Silviase/jgov_v1.4_full_ann huggingface dataset:
U.S., the analysis conducted by the U.S. DOJ focused on the potential competitive effects of the transaction for skiers living in the counties in and near Denver.

The focus on this group of consumers made sense because they typically drive to the resorts from where they live, and they ski for the day or perhaps the weekend. Thus, their choices are likely to be limited to the Denver area. Moreover, they are typically the target for weekly discounts and coupons for weekend skiing, and the DOJ was concerned that the transaction could lead to a reduction in those discounts. Whether this was likely and by how much the discounts could potentially have dropped depended on whether Ralston's resorts constrained the prices that Vail charged to Denver-area skiers. Although there was the potential for prices to rise at all five resorts, the DOJ focused on the potential for unilateral competitive effects and, specifically, the possibility that prices would increase at Vail Ski's resorts only.

How would the merger change Vail Ski’s pricing decision? Prior to the merger, Vail Ski would have refrained from raising its price because a price increase might have caused a number of skiers to ski at other resorts, including Ralston’s resorts. Thus, even if Vail Ski could have earned higher margins on each Denver-area skier who decided to pay the higher price and ski at a Vail Ski resort anyway, there will be “lost sales” as some skiers decide to go somewhere else.

After the merger, however, an increase in the price of a lift ticket at Vail Ski might lead skiers to switch, but those who choose to go to a Ralston resort would no longer be counted as “lost sales” from the point of view of the merged company. Indeed, it is the additional profit that Ralston would make on those skiers who switch that may make it profitable for Vail to raise its prices after the merger. The percentage of skiers who are likely to switch from Vail to Ralston was therefore the diversion ratio of interest. If the diversion ratio is high, then raising prices after the merger becomes much more attractive because the “lost sales” at Vail Ski would be offset by increased sales at a Ralston resort.

To assess the competitive implications of the proposed transaction, the economists for the merging parties designed a consumer survey to estimate the own-and cross-price elasticities of demand for the ski resorts in the area. A consumer survey was conducted because historical market data were not capable of yielding reliable estimates of the various elasticities of demand for skiers living in the Denver area. The survey, which asked respondents to compare and rank various resorts under differing price and snow conditions, provided the data needed to calculate the extent to which Front Range skiers were willing to switch from one resort to another, or not to ski at all, in response to changes in prices and snow conditions. Based on
these results and other market information, DOJ staff estimated that if the merger were allowed to take place without any divestiture, the acquisition would lead to an average price increase of approximately four percent. To resolve these concerns, the merging parties agreed to divest one of Ralston's ski resorts, and the transaction was approved.

### B. Analyses of Customer Switching Data

### 1. Methodology

One method for determining whether two firms are in the same market is to see whether a significant fraction of consumers are readily willing to switch between them for the purposes of purchasing the same product. As described below, analyses of customer switching data often require an inquiry that is specific to the data that are collected.

### 2. Case Study: SunGard/Comdis co Merger

SunGard Data Systems Inc. (Sungard) and Comdisco Inc. (Comdisco) were companies that supplied disaster recovery services. Disaster recovery services allow customers to restore the contents of their computer systems at another location if those systems are subject to a shutdown due to a power outage or other “disaster.” There were a number of different types of disaster recovery services offered in the marketplace. Both SunGard and Comdisco offered “shared hotsites” which were computer facilities shared among several customers that could be used to restore the customers’ computer systems from backup tapes in the event of disaster. $ ^{156} $ The companies’ main competitor in this market segment was IBM. However, there were other types of disaster recovery services that customers could potentially use as alternatives to shared hotsites. For example, a customer could set up its own in-house “internal hotsite” or use “quick ship services” which involve the rapid shipping of replacement computer systems after a disaster has occurred.

In October 2001, SunGard won an auction of Comdisco assets which were being sold due to Comdisco's bankruptcy. The U.S. DOJ filed a motion seeking a temporary
restraining order blocking SunGard's acquisition of the assets. A hearing was held in front of a U.S. District Court judge. With regard to relevant market definition, the U.S. DOJ argued that the market consisted of hotsite services provided to customers with mainframe and midrange computer systems, thus excluding customers with small computer systems. Under this market definition, the number of competitors would fall from three to two (the merged firm and IBM) and the merged firm would hold a share of 71 percent. $ ^{157} $ SunGard contended, in contrast, that the relevant market should also include at least quick ship services and internal hotsites. In that case, the merged firm would hold a share of less than 35 percent given that internal hotsites accounted for approximately half of all hotsites. $ ^{158} $

