Was Mr. Hewlett Right? Mergers, Advertising and the PC Industry

Transcription

1 Was Mr. Hewlett Right? Mergers, Advertising and the PC Industry Michelle Sovinsky Goeree 1 Preliminary, please do not cite. March, 2005 (First Version June 2002) Abstract In markets characterized by rapid change, such as the personal computer industry, consumers may not know every available product. Failing to incorporate limited information and the strategic role of informative advertising into merger analysis may yield misleading results regarding industry competitiveness. This is of particular importance when accessing the welfare impact of mergers. I use the parameters from a model of limited consumer information to (1) estimate the effect on profits and consumer welfare from mergers and (2) to examine the role of advertising as it relates to market power and the implications for antitrust policy. The methodology used to evaluate the impact of mergers follows Nevo(2000), but incorporates limited information and strategic choices of advertising. I simulate postmerger price and advertising equilibria for the Compaq-HP merger and for a hypothetical merger. I decompose the change in prices into changes due to increased concentration and changes due to the influence of advertising. The results indicate advertising can be used to increase market power when consumers have limited information, which suggests revisions to the current model used to access the impact of mergers in antitrust cases. JEL Classification: L15, D12, M37, D83 Keywords: merger analysis, informative advertising, discrete-choice models, product differentiation, structural estimation 1 This paper is based on various chapters from my 2002 dissertation. Special thanks to my dissertation advisors, Steven Stern and Simon Anderson, for their guidance. I am grateful to Gartner Inc. for making the data available, and specifically to Sandra Lahtinen. I also gratefully acknowledge financial support from the University of Virginia s Bankard Fund for Political Economy. Address for correspondence: Claremont McKenna College, 500 E. Ninth Street, Claremont, CA ( msgoeree@mckenna.edu)

2 1 Introduction On May 7, 2002 Hewlett-Packard (HP) Company launched the new Hewlett-Packard with an ad titled We are Ready. The new Hewlett-Packard is a result of a merger with Compaq Computer Corporation, the largest ever in the information technology sector. The $19 billion deal has drawn a lot of media attention for a number of reasons. Investors and rival firms are interested in its impact on shares and profits. Consumers are interested in the effect on prices. Regulators are interested in its implications for competition in an already concentrated industry. Originally proposed in June 2001, the merger prompted a bitter battle between Hewlett and Packard family interests and corporate executives. It was ultimately approved by a slim majority of shareholders (only 3%). Many HP shareholders opposed the deal because they thought the time lost in absorbing Compaq and incorporating cost synergies would distract from winning new orders at a time when the market was slowing. Walter Hewlett, whose father cofounded HP, launched a court battle against HP arguing the merger would result in lost profits in the long run. As further evidence of his conjecture, Hewlett pointed to his competitors, We believe that HP stockholders should be concerned when competitors, like SUN, Dell, and IBM don t object to a transaction that is supposed to add value to HP. Meanwhile, the Federal Trade Commission (FTC) voted unanimously to approve the merger. Likewise, the European Commission approved it without placing any conditions on the two companies, saying A careful analysis of the merger... has shown that HP would not be in a position to increase prices and that consumers would continue to benefit fromsufficient choice and innovation. 2 The analysis used by antitrust authorities to evaluate the impact of mergers is based on a model which assumes consumers are aware of all products for sale when they make their purchase decision. However, it is reasonable to think consumers may have limited informa- 2 HP-Compaq Merger Wins European Approval, NewsFactor Network, Feb.1,

3 tion regarding the products available, especially in markets characterized by a high degree of change such as the PC industry. Price elasticities calculated under the assumption of full information may yield misleading results regarding the degree of competition in the market. Incorrect conclusions regarding the competitive effects of a merger could lead antitrust authorities to approve a merger which has negative consequences for consumers. Goeree (2002) showed that assuming consumers are aware of all products in a market characterized by rapid change generates estimates of product-specific demand curves that are biased towards being too elastic. This is of particular importance when considering the welfare impact of mergers, one of the primary stated interests of the FTC when examining mergers. The goal of this empirical work is twofold: (1) to estimate the effect on profits and consumer welfare from mergers when consumers have limited information and (2) to examine the role of advertising as it relates to market power and the implications for antitrust policy. The methodology used to evaluate the impact of mergers is based on previous work, 3 but allows for limited information and strategic choices of advertising. I use the estimated parameters from a structural model of informative advertising 4 to simulate post-merger equilibrium price and advertising levels. I calculate the effect of mergers on the profits of merging and non-merging firms as well as the cost-synergies necessary to offset losses. I decompose the change in prices and markups into the changes due to increased concentration and the changes due to the influence of advertising. Perhaps surprisingly, the results suggest the latter effect is the strongest. The post-merger equilibrium results indicate advertising can be used to increase market power, which suggests revisions to the current model used by the FTC to determine market power in antitrust cases. I examine both the HP-Compaq merger and a hypothetical merger between IBM and Dell. 5 Considering a merger between these two firms is of interest for two reasons. First, 3 For example, see Baker and Bresnahan, 1985; Berry and Pakes, 1993, Hausman, Leonard and Zona, 1994; and Nevo, See Goeree (2002) for more detail. 5 While the merger is supposed, IBM and Dell entered into a $16 billion cross-licensing agreement in 2

