Estimating the Innovator's Dilemma: A Dynamic Structural Analysis of the HDD Industry (Dissertation Proposal)

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1 Estimating the Innovator's Dilemma: A Dynamic Structural Analysis of the HDD Industry (Dissertation Proposal) Mitsuru Igami February 17, 2010 Abstract The proposed research analyzes industry evolution with disruptive innovations, in which conventional technologies are replaced by new (but initially inferior) technologies. The goal is to understand the relationship between competition and innovation, for the purpose of designing pro-innovation competition policies. I develop a dynamic industry equilibrium model in which rms can invest in the quality improvement of conventional product and/or new product. The model is applied to the global hard disk drive (HDD) market, which has experienced many cycles of disruptive innovations. Parameters are estimated following Benkard's (2004) approach. Policy experiments (are expected to) indicate that (i) entry restriction, (ii) protectionist trade policy, and (iii) ban on oshoring will all have signicant negative impact on the pace of innovations. 1 Introduction This paper aims to address the following questions: (i) does competition foster innovation? and (ii) what public policies help innovation? In contrast to most of the existing literature, which analyze gradual improvement in productivity (process innovation) or product quality (product innovation), my main focus is on disruptive innovations, in which conventional technologies are replaced by new (initially low-quality) technologies. This type of innovation can potentially reshape an industry. Reecting on his managerial experience at Intel, an American rm that is the world's biggest chipmaker, its former CEO observes that: Of all the competitive forces that a company faces, that of `substitution,' or new techniques / approaches / technologies, is the deadliest. (...) The old winners tend not to adapt well; the new entrants face lower cost of entry, and quickly become successful. (Grove 1996, pp.27-29) Likewise, in discussing pro-innovation competition policies, Bresnahan (2003) stresses the importance of innovation by industry outsiders and new entrants that often results in Schumpeterian changes. Anderson School of Management, UCLA. mitsuru.igami.2012@anderson.ucla.edu. 1

2 More than a decade ago, Christensen (1997) proposed the concept of disruptive innovation as a set of stylized facts based on case studies. Although a similar notion of creative destruction has been around for much longer, it is only recently that IO economists started to formalize its analysis (Adner and Zemsky 2005, Klepper and Thompson 2006). To better understand the relationship between competition and (disruptive) innovation, I develop a dynamic oligopoly model, in which rms can invest in the quality improvement of conventional product and/or new product. The model is applied to the global hard disk drive (HDD) market, which has experienced many cycles of disruptive innovations. Parameters are estimated by the method of simulated moments. Policy experiments [are expected to] indicate that (i) entry restriction, (ii) protectionist trade policy, and (iii) ban on oshoring will all have signicant positive/negative impact on the pace of innovations and welfare. 2 Related Literature 2.1 Competition and Innovation As Gilbert (2004) observes in his survey, theoretical predictions are mixed as to whether competition promotes innovation. The results depend on the specics of the industry. Empirical studies based on cross-industry data are not denitive. Hence, one promising approach is a structural empirical analysis focused on a particular industry. 2.2 Ericson and Pakes (1995) Time is discrete in the original EP model. It contains incumbent rms and a potential entrant. Each rm produce a single product of diering qualities. In each period, each of the incumbent rms chooses price, investment in quality, and whether to exit the market. A potential entrant decides whether to enter. Doraszelski and Satterthwaite (2009) provide conditions under which a symmetric, pureinvestment-strategy Markov-Perfect Equilibrium exists. 2.3 Goettler and Gordon (2009) To study the dynamic duopoly (of Intel and AMD in the microprocessor market), Goettler and Gordon (2009) extend the EP model to include forward-looking consumers. 2.4 Doraszelski and Judd (2009) Doraszelski and Judd (2009) provide a continuous-time version of EP model. The advantage is the drastic reduction of the state space in the computation of MPE. 2.5 Weintraub, Benkard, and Van Roy (2008) Weintraub, Benkard, and Van Roy (2008) propose a new concept of Oblivious Equilibrium (OE) as an approximation to MPE. 2

