Competition and Quality Choice in the CPU Market

Transcription

1 Competition and Quality Choice in the CPU Market Chris Nosko Harvard University June 2011 Abstract This paper uses the CPU market to study how multiproduct firms generate returns from innovation. Using a new dataset, I estimate a discrete-choice model of CPU demand and then recover estimates of the sunk cost of product introductions. I combine these estimates with a model of firm product choice to examine how product line decisions change with asymmetric technological capabilities and with the competitive environment. I use the model to show how technological leaders can use product lines as strategic weapons, isolating competition to less desirable areas of the product spectrum. I apply this insight to a large shift in technological leadership Intel s introduction of the Core 2 Duo and quantify the portion of returns that came from Intel s ability to push its principle competitor, AMD, into lower-margin product segments. I find that competition plays a key role in determining firms product line decisions and that these decisions are important in generating returns from innovation. Ignoring endogenous product choices leads to underestimates of the social welfare losses from monopoly. I am grateful to Ulrich Doraszelski, Lisa Kahn, Greg Lewis, Julie Mortimer, Ariel Pakes, and Alan Sorensen for invaluable advice. Also, I thank Brett Gordon for his help in putting together the pricing data, Che-Lin Su for helpful discussions about computational methods, and especially Jeremy Davies of Contextworld for generously supplying downstream data. All errors are solely mine. 1

2 1 Introduction When firms innovate, they often don t just introduce products at the top-end of the market. Instead, they tend to reset their whole product line in an effort to extract the most possible profit from their innovation. Their incentives to reshape lower market segments depend on the industry structure, especially whether it is a monopoly or an oligopoly, and the technological capabilities of rival firms. In oligopoly, one way that firms generate profit is by strategically using product choices to change the nature of competition in the industry. Our ability to understand this phenomenon requires knowledge of how firms make product choices and how these product choices change with market structure. Despite the evident importance of competition for driving decisions about product lines, and the effect that these decisions have on consumer welfare, most antitrust analyses downplay them, and models of innovation almost completely ignore them. I use the CPU market to study how imperfectly competitive firms make product decisions and how these decisions affect their ability to generate returns from innovation. In this market, firms offer a menu of quality-differentiated products that is often reset to integrate new technology and to respond to actions of competitors. In 2006, Intel introduced a new product line, called the Core 2 Duo. Hailed as the most impressive piece of silicon the world has ever seen 1 the market went from relative equality between Intel and its rival, AMD, to one that was firmly dominated by Intel. Interestingly, it wasn t just Intel s ability to produce faster chips that led to dominance. Instead, much of Intel s increased profit came from pushing AMD out of mid-range market segments, areas where AMD still had the technological capability to compete. I use variation from the introduction of the Core 2 Duo, and the relative frequency of product line changes in general, to ask the following questions: In this industry, how do firms choose the number and quality of their products? How would these choices be different if the industry were a monopoly? And how do firms use strategic product choices to increase returns from innovation? 1 See the CNET article, Intel s Core 2 Duo lives up to hype from July 16, 2006: 2

3 My first finding relates to the role that competition plays in driving product line decisions. When marginal costs rise relatively slowly with quality level a stylized fact in the CPU industry a monopolist has little incentive to introduce a broad spectrum of products. Instead, a monopolist can extract almost all feasible profit with a limited number of products at the high-end of the market. In oligopoly, this strategy is no longer optimal because a competitor can steal marketshare by introducing products at lower price points. This process leads to a competitive equilibrium with more products spread throughout the product line. Thus, in markets like CPUs, quality-based product separation can be driven largely by competitive interaction rather than a desire to discriminate between consumer types. 2 This contrasts with the standard literature on price discrimination, which sees the introduction of quality-differentiated products as a mechanism for extracting more revenue from highvalued consumers. 3 This finding implies that, because product line decisions matter little for a monopolist s profitability, when they innovate, resetting a product line will play much less of a role in extracting profit from that innovation than for firms in an oligopoly. I next find that, in oligopoly, returns from innovation come not only from the ability to produce a better product at the top end, but also from an innovator s ability to steal business from rivals throughout the product line. Using a simple model, I construct an example showing how a technological leader can isolate competition to lower margin portions of the market, thereby increasing market power over a larger product space. I combine my model with data from the introduction of the Core 2 Duo to quantify that role that these business stealing effects played in generating profit for Intel. I break apart the portion of returns that came from Intel s introduction of new top products from the returns that came from strategic quality choices throughout the product spectrum. This comparison gives an estimate of how much we would underestimate the effect of competition on innovation incentives if we held product lines fixed. Next, I compare the profits that Intel generated in 2 In a series of theory papers, Johnson and Myatt (2003, 2006) discuss the role of competition in driving quality choices in a Cournot setting with differentiated products. 3 There is a long literature on using quality as a price discrimination mechanism going back at least to Jules Dupuit in the 19th century. The foundational modern work is Mussa and Rosen (1978), with generalizations to multi-dimensional consumer types and/or multiple firms by Rochet and Stole (2002), Armstrong (1996), Rochet and Chone (1998), and Armstrong and Vickers (2001). 3

4 the duopoly structure with profits that a counterfactual monopolist would have made with the same innovation. I show that, while these returns are substantially lower than that of an oligopolist in percentage terms, in absolute dollars, the returns are very similar. My empirical strategy relies on a combination of institutional details, a rich dataset containing some exogenous shifts, and a structural model. This industry has two firms, and these firms compete over products that differ in relatively straightforward ways, allowing me to write down a model that is simple enough to take to the data, but that still captures key aspects of the industry. By using this model to estimate primitives that we don t generally observe in datasets consumer preferences, marginal and sunk costs I attempt to untangle some of the key drivers of product line decisions. I then use these estimates to shed light on Intel s introduction of the Core 2 Duo, an event that I treat as the result of an exogenous innovative process. 4 Using the structural model, I compute how a counterfactual monopolist would choose products and compare these and the competitive outcomes to a social planner. My dataset contains CPU list prices (when purchased in 1,000 lot units) combined with European country and time specific data on desktop sales. Because the sales data contain information on the CPU that shipped with the computer, I am able to construct a monthly dataset of CPU prices and quantities sold across 8 European countries from I exploit the cross country variation to generate estimates of a horizontal taste for Intel s vs. AMD s products and the near constant flow of new chips provides variation in quality levels. As mentioned, I use the introduction of the Core 2 Duo to consider how innovative activity interacts with quality choice to generate returns. The basis of the structural model is an underlying utility framework that allows for consumer heterogeneity in willingness to pay for quality (vertical differentiation) and heterogeneity in brand preference across CPU companies (horizontal heterogeneity). Demand 4 In this paper I treat innovation outcomes as exogenous events, focusing on how firms generate returns from them. With respect to the Core 2 Duo in particular, this is probably not a bad assumption: The project that led to the Core 2 Duo, codenamed Banias, began at least as early as 2001 to develop a CPU for laptops (which later became the Pentium M). It was only later that this technology was thought appropriate for the desktop market. See the Seattle Times, How Israel saved Intel, More generally, the results in this paper can be seen as an input to a dynamic game that endogenizes innovation decisions. 4

