Silviase/jgov_v1.4_full_ann huggingface dataset:
itself—some of the loss of marginal customers has been internalized by merger. After the merger, the gain and loss that would result from a price increase are no longer balanced: the gain exceeds the loss. Thus, the merged firm has the incentive to raise the price of firm A's product until the balance is restored. The strength of this change in incentives due to the merger depends on the own- and cross-price elasticities of demand for the products of the merged firm.

Merger simulation also accounts for a second effect: the marginal cost efficiencies. To the extent that a merger will lead to lower marginal costs for any of the merging parties' products, the merged firm will have the incentive to lower its prices on those products, all else equal. The reasoning is as follows. As described above, each firm sets its price at the level where lowering or raising the price would not increase profits. A price reduction would not increase profits because the gains associated with the profit margin on added marginal consumers is just offset by the losses from charging the lower price on inframarginal consumers. If the merger leads to a reduction in marginal cost, however, this calculus is changed post-merger. In particular, the profit margin on each marginal consumer increases (because of the lower marginal cost). At premerger prices, the merged firm would gain more from lowering its price in terms of the profit margin on additional marginal consumers than it would lose from having a lower price on the inframarginal consumers. Thus, marginal cost efficiencies lead the merged firm to lower prices, all else equal.

The third effect focuses on how changes in the merging firms’ incentives may affect the incentives of other firms in the industry. Specifically, to the extent that the merging firms have the incentive to raise their prices post-merger, competition could be softened and the merging firms’ rivals may have the incentive to raise their prices as well. Conversely, if the merging firms’ have the incentive to decrease their prices post-merger, competition may be sharpened and the merging firms’ rivals will have an incentive to decrease their prices.

While merger simulation can be a powerful tool for analyzing the competitive effects of mergers, it has non-trivial data requirements. Moreover, as with most economic modeling, it is not possible to incorporate every aspect of the “real world” into the model. Finally, the simulation model may require that certain assumptions be made, e.g., about the form of competition between firms.

What is one to do if extensive data are not available? For example, it is important to understand the structure of demand. If the necessary data are not available or reliable, one may be able to use the antitrust logit model or PCAIDS approaches, which have substantially lesser data requirements. However, the
restrictions inherent in these models may be inappropriate. In that case, one may have to abandon merger simulation and turn to an alternative form of competitive analysis.

How does one know whether the assumptions in the model are valid? One must remember that the validity of an economic model is measured by its ability to predict outcomes, not by whether its assumptions are “realistic.” Indeed, the purpose of a model is to abstract from many real-world details to isolate the key factors that determine economic outcomes. The validity of the model itself, and thus its underlying assumptions, can often be tested. For example, if the Nash-Bertrand model is used to describe competition pre-merger, the validity of this assumption can be tested by comparing the gross margins implied by this model to actual gross margins. If there is a substantial divergence, one may question the validity of the model. In other situations, other tests might be available. One such test follows from the entry of a new product. $ ^{170} $ Under the Nash-Bertrand model, the prices of existing brands should react in a particular way. These predicted reactions can be compared to the actual price reactions. Again, if there were a substantial divergence, one may question the validity of the Nash-Bertrand assumption.

### 2. Case Study: Merger Simulation Applied to the Kimberly-Clark/Scott Merger

In the antitrust analysis of the Kimberly-Clark/Scott merger described above, merger simulation was employed by economists retained by the merging parties to analyze the likely competitive effects. The structure of consumer demand was econometrically estimated using retail scanner data. Pre-merger product marginal costs were inferred from the product prices and the estimated own-price elasticities of demand under the assumption that a Nash-Bertrand model was an appropriate description of the nature of pre-merger competition. The gross margins implied by the inferred marginal costs matched well with the gross margins obtained from the financial statements of the companies. This suggests that the Nash-Bertrand assumption was valid in this case. In addition, as part of their business planning, the companies performed an analysis of the cost savings they expected to generate as a result of the merger. Cost savings were expected from improving production yields, rationalizing production across plants to reduce shipping costs, and obtaining better materials prices through increased purchasing power.
The post-merger world was modeled using (1) the estimated demand structure, (2) the inferred marginal costs less the estimated cost savings, and (3) the Nash-Bertrand model of competition.