4 IBM is the world leader in sales of non-pc s, while Dell is the world leader in sales of PC s. They focus on different aspects of the computer industry. expected to be lower than those between HP and Compaq. As a result, cost synergies are Secondly, and more directly related to the topic of this research, IBM and Dell have very different ad-to-sales ratios. IBM is a high-advertising intensity firm, while Dell is a low-intensity firm. In contrast, HP and Compaq are thought to have more cost synergies and are closer in their ad-sales concentration. Comparison of the outcomes from the two very different mergers yields insight into the role that advertising plays in this industry and how firms use it when industry concentration increases. The remainder of the paper is organized as follows. In the next section, I present an overview of the PC industry and discuss the data used in estimation. In section 3, I describe the model used in the counterfactual merger simulations. In section 4, I discuss the impact of mergers on welfare and profits, the role that advertising plays as concentration increases, and the implications for antitrust policy. 2 Trends in the PC industry and Data The PC industry, as we know it today, has been growing since 1971 when Intel introduced the first microcomputer. It utilized the 4004 microprocessor, an integrated circuit able to process four bits of data at a time (hence the 4 s). Intel was the first to imbed all components of a computer central processing unit (CPU), memory, and input-output controls on a single chip. The advantage was that, simply by changing the external program, the same device could be used for a multitude of projects. The first generally available microcomputer, the Altair 8800, was on the market only four years later. It retailed for $439, which made it affordable, but it was not easy to use. The kit required assembly and software was not available. Two young hackers tackled the second problem and began the process of writing software. The language was the Beginners All-purpose Symbolic Instruction Code, BASIC, The agreement will last until 2006 and calls for broad patent cross-licensing between the two firms and collaboration in the development of product technology. 3

5 and the hackers were William Gates and Paul Allen. Three years later Steve Jobs and Steve Wozniak introduced the Apple II. The first PC that was both affordable and usable. It had 4K RAM, built-in BASIC, color graphics, and sold for about $1300. Apple controlled the market until IBM introduced the (modestly named) PC. The PC featured a modular design so that pieces could be added easily. It soon surpassed the Apple II in popularity and allowed IBM to dominate the market for several years. In 1984, Apple introduced the first generation Macintosh during a SuperBowl commercial. The new Macintosh came with a graphical user interface and a mouse, which made it easy to use. As a result, sales of the Macintosh boomed. Competition and technological improvement has continued to spur innovation, and the PC industry has seen numerous product introductions in the past years. Due to the frequency with which new products are brought into the market, consumers may not be aware of all products offered. Indeed every year over 200 new PCs are available from the top 15 firms alone. Due to the large number of brand introductions and the competitive nature of the industry, it is has become increasingly important for firms to advertise to inform consumers about their products. As Figures 1 and 2 illustrate, prices dropped from an average of close to $2700 in early 1997 to under $1600 in 1999, while advertising expenditures grew by over $0.5 billion. Advertising has been an important dimension of competition in this industry since its beginnings. Between 1995 and 1999, advertising expenditures grew by nearly 100% to $2.3 billion. In 1998 over 36 million PCs were sold, generating over $62 billion in sales $2 billion of which was spent on advertising. There is much variation in advertising expenditures across firms and across media. For instance, in 1998, fifty percent of the industry expenditures are by IBM, resulting in an ad-to-sales ratio of over 19 percent. While the top two firms (in terms of market share), Dell and Compaq, have much lower ad-to-sales ratios of under 3%. The data are from , a period of tremendous growth in the PC industry. During 4

6 this time there were a number of mergers. In 1996 Packard Bell 6 was a 4.5 billion company and its 15% market share made it the largest PC manufacturer in the US. Compaq passed Packard Bell in mid 1996 and price pressure from Compaq and emachines along with poor showings in consumer satisfaction surveys made it difficult for the company to remain profitable. 7 In 1997, 3 mergers occurred: Packard Bell, NEC, and ZDS; Acer and Texas Instruments; and Gateway with Advanced Logic Research. After this period, there was a slowdown in the PC market. Demand in the home market sector (as well as other sectors) declined. In part because there was not as much of a need toupgradeasoftensincethepcsweresowellmadeandinpartduetotheslumpinthe economy. This data is from , a period of tremendous growth in the PC industry. Immediately after this period, Compaq merged with DEC, which proved to be a mistake for Compaq in that they never fully recovered their pre-merger market position. It is this Compaq with which HP merged in In this sense the results of this study are not as insightful as they could be given more recent data. However, these results indicate what would have happened as a result of the merger during a period of incredible growth. Even during this period of tremendous growth, I find that the merger would increase profits for the merged companies, but not by a lot more than it would for the other companies. It is perhaps not surprising then, that the HP-Compaq merger as it currently stands is not working so well. I also simulated the effect of a 3 way merger between Compaq, DEC, and HP. Finally, the in-sample mergers are used to test the predictions of the model, that is I can examine how well the model predicts new prices and advertising outcomes for the mergers that occurred over the period of the data. The data come from three primary sources. The product-level data are from Gartner Inc. and consist of prices, market shares, and product characteristics from the first quarter of 1996 to fourth quarter of The Gartner data detail sales across sectors: home, 6 Packard Bell was an American radio manufacturer (and has no association with Hewelett Packard). 7 source 5