3 2.6 Xu (2008) Xu (2008) applies Weintraub, Benkard, and Van Roy's (2008) framework to the Korean electric motor industry to evaluate impact of competition on the pace of innovation. Specically, he studies the eects of (i) higher substitutability among competing products and (ii) lower entry barriers. 3 Model: Dynamic Oligopoly Ericson and Pakes's (1995) dynamic oligopoly model (hereafter EP) provides a framework for empirical studies. I extend it to incorporate multi-product rms. ˆ Time is discrete with an innite horizon, t = 1, 2,... Each rm j {1,..., J} can sell a single product in each of G dierent categories. ˆ Control variables are investment (x jt ) and exit decision (χ jt ) for an incumbent rm, and whether to enter (χ e ) for a potential entrant. ˆ A rm's state is the qualities of its products across G dierent categories, q jt = (q j1t, q j2t,..., q jgt ). So the industry state is its collection, q t = (q 1t, q 2t,..., q Jt ). ˆ The law of motion is given by q jg,t+1 = q jg,t + τ jgt, where the quality improvement τ jgt {0, } takes the value of either zero or a xed increment. The outcome is stochastically increasing in x jgt. The industry equilibrium concept is Markov-Perfect Nash Equilibrium (MPE), where a rm's strategy maps the current state to actions. ˆ Even if I use monopolistic competition and allow no strategic interaction between entrants and incumbents, I can still preserve the essence of incumbent's dilemma based on cannibalization and/or higher cost of adoption. I will probably miss the rst-mover advantage in adopting new technologies, though. 3.1 Timing Each period, the following sequence of events happen: ˆ Entry and exit decisions: (i) each incumbent receives a (private) draw of its sell-o value, φ jt, from a common distribution and chooses whether to exit, χ jt {exit, stay}; (ii) a potential entrant receives a (private) draw of its entry cost, κ jt, from a common distribution and chooses whether to enter, χ e {enter, not}. ˆ Price competition: given q t, all surviving incumbents set prices p jt = (p j1t, p j2t,..., p jgt ) at the levels consistent with the static Bertrand competition (Nash equilibrium in prices) among dierentiated products. Each incumbent earns prot equal to π jt (q t, p t ) = G g=1 π jgt (q t, p t ) = M G t g=1 s jgt (q t, p t ) [p jgt mc jgt ], where M t denotes the market size, s jgt (q t, p t ) the market share of rm j's product in category g, and mc jgt the 3

4 marginal cost. The exact form of s jgt (q t, p t ) depends on the demand side (to be specied later). ˆ Investment: all surviving incumbents choose the investment level x jt = (x j1t, x j2t,..., x jgt ). ˆ The realization of the stochastic outcomes: (i) the R&D investment outcome, (ii) exit, and (iii) entry. Goettler and Gordon (2009) models the microprocessor market as a dynamic duopoly between Intel and AMD, without entry or exit. They characterize chips as durable goods and consumers as forward-looking agents. Prices become dynamic control variables, together with investments. Intel and AMD simultaneously set both prices and investment levels. Hence there are no subperiods in their model. 3.2 Demand [Should be Homogeneous within Category] Demand is characterized by the nested logit model (McFadden 1976). Intuitively, a consumer rst selects a product group (dened by form factors such as 8-inch, 5.5-inch, or 3.5-inch), and then a particular rm's product within that category. This modeling choice is based on two considerations: (i) a logit model is simpler but will not capture dierent substitution patterns across technological generations (form factors); and (ii) a random-coecient model allows for even richer substitution patterns but will be too demanding for my dataset. 1 By purchasing product l at time t, consumer i enjoys the following utility: u ilt = α t p lt + β t w (q lt ) + γ t g lt + X lt κ t + ξ lt + σ gl tζ itgl + (1 σ gl t) ε ilt, where p lt, q lt, g lt, X lt, and ξ lt are product l's price, quality (areal density), product group (form factor), other observed characteristics, and an unobserved characteristic, respectively, at time t. The function w (q lt ) represents the decreasing marginal utility in product quality: { q if q q w (q) = ln (q) if q > q. Taste parameters (α t, β t, γ t, κ t ) are allowed to vary over time but common across consumers. Together, δ lt α t p lt + β t w (q lt ) + γ t g lt + X lt κ t + ξ lt represents the mean utility from product l at time t. 1 Fershtman and Pakes (2000) use a logit model, u ilt = g t (q lt ) + (y it p lt ) + ε ilt, so the market share equation is given by: exp (g t (q lt ) p lt ) s lt (q t, p t ) = 1 + J k=1 exp (g t (q kt ) p kt ). Goettler and Gordon (2009) species: u ilt = βq lt αp lt + ξ l + ε ilt, where ξ l is interpreted as a brand preference for product l. Since the consumer's problem is dynamic, the demand side is more complicated than in usual discrete-choice models. Xu (2008) uses CES demand function and imposes monopolistic competition. Hence a single parameter represents the whole substitution patterns. 4

