American Economic Association
The Schumpeterian Tradeoff Revisited
Author(s): Richard R. Nelson and Sidney G. Winter
The Schumpeterian Tradeoff Revisited
The image of the competitive process pre-
I. TheComplexStructureof the
Before pr...
would drive the real innovators out of busi-
Of course, as a number of c...
C - d
I d
firm actually producing (cost c'...
rectly, and also indirectly by influencing the
structure that evolves, which i...
R&D than a small firm. Similarly, require-
ments to work with ...
prevailing market share, within constraints
set by the assumed physical deprec...
Here, 8 is the physical depreciation rate and
the gross inves...
its logic so that it is easy to develop reason-
able hypotheses about its beha...
ing, given the other parametersof the model,
is sufficiently ...
successesfor a firm was clusteredon tech-
niquescloseto its existingproductivi...
(1) (2) (1) (2) (1) (2) (1)...
paredwith the sixteen-firmruns. However,
averagepracticetendedto be higherin th...
efforts and less influenced by developments
outside the industry than has been...
Science Based Cumulative
Aggressive Res...
the kinds of regimes for technical progress
under which the s...
Phillips, Almarin, Technology and Market
Structure:A Study of theAircraftIndus...
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  1. 1. American Economic Association The Schumpeterian Tradeoff Revisited Author(s): Richard R. Nelson and Sidney G. Winter Source: The American Economic Review, Vol. 72, No. 1 (Mar., 1982), pp. 114-132 Published by: American Economic Association Stable URL: Accessed: 26/10/2010 17:06 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact American Economic Association is collaborating with JSTOR to digitize, preserve and extend access to The American Economic Review.
  2. 2. The Schumpeterian Tradeoff Revisited By RICHARD R. NELSON AND SIDNEY G. WINTER* The image of the competitive process pre- sented by Joseph Schumpeter in his Capi- talism, Socialism, and Democracy has direct and obvious relevance to the economy of today, a straightforwardplausibility that the textbook account of competition cannot match. In electronics, pharmaceuticals, and many other industries it is plain that compe- tition among firms centrally involves their R&D policies, successes, and failures. And, as Schumpeterstressed, over the long run the gains to society from continuing innovation are vastly greater than those associated with competitive pricing. In articulating this view of competition, Schumpeter also put forth what has been called "The Schumpeterian Hypothesis": A market structureinvolving large firms with a considerable degree of market power is the price that society must pay for rapid techno- logical advance. Thus there is a tradeoff be- tween static efficiency, in the sense of prices close to marginal production cost, and dy- namic progressiveness. It is not clear how much choice Schumpeter thought society ac- tually had regarding the mix between static efficiency and dynamic progressiveness, but many contemporary economists clearly write as if they think that market structure is a variable potentially under tight public con- trol. It is important here to distinguish between Schumpeter's general propositions about the nature and social value of competition in technologically progressiveindustries and the specific viewpoint on the role of market structure represented by the Schumpeterian hypothesis. One can accept the value of the formerwhile remainingopen-minded, or even skeptical about the latter. And one can re- gard the analysis of Schumpeterian competi- tion as constituting a promising research agenda on which a start has barely been made, while considering that sharply di- minishing returns may have set in some time ago for certain lines of effort directed to the narrowerquestion.' This is the third of a series of papers in which we have set forth a model of Schumpeterian competition, explored some of the links between technological advance and marketstructurecontained in that model, and examined the influence of background conditions upon those links and on industry performance more generally. In our 1977 article, we studied how initial market struc- ture influenced industry technological pro- gress and price behavior, under a particular set of assumptions about the cost of innova- tion and appropriability conditions. In that paper we noted a tendency for concentration to grow in industries that start out initially unconcentrated. The evolution of industry structure over time, and in particular the conditions stimulating and retarding growth of concentration, was the topic of our 1978 article. In the present paper the inquiry is extended in several ways, most notably in the treatment of different regimes of exoge- nous change in technological opportunity. The focus is on the competitive contest among innovators and imitators, on how various technological and institutional condi- tions, some of which may be subject to in- fluence by government policy, determine the nature of that contest, and on the innovation and price performance of the industry. *Professors of economics, Yale University. We are indebted to Peter Reiss for research assistance and to members of the Yale microeconomics workshop for useful comments on an earlier draft. Our colleague, Richard Levin, was especially helpful. We also benefited from the comments of an anonymous reference. The work has been supported by grants from the Sloan Foundation and the National Science Foundation. IFor an early review of the discussion, see Nelson, Merton Peck, and Edward Kalachek. For more recent reviews, see Morton Kamien and Nancy Schwartz, and F. M. Scherer. Theoretical models of Schumpeterian competition have recently been put forward by Partha Dasgupta and Joseph Stiglitz (1980a, b), Glenn Loury, Carl Futia, and M. Th&ese Flaherty. 114
  3. 3. VOL. 72 NO. I NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 115 I. TheComplexStructureof the SchumpeterianArguments Before presenting a specific model, it is helpful to consider the issues more generally. It is plain that the particular view of tech- nological change taken in most analyses of Schumpeterian competition involves a con- siderable abstraction. Many variables in- fluence the pace and pattern of technological progress in an industry. There are many dif- ferent sources of innovation, and many kinds of policies impinge upon them. However, it is characteristic of discussion relating to the Schumpeterian arguments to focus on one class of innovators-firms in the industry and on policy influencing the structure (in some sense) of the industry. We will stick with these conventional ground rules. Even within these constraints, a wide range of possible relationships need to be articu- lated and examined. We propose there are three connected, but distinguishable, classes. One of these concerns the relations between market structure and the innovation that is generated. A second involves the nature of possible tradeoffs between static efficiency and technological progressiveness implicit in these relationships. Third, there are ques- tions regardingthe policy tools available and their influence over time on structure and innovativeness. A. TheRelationshipBetweenMarket Structureand Innovation Much