Lecture 9: Antitrust Policy & Merger Analysis with Differentiated Products I.An Overview II.The Antitrust Framework. III.Simulation analysis in mergers involving differentiated products I. An Overview In 1998, completed mergers and acquisitions totaled $1.273 trillion, a new record. This amount was 50% above 1997 levels. Indeed, the merger wave of the 1980s peaked with completed mergers and acquisitions equivalent to 10% of GDP, before falling to 3% in Since then, the merger/GDP ratio has risen steadily each year, reaching 22% of GDP in Hence mergers are a significant part of economic activity.

The analysis of mergers using non-cooperative game theory seems very compelling. Yet up until the early 1980s, the dominant approach was based on the merger’s likely effect on collusion in the market. Now, the Antitrust division typically focuses on unilateral effects rather than the potential collusion effects. Early work using game theory treated firms as Cournot competitors, and a merger between two firms was equivalent to one of the firms exiting the market. This approach has been criticized in recent years. New merger analysis assumes differentiated products and models the firms as independently setting the prices of each of their brands. The typical assumption is that the merging firms continue to sell all products.

II. Mergers: The Antitrust Framework Mergers fall under section 7 of the Clayton Act and the general criterion for illegality is whether the effect of the merger would be to substantially lessen competition. For at least 50 years, the U.S. Courts have favored a structural test to see whether such an outcome was likely, i.e., a test based on concentration and market shares. By the 1960s mergers involving firms with small market shares were routinely being blocked by the courts. Because of inconsistent (and overly interventionist) decisions, the U.S. Dept. of Justice tried to codify the practice of merger enforcement with the issuance of a series of merger guidelines beginning in The first part of the guidelines involved concentration and safe harbors, based on measures of market concentration.

For example, the 1982 guidelines created safe harbors for mergers in markets with postmerger HHIs below 1000 and for mergers that increased the HHI by less than 50. Problem is that it was assumed that pre and post merger market shares would remain the same. The 1982 guidelines introduced a major innovation in the methodology of market definition. A market is defined “as a product or a group of products and a geographic area in which it is sold such that a hypothetical, profit-maximizing firm, not subject to price regulation, that was the only present and future seller of those products in that area would impose a small but significant and non-transitory increase in price above prevailing or likely future levels.” The purpose is to find a gap in the chain of substitute products.

The market that results from this test, is termed the relevant antitrust market and critical battles have been waged over this issue. One problem with the guidelines is that the focus was typically on a static oligopoly, that is, the guidelines did not place too much emphasis on the possible impact of technological change. Now, a requirement of the government as part of a consent order to a merger is that steps should be taken to maintain rivalry in innovation. This can be ensured via divestiture or licensing of a key proprietary technology. The 1992 revision to the Merger Guidelines added a new focus on the role of entry. The guidelines require that entry be timely, likely and sufficient enough to counteract the competitive effects of concern.

There is a growing emphasis on efficiencies. Without efficiency gains from a merger, prices will likely increase. Synergies are often critical if a merger is to increase total surplus. A 1997 revision to the guidelines states that the primary benefit of mergers to the economy is their potential to generate efficiencies. Only the efficiencies that cannot be achieved without mergers will be considered in the merger analysis.

So how does all of this work in practice? Case Study: Staples-Office Depot In September 1996, Staples and Office Depot, the two largest office superstar chains announced an agreement to merge. The FTC opposed the merger and presented one of the first cases to use modern economic and econometric analysis. This case established the use of unilateral-effects analysis (as opposed to coordinated-effects analysis). The case also confirmed that U.S. courts will primarily apply a price standard, that is, a contested merger will only be approved if the defendants can show that prices will not rise as a result of the merger. There is a minor role for efficiency gains arising from a merger.

The Case in Detail By the mid 1990s, there were only three effective competitors: Staples, Office Depot, and Office Max. Since these stores were not always in the same geographical market, larger cities consisted of monopolies, duopolies, and triopolies. (This variation was critical to enable the use of econometric analysis.) The FTC experts first established that Office Superstores (OSSs) were the relevant market. This was a key step, because OSSs only accounted for 6% of total office supply sales. Data obtained from the three firms indicated that they feared competition from the other two, but not traditional stores. By comparing prices in monopoly, duopoly, and triopoly markets, FTC economists obtained a crude measure of the likely effect of both a hypothetical monopoly and the proposed merger itself.