### a. Use of Customer Switching Data

SunGard offered customer switching data as evidence. Both SunGard and Comdisco gathered information on the alternatives chosen by the customers they lost. The customer switching data revealed that each company had lost more customers to internal hotsites (i.e., customers taking the business in-house) than to all outside suppliers of disaster recovery services combined. $ ^{159} $ This evidence of extensive customer switching from shared hotsites to internal hotsites provided strong support for SunGard's position that the relevant product market for shared hotsites must include internal hotsites as well. In part based on this analysis, the U.S. District Court ruled in favor of SunGard and allowed the merger to proceed.

### b. Diversion Ratios

As mentioned above, customer switching data of the type described above can also be used to calculate diversion ratios. For example, suppose that a hypothetical customer survey (like the survey discussed above) were to indicate that 5 percent of SunGard's lost sales switched to Comdisco. In this case, the diversion ratio would be 0.05. Now suppose that the profit margin for the two retailers was 30 percent. If we apply the following formula for the post-merger price increase,
$$ \frac{mD}{1-m-D} $$

then the predicted post-merger price increase would be 2.3 percent. $ ^{160} $

### C. Estimation of Own- and Cross-Price Elasticities of Demand

### 1. Methodology

Under the U.S. Merger Guidelines, an important first question is whether the merging parties' products are each other's "next best substitutes." This question can be answered by examining each product's cross-price elasticities of demand. The cross-price elasticity of demand between product A and product B measures the extent to which consumers are willing to switch between product A to product B in response to a change in the relative prices of the two products. The cross-price elasticities (along with the own-price elasticities of demand) can be estimated using econometric techniques if data on the sales and prices of the various products are available over time.

Retail scanner data provide precisely such information. $ ^{161} $ If such data are available, an econometric analysis would involve measuring the extent to which the demand for product A changes when the price of product B changes (e.g., when product B goes on sale). $ ^{162} $ For example, if the cross-price elasticities of product A with respect
to the prices of product B, product C, and product D were known, the closest substitute (i.e., the “next best substitute”) for product A will be the product (out of B, C, and D) with the largest cross-price elasticity.

### 2. Case Study: Kimberly-Clark/Scott Merger

In 1995, Kimberly-Clark Corporation (Kimberly-Clark) announced its intention to acquire Scott Paper Company (Scott). The DOJ reviewed the transaction. Kimberly-Clark and Scott participated in both the commercial and retail tissue businesses, manufacturing a wide range of retail tissue products including facial tissue, bath tissue, paper towels, napkins, and baby wipes. The companies' shares varied substantially by product. For example, Kimberly-Clark with its Kleenex product held a large share of the facial tissue market, while its share of the paper towel market was relatively small because it did not have a product with a strong brand name in this market. Beyond Kimberly-Clark and Scott, there were four other major players in the retail tissue market, namely, Procter & Gamble Co. (Procter & Gamble), Georgia-Pacific Corporation (Georgia-Pacific), James River Corporation (James River), and Fort Howard Corporation (Fort Howard). $ ^{163} $

We will focus our discussion on bath tissue, because this segment was central to the merger analysis. There was little debate that bath tissue formed a separate relevant market. The merging parties' business people believed that there was little substitution between bath tissue and other segments. No further analysis was done and both the parties and the DOJ adopted this market definition.

Within the bath tissue market, the brand shares were as follows: $ ^{164} $

## Table 1 Bath Tissue Brand Shares

Kleenex	Cottonelle	Charmin	Northern	Angel Soft	ScotTissue	Brands
Share	8%	7%	31%	12%	9%	17%	16%

Kleenex was the Kimberly-Clark brand and Cottonelle and ScotTissue were the Scott brands. Charmin was the Procter & Gamble brand, Northern was the James Rivers
brand, Angel Soft was the Georgia-Pacific brand, and the economy brands (here aggregated into a single group) were made up generally of store brands (private label) and regional brands. After the merger, the parties' combined share would be 32 percent. The HHI would increase by 384 points in a market where the HHI already exceeded 1800.

The bath tissue market was segmented into premium and economy brands. The major premium brands were Kleenex, Cottonelle, Charmin, Northern, and Angel Soft. The economy segment, as mentioned above, consisted of private label products (also known as store brands) and regional brands. In a separate segment of its own, was Scott's ScotTissue brand, which was thought to appeal to a distinct set of consumers who desired a large number of sheets per roll at a value price. The premium segment was differentiated from the other segments by the quality of the tissue used to create the product. A higher quality bath-tissue product is softer and thicker, and is also more costly to produce.