7 business, educational institutions, and government. I use the home market data, which accounts for over 30% of all PCs sold, to estimate the model. The product level data are combined with advertising data obtained from Competitive Media Reporting s LNA/ Multi-Media. Advertising data are quarterly expenditures by firm across media 8 with some brand level information. 9 The final dataset is from Simmons Market Research and includes consumer-level purchases across manufacturers, information on media habits, and consumer characteristics for about 20,000 households annually. I restrict my attention to the top 10 firms (based on market share) and to 5 others to make full use of the micro-level data. 10 These 15 included firms account for over 85% of PC sales to home users. Details of the data and its construction are given in Appendix 1 and in Goeree (2002). 3 Model The econometric model follows those found in recent studies of differentiated products, such as Berry, Levinsohn, and Pakes (1995, 2004) and Nevo (2000) and is presented in more detail in Appendix B and Goeree (2002). 3.1 Consumer Behavior Individual i =1,...,N chooses from j =1,..., J products at time t =1,...,T. Gartner collects information on five main PC attributes: manufacturer (e.g. Dell), brand (e.g. Latitude LX), form factor (e.g. desktop), CPU type (e.g. Pentium II), and CPU speed. A product pertains to a specific PC model defined as a manufacturer-brand- CPU type-cpu 8 The expenditures are reported for 10 media, from which I construct 4 main categories: newspaper, magazine, television, and radio. 9 In the PC industry, it is common for manufacturers to advertise groups of products simultaneously. For example, in 1996 one of Compaq s advertising campaigns involved all Presario brand computers (of which there are 12). See Appendix 1 for more detail regarding the treatment of group advertising. 10 The included manufacturers are Acer, Apple, AST, AT&T, Compaq, Dell, Epson, Gateway, HP, IBM, Micron,NEC,PackardBell,andTexasInstruments. 6

8 speed-form factor combination. 11 The indirect utility consumer i obtains from product j at time t is given by u ijt = α ln(y it p jt )+x 0 jβ it + ξ jt + ijt (1) The characteristics of product j are represented by (p jt,x j,ξ jt ), these are price, non-price observed characteristics (such as CPU speed, laptop and Pentium dummies, firm fixed effects, and a constant), and unobserved (to the econometrician) characteristics, respectively. Income is represented by y it, ijt is a mean zero stochastic term which is assumed to be independent and identically distributed across products and consumers, and β it are individual specific components. The individual specific components are random coefficients where β it = β + ΠD it + Σν i, ν i N(0,I k ) where mean preferences for observable product attributes are captured by β, thematrixof coefficients, Π, measures how tastes vary with these attributes and Σ is a scaling matrix. Characteristics not observed by the econometrician that may influence tastes are captured by the ν i. Consumers may decide not to purchase any of the products. The indirect utility provided from purchasing the outside option is u i0t = α ln(y it )+ξ 0t + i0t, where the price of the outside good is normalized to zero. I assume the consumer purchases at most one good per period, that which provides the highest utility from all the goods in her choice set. 12 The set of variables that results in the purchase of good j : R jt {(y it,d it,ν i, ijt ):U ijt U irt r 6= j}. If a consumer has fullinformation regarding the products for sale and, assuming ties occur with zero probability, 11 The data allow for a very narrow model definition. For example, Compaq Armada 3xxx Pentium 150/166 laptop and Compaq Armada 4xxx Pentium 150/166 laptop are two separate models, as are an Apple Power Macintosh Power PC /200 desktop and deskside. 12 This assumption may be questionable in markets where multiple purchase is common. However, it is not unreasonable to restrict a consumer to purchase one computer per quarter. Hendel (1999) examines purchases of PCs by businesses and presents a multiple-choice model of PC purchases. 7

9 the market share of product j is s jt = Z df (y, D, ν, ) = R jt Z df y,d (y, D)dF ν (ν)df ( ) R jt (2) where F ( ) denotes the respective known distribution functions. To derive the market share of product j, I integrate over the observed joint distribution of (y it,d it ) and the assumed distribution of (ν i, ijt ) in the population, where the second equation follows from independence assumptions. I assume the are distributed i.i.d. type I extreme value in order to obtain simple expressions for choice probabilities. In industries where introductions of new products arefrequent(likethepcindustry), the assumption that consumers are aware of all products for sale is not an innocuous one. As in Goeree (2002), I develop a model of random choice sets: the probability consumer i purchases product j depends upon the probability she is aware of product j, the other products competing with j of which she is aware, and the probability she would buy product j given her choice set. Let C j be the set of all possible choice sets that include product j. Assuming consumers are aware of the outside option with probability one, the (conditional) probability that consumer i purchases product j is given by s ijt = X Y Y (y it p jt ) α exp{x 0 jβ it + ξ jt } φ ilt (1 φ ikt ) y S C itα + P j r S (y it p rt ) α exp{x 0 rβ it + ξ rt } l S k/ S (3) where φ ijt is the probability consumer i is informed about product j, the outside sum is over the different choice sets that include product j, andthey α it in the denominator is from the presence of an outside good. Advertising affects demand through the information technology function, φ ijt which describes the effectiveness of product j advertising at informing consumer i. The information technology is a function of medium-specific advertising, observed consumer attributes, and (unobserved) consumer-advertising- medium specific effects. 13 The information technology 13 For more details regarding the specification of the information technology see Appendix 2 and Goeree(2002). 8