5 estimation proceeds following the Pure Characteristic Model of Berry and Pakes (2007) with some modifications tailoring the problem to my setting. I recover sunk costs of product introductions from observing decisions on whether and when firms introduced new products into the market. Moment inequalities (Pakes, Porter, Ho, and Ishii (2006) ) allow me to recover bounds on the sunk cost parameter. I find that a counterfactual monopolist has little incentive to introduce a whole product line: With a single product he can capture 98% of the profit that he earns with a full, optimally-placed product line. Given the sunk cost estimates, a monopolist would introduce between 1 and 3 products compared to the 8 to 10 products that exist in the competitive market. Consumer surplus from a monopolist is found to go down by 65% compared to the competitive outcome. Much of that comes from the increased monopolist prices, but a nontrivial 13% comes from the reduction of products and their inefficiently high quality levels. I further find that the returns to innovation are higher in percentage terms in an oligopoly than in a monopoly. My estimates indicate that Intel s profits increased by 96% with the introduction of the Core 2 Duo (from 95 to 180 million dollars monthly). 49% of that came from the introduction of new products (holding old products fixed), and the rest came from the realignment of products throughout the spectrum. Finally, a monopolist with the same innovation would have increased profits by 17% (from 488 to 573 million). Even though a monopolist has lower percentage returns, in dollar values, the amount is very similar to the oligopoly outcome. These results speak to recent antitrust enforcement in this industry. The market leader, Intel, has been widely accused of actively working to exclude AMD from the market. A number of regulatory agencies including the European Union Competition Commission, the U.S. Federal Trade Commission, and the Fair Trade Commission of Japan have either fined or investigated Intel s behavior. Naturally, in analyzing the possible effects of a market dominated more strongly by Intel, we would like to know how the product landscape would change. My results indicate that the current market is quite competitive. An Intel monopoly would result in substantial lost consumer welfare, mostly because of higher monopoly prices, 5

6 but also because of a decrease in the number of products on the market. There are a number of recent empirical IO papers that examine topics related to mine. Eizenberg (2008) estimates a game where downstream OEMs choose a discrete portfolio of CPU options to offer with their PC products in a first stage, and then set prices in a second stage. 5 In contrast, I focus on competition between the upstream firms, Intel and AMD, and on how product line decisions affect their ability to generate returns from innovation. Fan (2009), Crawford and Shum (2006), Mazzeo (2002), and Draganska, Mazzeo, and Seim (2009), consider endogenous product choice in newspapers, cable television, hotels, and ice cream, respectively. Berry and Waldfogel (1999, 2001) explore firm choice of radio station formats using merger activity brought on by the Telecommunications Act of 1996, and Sweeting (2007) models single-product firms and estimates sunk cost of format switching. Two papers use the CPU market to study different topics. Song (2007) estimates a demand system closely related to the one I use below in order to compare consumer welfare measures to more widely used models. Goettler and Gordon (2009) model firm innovation incentives in the face of dynamic consumers. Because of the complexity of the dynamic model, they are forced to limit firm heterogeneity, modeling Intel and AMD as single-product firms, which doesn t allow for the segmentation incentives that I focus on. This paper is organized as follows. Section 2 describes the industry, introduces the data that will be used, and discusses changes in the CPU market that make it a suitable environment to study nonlinear pricing and competition. Section 3 details utility primitives and goes through their estimation. Section 4 lays out the quality choice model which includes the second-stage pricing game and estimation of marginal and sunk costs. Section 5 lays out and solves counterfactuals using the estimates from earlier sections. The counterfactuals simulate what the market would look like if it were a monopoly, run by a social planner, or had different innovation outcomes. 5 Eizenberg shows how to account for issues of self-selection and partial identification in these sorts of games, an estimation problem closely related to the one in this paper. 6

7 2 The CPU Market The market for desktop, laptop, and server CPUs is dominated by two companies: Intel and AMD, with (respectively) approximately 80% and 18% market share as of January This paper concentrates on the market for desktop CPUs. These are CPUs that go into home and business machines that are used for everyday tasks. I concentrate on this market rather than the market for laptop chips because it is more competitive (Intel dominates the market for laptop chips) and more stable (laptop growth has been explosive over the last few years). More data are also available for this market because enthusiasts tend to buy desktop chips and chart their performance extensively. Within the desktop market, each firm typically offers between 10 and 15 chip varieties at any given time. By far the largest difference between these chips is performance. Higher performing chips tend to have higher clockspeed (operate at a higher frequency), more highspeed cache memory available, include multiple cores, and use more advanced process technology. Firms can and do use all of these levers to manipulate performance, but at the end of the day a consumer need only look at how the chip performs based on some benchmark to determine it s product quality. 6 The CPU market has long been known for offering quality-differentiated product lines. The Intel was a popular example in both the economics literature (Deneckere and McAfee (1996) ) and the popular press. Introduced in 1989, Intel created low-quality and high-quality versions of this chip. Strikingly, in order to create the low-quality chip, they went to some cost to destroy a perfectly good high-quality chip. 7 Recent examples include 6 This is, of course, a simplification. E.g., chips that have differing number of cores appeal to consumers who do different sorts of tasks with their computer (making it not perfectly collapsible to a one dimensional metric). Nevertheless, this is a product that comes about as close as possible to differing on a single vertical dimension. 7 More specifically, Intel released a DX version that included a math co-processing chip, and an SX version that did not. The co-processing chip gave a performance boost to power users but was ignored by the mainstream software of most users. The DX version sold at a substantial premium. The story goes that in order to create an SX chip, Intel manufactured a DX version and then incurred some cost to destroy the connection between the CPU and the co-processor. This manufacturing process is somewhat apocryphal. At first Intel used DX chips with properly functioning CPUs but with manufacturing defects in the co-processing unit. Later, they created a separate mask to exclude the co-processing unit completely, which decreased the die size and hence the cost. 7

8 Intel s selling of a code that unlocks features of their G6951 processor. Consumers purchase the chip at a stock level, and should they wish to access additional performance increases, they can buy the magic code (which costs Intel nothing) that makes the chip perform better. 8 While these anecdotes illustrate isolated incidents, the industry has eluded systematic study on the quality-choice dimension. I believe this is because up until the end of 2003, Intel and AMD used a rather crude segmentation mechanism: chip manufacturers concentrated on their top chips and left their older chips to serve more price sensitive segments of the market (at reduced prices). While segmentation was occurring, the quality choice itself was not being made every period instead, firms were constrained by their top performing chips from the last period, a strategy known as waterfalling. This changed toward the end of 2003 when firms began adjusting price and quality on a regular basis to hit different segments of the market. Appendix 1 documents this shift in the industry. The competitive nature of the industry has fluctuated markedly over the 2000 s. The mid 2000 s were the height of AMDs competitiveness, peaking with 30% marketshare at the beginning of This is up from around 10% at the beginning of 2003 and 20% at the beginning of Figure 1 plots Intel and AMD marketshare from the end of 2003 through the beginning of [Figure 1 about here.] Changing technical leadership is partially responsible for the marketshare fluctuations. From , AMD consistently released products whose price/performance characteristics were similar to or beat Intel s products. However, with the release of the Core 2 product line at the end of 2006, Intel regained technical leadership, a position which they ve held every since. Figures 2 and 3 tell the story of a rapidly and significantly changing market. In June 2006, Intel and AMD both offered products throughout the quality spectrum. In many parts of the spectrum, AMD offered products that were better and cheaper than comparable Intel products (the top panel of 2). In July 2006, Intel released a number of Core 2 Duo 8 See: I thank Kelly Shue for pointing this out to me. 8