### a. Analysis of the Competitive Effects in the Bath Tissue Market

The differences in characteristics across products and the companies' investments in building their brand names implied that a differentiated products analysis was the appropriate approach to assessing the competitive effects of the merger. $ ^{165} $ Bath tissue products are predominantly sold through the supermarket, drug store, and mass merchandiser channels. ACNielsen and IRI are market research companies that collect sales data from the bar code scanners in a scientifically valid sample of stores in each of these channels. After processing and packaging the data, AC Nielsen sells the data (and analytic services) to the manufacturers, who then use the information in their market research efforts. For the purposes of antitrust analysis, the availability of retail scanner data provided an opportunity to analyze empirically a merger's likely competitive effects. In this particular case, weekly data were available on each individual bath tissue product (defined by brand and package type and size) in 26 cities for a period of 154 weeks. The variables included in the data were: product ID, city, week, dollar sales, and quantity sales. In general, Nielsen and IRI can also provide information on the percentage of stores within the city (weighted by total sales volume) and week that had a promotional display for the product or an advertisement for the product in the store circular.
Below, we reproduce the cross-price elasticities for Kleenex bath tissue that were estimated using econometric techniques applied to retail scanner data as described above: $ ^{166} $

Table 2

Kleenex Cross-Price Elasticities

Angel
Charmin	Cottonelle	Northern	Soft	ScotTissue
Kleenex	Cross-Price
Elasticity	0.69	0.19	0.21	0.13	0.18

For example, the cross-price elasticity of Kleenex with respect to Charmin was estimated to be 0.69. Being the largest among all other brands, this indicated that Charmin was the “next best substitute” for Kleenex. Northern was the second best substitute and the Scott brands Cottonelle and ScotTissue were the third and fourth, respectively.

One must also look at the cross-price elasticities for Cottonelle and ScotTissue, since the “next best substitute” need not be symmetric; in other words, Kleenex might still be the next best substitute for Cottonelle or ScotTissue even though neither of these brands is the next best substitute for Kleenex. An examination of the cross-price elasticities for Cottonelle and ScotTissue (not reproduced here) showed that the top two substitutes for Cottonelle were Northern and Angel Soft and the top two substitutes for ScotTissue were Charmin and Northern. Thus, the estimated cross-elasticities of demand demonstrated that the Scott brands and the Kimberly-Clark brand were not the next best substitutes for each other.

Analyses of this type were conducted by the economists retained by the parties and also by the DOJ economists. A consent decree was ultimately negotiated and the merger was allowed to go forward. No divestitures were required in the bath tissue segment, an outcome that is supported by the cross-price elasticity analysis described above. Similarly, no divestitures were required in paper towels or napkins where the companies’ combined shares were relatively low.

In baby wipes and facial tissue, however, where the parties’ combined market shares would have been over 50 percent, divestitures were required. In particular, the consent decree required that Kimberly-Clark and Scott (1) divest Scott’s baby
wipes business, (2) license the Scott facial tissue brand name, and (3) divest two tissue manufacturing plants. The outcome in these two segments seems to have been based primarily on the fact of high combined market shares in baby wipes and facial tissue. However, with regard to these markets, no substantive economic analysis was exchanged between the merging parties and the U.S. DOJ.

### I V. Empirical Approaches to Assessing the Competitive Effects

### A. Merger Simulation

### 1. Methodology

Estimating and comparing the cross-price elasticities as described above is only the first step in a complete analysis of the competitive effects of a merger. The reason that the analysis cannot stop with an identification of “next best substitutes” is that the merger of two products that are not each other’s next best substitute can nevertheless still lead to substantial post-merger price increases if the two products are sufficiently close competitors. Moreover, the merger of two products that are each other’s next best substitute may not lead to substantial post-merger price increases if the competition between the two products is relatively weak, there are other closely competing products in the market, entry and expansion (including the relocation or repositioning of substitutes) is readily accomplished, or if the efficiencies generated by the merger are sufficiently strong to offset what would otherwise be price increases.

