversion concept has always played a role in unilateral-effects analysis, although the 2010 revisions in the Guidelines could lead to more aggressive enforcement if a lower critical diversion rate is integrated into an UPP model. On the other hand, the historical record is almost completely silent on margins. To the extent that the new Guidelines stand for an aggressive policy in high-technology, high-margin industries and a less aggressive policy in low-margin, retailing style markets, the reforms would represent a substantial change in enforcement policy. However, before even considering this change, it is useful to undertake a more detailed study of unilateral-effects analyses to more fully explore the impact of fact-based considerations on the challenge decision. This question is addressed in the next section. V. STAFF ANALYSES THAT REJECT UNILATERAL-EFFECTS CONCERNS To more fully understand how an UPP analysis can be applied to merger investigations, a research file of 220 differentiated-product mergers studied at the FTC between 1993 and mid-2010 was created. Within this sample, the BC staff identified 142 differentiated-product markets in which unilateral concerns appeared to be the primary concern (this sample forms the core of the 184 matters reviewed in Parts III and IV). The bulk of these investigations (109) led to enforcement action, and in another six, the unilateral theory was at least partially supported even though the investigation was closed (two involved merger to monopoly and four others involved valid theories of unilateral concern, precluded from challenge by the ease of entry). Files for the remaining 27 differentiated-product markets were closely studied to determine why the unilateral-effects theory was rejected. This sample was supplemented by closely reviewing the 78 matters in which the staff analysis found that the product at issue was relatively differentiated, but implied collusion was the primary concern. In 15 matters, the file identified both a viable collusion and unilateral concerns (the staff focused mostly on collusion concerns), and these matters were deleted from the study. This left 63 analyses, 50 matters in which the unilateral theory was judged weaker than a rejected collusion theory, and 13 matters in which the unilateral theory was rejected, although the matter proceeded with a collusion concern. Five of these matters were excluded from this sample, because the Herfindahl fell below 1,500, suggesting detailed unilateral-effects analysis was unnecessary. 25 Thus, the supplemental 25 The 2010 Guidelines define a market exhibiting a Herfindahl of under 1500 as unconcentrated; therefore, matters with low Herfindahls are not likely to raise competitive concerns. MERGER GUIDELINES, supra note 1.