9 products. The bottom panel of figure 2 shows that these products completely dominated AMD s offerings, substantially altering the price/quality landscape. AMD responded by slashing prices and removing products that were no longer competitive (figure 3). By January of 2008, the competitive nature of the market had changed so significantly that AMD was relegated to the bottom portion of the quality spectrum, offering almost no chips at the medium to high end. Interestingly, while AMD s overall marketshare did not drop all the much. Its share of more expensive chips dropped almost to 0 (figure 4). [Figure 2 about here.] [Figure 3 about here.] [Figure 4 about here.] I exploit this shift in technological leadership in two ways: First, it provides natural variation in product characteristics that helps identify the demand system. Second, I explore firms reaction to this technological change, focusing on ways that this affected quality choices and the strategic interactions between firms. The section on counterfactuals discusses this in more detail. In addition to variation in marketshare across time, there is also substantial variation across country. Figure 5 shows that AMD s marketshare in France is consistently higher than in other countries, especially when contrasted with their Southern neighbors, Spain. This is a useful source of variation that will play a key role in identifying the horizontal aspect of consumer heterogeneity. [Figure 5 about here.] 2.1 Data Data for this paper come from a variety of sources. Prices were gathered from websites devoted to tracking the wholesale prices of Intel and AMD chips. These are prices paid by 9

10 distributors and system builders when purchased in lots of 1, Quantities come from Contextworld, a European firm that tracks computer sales. Contextworld contracts with major retailers and distributors across the region to receive point of sales data. They then extrapolate to include retailers that they do not have contracts with. To check the accuracy of these extrapolations, I compared aggregate Contextworld data to other consulting firms that report their versions of these numbers, such as IDC, and the numbers were quite similar. The Contextworld data contain characteristics such as hard drive size, ram, screen, and important for my purposes, the exact CPU that went into the computer. These data can be broken down across country, distribution channel, and computer type (laptop or desktop). One downside to using downstream sales data is that there is a lag between when a CPU is purchased from the upstream supplier and when it is sold to the end user (making it into the quantities data). Performance data were collected from various enthusiast websites. These sites and forums take CPUs and run them through a series of benchmarks and performance tests. The end result is a number of metrics that allows chips to be compared to each other. It is important for this paper to use actual benchmark numbers instead of CPU speed (or some combination of CPU speed and cache) because I am explicitly comparing performance across CPU manufacturers with substantially different architectures. Simple clockspeed doesn t reflect actual performance in this case because the chip architecture interacts with numerous characteristics of the chip in complex ways to actually move information through the pipeline. 9 There is plenty of evidence that the large OEMs do not pay these list prices. Instead, at a minimum, they get percentage discounts off of them depending on their size. As long as these are the same across the OEMs in my data, then my substantive empirical conclusions will not be affected. It would be more problematic if specific OEMs got specific discounts off of certain chips and not on others. I have not heard of instances of this occurring, but given the bilateral nature of these deals, I certainly cannot rule it out. 10

11 3 Demand Demand follows the Pure Characteristics Model of Berry and Pakes (2007) (See also Song (2007)). 10 Consumers choose a single product from the set of available options. Conditional on purchasing, consumers get utility as specified in equation 1. u ij = β i x j 1 p j + ξ j (1) α i Individual: i, Product: j x j are observable product characteristics and ξ j a product level unobservable. The vertical random coefficient, α i, measures a consumer s willingness to pay for quality (alternatively, one could interpret it as price sensitivity). Because I am primarily concerned with the level of competition between Intel and AMD, I break out a dummy, d j from the product observables where d j = (d Intel d AMD ). d Intel and d AMD are dummies for whether the product is made by AMD or by Intel. u ij = βx j + ν i d j 1 p j + ξ j (2) α i I allow for consumers to have varying tastes for purchasing an Intel or AMD product by including the random coefficient, ν i on d j. If an Intel product, ν i will enter the utility function positively and if an AMD product, negatively. ν i determines how substitutable products are across firms. At one extreme, if ν i is a constant with value 0, then two products (from different firms) with the same characteristics would be identical from a consumer s 10 The key difference between this model and the more commonly used discrete choice model of Berry, Levinsohn, and Pakes (BLP) is the absence of a product and individual specific error term (generally denoted ɛ ij ). Although appending an ɛ ij would ease the computational burden in estimation (a point to which I return below), it is problematic in this context. Here, a product is defined at the chip level. Adding a BLP-style error term would oddly imply that consumers received independent utility draws for closely related chips (say products that only slightly differed in speed). The larger problem comes from considering the supply side: Consumers with iid product level error terms provide strong incentives for firms to introduce new products, even if they are very closely related to existing products. Firms can generate utility from simply introducing a product because some consumers will get a high draw from the idiosyncratic shock even if they got a low draw from a product with identical observables. If these shocks don t accurately represent reality, then a model which considers firms incentives to introduce new products will overstate profit opportunities. These reasons for using the PCM are stressed in the original paper by Berry and Pakes. They are especially important here because the supply side and counterfactuals explicitly consider product introduction choices. 11

12 perspective, which gives Bertrand pricing implying zero markup. On the other hand, the higher the mean value of ν i for a given firm, the more market power that firm enjoys as consumers will prefer that firm s products over a competitor s even with inferior product characteristics. As the variance of ν i increases, price competition softens because the tails of the distribution more heavily favor one firm or the other. In the CPU market, there are a number of reasons that consumers might prefer one firm s product over another s (holding observables constant). First, both Intel and AMD advertise heavily. The Intel Inside campaign was widely credited with generating consumer awareness of the CPU, moving it from a commodity semiconductor part to a major part of the consumer buying decision. Second, due to differences in architectures, AMD and Intel chips perform different types of tasks at different speeds. For instance, those who play computer games tend to prefer AMD chips, while those with business oriented tasks prefer Intel. Lastly, there are complementarities between the CPU and other components inside the computer. CPU s from a given company can often be upgraded without changing the motherboard, video card, etc, while changing to a CPU from a different company would require re-purchasing those components. Splitting apart the random coefficients into a mean and variance term and assuming that α i is distributed lognormal and ν i normal gives (where n ν and n α are standard normals): u ij = βx j + ( ν + σ ν n ν )d j 1 exp(ᾱ + σ α n α ) p j + ξ j (3) Because utility is only defined up to a monotonic transformation, two normalizations are needed in order to identify the underlying components of the demand system. I first normalize the utility of the outside option to 0. Without this normalization, an additive shift to all prices would unrealistically not affect market shares. The second normalization setting the mean of the underlying normal on the price coefficient, ᾱ to 0 pins down the multiplicative scale. Without it, multiplying both sides of the utility function by the same constant would not change the implications of the model. 12

13 Define δ j as the mean product level quality: δ j = βx j + νd j + ξ j (4) That leads to the underlying utility function that I use: 3.1 Estimation u ij = δ j + σ ν n ν d j 1 exp(σ α n α ) p j (5) The parameters to be estimated are the mean utility levels (δ j ), and the variances of the random coefficients (σ α, σ ν ). α i is assumed to be log normal and, one of it s parameters, µ, normalized to 0. ν i is assumed to be normal with both its mean and variance estimated. As in Berry and Pakes, to generate aggregate market shares, first consider a consumer s product choice conditional on purchasing from firm n. I construct upper and lower cutoff values for each product: jn = max k<j p j p k δ j δ k jn = min j<k p k p j δ k δ j (6) Then all consumers with: jn < α i < jn (7) Will purchase product j. Here I diverge from the original Berry and Pakes estimation routine. Because of the structure of my model, one horizontal and one vertical characteristic with two firms, I am able to calculate an analytical cutoff value for each α i type that determines which consumers purchase from each firm. Let u j n be a consumer s preferred product conditional on purchasing from firm n. Then, for every α i, there is a cutoff value, ν, such that u j 1 = u j 2. Using the functional form of utility gives: ν = (δ j 2 δ j 1 ) + 1 (p j α 1 p j 2 ) (8) i 13