10 approaches one as advertising increases, but may be positive even when no advertising occurs. The latter flexibility allows for the possibility that a consumer may be informed, even if she hasn t seen an advertisement. For instance, she may have received information by word-of-mouth, through experience with the product, or exposure to other non-advertising media coverage Firm Behavior I assume there are F firms in an oligopolistically competitive industry and that they are noncooperative, Bertrand-Nash competitors. Each firm produces a subset of the J products, J f. Suppressing time notation, the profits of firm f are X X (p j mc j )Ms j (p, a)+ j J f Π nh j j J f (p nh,a) X m mc ad jm( X j J f a jm ) C f (4) where s j is the vector of home market shares, which is a function prices and advertising for all products; mc j is the marginal cost of production; Π nh j is the gross profit (before advertising) from sales to the non-home sectors; mc ad jm is the marginal cost of advertising in medium m; a jm is the number of medium m advertisements; and C f are fixed costs of production. The potential market size, M, isgivenbythenumberofushouseholdsinagivenperiod,as reported by the Census Bureau. Given their products and the advertising, prices, and attributes of competing products, firms choose prices and advertising media levels simultaneously to maximize profits. PC firms may sell to non-home sectors (such as the business, education, and government sectors). I assume marginal costs are constant, which implies pricing decisions are independent across sectors. 15 Any product sold in the home market sector will have prices that satisfy the 14 See Anand and Shachar (2001). 15 There are reasons to believe that pricing decisions may not be independent across sectors. See Goeree(2002) for more discussion. 9

11 following first order conditions In vector form, the J first-order conditions are s j (p, a)+ X r J f (p r mc r ) s r(p, a) p j =0 (5) s (p mc) =0 where j,r = s r p j I j,r with I j,r an indicator function equal to one when j and r are produced by the same firm and zero otherwise. These FOC s imply marginal costs given by mc = p 1 s (6) An advertisement intended to reach a home consumer may affect sales in other sectors. Optimal advertising choices must equate the marginal revenue of an additional advertisement in all sectors with the marginal cost. Optimal advertising medium choices a jm must therefore satisfy M X r J f (p r mc r ) s r(p, a) a jm + mrj nh (p nh )=mc ad jm (7) where mr nh is the marginal revenue of advertising in non-home market sectors. 4 Post Merger Equilibrium This section presents the methodology used and the results from two mergers in the PC industry. I consider a merger between HP and Compaq and of a hypothetical merger between IBM and Dell. All post-merger computations use the estimates from the model of demand and supply presented in the above section. These parameter estimates are given in Tables 3-5 and discussed in more detail in Goeree (2002). To simulate the effects of a merger, I follow the same strategy as Nevo (2000). First, I estimate the parameters of the (pre-merger) model and use the implied price elasticities to recover the marginal costs of production. I use these marginal costs and the post-merger 10

12 industry structure to simulate a new price and advertising equilibrium. I simulate postmerger equilibria under two assumptions of post-merger behavior on part of the firms. First, I compute the new price equilibrium under the assumption that advertising choices remain at pre-merger levels. That is, I do not allow firms to reoptimize over advertising choices. This is used as the benchmark case. Notice that this benchmark case will provide an accurate picture of the post-merger industry only if firms do not change their advertising strategy or if advertising does not impact demand. I compare the benchmark case to the new price and advertising equilibrium that arises post-merger allowing firms to reoptimize over both prices and advertising. For the first two parts of the analysis I use estimated costs and, hence, assume the cost structure is the same both before and after the merger. Firms may experience decreased costs as the result of a merger. Therefore, in the final part of the analysis (following Nevo, 2000), I determine the magnitude of cost savings that would be necessary to return to the pre-merger price and advertising equilibrium. I assume post-merger market conduct is the same as pre-merger, and use the estimated pre-merger parameters to simulate the (counterfactual) post-merger equilibrium. The predicted post-merger equilibrium price p post solves p post = cmc + post (p post ) 1 s(p post ) (8) where the matrix post is constructed to reflect post-merger ownership structure, cmc are the predicted marginal costs and p post is the vector of post-merger predicted equilibrium prices. The predicted post-merger advertising levels solve (7) under the new ownership structure, holding marginal costs constant at their estimated levels. The post-merger equilibrium levels of prices and advertising are simulated jointly using the data from the last quarter of

13 4.1 Advertising and Market Power Hewlett-Packard Co. s PC division more than doubled in size with the acquisition of Compaq Computer Corp., instantly making the company the top PC supplier. Table 6 presents firm and industry level changes in prices, advertising, markups, and variable profits after the mergers. The first column presents average percentage changes in prices under the assumption that post-merger advertising levels are unchanged. Predicted price changes are higher for the merging firms, especially for the Compaq-HP merger. In addition all firms increase their prices under the HP-Compaq merger. We might expect prices to increase for all firms in the industry, as the industry is more concentrated allowing firms to exercise more market power. However, counter to intuition, under the Dell-IBM merger some firms charge lower average prices and overall industry prices experience only a small increase in price (0.2%). This unexpected result may be due to the assumption that firms leave advertising expenditures unchanged. The second and third columns present results based on the assumption that firms choose new prices and advertising levels after the merger. As the second column shows, the pricing outcome is more intuitive. All firms increase their prices as industry concentration grows. The overall price increase in the industry is 9% and 6% under the HP-Compaq and Dell-IBM mergers, respectively. Surprisingly, Dell-IBM do not raise prices as much as Compaq under the Dell-IBM merger. for selected products. The first column of Table 7 provides a breakdown of price changes As the table indicates, price increases are greatest for the HP and IBM products under their respective mergers. One possible reason for this distribution is that both HP and IBM have lower pre-merger prices than their merging counterparts. As the third column of Table 6 indicates, prices are 2% and 6% higher under the HP-Compaq and Dell-IBM mergers respectively relative to when firms don t reoptimize over advertising. The fourth column presents percentage changes in advertising. All firms choose to advertise more in both post-merger environments. Advertising increases by 3.3% under the HP-Compaq merger and 1.8% under the Dell-IBM merger. Prior to merging HP-Compaq 12