5 ˆ Alternatively, Goettler and Gordon (2009) species relative quality (with respect to frontier level) to avoid w (q) form. Consumer heterogeneity manifests itself in the form of taste shocks ζ itgl (taste for l's product group g l ) and ε ilt (taste for product l itself), which are weighted by σ t, the parameter representing the importance of product groups relative to individual products. ε ilt is assumed to be i.i.d. extreme value across consumers, products, and time. ζ itgl is distributed in such a way that the marginal distribution of the composite taste shock, ν ilt σ gl tζ itgl +(1 σ gl t) ε ilt, is also i.i.d. extreme value. 2 In each period, a consumer may purchase up to one unit of a single product, or purchase nothing, in which case the utility is u i0t 0. Product l's market share in period t will then be expressed as: ( ) s lt = s l gl ts gl t = exp [ δ lt ( )] 1 σ gl t ( ) exp 1 σgl t δ kt k gl 1 σ gl t [ ( )] k g l exp k g exp 1 σgl. t δ kt 1 σ gl t g=1,...,g δ kt 1 σ gl t The rst part represents product l's market share within its product group g l, while the second part represents product group g l 's share among all the product groups g = 1,..., G. Thus, product l's market share is a function of all products' prices, qualities, categories, other observed characteristics, and the unobserved characteristic, (p t, q t, g t, X t, ξ t ), plus the demand-side parameters θt D (α t, β t, γ t, κ t, σ t ). My dataset contains information on (p t, q t, g t, X t ), from which θt D can be estimated together with ξ t by Berry's (1994) inversion: ( ) slt ln = α t p lt + β t w (q lt ) + γ t g lt + X lt κ t + σ gl t ln ( s l gl t) + ξlt. s 0t Let ˆθ D t represent the IV estimate for θ D t. The estimation of the demand model has to address the following issues: ˆ Because p lt is endogenously chosen by the rms in the Bertrand competition, and because s l gl t is also aected by that choice, they need to be instrumented. Typical IVs are: (i) cost shifters, (ii) market structure variables, such as the number of rival products in the same category or the proximity of rival products in the characteristics space, and (iii) prices in other local markets. ˆ Although product characteristics such as q t, g t, and X t are treated as exogenous in a typical demand estimation, this assumption requires extra caution in the current dynamic model, in which rms invest in the improvement of q t. ˆ The competition in the HDD industry is (geographically) global. The variation in data across local markets will be limited. Likewise, the availability of IVs, of type (iii) in the above, may be limited. 2 Cardell (1996) shows that there exists a unique distribution of ζ that satises this condition. 5