14 Assuming marginal distributions for α, ν:f(α), g(ν), market shares for product j from firm 1(2) are given by: S j1 = S j2 = j1 j1 [1 G ( ν(p, δ, α) α)] df (α) (9) j2 j2 [G ( ν(p, δ, α) α)] df (α) (10) This formulation is helpful for two reasons: First, because I don t need to simulate consumer types in order to calculate predicted marketshares (I can use numerical quadrature), I reduce noise in the model. Second, the relative ease of computing marketshares allows me to more easily formulate the problem as a constrained maximization problem and use the Mathematical Programming with Equilibrium Constraints (MPEC) techniques as discussed in Su and Judd (2008). It is common in the discrete choice estimation literature to assume that observed and unobserved product characteristics are uncorrelated with each other. This is useful for at least two reasons: 1) The estimation routine usually involves a regression where observed characteristics are regressors and the unobserved characteristic is the error term, resulting in biased coefficients without this assumption (and in the absence of further instruments). 2) It allows for characteristics of products from firm j i to be used as instruments for prices of products from firm j. This relies on other firms characteristics being correlated with price (which makes sense given that more crowded regions of the product space should have lower markups) and uncorrelated with unobservables. If firms choose characteristics knowing the unobservables of products produced by themselves and their competitors, then these instruments will not be valid and the estimated parameters will be biased. Assuming exogenous product characteristics is problematic in my model because the whole point of the supply side is to investigate how quality choices are made. Using product characteristics as instruments creates a situation where, without further assumptions, the demand estimation is incompatible with the quality-choice model. My solution is to exploit 14

15 the panel structure of the data which allows me to insert product-level dummy variables in the estimation. As in Nevo (2001), product level dummies change the structural error in the estimation. The unobserved product characteristics are soaked up by the dummies, which subsume all characteristics that do not change from market to market. The structural error term is now the market-level deviation from the mean utility level. In my case this means time/country specific deviations for an individual chip. Estimation proceeds by minimizing a GMM objective function subject to simulated market shares equaling actual market shares: Subject To: min ξ(β, σ) ZW Z ξ(β, σ) (11) σ ν,σ α, δ,p,β s j = S j j (12) For each candidate value of the standard deviations of the random coefficients, the routine calculates the mean utility levels that equate actual with observed marketshares and finds the value of the objective function by taking the residuals from an IV regression. The IV regression has the mean product utilities on the left-hand side and the right-hand side includes product dummies (k j ), time dummies (k t ), and market dummies (k m ). See equation 13. ξjtm is the market/time specific error term. The product dummy variables, β j give the δ j s from the underlying utility model and are used as inputs to the supply side of the model. 11 δjtm = β j k j + β t k t + β m k m + ξ jtm (13) This estimation routine presents a computational challenge: Unlike in BLP, where the idiosyncratic error term ensures positive market shares for all products at all potential parameter values, this model often predicts that some products will have 0 market share. This 11 The downside to this estimation strategy is that recovering the underlying utility coefficients requires an extra step of regressing the dummies on product characteristics a regression that is only valid if the unobserved product characteristics are uncorrelated with observed product characteristics. In this context this means firms don t know ξ when they choose product characteristics, an assumption that is unlikely to be true in my case. Fortunately, I m not actually concerned with the coefficients on product characteristics recovering the δ s is sufficient for the supply side estimation and counterfactuals. 15

16 occurs when the mean utility levels become un-ordered, making some products dominated by their neighbors. When this happens, the gradient of the constraints for those products are 0 and standard computational techniques no longer work. However, for parameter values where the marketshares are all non-0, this is a straightforward constrained maximization problem. The key, then, is to get good starting values. To do this, I implement a routine that smoothes out the marketshare constraints by viewing consumer s choices probabilistically. 12 I then use these as starting values for the analytical market share routine. I solve this as a mathematical program with equilibrium constraints (Su and Judd 2008) using the optimization routine KNITRO where marketshares are computed with quadrature. Appendix 2 details this process. 3.2 Instruments Instruments other than the X s themselves are necessary both because I estimate the standard deviations of the random coefficients (requiring at least two extra instruments) and because firms choose prices knowing the demand shocks, leading to potential correlation between prices and the unobservables. Price correlation is less of a problem than in demand system estimation without product dummies because the unobservable here is not the mean unobservable quality level (which I estimate as part of the δ s), rather its idiosyncratic deviations. The instruments I use come from the downstream data and consist of products that are bundled in the computer systems that OEMs sell. Specifically, I use hard drive size, amount of ram, screen size, presence of discrete video card, and the version of installed operating system. These are all characteristics that have clear ordinal rankings where consumers prefer more to less. Firms tend to bundle higher priced chips with better characteristics, creating the correlation between the instruments and price that is necessary. Furthermore, decisions about which products to bundle are made by the OEM at much less frequency than the frequency with which CPU prices change, making it highly plausible that the instruments 12 This probabilistic formulation was developed by Che-Lin Su in currently unpublished work. I thank him for sharing it with me. 16

17 are uncorrelated with the unobserved demand shocks. 3.3 Results Table 1 present the results of the demand estimation routine. The left-hand panel lists the estimated quality for a selection of chips that were available at the end of 2006 and in early Because the mean of the price coefficient is essentially 1, the coefficients can roughly be thought of as the average consumer s willingness to pay (relative to the outside option) for each chip. Using estimated mean quality levels (as opposed to performance comparisons tied to CPU clockspeed) allows for the easy comparison of chips across generations and between firms. I ve sorted the table within firm in a manner that corresponds with my a priori view of how these chips should be ranked. 14 For the most part, the estimated coefficients line up with this informal ranking. 15 The right-hand panel of 1 shows a selection of month dummies, the country dummies, and the estimates of the standard deviations of the random coefficients. This industry is highly seasonal resulting in month dummies that are higher in December compared to the rest of the year. [Table 1 about here.] 4 Quality Choices Supply side competition is assumed to proceed in two stages. In a first stage, firms choose product qualities, paying a sunk cost for moving products from their previous spot (products are moved by introducing a new product and taking out an old one that was at the same price level). In a second stage, firms compete in prices taking quality choices as given. 13 I list these as being illustrative examples that come out of the estimation. A table listing the quality levels of the 91 chips that exist in my data would not add all that much and would significantly detract from the readability of the table. 14 That is to say, by chip generation in the order of better chip generations and by product number within chip generation because product numbers often give a rough idea of how the CPU companies themselves view which products are better than others. 15 It is somewhat interesting to see that the high end products from lower chip lines sometimes are valued higher than the low end chips from higher chip lines (compare, for example, the Celeron 360 and the Pentium 4 531). 17

18 In reality, product quality choices are dynamic. Because products live for more than one period, their placement today affects sunk costs that would need to be paid tomorrow should they be moved. I follow most of the literature in assuming a two-period model for a number of reasons: First, the main point of this paper concerns ways in which competition interacts with product placement decisions, and to understand these incentives, dynamics are not necessary. Second, in order to explore changes in market primitives it is necessary to solve counterfactuals under different circumstances. Running these counterfactuals as full dynamic games is computationally impossible because the state space increases with the number of products. My approach is to solve the full game as a two-period model and explore how the game shifts with dynamics with a more limited number of products. I note with footnotes throughout where I have also computed dynamic versions of the problem. This section starts by laying out the second stage pricing game and discusses marginal costs (which fall out of the pricing game). Then, the first stage quality choice game is formalized and used as the basis of the sunk cost estimation routine. 4.1 Second Stage: Pricing equation Firms are assumed to make pricing decisions simultaneously with full knowledge of the structural error terms. Because of the horizontal heterogeneity embedded in the demand system, a product from AMD and a product from Intel with exactly the same observable and unobservable characteristics will not be perfect substitutes. Taking qualities and number of products as given, the profit maximization problem for firm 1 is: max p 1 Π 1 = M m=1 j=1 J p j1 MS mj1 (p, δ) C j1 (14) Country: m, Product: j The maximization problem assumes that prices don t vary from country to country (arbitrage would prevent prices from diverging too much) and that marginal costs are the same across countries The p s and C s have product but not country subscripts and the problem for the firm maximizes over 18