14 have a combined ad-to-sales ratio of 7%, while Dell-IBM s is on the order of 14%. HP- Compaq increases their advertising by 10% (or $75 million) after the merger, yielding a post-merger ad-to-sales ratio of 20%. 16 Dell-IBM increases their advertising by 0.9% (or $12 million) post-merger, yielding a post-merger ad-to-sales ratio of 22%. It is interesting that post-merger ad-to-sales ratios are similar for the merging firms. Column two of Table 7 provides an idea of how these advertising changes are distributed across the products offered by the merging firms. The table shows that advertising increases are larger for HP under the firstmerger themoreadvertisingintensivepre-mergerfirm. While under the Dell-IBM merger, advertising increases are greatest for the Dell products the less advertising intensive pre-merger firm. One thing is clear from these results. Had we considered only one merger we would have come away with a partial picture of the role of advertising in this industry. The fifthcolumnoftable6presents abreakdown of post-merger markup percentages, which include advertising costs. Pre-merger summary statistics are presented in Table 8. The industry average markup is higher (24%) relative to pre-merger markups (10%) and post-merger price only markups (20%). The percentage markups for Compaq-HP is about 50% higher than pre-merger levels, while for Dell-IBM it is roughly equivalent. These results shed light on the implications for informative advertising in the PC industry. The results suggest that firms will charge higher prices in conjunction with more advertising. Markups are about 10% higher on average when firms are permitted to reoptimize over advertising choices relative to optimizing over price only. Part of the post-merger higher markup is due to industry concentration, and part is due to the effect of advertising in this industry. If the firms were prohibited from changing their advertising, the industry would look much more competitive. The results indicate that advertising is used to reduce competition in the market. The above analysis assumes costs do not change after the mergers. We may expect 16 This is calculated using the post-merger shares, which are lower than pre-merger combined shares. 13

15 there to be some synergies between companies resulting in decreased post-merger costs. I calculated the percentage change in costs that would induce post-merger prices to equal premerger prices. I do not make any assumptions regarding which products of the acquiring firms would enjoy cost reductions. The median percentage changes are given in Table 11. The results indicate that a cost savings of 4% for HP-Compaq is enough to reach pre-merger equilibrium prices. The cost savings would be mostly on HP products. Even smaller costs savings, 2.7%, are enough to keep prices constant at pre-merger levels for the merger of Dell and IBM. Under the Dell-IBM merger, cost savings are spread more evenly over the products of both firms, relative to the HP-Compaq outcome, but are still more intense for Dell products. In addition, the costs savings are not of an unattainable magnitude. HP and Compaq or Dell and IBM may experience a reduction in costs of close to 4% and 3% respectively after merging. (more analysis of the results forthcoming...) 4.2 Consumer Surplus and Profits Remarking on the HP-Compaq merger, a research fellow at Gartner noted, There s nothing good for the consumers. They ve eliminated one of two fiercely competitive brands in the market. And what that means generally is higher prices and less choice. The previous subsection uses the structural estimates to produce a counterfactual of what the market equilibrium would look like under the merger. In this section, I examine the impact of the mergers on consumer surplus and profits to provide further insight into the social welfare effects of these mergers, the primary stated interest of the FTC. Consumers face different prices and (possibly) different choice sets as a result of a merger. Evaluating the change in utility generated by differences in choice sets is possible since preferences are defined over characteristics. I use compensating variation to measure changes in consumer welfare. Compensating variation is the amount of money an individual would need to be compensated at the new equilibrium prices and choice set to be as well off as they were under the pre-merger equilibrium. If household welfare is improved as a result of 14

16 the merger, the expected compensating variation measure is negative. Compensating variation for individual i, cv i, is implicitly defined by Max j J pre i U(y i p pre j )= Max j J post i U(y i p post j cv i, ) (9) where the superscripts pre and post are used to distinguish the original versus the new conditions associated with the merger. Recall, consumers may not know all products available to them, J i denotes the choice set facing consumer i. 17 Thepre-andpost-merger choice set may differ because the equilibrium level of advertising may change. Under assumptions of full information, the effects on consumer welfare (as measured by the area under the Hicksian or Marshallian demand curves) will be understated. The resulting compensating variation is a random variable, which will depend upon in a nonlinear fashion. I am interested in the expected value of this random variable, E(cv). Since marginal utility of income varies with income and prices there is no closed form solution for E(cv). IsimulateE(cv) using a procedure developed by McFadden (1999). The simulation requires sampling from the underlying distribution of errors and employing a numerical algorithm to solve for the implicitly defined cv as given in equation (9). I compute cv i for R draws from the underlying distribution of errors. Mean compensating variation for an individual is the average over these draws. 18 I average across the sample to obtain the mean compensating variation for the population. The change in welfare is computed by comparing consumer and producer welfare across the two economic environments. For each 17 The choice set for an individual is determined by comparing the value of the advertising technology, φ ij, to a draw from a uniform distribution. The advertising technology is evaluated at the estimated value of the parameters and equilibrium advertising levels. If the uniform draw is larger, the product is not in the choice set. If the uniform draw is smaller the product is in the choice set. Specifically, the pre-merger choice set is constructed as follows: given φ ij ( b θ, a pre ) and a draw from a uniform, u ij, construct a J dimensional Bernoulli vector, b i. This defines the choice set, where the jth element is determined according to ½ 1 if φij >u b ij = ij 0 if φ ij <u ij analogously for the post-merger choice set. 18 McFadden (1999) demonstrates in a monte-carlo experiment that the number of iterations required to obtain a 5% root mean squared error when are distributed extreme value 755. For this reason I choose R =