The FTC also constructed an econometric model of the industry, including both large and small non-OSS. The model predicted that a merger to monopoly would raise prices by 8.49%, more than needed for an antitrust market. Once the relevant market had been established, the FTC used the econometric model to predict the effect of the proposed Staples- Office Depot merger. The model predicted that the merger would increase prices by an average of 7.3% in in the two and three firm market cities where both firms were present. The FCC also used an event study of stock market prices immediately preceding and following the proposed merger announcement to show that their econometric estimates were consistent with (stock) market expectations of the possible merger.

The FCC also demonstrated that barriers to entry were very high in the OSS market. The defense claimed that efficiency gains (primarily from economies of scale) would cause prices to fall by 3.0% following a merger. (The defense also claimed that the gross price effect of a merger ignoring efficiencies was less than 1%. The FTC argued that economies of scale were nearly exhausted and that they would amount to less than 1%. The judge agreed with the FTC in almost every respect and the proposed merger was not approved.

III. The use of simulation analysis in mergers involving differentiated products Economic Analysis regarding the unilateral effects is more amenable to quantification than is economic analysis of the dangers of collusion. Ultimately, we are trying to measure the added incentive to raise price caused by the merger. Employing a combination of game-theoretic and econometric methods, we now have the capability to estimate consumer demand using industry data, and based on these demand estimates to derive specific predictions regarding post merger prices. This contrasts sharply with with the analysis of the dangers of collusion. While we can easily list the factors that facilitate collusion, there is no accepted method of quantifying the increased likelihood of collusion as a result of a merger.

In the mid 1990s, the Justice Department showed an interest in a completely new approach to conducting merger analysis. Pioneered by Froeb and Werden, the simulation methodology using discrete choice models of product differentiation removes the need for defining a relevant antitrust market. In the same way, market shares have no particular meaning. While we’ll focus on the methodology, it is important to keep the pitfalls in mind: One problem with this approach is that it may take decades before judges and antitrust lawyers become comfortable with the methodology. It remains to be seen that this approach leads to better decisions that an approach based on market definition and market shares.

Methodology Econometrically simulating the effects of differentiated product mergers involves four steps. 1.Make an assumption about the type of conduct (competition) in the industry. In most cases, Bertrand, or price competition is employed. 2.Econometrically estimate the relevant demand parameters. Here a specification needs to be chosen. (We will not do this now.) 3.Use the estimated demand parameters and information on prices and shares to get marginal cost estimates from first order conditions 4.Use demand and cost information to simulate the effect of a merger.

Estimating the Effects of a merger Suppose that two firms merge in a four firm industry where firms sell differentiated products: (Step 1: Assume Bertrand competition.) Before merger:  i =(p i – mc i ) q i, i=1,4. After merger:  12 =(p 1 – mc 1 ) q 1 + (p 2 – mc 2 ) q 2  3 =(p 3 – mc 3 ) q 3  4 =(p 4 – mc 4 ) q 4 Step 3: If we know the demand function and have information on prices and quantities, we can we can back out the marginal costs using the “before merger” industry structure. Step 4: Once we know marginal costs, we can simulate the effect of a merger, by solving for prices (and quantities) in “after merger” industry structure. Hence, we have estimated the effect of a merger.

Step 2: Estimating Demand Functions Following Berry (1994) we employ a random utility model of the form: x j vector of observable characteristics, p j is the observed price of automobile j,  and  : mean valuations of the observable characteristics, The last two terms of the above equation are error terms:  j is the average value of product j's unobserved characteristics  ij represents the distribution of consumer preferences around this mean. (  ij introduces heterogeneity and its distribution determines the substitution patterns among products.)

We’ll assume that the are i.i.d extreme value (Weibull) distribution function. This (multinomial logit) model is popular because of a closed form solution. The probabaility of choosing product j is: (2)

Demand: If we normalize the mean utility of the outside good (k=0) to be 0, and take logs of equation (2), we obtain the demand function for each product: (3) Where s 0 is the share of the outside good. Equation (3) will be estimated. This will yield estimates for  and .

Oligopoly (Bertrand) price competition Let p j =price of product j, q j =quantity of product i, The profits of a single product firm are:  j =(p j - mc j ) q j FOC for profit maximization imply: q j +(p j - mc j )  q j /  p j =0. For the logit model, the FOCs are: p j = mc j – 1/  (1-s j )  p j =0 (4) Given p j,s j, and , marginal costs can be estimated from (4).