6 ˆ All the parameters in θt D are allowed to change over time, to reect the rapid development in the use of HDDs. This modeling decision will limit the extent to which I can exploit the variation over time for the identication purpose. These concerns might call for a simpler demand model. 3.3 Supply With the market share equation from the demand side, prot from product l can be expressed as: ( ) ( ) π lt pt, q t, g t ; θt D = Mt s lt pt, q t, g t ; θt D [plt mc lt ], where M t is the market size and c lt is the marginal cost. Letting B jt represent the set of products produced by rm j at t, prot earned by rm j at time t is: π jt = ( ) ( ) π lt pt, q t, g t ; θt D = M t s lt pt, q t, g t ; θt D [plt mc lt ]. l B jt l B jt Let us assume that, in each period, rms compete in prices given ( ) q t, g t, c t ; θt D, so that the market shares represent a Nash equilibrium in prices. Together with the demand estimate ˆθ t D, I can recover the marginal costs for all products, mc t, from which I can further estimate the parameters, θt C, of the marginal cost function, mc ( ) q, g; θt C. Let ˆθC t represent its estimates. I model the relationship between the actual cost mc lt and its expectation conditional on (q lt, g lt ) as mc lt = mc ( ) q lt, g lt ; θt C + ηlt, where η lt is the cost shock, i.i.d. across product and time. Now, rm j's prot can be expressed as: π jt = π j ( qt, g t, M t ; θ D t, θ C t ). 3.4 State Variables, Laws of Motion, and Investment From the perspective of rm j, the qualities and categories of its own products, q jt and g jt, are endogenous state variables, i.e., their future values can be directly controlled. Suppose a rm may supply only one product within each category, and let q jgt = 0 indicate that rm j does not produce in category g {1, 2,..., G} at time t. Then rm j's state (product portfolio) can be summarized by a 1 G vector q jt (q j1t, q j2t,..., q jgt ). Regarding the R&D, I follow Goettler and Gordon (2009), and Pakes and McGuire (1994) in the specication of the quality improvement process (i.e., the law of motion for q jgt ). Investment in quality is denoted by x jgt R +. Its outcome (the improvement in quality) is τ jgt = q jg,t+1 q jg,t, where the improvement takes the value of either zero or a xed increment (or a proportional increment, if the quality grid is dened on a log scale), i.e., τ jgt {0, }. The outcome is stochastically increasing in x jgt : f (1 x) = ax 1 + ax (success), and f (0 x) = ax (failure). 6

7 ˆ Additionally, a spillover eect may be embedded, by further specifying the benet of being a follower relative to being a leader. One specication is to include the distance to the frontier product quality in f ( x) (Goettler and Gordon 2009). Another is to include the amount of R&D investment by the rival rms that are technologically more advanced (Xu 2008). The rival rms' product qualities and categories, q jt, cannot be directly controlled by rm j. Firm j forms expectations over their future values. The perceived law of motion is required to be consistent in equilibrium. { Finally, } M t, θt D, and θt C are exogenous state variables. Their entire paths over time, Mt, θt D, θt C, are assumed to be common knowledge. In other words, I assume the rms' t perfect foresight on these variables. They are collectively denoted by s t for notational simplicity. 3.5 Incumbent Firm's Dynamic Programming Problem Let V j represent rm j's value function. For notational simplicity, time subscripts suppressed. The Bellman equation is given by: ( V j qj, q j, s; θ dyn) [ { = max φ j, sup πj (q j, q j ; s) cx j + βe [ V ( q j, q j, s ; θ dyn) q j, q j, s ]}], x j 0 where θ dyn represents the dynamic parameters of the model (such as the distributions of sell-o value and entry cost), and c is the unit cost of investment. The expectation is over the rm's own investment outcome, τ j, and the rivals' future quality levels, q j, conditional on today's state (q, s): E [ V ( q j, q j, s ; θ dyn) q, s ] = ( V j qj + τ j, q j, s ) f (τ j x j ) h ( q j q, s ) ˆ May I deal with { M t, θ D t, θ C t } t τ j,q j using a gigantic transition matrix like this? T = ˆ The rst-order condition for investment, V j x j c + β τ j,q j = 0, is given by: V j ( qj + τ j, q j, s ) f (τ j x j ) h ( q j q, s ) f (τ j x j ) x = 0. 7

8 ˆ But, is Goettler and Gordon's (2009) proof applicable here? They need this FOC because price is another dynamic control in their model, but I do not. And, is x really continuous? My model includes the change from zero to positive quality, representing an introduction of a new product. Maybe this initial production should be modeled like entry. Let's see. 3.6 Potential Entrant's Problem A potential entrant chooses to enter if V e > κ, where and κ is a private draw of the entry cost. 3.7 Equilibrium V e ( q, s; θ dyn) = βe [ V ( q, s ; θ dyn) q, s ] { {V } } A Markov Perfect Equilibrium is j, x j, χ j, h J j, j=1 χe. The rm's belief must be consistent: ( q j q, s ) J ) = f (τ = q j q j q j,x j h j j j ˆ For computational feasibility, I might have to further focus on symmetric equilibria, i.e., no j subscripts in a MPE. 4 A Simple Model for Pilot Simulation Before plunging into the full model, data, and estimation, a simple model and its simulation would help clarify basic economic forces at play: ˆ An incumbent faces an investment tradeo between the quality improvement of its conventional product and the introduction and improvement of a new-generation product. ˆ In order to replicate the innovator's dilemma, I guess there need to be: (i) sucient cannibalization between the old and new products, (ii) some additional cost in developing a new product, probably greater than that incurred by an entrant, and/or (iii) a low discount factor (a high discount rate) such that an incumbent would rather stick to the old product and then exit. ˆ Maybe the time-varying consumer tastes are not essential to this tradeo. The recent modeling attempts by Adner and Zemsky (2005) Klepper and Thompson (2006) may help me develop basic intuitions. Also, Schmidt-Dengler's (2006) simple dynamic empirical model of technology adoption may serve as a reasonable starting point. 8