17 merger, I calculate two compensating variations: one in which advertising is not a strategic variable and one in which it is. I compare consumer welfare to pre-merger welfare under both environments. (Consumer surplus and profit results are forthcoming...) References Ackerberg, Daniel (2003) Advertising, Learning, and Consumer Choice in Experience Goods Markets: A Structural Empirical Examination, International Economic Review 44: Anand, Bharat and Ron Shachar (2001) Advertising, the Matchmaker, Harvard Business School Working Paper No Anderson, Simon, A. de Palma, and J.F. Thisse (1989) Demand for Differentiated Products, Discrete Choice Models, and the Characteristics Approach, Review of Economic Studies 56: Anderson, Simon, A. de Palma, and J.F. Thisse (1992) Discrete Choice Theory of Product Differentiation, Cambridge: MIT Press. Baker, J. and T. Bresnahan (1985) The Gains from Merger or Collusion in Product Differentiated Industries, The Journal of Industrial Economics, 33(4): Berry, Steven (1994) Estimating Discrete Choice Models of Product Differentiation, Rand Journal of Economics 25(2): Berry, Steven, James Levinsohn and Ariel Pakes (2004) Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market, Journal of Political Economy 112(1,1): Berry, Steven and Ariel Pakes (1993) Some Applications and Limitations of Recent Advances in Empirical Industrial Organization: Merger Analysis, American Economic Review 83(2): Bresnahan, T. and S. Greenstein, (2000) Technological Competition and the Structure of the Computer Industry, Journal of Industrial Economics Bresnahan, T., S. Stern, and M. Trajtenberg (1997) Market Segmentation and the Sources of Rents from Innovation: Personal Computers in the Late 1980s, Rand Journal of Economics 28(0): S17-S44. Chamberlain, G. (1987) Asymptotic Efficiency in Estimation with Conditional Moment Restrictions, Journal of Econometrics 34: Erdem, Tulin and Michael Keane (1996) Decision-making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets, Marketing Science 15(1):

18 Genakos, Christos (2004) Differential Merger Effects: The Case of the Personal Computer Industry, mimeo, London Business School. Goeree, Michelle S. (2002) Advertising and the US Personal Computer Industry, Claremont Colleges Working Paper Gourieroux, Christian, et al. (1987) Generalized Residuals, Journal of Econometrics, 34: Grossman, G. and Carl Shapiro (1984) Informative Advertising with Differentiated Products, Review of Economic Studies 51: Hausman, J., G. Leonard, and J. Zona (1994) Competitive Analysis with Differentiated Products, Annales D Economie et de Statistique 34: Hendel, I. (1999) Estimating Multiple-Discrete Choice Models: An Application to Computerization Returns, Review of Economic Studies 66(2): Imbens and Lancaseter (1994) McFadden, Daniel (1999) Computing Willingness-to-Pay in Random Utility Models, in J. Moore, R. Riezman, and J. Melvin (eds.), Trade, Theory and Econometrics: EssaysinHonorofJohnS.Chipman, Routledge. Nevo, Aviv (2000) Mergers with Differentiated Products: The Case of the Ready-to- Eat Cereal Industry, Rand Journal of Economics 31(3): Nevo, Aviv (2002) New Products, Quality Changes and Welfare Measures Computed from Estimated Demand Systems, forthcoming, Review of Economic Studies. Pakes, Ariel and David Pollard (1989) Simulation and the Asymptotics of Optimization Estimators, Econometrica 57(5): Petrin, Amil (2002) Quantifying the Benefits of New Products: The Case of the Minivan, Journal of Political Economy 110(4): Shapiro, C. (1996), Mergers with Differentiated Products, Antitrust, 10(2), Small, Kenneth, and Harvey Rosen (1981) Applied Welfare Economics with Discrete Choice Models, Econometrica 49: Werden, G.J. (1997) Simulating the Effects of Differentiated Products Mergers: A Practictioners Guide, Proceedings of the NE-165 Conference. Werden, G.J. and L.M. Froeb (1994) The Effects of Mergers in Differentiated Products Industries: Logit Demand and Merger Policy, Journal of Law, Economics, and Organization, 194, Willig, R.D. (1991), Merger Analysis, Industrial Organization Theory, and the Merger Guidelines, Brookings Papers on Economic Activity, Microeconomics,