19 This yields a first order condition of: Π 1 p j1 = M (p (j 1)1 C (j 1) 1 ) S m(j 1) 1 (p, δ) m=1 p j1 + (p (j+1)1 C (j+1) 1 ) S m(j+1) 1 (p, δ) p j1 + (p j1 C j 1 ) S mj 1 (p, δ) p j1 + S mj1 (p, δ) (15) The equilibrium is a fixed point in p for the two firms. The own price derivative for product j is given by: S j1 p j1 = [ 1 G( ν(p, δ, j1 ) j1 ) ] f( j1 ) j 1 p j1 [ 1 G( ν(p, δ, j1 ) j1 ) ] f( j1 ) j 1 p j1 j2 j2 g( ν(p, δ, α) α) ν(p, δ, α) f(α) dα p j1 (16) This is a very natural equation. The first two terms quantify the consumers that are lost to the products above and below in the product space. However, not all consumers are lost: only those that were already purchasing products from firm 1. If all consumers purchased from firm 1 so that G( ν(p, δ, j1 ) j1 ) = 0, then the model collapses back to the vertical model. The third term quantifies consumers that are lost to firm 2 through a change in ν. Notice that, while only consumers at the boundary of indifference between products are lost to neighboring products, consumers throughout the spectrum are lost to firm 2. Because neighboring products are also owned by firm 1, losing consumers above and below is internalized by the firm. It s straightforward to compute the cross price derivatives in these directions: S (j+1)1 p j1 = [ 1 G( ν(p, δ, (j+1)1 ) (j+1)1 ) ] f( (j+1)1 ) (j+1) 1 p j1 (17) the sum of profits from the individual countries (m). 19

20 4.1.1 Marginal Costs Marginal costs are assumed to follow a Markov process. A chip s cost in any given period is a function of last period s cost and an idiosyncratic error term: c j(t+1) = β j + ρc jt + ɛ c(jt) (18) Given the demand parameters, the first order conditions (equation 15) define a set of nonlinear equations that can be used to back out the marginal cost of a chip in any period. 17 Estimation begins by solving these systems of equations period by period, giving estimates of each chip in each period. 18 These estimated costs are then regressed on last period s costs to get an estimate of ρ. Figure 6 graphs estimated marginal costs for AMD and Intel for two months: 2006 and May At the low end of the product spectrum, costs for AMD and Intel are relatively similar, but AMD s costs rise faster with quality than Intel s, resulting in significantly higher costs for producing high quality chips. This asymmetry in costs is a large part of Intel s competitive advantage. Between May 2006 and May 2007, costs for both companies fell. These reductions came from the introduction of new technology that allowed for higher quality chips to be produced at lower cost. [Figure 6 about here.] Using estimates of marginal costs across all months, I can run equation 18 to get an estimate of ρ. Doing this yields a reasonable value of.9102 with a standard error of (.04). This indicates that marginal costs are falling over time, consistent with the earlier graph. 17 While Caplin and Nalebuff (1991) show a unique pricing equilibrium for this class of games with singleproduct firms, as far as I know, there is no equivalent proof for multi-product firms. That potential for multiple equilibria does not affect the estimation because, if there were multiple equilibria, the routine would simply pick out the one that is played in the data. However, this could be more problematic in the sunk cost estimation (where I compute counterfactual pricing equilibria). I fall back on the standby of computing equilibria for a wide variety of starting values and see that they always converge to the same point, leading me to hypothesize that, at least at the estimated demand parameters, the equilibrium to this pricing game is unique. 18 In this industry, lower quality chips are sometimes the byproduct of higher quality chips. Because of variation in the production process, sometimes chips that are designed to be high performance chips don t pass quality checks but can be salvaged and used at lower performance levels. To the extent that firms take into account this process when pricing, my marginal cost estimates will accommodate this behavior. Indeed, it s one of the advantages of estimating marginal costs. 20 May

21 4.2 First Stage: Quality Choices I extend the game back to a first stage where firms choose quality levels and number of products to be offered. When deciding whether to introduce a product of a given quality, firms have at least three things to consider: 1) Every product introduction incurs a sunk cost. 2) If they introduce a product with similar characteristics to the competition, then markups will be lower due to closer substitution patterns. And 3) Firms would like to use quality to 2nd-degree price discriminate. Factors 1 and 2 push the firm toward having fewer products in spaces that are uninhabited by their competition. Factor 3 pushes toward having a broad closely-spaced product line. The counter-balancing of these forces will determine the equilibrium. The formal problem for firm 1 is laid out in equations 19 and 20. [ max E π(δ 1t, δ 2t, p δ 1 1t, p 2t, ξ ] t, ɛ ct ) SC(δ 1(t 1,t) ) (19) max(δ 1t ) δ 1t (20) Firm 1 chooses its vector of qualities (δ 1 ) to maximize the sum of the profit function. Profit depends on the chips that each firm offers in that period, optimal prices as specified in equation 14, and the structural error terms. The maximum quality that the firm can produce in every period is given by δ 1t. I assume it evolves exogenously and that firms are aware of its evolution. Since firms make quality choices without knowledge of the structural error terms, the expectation operator is over the realization of demand and cost shocks. 19 Firms are forced to pay a sunk cost to change chip qualities (SC) that depends on their previous set of products in the market Sunk Cost Estimation I use information on products that were introduced and potential products that were not introduced to estimate a sunk cost of product introduction. Consider a firm s decision to 19 See Eizenberg (2008) for a full discussion of the sample selection problems involved in estimating these kinds of models. I follow him in assuming that firms do not observe the per-period shocks when they make their product decisions. 21

22 introduce a new product. If they introduce it, then it must have been the case that it was more profitable than not introducing it and not paying the sunk cost. Similarly, if they decide not to introduce a product, then it must have been the case that the firm would have been worse off introducing the product and paying a sunk cost. These two optimality conditions allow me to implement an inequality estimator in the style of Pakes, Porter, Ho, and Ishii (2006). Firms are assumed to pay a constant sunk cost for introducing a new product irrespective of where in the quality space that product is located. Letting δ jp denoted the location of products in the previous period, sunk costs are given by: SC = J I(δ j δ jp )θ (21) j=1 This procedure uses the estimated demand system to calculate a pricing equilibrium and consequent profit for actions that the firms could have taken but decided not to take. On average, observed actions are assumed to be more profitable than the unobserved potential actions. The difference between profit from observed and unobserved actions is used to form moments. Without further assumptions, sunk costs are not point identified, rather the moment conditions allows me to identify bounds. One side of the bound comes from looking at products that a firm could have moved (by adding a new product to the line and removing the old product) but decided not to. If they moved the product, they would in expectation increase profits but also incur the sunk cost of product introduction, θ. Letting π(δ j) represent profits for a product movement to any other position, then inequality 22 must hold. θ E [ π(δ j) π(δ j ) ] (22) The other side of the bound comes from looking at products that firms did indeed decide to move. In this case, they could have left the product in its old position and foregone the sunk cost, θ. That they decided to move the product indicates that the firm expected to make more profit from its movement than in keeping it in the same place. Inequality 23 formalizes 22