9 4.1 Incumbent's Dilemma in 2 2 Matrix Sources of incumbent's dilemma can be seen in a simple technology adoption problem. ˆ Three technologies: {Low, High, N ew}. The incumbent starts with low-quality old technology, but can either upgrade to high-quality old technology or introduce new technology. The potential entrant can either stay out or enter with new technology. (Can expand their action sets, but not much to gain.) ˆ Period prot: π (Low, Out) is incumbent's prot when his technology is low-quality old and potential entrant stays out; π e (High, In) is entrant's prot when incumbent's technology is high-quality old and she enters with new technology. ˆ R&D expenditure: x High is incumbent's R&D cost for upgrading (Low High quality); x New is for introducing new technology (Low New); x e is entrant's R&D cost for entering with new technology (Out In). With π e (, Out) 0, the game is expressed in normal form as follows: Incumbent / Entrant Stay out Enter with new tech π (Low, Out) x High π (Low, Out) x High Upgrade to high + β π (High, Out), + β π (High, In), 1 β 1 β 0 x e + β π 1 β e (High, In) π (Low, Out) x New π (Low, Out) x New Try new tech + β π (New, Out), + β π (New, In), 1 β 1 β 0 x e + β π 1 β e (New, In) ˆ The innovator's dilemma in data: (High, In) is most often observed. This is a Nash equilibrium if Incumbent : Potential entrant : β 1 β [π (High, In) π (New, In)] [x High x New ], and β 1 β π e (High, In) x e, which can be the case when: (1) π (High, In) is big ( huge customer base, rst-mover advantage in quality race) (2) π (N ew, In) is small ( cannibalization, competition, too small a market); (3) x New is big ( managerial/organizational mis-match); (4) π e (High, In) is big ( rst-mover advantage in new technology); and/or (5) x e is small ( managerial/organizational match). ˆ The incumbent's cannibalization (replacement) eect explains (1) & (2). ˆ The competition (eciency, rent dissipation) eect due to entry can also explain (2). 9

10 ˆ The emerging nature of the new submarket may explain (2) & (4). ˆ The dierential R&D cost may explain (3) & (5). β In place of the present discounted value of (constant) prot stream, π (, ) here, the 1 β fully dynamic model will have βev (, ). But most of the incentives to innovate are already at play in this simple static example. 5 Data: Hard Disk Drive Industry Prominently featured in the work of Christensen (1997), the hard disk drive industry oers a laboratory of disruptive innovations. Lerner (1997) presents an analysis of the same dataset. 6 Estimation 6.1 à la Benkard (2004) Benkard (2004) rst estimates demand (nested logit) and cost (with learning eect) separately. Dynamic parameters such as β (discount factor) and the distribution of κ (entry cost) are simply calibrated. Then he runs simulations that replicates data fairly well. Policy experiments, too. 6.2 Method of Simulated Moments As exemplied by Goettler and Gordon (2009), and Xu (2008), Method of Simulated Moments (McFadden 1989, Pakes and Pollard 1989) provides a bridge between data and model through simulation. Ackerberg and Gowrisankaran (2007) apply Indirect Inference method, a similar approach. 6.3 Two-Step Approach [Not Feasible Here] The current dataset may not match the data requirement of the two-step approaches such as Bajari, Benkard, and Levin (2007), and Aguirregabiria and Mira (2007). But the use of stock prices (for values), accounting and nancial data such as cashow and R&D expenses (for per-period prot and investment) may mitigate the issue. And I am expert on that. Moreover, if I can (persuasively) really simplify the discretized state space, there will be enough observations for each state. If so, I can estimate expected policy functions and then calculate expected value and simulated policy functions. 10