With estimates of the own- and cross-price elasticities, an analysis of the competitive effects of a merger can be performed using a method called “merger simulation.” Merger simulation involves building a model of the post-merger industry and then solving the model to determine the firms' optimal (post-merger) prices. These predicted post-merger prices are compared to the actual pre-merger prices to assess the likely effects of the merger. This type of methodology—building models of markets and using these models to predict the effects of a change in economic conditions or economic policy—has a longstanding history in economics.

A straightforward example illustrates how a merger simulation works. Suppose that an industry consists of two firms selling a homogeneous product, each with a 50 percent market share. Pre-merger they both charge a price of 1 and they both have a marginal cost of 0.75. We observe (perhaps through econometric estimation) that the market demand curve is $ Q=(300-200P) $ . To model the
post-merger world, we could start by assuming that the market demand curve would remain the same and the marginal cost of the merged firm would be 0.75 (i.e., no merger-related efficiencies). However, the merged firm would be a monopolist. Thus, in this model of the post-merger world, we would predict that the merged firm would choose a price to maximize its profits, which, in mathematical terms, is $ (P - 0.75)(300 - 200P) $ . Solving this maximization problem yields a price of 1.13. Comparing this price to the pre-merger price suggests that the merger will lead to a 12.5 percent price increase.

As a general matter, economic principles demonstrate that market prices are a function of demand conditions, cost conditions, and the nature of competitive interaction between firms. Thus, in performing a merger simulation, the economist must model these aspects of the industry. Specifically, a merger simulation requires three primary inputs: (1) the own- and cross-price elasticities between the products in the market or, more generally, the structure of consumer demand; (2) the cost conditions of firms in the market; and (3) the nature of strategic interaction between firms in the market. We now discuss how each of these inputs can be obtained in practice.

Structure of Consumer Demand. There are several ways for the economist to obtain information on the structure of consumer demand. The preferred method among economists is to econometrically estimate the elasticities of demand using retail scanner data, consumer survey data, or other data on price and quantity, as described in the previous section. This method is preferred because it requires relatively few restrictive assumptions and those that are needed can typically be statistically tested to ensure their validity. On the other hand, the data required for econometric estimation are not always available. In that case, a less satisfactory, but at the same time less data-intensive, method involves inferring the elasticities from data on company prices, gross margins, and shares. The “antitrust logit” and “PCAIDS” models are examples of this method. $ ^{167} $ The relatively low data requirements come at the cost of restrictive assumptions that typically cannot be tested.

Cost Conditions. Data on firms' pre-merger costs can be obtained from the firms' financial statements, although one must take care that the accounting data conform with the economic concepts of marginal or incremental cost. A second method is to infer the firms' pre-merger marginal costs based on their prices, the
structure of demand, and an assumed model of pre-merger competition. For example, if one were to assume the Nash-Bertrand model of competition in a differentiated products industry, a single product firm's margin of price over marginal cost should equal the inverse of the product's own elasticity of demand. If the price and demand elasticity are known, the marginal cost can be derived based on this relationship. The post-merger marginal cost for each product can be computed as the pre-merger marginal cost less any cost-saving efficiencies that the merger is expected to generate. The merging parties typically estimate such cost savings as part of their business analysis supporting the proposed merger.

Strategic Interaction. The nature of strategic interaction between firms in the market is often assumed to be of the static Nash-Bertrand variety in practice when the market being analyzed is a differentiated products market. $ ^{168} $ However, methods exist for testing this assumption and doing so when possible is good practice. $ ^{169} $

Having specified a model of the post-merger world, merger simulation allows us to simultaneously assess three effects of the merger on market prices. The first effect is the change in incentives for the merged firm when setting its prices. This change in incentives arises due to the relaxation of competitive constraints caused by the merger. For instance, we can start with the premise that, prior to the merger, the firms have priced their products to maximize their profits. In doing so, each firm is constrained by the fact that an increase in its price will cause some consumers (“marginal consumers”) to switch to other brands (or leave the market altogether), in which case the firm would lose the profit margin on these customers. Prior to the merger, the firm chooses its price so that the gain from an increase in price to customers who would not switch (“inframarginal consumers”) is exactly offset by the loss in profit margin from the marginal consumers.

Now suppose that there is a merger of two firms, A and B. Before the merger, firm A was constrained in setting its price by the fact that a higher price would cause some marginal consumers to switch to firm B's product. After the merger, however, an increase in the price of firm A's product will lead to those marginal customers going from one pocket (i.e., firm A's product) to another (i.e., firm B's product). In other words, the merged firm does not lose as many marginal consumers as firm A did by