The predicted price changes resulting from the merger, with and without marginal cost efficiencies, were as follows: $ ^{171} $

Table 3

Post Merger Price Changes

Kleenex	Cottonelle	ScotTissue
Price	Change	Absent
Efficiencies	2.4%	1.4%	1.2%
Price Change With Efficiencies	0.4%	-0.3%	-1.8%

Without any efficiencies, the merger would be expected to cause only small increases in the prices of the products. Kimberly-Clark estimated the marginal cost efficiencies to be a 2 percent reduction in the marginal cost of Kleenex and Cottonelle and a 4 percent reduction in the marginal cost of ScotTissue. With these efficiencies, the prices of Cottonelle and ScotTissue were expected to decrease by a small amount while the price of Kleenex was expected to increase slightly.

The economists for the merging parties presented the foregoing results. Presumably based on these results or similar results obtained by its own economists, the U.S. DOJ concluded that the merger was unlikely to have any substantial anticompetitive effect in the bath tissue market. Indeed, with the prospect for efficiencies and cost savings, the merger was likely to have a procompetitive effect, benefiting consumers on net through lower prices for ScotTissue. The U.S. DOJ allowed the merger to proceed without requiring any remedy in the bath tissue product area.

B. Analyses of Historical Events and “Natural Experiments”

### 1. Methodology

Empirical analyses can sometimes be based on historical events that allow one
to disentangle one hypothesis from another or to estimate the effects of certain types of actions in the marketplace. For example, after the entry of a new type of product into the marketplace, one can examine which existing products' prices decreased and by how much. Such an analysis may be useful in studying market definition. The entry of a new competitor results in a change in market structure, and the event may allow one to examine whether and by how much prices fell due to the increase in the number of competitors in the marketplace or due to the particular characteristics of the entrant. The data to examine these "natural experiments" may not always be readily available, but when they are, they can be a useful basis for an empirical analysis.

### 2. Case Study: Staples/Office Depot Merger

In 1995, Staples Inc. (Staples) and Office Depot Inc. (Office Depot) announced their intention to merge. The two companies operated what are called "office superstores." Office superstores are retail stores that specialize in the selling of office supplies and equipment such as computers, business software, office furniture, paper, pens, paper clips, etc. Office superstores were a relatively new phenomenon in the U.S. at the time of the merger. The business strategy behind office superstores (as well as other types of superstores) was to create purchasing power by building scale, which in turn was accomplished by specializing in one product segment (office supplies) and opening numerous stores across the country. Historically, the office superstores had succeeded in substantially decreasing the wholesale price for office supplies. These savings, in turn, were passed on to consumers in terms of lower retail prices.

At the time of the proposed merger, there was one other national office superstore chain called Office Max Inc. (Office Max). The merging companies also competed with mass merchandisers who carried office supplies, such as Wal-Mart Stores, Inc. (Wal-Mart) and K-Mart Corporation (K-Mart), mail order office supply companies such as Viking Office Products, Inc. (Viking), and local independent office supply retailers.

None of the three existing office superstore chains was represented in every major U.S. metropolitan area. Office Max and Staples were more prevalent in the Northeast while Office Depot was more prevalent in the South. However, there were numerous metropolitan areas where the chains overlapped with each other. For example, in the area around Boston, Massachusetts, Staples and Office Max both had stores in close proximity. In Los Angeles, all three chains operated stores.
### a. Use of a Natural Experiment to Analyze Market Definition and Likely Competitive Effects

The key question in the antitrust analysis of the proposed transaction was whether other suppliers of office supplies, such as Wal-Mart, K-Mart, Viking, and local office supply stores, provided a significant competitive constraint on the pricing of the office superstore chains. If so, office superstores would not comprise a separate relevant market, and in a broader market, the Staples/Office Depot merger would not have any anticompetitive effect. If, on the other hand, the other sources of office supplies did not provide a significant competitive constraint on the prices of office superstores, the merger would likely lead to higher prices. This is because, prior to the merger, Staples and Office Depot would then have been the primary competitive constraints on each other's pricing in the geographic markets where they overlapped and particularly in the subset of geographic markets where Office Max was not present.

All three office superstore chains had been opening stores around the country regularly in the years prior to the merger. Often, one chain would open a store within the “market area” of an existing office superstore operated by a competing chain. The market area for a given store is fairly localized since customers are willing to drive only limited distances to buy office supplies. The occurrence of a new store opening in the presence of an existing competing store was a “natural experiment” that allowed economists to test the question of whether office superstores were competitively constrained in their pricing by other sources of office supplies. $ ^{172} $

Consider, for example, a Staples store operating in a market area that had no other competing office superstores. If the pricing at this Staples store was competitively constrained by a nearby Wal-Mart, we would expect that the opening of an Office Depot store nearby would have little or no effect on Staples' pricing—the Staples store would already be pricing at the competitive level. If, on the other hand, the local Wal-Mart store had little constraining effect on Staples' pricing, then we
would expect the opening of the new Office Depot store to substantially reduce prices at Staples.