19 A Data Appendices Gartner collects information on five main PC attributes: manufacturer (e.g. Dell), brand (e.g. Latitude LX), form factor (e.g. desktop), CPU type (e.g. Pentium II), and CPU speed. Idefine a model as a manufacturer, brand, CPU type, CPU speed, form factor combination. Even though I do not have data on some product attributes, the richness of the Gartner data still allows for a very narrow model definition. For example, Compaq Armada 3xxx Pentium 150/166 laptop and Compaq Armada 4xxx Pentium 150/166 laptop are two separate models, as are an Apple Power Macintosh Power PC /200 desktop and deskside. Treating a model/quarterasanobservation,thetotalsamplesizeis In the PC industry, it is common for manufacturers to advertise groups of products simultaneously. For example, in 1996 one of Compaq s advertising campaigns involved all Presario brand computers (of which there are 12). I construct effective product advertising expenditures by adding observed product-specific expenditurestoaweightedaverage ofall group expenditures for that product, where the weights are estimated. Let G j be the set of all possible product groups that include model j. Let ad H be total effective advertising expenditures for H G j. Define ad H ad H. Then effective advertising expenditures for H product j are given by ad j = X γad H + πad 2 H H G j where the sum is over the different groups that include product j. The parameters γ and π are estimated if the product is advertised in a group, otherwise γ is restricted to one and π to zero. Finally, I use data from the Consumer Population Survey in combination with the aggregate product level data to define the distribution of consumer characteristics. More detail about the various datasets, their construction, and descriptive statistics can be found in Goeree (2002). B Model and Estimation Details Themarketshareforproductj is obtained by integrating over the relevant distributions in the population Z s jt (p, a) = s ijt df (y, D)dF (ν)df (κ) (10) and is a function of prices and advertising of all products, where s ijt is given in equation (3). The advertising technology for product j for consumer i is given by φ ijt (θ φ, Υ) = exp(τ ijt) 1+exp(τ ijt ) τ ijt = ed 0 itλ + a 0 jmt(ϕ m + ρ m a jt + Ψ f + Υ m D s it + κ i ) lnκ i N(0,I m ) 19 This is the sample size after eliminating observations with negligible quarterly market shares.

20 where θ φ = {λ, γ, π, ϕ, ρ, Ψ} and Υ are parameters to be estimated. The m-dimensional vector a is the number of advertisements in each medium, ed is a subset of consumer characteristics, and κ are unobserved consumer heterogeneity with regard to advertising medium effectiveness. Under the assumption that the data are the equilibrium outcomes, the parameters {θ, β, η, η AD } are estimated by generalized method of simulated moments. The demandside parameters are β and θ = {α, σ, Π,θ φ } and the supply-side parameters are η and η AD, where η AD is the vector of parameters associated with advertising medium choices. 20 The first two sets of moment conditions are analogous to those in BLP. I restrict the model predictions for product j s market share to match the observed market shares. Using a contraction mapping suggested by Berry (1994) I compute the vector δ (θ), whichisthe implicit solution to St obs s t (δ, θ) =0, and use it to solve for the demand side unobservable, ξ jt. I assume marginal costs of production, mc j, are composed of unobserved (ω jt )and observed characteristics (w jt ),whereln(mc j )=wjη 0 + ω j. I use the pricing FOCs to solve for the cost side unobservable, ω jt. Marginal costs of advertising in medium m areafunctionoftheaveragepriceofan advertisement and unobserved product-specific components, ln(mc ad jm) =ψ ln(p adm )+υ j υ j N(0,σ 2 υi m ) Some firms choose not to advertise some products in some media. To allow for corner solutions, I use the marginal costs associated with advertising and the interior FOCs to construct a tobit maximum likelihood function, $(Ω) = Y j:a jm >0 1 σ υ φ N µ ln hj ψ ln p admt σ υ Y j:a jm 0 µ ln hj ψ ln p admt 1 Φ where φ N is the standard normal pdf, Φ is the cdf, and h j is the marginal revenue of advertising (the left-hand side of equation 7). For ease of exposition rewrite the advertising medium FOC as ln h jm (a jm ) ψ ln p admt = υ jm where the latent variable, a jm is its implicit solution. We observe a jm if there is an interior solution and zero otherwise. Notice the errors, υ jm, are linked with the latent variable. Since they depend on unobserved variables, they cannot be used to construct moment conditions. Gourieroux et al.(1987) suggest an alternative method: replace the errors by their best prediction conditional on the observable variables and use these to construct moment conditions. They show the moment conditions then express an orthogonality between the generalized residuals, eυ jm (bω) =E[υ jm (bω) a jm ], and the instruments, where bω is the maximum likelihood estimator of Ω. Let T be the vector formed by stacking the resulting sample moments over media and over products. The micro-moment conditions use the Simmons data. Let B i be an F 1 vector of firm choices for individual i. Let b i be a realization of B i where b if =1if a brand produced by firm f was chosen. Define the residual as the difference between the vector of observed choices in the data and the model prediction given (δ, θ). The residual for individual i, denoted G i (δ, θ), can be written as b i E ν E[B i D i,ν,δ,θ]=g i (δ, θ) σ υ 20 The additional parameters of the model, Υ, are estimated separately using maximum likelihood, see Goeree (2002). 19

21 The population restriction for the micro moment is E[G i (δ, θ) (X, ξ)] = 0. Let G(δ, θ) be the vector formed by stacking the residuals G i (δ, θ) over individuals. I use generalized method of simulated moments to find the parameter values that minimize the objective function Λ 0 ZA 1 Z 0 Λ where A is an appropriate weighting matrix which is a consistent estimate of E[Z 0 ΛΛ 0 Z] and Z are instruments orthogonal to the composite error term Λ = 0 1 ξ(δ, β) 1 ω(δ, θ, η) 1 T (δ, θ, η 1 J J J AD) G(δ, θ) N Chamberlain (1987) shows the optimal instrument for any disturbance-parameter pair is the expected value of the derivative of the disturbance with respect to the parameters (evaluated at the true value of the parameters). Product and cost characteristics are optimal instruments for the demand side parameter β andcostsideparameterη, respectively. The optimal instruments for the other parameters are functions of either price or advertising. Due to endogeneity of price and advertising, the instruments for the other parameters are not valid. Similar to the approach taken in Berkovec and Stern (1991), I form exogenous instruments for the disturbance-parameter pairs by simulating the expectations of the disturbance-parameter pairs and regressing the simulated value on exogenous regressors. 20