23 this concept. θ E [π(δ j ) π(δ jp )] (23) Firms make quality choices in expectation without knowing the realization of demand and cost shocks. It is also assumed that firms make quality choices without knowing the quality choices that their competition will make. Let ν sc denote the difference between profit expectations and realized profit. I assume that ν sc is unobserved by both the econometrician and the firm. 20 Denoting r(δ j ) as the estimate of observed profit that comes from the demand and costs estimates, the relationship between π(δ j ) and r(δ j ) is given by: E [π(δ j )] = r(δ j ) + ν sc (24) As long as firms have correct expectations on average, ν sc will go to 0 as the sample size goes to infinity. This gives: δ j δ 1 jp plim J J j 1 (r(δ j ) r(δ jp )) θ plim J J δ j =δ jp j ( r(δ j ) r(δ j ) ) (25) For δ j, the strategy that a firm could have used but decided not to, I find the best possible placement for a product by searching across the δ space and recomputing the competitive pricing equilibrium for each possible spot. Using this procedure, I estimate that the sunk costs of introducing a product fall in the range of $1, 236, 000 $3, 412, 000. This is rather small compared to profits but consistent with the idea that once a chip generation is introduced, adding an additional product doesn t require a whole lot of extra work. 5 Counterfactuals Putting together the estimates described above with the structure of the model, allows me to construct counterfactuals that speak to the role that competition plays in this market. 20 Pakes, Porter, Ho, and Ishii allow for a second error that firms know but that is unobserved by the econometrician. With appropriate instruments, this can be included in the model. I don t allow for this second error, implicitly assuming that sunk costs are the same across firms and across time. 23

24 Key parts of the counterfactuals examine how consumer welfare changes under different scenarios. To be specific about this, consumer welfare is given by: CS intel = j1 α i ν [δ j 1αi p j + ν i ] f(ν i )f(α i ) dν i dα i (26) j1 ν CS amd = j1 α i ν [δ j 1αi p j + ν i ] f(ν i )f(α i ) dν i dα i (27) j1 ν Where CS intel and CS amd breakout the consumer surplus from consumers who purchase each firm s products. I multiply by the price coefficient, α i to translate utility into a dollar value. 21 For many of the counterfactuals, I use the month of May 2007 as a starting point for the analysis. In May 2007, Intel offered 9 products with differing prices while AMD had 7. Estimated profit for Intel was $186 million and for AMD, $23 million. Total estimated consumer welfare was $1.63 billion. Figure 7 shows Intel s estimated markup for May The solid line is Intel s estimated marginal cost for that month. The dashed line shows prices. Margins are relatively low at the low-quality end of the spectrum where AMD competes strongly but jump up higher as quality goes up both in response to consumers with less price sensitivity and because AMD doesn t have products that can compete as strongly. [Figure 7 about here.] 5.1 Monopoly Moving to a monopoly from an oligopoly has (at least) 3 effects on consumer welfare: 1) If consumers have a taste for AMD s products, substituting to Intel s products or the outside good will cause a welfare loss for those consumers. 2) Prices will be higher. 3) Monopolists have fewer incentives to introduce products into the market and for the products that are 21 Because ν i is assumed to be distributed normal and therefore has infinite support, the very tail ends of this distribution massively change the consumer surplus calculation. In order to prevent this, I define ν and ν as the inverse of the normal distribution at p=.001 and.999. In other words, I chop off the very extreme tail of the ν i distribution. 24

25 introduced, monopolists will choose different quality levels. 22 The goal of this counterfactual is to separate out the different components of consumer welfare change and quantify potential social loss or gain. The first set of counterfactuals, detailed in table 2, decomposes a shift to monopoly into the profit gains and welfare losses from each of these mechanisms. Removing AMD but fixing products and prices doesn t change profit (up 2.8%) or consumer welfare (down 1.2%) very much. The relatively small change in consumer welfare indicates that consumer taste for AMD products is not all that strong. Individuals who were purchasing AMD products suffer some loss from purchasing an Intel product or substituting to the outside option, but their lost utility is not large. [Table 2 about here.] Next, I solve the monopolist s profit maximization problem, keeping product qualities fixed at the competitive level. Figure 8 plots markups as a function of quality (δ). Prices rise and Intel s profit goes to $562.2 million. Meanwhile consumer welfare drops 51% to $791.3 million. The increase in prices is telling: It indicates that despite AMD s relatively small marketshare, this industry is quite competitive. Removing AMD from the market would allow Intel to significantly raise prices leading to large consumer welfare losses. [Figure 8 about here.] Optimal Product Placement Consistent with equation 19, a monopolist solves for optimal quality choices knowing the markov process that costs follow (and the ρ parameter of that process) but without knowing 22 In theory not all consumers are necessarily made worse off by a monopolist: if quality levels change such that consumer types are served that weren t served under oligopoly or prices go down on some products in response to monopolist segmentation, then welfare for those consumers could go up. In practice, it is highly unlikely that these gains will swamp consumer welfare losses for other consumers, and with the structure that I impose I haven t been able to find parameter values for which this happens. Of course, a monopolist will also be more profitable than an oligopolistic and that profit may offset the consumer welfare change leading to greater social surplus. 25

26 the idiosyncratic period-by-period demand and cost shocks. [ max E π(δ t, p t, ξ ] t, ɛ ct ) SC(δ (t 1,t) ) (28) δ max(δ t ) δ t (29) In the first stage, the monopolist chooses the number and quality of products. In the second stage, idiosyncratic cost and demand shocks ( ξ and ɛ c ) are realized and the firm choose optimal products with full knowledge of those realizations. counterfactual choices are computed in the following steps: Simulate from the empirical demand and cost residuals Computationally, the Regress costs on quality (along with quadratic and cubic terms) and use the estimated parameters to construct a mapping from quality to cost for any potential quality level. This smooths out the cost function and prevents lower quality products from being more expensive to make than higher quality products. Compute optimal prices by setting the first order conditions to 0 and solving the resulting system of nonlinear equations Compute the profit function given the optimal prices, quality levels, and costs Do this 1,000 times for each candidate quality vector and take the average across simulations Choose a new candidate vector and repeat the above steps Take the maximum of the candidate vectors In practice, the uncertainty changes the problem very little: The profit function as computed above for a given candidate vector is very similar to the profit computed by ignoring the uncertainty (assuming demand and cost shocks are zero). Thus, in practice, I first solve the problem as a constrained optimization problem without uncertainty by handing it off to 26

27 the solver KNITRO, and then compute how the profit function changes with uncertainty by permeating around the solution from the constrained optimization problem. To examine how many products a monopolist would like to introduce, I compute the optimal products and consequent profits for N=1 through N=9. Table 3 lists these profits. It is immediately apparent from the table that as long as sunk costs are of even moderate size, a monopolist will introduce very few products. Profits increase for each additional product, but at a very slow rate. Furthermore, gains to re-optimizing over the 9 products are very small relative to the competitive offerings. Using the sunk cost estimates from above indicates that a monopolist will introduce between 1 and 3 products compared to the 9 that exist in the data. [Table 3 about here.] 5.2 Social Planner Considering the problem from the social planners perspective serves as a useful benchmark from which to compare the monopoly counterfactual and the oligopoly data. A social planner chooses the number of products for Intel and AMD up to the level where introducing a new product decreases consumer surplus by more than the sunk cost of product introduction. Social welfare is maximized when prices are equal to marginal costs, and because marginal costs are assumed to be constant with respect to quantity at any given quality level, firm profits are equal to 0. [ ] max E CS(δ t, c t, ξ t ) SC(δ (t 1,t) ) (30) δ max(δ t ) δ t (31) Table 3 displays the social welfare gains from each product introduction. Similar to the monopoly case, most social welfare is generated by the introduction of the first product. Subsequent products generate additional social welfare, but the gains drop off pretty quickly. A social planner would introduce more products (somewhere around 5) than a monopolist but fewer than the competitive outcome. 27