The data used for this type of analysis was the following: (1) information on pricing by store and product (for a “basket” of products) over an extended period of time, (2) the location of each office superstore, (3) the date when the rival office superstore opened, and (4) similar information for other types of competing suppliers of office products. The econometric specification that was estimated was as follows:

$$ \log P_{_{it}}=\alpha_{_{i}}+\gamma OD_{_{it}}+\delta OM_{_{it}}+\theta WM_{_{it}}+\varepsilon_{_{it}} $$

where $ \log P_{it} $ is the log price index for Staples in store i in week t, $ \alpha_{i} $ is a fixed effect for store i, and $ OD_{it} $ , $ OM_{it} $ , and $ WM_{it} $ are “competitor variables” that represent the presence of Office Depot, Office Max, and Wal-Mart, respectively, nearby store i in week t. The latter variables could be simply indicator (zero-one) variables, or could be the number of competing stores. The concept of “nearby” stores could be defined as stores within several miles or stores within the same metropolitan area. The coefficients on the competitor variables were interpreted as measures of the extent to which the presence of nearby competitor stores affected Staples’ pricing.

The fixed effect for store i was important in that it captures all time-invariant factors that might affect the price of store i. Thus, a store located in a high cost area (labor cost or rent) might be expected to have higher prices over the entire time period. This factor will be accounted for by the fixed effect. The presence of the fixed effects in the equation also implies that the coefficients on the competitor variables will be econometrically identified only through changes in the competitor variables. In other words, these variables changed only when the respective competitors opened or closed nearby stores.

The economists consulting for the merging parties performed this type of analysis using econometric methods. $ ^{173} $ They found that the opening of a competing office superstore had no statistically significant effect on the prices at nearby existing Staples stores. This result supported the conclusion that office superstores did not constitute a separate relevant market, that Wal-Mart and other suppliers of office supplies competed with the office superstores, and that the merger was therefore unlikely to have any significant anticompetitive effect.

The economists consulting for the FTC performed a similar analysis using a somewhat different set of stores and variables. They found that the opening of an
Office Depot tended to reduce Staples' pricing. However, the economists consulting for the merging parties demonstrated that the FTC's economists' study failed well-known econometric tests. The U.S. District Court judge who heard the case did not mention the econometric analysis of either side in his written opinion. Thus, there were no conclusions drawn by the Court regarding the merits of either side's empirical approach and analysis.

### C. Analyses of Bidding Data

### 1. Methodology

In bidding markets, it is particularly important to assess the competitive significance of individual bidders in the marketplace because aggregate market share data may not be reliable or sufficient. The principal issue is that in situations where bidding yields just one winner, it is often difficult to identify the firm or firms whose presence was important in determining the final price. That is because market shares essentially count the instances in which an individual firm actually won a competitive bid; as there is only one winner in many bidding situations, such a count could understate the competitive role played by the losing bidders.

Bidding situations are also complex to analyze because they are often influenced by the rules of the bidding process. These rules are especially important because they are often designed to encourage or promote competition among bidders. Moreover, if the project or contract is long term or likely to yield large revenues or profits, the bidding tends to be particularly vigorous. In these circumstances, there may not be much, if any, relationship between the number of bidders in a particular contest and the resulting outcome (e.g., price paid by the purchaser).

Bidding data therefore has been an important part of the analysis conducted by the competition policy agencies in numerous mergers and acquisitions. Bidding data may shed light on the frequency with which the merging parties had confronted each other in bidding situations and were, therefore, each other's closest competitors. The data also may be rich enough to determine whether the presence of a particular bidder has an important impact on the bids offered by rivals. For instance, if the merging parties find themselves in the first or second place spots for many customers, then the antitrust agencies may be concerned that the merger could lead to higher prices by eliminating the purchaser's next best alternative to the winning bidder. On the other hand, if the merging parties are typically the first and third place bidders, then the
merger may not change competitive conditions, assuming the second bidder is likely to continue to bid as vigorously as it did prior to the merger. Of course, if there are few bidders, then it is possible that the elimination of even a third place rival could lead to less aggressive bidding by all rivals. As the examples below illustrate, the analysis can be persuasive in assessing the degree to which the merging rivals are close competitors as well as the potential effect of the transaction on the prices paid by customers.