22 C Figures and Tables $2,700 Industry Trends 13 Average Price (98$) $2,500 $2,300 $2,100 $1,900 $1,700 $1, Total Units (in millions) Average Price 1998 Figure Total Units Sold Advertising Expenditures Millions of 98 Dollars $2,500 $2,250 $2,000 $1,750 $1,500 $1, Figure 2: Advertising Trends 21

23 Manufacturer Advertising Total Ad to Sales Ad$ per Market Expenditures Market Share Ratio Share Point IBM $1, % 19.55% $ Hewlett-Packard $ % 10.28% $48.44 Gateway $ % 5.99% $23.75 Dell $ % 2.28% $14.17 Compaq $ % 2.56% $14.91 Apple $ % 4.90% $20.37 Total for PC market $2, % Note: Dollars are in millions, Market Share is dollar market share of all sectors (home, business, education, and government) Figure 1: Table 1: 1998 Advertising Expenditures for Selected Firms Manufacturer Percentage Dollar Share Percentage Dollar Share All Sectors Home Market Sector Acer Apple AST Research Compaq Dell Gateway Hewlett-Packard IBM Micron NEC Packard Bell Packard Bell-NEC Sony Texas Instruments Toshiba ZDS-Groupe Bull Total Sales $58,515 $66,636 $62,118 $16,529 $18,610 $17,673 Notes: Total sales are in millions of dollars. In 1997 three mergers occurred: Packard Bell, NEC, and ZDS; Acer and Texas Instr.; Gateway and Advanced Logic Research Table 2: Market Share for All Sectors and Home Sector 22

24 Variable Means Standard Coefficient estimates for interactions Deviation utility coefficients interactions with demographic variables household size income > $100,000 30<age<50 white male cpu speed (MHz) ** ** ** (1.1262) (0.0283) (0.8774) pentium (5.8061) (0.7234) (3.3086) laptop ** (1.6484) (0.8553) (5.4335) (3.0218) constant ** (4.5332) ln(income-price) ** (0.1026) acer ( ) apple ** (1.2641) compaq (4.8206) dell ** (0.1744) gateway (8.2857) hewlett packard (3.3285) ibm ** (1.0363) micron (4.5577) packard bell ** (1.6603) advertising coefficients group advertising ** (0.2265) (group advertising) ** (0.0007) information technology coefficients interactions with total advertising See table 9 for the constant ** acer coefficients on interactions (0.0872) ( ) between advertising and high school graduate ** apple * demographic variables (0.0187) (0.2066) income < $60, ** compaq ** (0.1048) (0.1607) income > $100, dell * (0.3926) (0.3533) gateway ** np and mag advertising (0.1385) (2.1168) hp tv advertising ** ( ) (0.1418) ibm ** (0.1383) (np and mag advertising) * micron (0.0110) (3.7633) (tv advertising) ** packard bell ** (0.0169) (0.0861) Notes: ** indicates significant at the 5% level; * significant at the 10% level. Standard errors are given in parentheses. Table 3: Structural Estimates (Home Sector) 23

25 Media Magazine Newspaper Television Radio Variable Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error constant ** (0.0385) ** (0.0378) ** (0.0392) ** (0.0412) midage * (0.0234) ** (0.0242) (0.0236) (0.0241) mature (age>50) (0.0239) ** (0.0243) ** (0.0238) ** (0.0242) married (0.0182) ** (0.0184) ** (0.0182) (0.0187) hh size ** (0.0057) ** (0.0060) ** (0.0058) * (0.0061) inclow ** (0.0195) ** (0.0197) ** (0.0201) ** (0.0205) inchigh ** (0.0294) ** (0.0283) (0.0305) ** (0.0304) malewh ** (0.0170) (0.0171) (0.0169) (0.0173) eduhs ** (0.0254) ** (0.0251) ** (0.0256) ** (0.0260) eduad (0.0265) ** (0.0261) ** (0.0269) ** (0.0273) edubs (0.0260) ** (0.0253) ** (0.0265) ** (0.0269) edusp ** (0.0034) ** (0.0035) ** (0.0033) ** (0.0034) Log Likelihood Notes: Estimates include time dummies. ** indicates significant at the 5% level; * significant at the 10% level. Table 4: Estimates of Media Exposure Variable Coefficient Standard Error ln marginal cost of production constant ** (0.1784) ln(cpu speed) ** (0.0062) pentium dummy ** (0.0926) laptop dummy ** (0.2691) quarterly trend ** (0.0214) ln marginal cost of advertising constant ** (1.9230) ln(price of advertising) ** (0.0002) non-home sector marginal revenue constant * (1.2869) non-home sector price ** (0.0749) cpu speed ** (0.0028) non-pc sales * (2.8202) Notes: ** indicates significant at the 5% level; * significant at the 10% level. Table 5: Structural Cost and Non-Home Sector Estimates 24