28 In both the monopolist and the social planner cases, there is not a lot of reason to introduce a large number of products. The large majority of surplus and profit comes from a single product placed at the high end of the market. This result is largely driven by the shape of the marginal cost curve. When costs rise relatively slowly with quality, as they do with the estimated marginal costs, segmenting the market with additional products doesn t add a lot of value. In the monopolist case, introducing products lower in the spectrum merely serves to cannibalize his own top-end product without generating significant new business from the outside option. In the social planner case, the highest quality to cost ratio is at the high end of the spectrum, and so introducing products lower in the spectrum, with lower quality to cost ratios, doesn t generate much additional welfare. It is also informative to consider the quality choices that a potential monopolist would make. Table 4 shows the product quality levels in the data compared to the optimal choices by a monopolist and social planner. The top panel lists the product levels that would be chosen conditional on the monopolist and social planner introducing the optimal number of products into the market taking into account sunk costs (5 in the social planner s case and 3 in the monopolist s). Because a monopolist introduces fewer products, he tends to introduce them at higher quality levels than the oligopoly outcome. The bottom panel of table 4 gives the optimal quality choices conditional on introducing 9 products to that market (so that they can be compared with the competitive outcome). As the number of products introduced goes up, a monopolist would push into the lower portions of the quality spectrum. The quality levels that a monopolist and a social planner would choose if they introduced 9 products are very similar. The Mussa and Rosen result that a monopolist wants to distort quality levels downward for every consumer type except the top one exists, but because price discrimination motives are so small at the estimated parameter values, the distortions are also small. [Table 4 about here.] 28

29 5.3 Technological Leadership Decomposing Returns to Innovation In the CPU industry, returns to innovation are driven by multiple factors. We typically think of innovation as allowing a firm to product a better top-end chip, and this is certainly true. But a second factor also plays a key role. A new chip technology, such as the Core 2 Duo, both allows a firm to produce better top-end chips, and it lowers the marginal cost of producing a given quality level along the whole quality spectrum. In order to understand how innovation changes marginal costs, it is necessary to discuss a bit of detail on the CPU manufacturing process. CPU s are produced in batches by etching transistors on a large wafer surface. The wafer is then chopped up into little pieces, with each piece representing an individual CPU. When firms innovate, they increase their ability to get a given level of performance out of a fixed number of transistors (the number of transistors fixes the size of the individual chip on the die). With this increased ability, they can keep the size of the chips constant and increase performance for each chip (producing better top-end chips). They can also shrink the size of the chip on the wafer, producing more chips on each wafer (lowering marginal costs). In practice, firms both produce higher quality chips at the same cost as an earlier generation lower quality chip by fixing the size, and they produce lower quality chips at lower marginal cost (by shrinking the size). This double process was in part responsible for giving Intel the ability to push AMD out of mid-market segments. AMD could produce chips that were as good as Intel s in this range, but they could only do so at a much higher price, which didn t allow them to price competitively. Of course, this is an equilibrium response: Intel didn t need to push AMD out of those markets. One response could have been to simply produce a better chip and leave older chips to serve the lower market segments. Another response would have been to raise the price of the highest quality chip and leave the newer lower quality chips at prices where AMD still could compete. However, these are not optimal solutions because there is a strategic effect: By pushing AMD out of lower market segments, Intel increased the length of the 29

30 product line over which it was a monopolist. In other words, by pushing AMD out of the lower segments, Intel could increase its markups on its higher end products because AMD was not longer a reasonable substitute for these products. To see how this can work in the model, consider a stylized example using the demand system. Intel and AMD both initially have the ability to produce at a top quality of δ = 5. In this case, one equilibrium has Intel with a single product at δ = 5 and AMD at δ = Profits are.8681 and Intel innovates and is now able to produce a product of quality δ = 7. Intel could simply sell a single product. In that case they will choose to sell their highest quality product, AMD will choose to sell a product of δ = 4.5, and profits will be 1.29 and.55 they go up for both firms. Intel could also choose to leave their old product on the market at δ = 5. AMD will keep their product at δ = 3.5 and profits will be 1.59 and.36. Lastly, Intel could lower the quality of it s lower-rung product to 4. AMD would respond by lowering it s quality to δ = 2.5 and profits would be 1.98 and.3. By strategically using its lower rung product, Intel can preserve markup on its new, higher margin, products by pushing AMD into being a lower-tier player Market Structure and Innovation It is well known that a monopolist s incentive to innovate may be different than that of a competitive firm. Arrow (1962) argued that a competitive firm generates returns from innovation by gaining market power and thus being able to extract additional profit (above the competitive equilibrium). This market power could come from an innovation that lowered marginal costs below that of the competition or from the ability to make a better quality or different product. On the other hand, while a monopolist would benefit from this innovation, because he was already capturing a large portion of the market surplus, the innovation would mainly serve to cannibalize sales he already had, generating a lower return from the innovation There are, of course, other equilibria in this simple game, including one with AMD at 5 and Intel at A durable goods monopolist may need to innovate in order to draw customers that had already purchased back into the market. That incentives is missing in this model as it would require dynamic consumer decisions. Goettler and Gordon (2009) discuss this in more detail. 30

31 In oligopoly, firms capture returns from innovation mainly by stealing business from the competition. This can come through high quality products that the competition can t match or it can come from new products introduced throughout the product spectrum. Once we view product quality choices as the outcome of a competitive equilibrium, it becomes clear that a post innovation equilibrium need not have quality choices staying at the same level as in the pre-innovation equilibrium. Indeed, firms have incentives to change quality levels to increase market power over as much of the product spectrum as they possibly can. In order to investigate how the returns to innovation change with market structure, I use the introduction of the Core 2 Duo. As discussed earlier, Intel s introduction of the Core 2 Duo changed the competitive landscape. Before its release, Intel and AMD were on relatively equal technological footing. Its introduction gave Intel a significant lead, allowing them to produce better quality chips than AMD. As shown in figures 2 and 3 discussed above, Intel didn t just release a chip at the top end of the spectrum. Instead they changed their whole product line. The end result was that AMD was relegated the bottom portion of the product spectrum. [Table 5 about here.] Table 5 lists estimated Intel profits under a number of different counterfactual scenarios. The first row addresses the question of what would returns have been were Intel a monopolist in the pre-innovation period and a monopolist in the post-innovation period. Profits increase, but only by 17%. The relatively small increase in profit comes because Intel was already capturing a large share of potential market surplus in the pre-innovation period. Postinnovation, Intel is able to produce better products, which they can then have higher markups on, but these gains are small, and for the most part they end up selling the new chips to the same people they sold the old chips to at a slightly higher price. The second row of table 5 gives Intel profits using the quality levels from the data (as estimated by the demand system). Here Intel benefits from the innovation both because they now can produce products that AMD can t compete with at the high end (and are therefore able to charge higher markups on them) and because they restructured their product line 31