### 2. Case Study: Acquisition of Agilent Healthcare Solutions Group by Philips Medical Systems

In 2001, Philips Medical Systems (Philips) purchased all of the assets of Agilent's Healthcare Solutions Group (Agilent HSG) in a transaction that combined two largely complementary suppliers of medical equipment. Worldwide sales for Philips were over Euro 31 billion, and total sales for Agilent HSG worldwide were Euro 1.3 billion. $ ^{174} $ Although both companies are generally known for making and selling medical imaging devices and equipment (e.g., x-ray, computer tomography (CT), magnetic resonance imaging (MRI), nuclear medicine, and ultrasound), the main competitive overlap was in the product area of cardiac ultrasound imaging equipment. The transaction was reviewed by the U.S. DOJ and by the EC. The discussion below focuses on the EC's review, which involved empirical analyses aimed at determining the potential for unilateral effects.

To assess the competitive effects of the merger, the EC relied on a combination of qualitative and quantitative evidence. Evidence on the merging parties' market shares and the presence of at least two or three major competitors were important in demonstrating that the proposed acquisition would not give Philips a dominant position in both the "overall ultrasound market" or in a market that might be more narrowly defined by application (e.g., cardiology) or price range (e.g., high end). $ ^{175} $ In addition, the EC relied on evidence that market shares tended to fluctuate following technological innovations that allowed competitors with the newest products to gain share quickly at the expense of older generation products.

To support its conclusion that the proposed transaction would not lead to
unilateral competitive effects, the EC also considered and accepted an economic study based on Agilent HSG's "win/loss" data, that is, the results of bids for cardiac ultrasound equipment that were won and lost by Agilent HSG. That analysis found that General Electric Company (GE) and Siemens Medical Systems (particularly after Siemens' acquisition of Acuson in November 2000) were Agilent HSG's two strongest competitors and that Philips generally ranked third. $ ^{176} $

In the EC’s analysis of Philips/Agilent HSG, the economic studies that were conducted using relevant win/loss data were not meant to quantify the potential unilateral effects that might arise from the proposed transaction. Instead, they were descriptive studies designed to determine whether the parties were close rivals or not. In both matters, the need to quantify the potential unilateral effects was not necessary in light of the finding that the merging parties were not each other's closest competitor.

### 3. Case Study: Acquisition of Instrumentarium by General Electric Company

In 2003, GE notified the Commission that it sought to acquire control of Instrumentarium, a Finnish company whose main business was in the areas of anesthesia and critical care, including patient monitors and anesthesia delivery machines. The proposed transaction raised a variety of competitive concerns because GE makes and sells patient monitors, including those that are designed to work with anesthesia delivery machines. $ ^{177} $ The transaction also was reviewed by the U.S. DOJ and by the EC. The discussion below focuses on the EC's review.

The EC's review of the proposed acquisition focused on three products: patient monitors, mobile fluoroscopic x-ray machines (i.e., mobile C-arms), and mammography devices. In each of these product categories, the analysis focused on distinct segments defined by either medical application or technology. The EC also found that the relevant geographic markets were national in scope. To illustrate the kinds of empirical analyses that were undertaken, the discussion that follows will focus on perioperative patient monitors (i.e., patient monitors used in or near the operating
room of a hospital). $ ^{178} $

In its evaluation of the competitive effects of the transaction in the area of perioperative patient monitors, the EC evaluated the potential for horizontal merger effects, as well as the potential for foreclosure stemming from concerns regarding the merged entity’s incentive to engage in technological tying. $ ^{179} $ As part of its review, the EC collected and analyzed market share data, as well as qualitative information regarding the nature of the process by which hospitals purchase patient monitors. In addition, the EC analyzed bidding data “to assess whether the market shares of the merging parties overestimate or underestimate their market power…” $ {}^{180} $ For instance, in an analysis that was similar to that which was conducted in connection with Philips’ acquisition of Agilent HSG, the EC analyzed the available bidding data to determine whether GE and Instrumentarium were close competitors. In this case, the EC found that across the EEA, the merging parties competed against each other in 50 to 60 percent of the cases. $ ^{181} $ The EC also found that at the EEA level, GE was the most frequent “runner-up” to Instrumentarium in customer bids for perioperative patient monitoring equipment. $ ^{182} $

More importantly, the EC’s analysis of the available bidding data also sought to quantify the possible price effect of the proposed acquisition. $ ^{[183]} $ As stated in its