11 7 Policy Experiments In most countries, the industry did not experience public-policy interventions. Hence, the actual data represent a clean, no-intervention case, to which various policy simulations can be compared. 7.1 Ban on New Entry (or unlawful predation, or underdeveloped venture capital) 7.2 Ban on International Trade 7.3 Ban on Oshoring 8 Conclusion References [1] Ackerberg, Daniel A., and Gautam Gowrisankaran (2006), Quantifying equilibrium network externalities in the ACH banking industry, RAND Journal of Economics, 37-3, [2] Adner, Ron, and Peter Zemsky (2005), Disruptive technologies and the emergence of competition, RAND Journal of Economics, 36-2, [3] Aguirregabiria, Victor, and Pedro Mira (2007), Sequential Estimation of Dynamic Discrete Games, Econometrica, 75-1, 153. [4] Bajari, Patrick, C. Lanier Benkard, and Jonathan Levin (2007), Estimating Dynamic Models of Imperfect Competition Econometrica, 75-5, [5] Benkard, C. Lanier (2004), A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft, Review of Economic Studies, 71, [6] Berry, Steven T. (1994), Estimating discrete-choice models of product dierentiation, RAND Journal of Economics, 25-2, [7] Bresnahan, Timothy F. (2003), Pro-Innovation Competition Policy: Microsoft and Beyond, CPRC Discussion Paper, Competition Policy Research Center, Fair Trade Commission of Japan. [8] Christensen, Clayton M. (1997), The Innovator's Dilemma, (NY: HarperBusiness). [9] Doraszelski, Ulrich, and Kenneth L. Judd (2009), Avoiding the Curse of Dimensionality in Dynamic Stochastic Games, manuscript, Harvard University. [10] Doraszelski, Ulrich, and Mark Satterthwaite (2009), Computable Markov-Perfect Industry Dynamics, manuscript, Harvard University. 11

12 [11] Ericson, Richard, and Ariel Pakes (1995), Markov-Perfect Industry Dynamics: A Framework for Empirical Work, Review of Economic Studies, 62, [12] Fershtman, Chaim, and Ariel Pakes (2000), A dynamic oligopoly with collusion and price wars, RAND Journal of Economics, 31-2, [13] Gilbert, Richard (2006), Looking for Mr. Schumpeter: Where Are We in the Competition-Innovation Debate?, NBER. [14] Goettler, Ronald, and Brett Gordon (2009), Computation and Innovation in the Microprocessor Industry: Does AMD spur Intel to innovate more?, manuscript, University of Chicago. [15] Grove, Andrew S. (1996), Only the Paranoid Survive: How to Exploit the Crisis Points that Challenge Every Company and Career, (NY: Currency Doubleday). [16] Klepper, Steven, and Peter Thompson (2006), Submarkets and the evolution of market structure, RAND Journal of Economics, 37-4, [17] McFadden, Daniel (1989), A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration, Econometrica, 57-5, [18] Pakes, Ariel, and Paul McGuire (1994), Computing Markov-perfect Nash equilibria: numerical implications of a dynamic dierentiated product model, RAND Journal of Economics, 25-4, [19] Pakes, Ariel, and David Pollard (1989), Simulation and the Asymptotics of Optimization Estimators, Econometrica, 57-5, [20] Schmidt-Dengler, Philipp (2006), The Timing of New Technology Adoption: The Case of MRI, manuscript, London School of Economics. [21] Weintraub, Gabriel Y., C. Lanier Benkard, and Benjamin Van Roy (2008), Markov Perfect Industry Dynamics with Many Firms, Econometrica, 76-6, [22] Weintraub, Gabriel Y., C. Lanier Benkard, and Benjamin Van Roy (2009), Computational Methods for Oblivious Equilibrium, manuscript, Columbia University. [23] Xu, Daniel Yi (2008), A Structural Empirical Model of R&D, Firm Heterogeneity, and Industry Evolution, manuscript, New York University. 12