32 in such a way that AMD was relegated to lower portions of the quality spectrum. In this scenario, Intel s profits rise by 97%. Next, I attempt to separate the profits that come because of changing product line decisions from those that come from products at the top end. To do this, what I would like to do is leave product quality choices fixed from the pre-innovation period at the low end of the market and simply tack on the high quality products that Intel was able to produce in the post-innovation period. This asks the question, what if Intel and AMD fixed their product characteristics and Intel simply introduced products at the top end without re-aligning throughout the spectrum. This is complicated here by the fact that marginal costs also change in between the pre and post innovation periods. To adjust for this, I take the quality of products that AMD and Intel produced in the pre-innovation period and predict would these products would cost in the post-innovation period under the estimated new marginal cost functions. I then give Intel the ability to re-optimize over it s top products (that it couldn t produce before) and ask what profits would look like. Row 3 of table 5 gives the profit that would result in this scenario. Profits still rise for Intel, but only by 47% (as compared to the 97% when Intel was able to flexibly adjust its products). This last counterfactual is informative for two reasons. First, it speaks to the question of where returns to innovation in oligopoly come from. The numbers indicate that with multiproduct firms, a substantial portion of profit can come from a firm strategically choosing its products to isolate competition and gain market power over larger stretches of the product line. Second, it argues that if we are interested in comparing the levels of innovation that might result under different market structures, fixing product characteristics will miss a piece of the story. Antitrust or merger analyses that compute a hypothetical innovation and attempt to quantify the returns would need to adjust for product quality levels to fully account for innovation incentives. 32

33 6 Conclusion This paper has examined the product line decisions of firms in the CPU market. Demand system estimation is based on the pure characteristics model with both horizontal and vertical consumer heterogeneity. Supply side estimation backs out marginal costs from period by period pricing first order conditions. Sunk costs are estimated by observing when and of what quality products are introduced (and products that firms decide not to introduce). I find that a monopolist has fewer incentives to introduce products compared to an oligopolist and when he does introduce products, they tend to be clustered at the high end. Compared to an oligopoly, monopoly reduces consumer surplus both because of higher prices, and because of different product introduction incentives. I also explore the incentives to innovate in multi-product firms. I find that returns to innovation are driven both by a firm s ability to introduce better products at the top end, and also by changes to product quality that are made throughout the product spectrum. In a monopoly, the incentive to steal business is not present and so strategic quality choices play much less of a role. While my results speak to a particular market with specific estimated demand and supply primitives, there is plenty of future work to be done in examining the theoretical underpinnings of these results. In particular, the extent to which the estimated slope of the marginal cost curve (with respect to quality) and the consumer valuation distribution are driving these results would indicate how much we might think of generalizing to other industries. A second area for future work comes from the vertical supply chain in this industry. My model will miss any product introduction incentives the come from the interaction between upstream and downstream firms. An explicit model of supplier interactions would be necessary to capture these incentives. 33

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37 Appendix 1: Changes in the market In November 2003, Intel released its fastest desktop processor to date, the Pentium 4 Extreme Edition. Designed to appeal to consumers who needed top of the line performance, the P4EE carried a relatively high price of $925. Over the lifecycle of this chip, Intel introduced higher performing chips but kept the price of this Extreme edition chip steady. When it exited the market in April 2005, it was still selling at its introduction price. This was in marked contrast with its earlier high performance chips, which had always debuted with a (relatively) high price that was quickly and consistently dropped over time. For instance, in December of 1999 Intel introduced an 800mhz Pentium III (Coppermine) chip for $851. Within two months it was selling for $647 and by the time it was discontinued in March of 2003 it was selling for $163. Far from being an isolated incident, this marked a change in strategy for Intel. 25 until November of 2003, Intel had offered relatively few chips at any given time, dropping their prices as new chips came onto the market. This strategy, known as waterfalling, introduced a new chip at the top end and reduced the prices of lower-end chips at the same time. Importantly, this meant that, while the older chips were hitting lower price points and being used to segment the market, Intel had little control over the quality measure of chips they were producing. Quality was determined by the highest performance mark they could hit and lower market segments were served by the quality level that was hit yesterday or the day before yesterday. From late 2003 forward, Intel pursued a strategy whereby both quality and price was chosen more frequently. Instead of using old chips to segment the market, new higher performing chips would be introduced to replace older chips which were then kept at the same price (making the new chips dominate the old ones). In any given period, Intel would release chips all along the price/quality spectrum, using both price and quality to segment the market. 25 The Extreme Edition (EE) chips are a stark example because they tend to not move in price at all. Other top chips do move in price, just not to the same extent as earlier chips. See below for more on this. Up 37

38 Figure 9 plots the price path of Intel chips introduced between 1999 and The top panel displays chips whose introduction price was above the 75th percentile of chip prices at the introduction month. The bottom panel plots price paths for those below the 75th percentile. [Figure 9 about here.] There is a clear pattern of price waterfalling pre-2004: Most chips are introduced at the high end and their price is rapidly reduced over the ensuing months. At any given time, Intel offered a full range of performance variation across chips, but this was accomplished by using yesterday s high end chips. There is a very different pattern in the data from 2004 forward: Chip introductions are spread throughout the price spectrum, most being introduced below the 75th percentile. Excepting a few anomalies, prices move once or twice over a chip s lifetime, which is quite different from the drastic price drops seen pre

39 Appendix 2: Computational Challenges Estimating the pure characteristics model presents a computational challenge because the marketshare equations predict no purchasers for many combinations of parameter values. In order to get around this problem, I introduce a number of incidental variables that smooth out the problem and ensure that the constraint gradients never become Equations lay out the estimation routine. Subject To: min ξ(β, σ σ) ZW Z ξ(β, σ) (32) ν,σ α,δ,p,β 1 N N p ij = S j j (33) J p ik u ik (α i, ν i, β, δ, σ) u ij (α i, ν i, β, δ, σ) 0 i, j (34) k=1 i=1 J p ik = 1 i, j (35) This formulation views an individual s purchase choice probabilistically p ij represents the probability that individual i will choose product j. At the optimum, incentive compatibility must hold: An individual must choose the product that maximizes his utility (equation 34), and his choice probabilities must sum to 1 (equation 35). The power of this approach comes from the fact that these constraints can be violated by the solver over the course of the search routine. For parameter values that would predict 0 market shares (and 0 constraint gradients) via direct calculation, this formulation allows for non-zero constraint gradients. Of course, this won t be true at the optimum, but it gives the solver sufficient flexibility to get to the optimum. The downside to this technique is that individuals must be simulated and the number of constraints grows with the number of simulated individuals. The researcher faces a tradeoff: a small number of simulations introduces noise into the estimates while a large number 26 I am indebted to Che-Lin Su for invaluable discussions and help in formulating the problem in this manner. 39 k=1

40 increases the computational burden, which at best increases computing time and at worse makes the problem unsolvable. It turns out that estimation of the direct formulation is relatively well-behaved for parameter values where all market shares are positive. In order to eliminate simulation error, I use the estimation routine in equations to get estimates that are close to the true parameter values and then use those as starting values for the direct calculation. This works because my model only has two random coefficients, making the region over which consumers prefer each product easy to calculate. With more random coefficients, this would be problematic and I would be forced to rely on the simulation method and incur the concomitant error. 40

41 Figure 1: Intel and AMD Marketshares 41

42 Price/Performance June 2006 Price/Performance July 2006 Figure 2 42

43 Price/Performance Oct 2006 Price/Performance January 2008 Figure 3: 43

44 Figure 4: Marketshare: Chips >=$150 44

45 Figure 5: AMD Marketshare By Country 45

46 Figure 6: Marginal Costs 46

47 Figure 7: Estimated Intel Markup May

48 Figure 8: Estimated Intel Markup May

49 Price paths of Intel chips Introduced above the 75th percentile Price paths of Intel chips Introduced below the 75th percentile Figure 9: 49