of Schumpeter's discussion in Cap- italism, Socialism, and Democracy stressed the advantages for innovation of a firm being large, and was not focused on market struc- ture per se. He argued that there were innovation "capability advantages" of large firm size stemming from economies of scale in R&D and management, greater capabili- ties for risk spreading, finance, etc. Almost certainly, he also had in mind "appropriabil- ity advantages" of large firms over small ones. In the economic world of Capitalism, Socialism, and Democracy, as in the eco- nomic world of his earlier work, The Theory of Economic Development, the returns to in- novation stem from the transient monopoly of a new product or process provided by imitator lag. Where patent protection is spotty and imitation may occur rapidly, the payoff to an innovator may depend largely on his ability to exploit that innovation over a relatively short period of time. Large firms have a level of production, productive capac- ity, marketingarrangements,and finance that enables them quickly to exploit a new tech- nology at relatively large scale. The argument that large firms can be more efficient in R&D, and can quickly reap the advantages of large scale use of an innova- tion, has been countered by arguments that the bureaucratic control structure of large firms may partially or even fully offset these latent advantages. While there are extant theoretical models that have tried to capture the roles of scale economies in R&D and appropriability advantages of large size, to our knowledge there has been no explicit modeling that tries to come to grips with the internal control issues.2 This remark applies to the model we shall present here, as well as to other models of Schumpeterian competi- tion. In the argument above what is favorable to innovation is firm size, not market power per se. To the extent that some minimal scale is necessary for innovation it is of course possible that the necessary scale may be achievable only by a monopolist in a product field, or by a structure involving just a few firms. But arguments that market power per se is important to induce innovation must be of a different stripe. One such argument is that absence of sig- nificant competitors is a variable which in its own right can increase appropriability. Put another way, market structureinfluences the speed with which transient quasi rents are eroded away by imitators. A related but dis- tinguishable argument is this: absence of competition, or restrained oligopolistic com- petition, by leading to high rates of return in the industry generally, can serve to shelter firms that do innovative R&D in circum- stances where, if competition were more ag- gressive, firms that aim for a "fast second" 2 David Teece has at least begun such an inquiry.
  4. 4. 116 THE AMERICAN ECONOMIC REVIEW MARCH 1982 would drive the real innovators out of busi- ness. Of course, as a number of commentators remarked,weak competition may reduce the spur to innovation. This argument, like the one about bureaucratic obstacles to innova- tion in large firms, has not yet been ade- quately modeled.3 Nor do we treat it in our model. Finally, we note that while most analyses of the connections between market structure and innovation have viewed the causation as flowing from the former to the latter, it is clear that under Schumpeterian competition there is a reverse flow as well. Successful innovators who are not imitated quickly may invest their profits and grow relative to their competitors. Similarly, a firm that plays an effective "fast second" strategy may come ultimately to dominate the industry. As we stressed in our 1978 paper, market structure should be viewed as endogenous to an analy- sis of Schumpeterian competition, with the connections between innovation and market structure going both ways. It is surprising that studies concerned with the Schum- peterian hypothesis have largely neglected the reverse causal linkage. An important ex- ception is Almarin Phillips' study of the aircraft industry. Recently, Richard Levin has obtained promising results in exploratory empirical work on Schumpeterian competi- tion that takes the endogeneity of structure into account. B. TradeoffIssues: Costsand Benefits Much of the economist's interest in the Schumpeterian controversy stems from the observation that aspects of structurethat are conducive to innovation may be detrimental to the achievement of Pareto optimality in the short run. However, like the analysis of the market structure-innovativenesslinks, the discussion of the "tradeoff" has tended to be oversimplified. With the exception of some recent theoret- ical work, economists have tended to identify the deadweight triangle losses associated with product market power as the sole static cost of a progressive (concentrated) industry; there are, however, several kinds of social costs involved in Schumpeterian competi- tion, not just one. One of these is, indeed, the social costs associated with less than com- petitive output levels. The presence of such costs is signaled by a gap between marginal production costs in the most progressivefirms and price. However, static inefficiencies also reside in the extent to which the "best tech- nology" is monopolized by one firm in the industry, independently of whether that firm is large enough to have market power. In- deed, there are two different kinds of costs associated with limits on the use of technical information imposed through the patent sys- tem, or simply by industrial secrecy. One is a higher average production cost than given technological knowledge would permit, a cost associated with a gap between best practice and averagepractice. Another is the presence of duplicative or near-duplicative R&D ef- forts, resulting in a lower "best practice" for a given amount of cumulative industry R&D (or more R&D needed to achieve a given best practice). In addition, of course, there is a possible distortion of the level of total R&D effort, which may be greater or less than it would be in a hypothetical "second best" optimum in which the other costs are accepted. Setting aside for the moment the last of these considerations, the other costs can be depicted as in Figure 1.4 Let C equal unit production costs if industry R&D were spent perfectly efficiently and all firms had access to the best known technology. With a de- mand curve A-B, potential consumer's plus producer's surplus is triangle ABC. Let c equal unit production costs with the actual best practice technology (given some nearly duplicative R&D efforts) and the jagged line c-c' represent an industry production cost schedule arraying costs by firms from the most efficient (cost c) to the least efficient3More precisely, this issue of the "spur" to innova- tion provided by more competitive market structure has been formally treated in the context of models that assume profit maximization. We do not think that this is what the spur discussion is all about. 4This graphic representation was suggested to us by Levin.
  5. 5. VOL. 72 NO. ] NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 117 P A pS C - d I d 4 FIGURE 1 firm actually producing (cost c'.) Let actual output be Q. Actual surplus then is AP'c'c. The difference between actual and potential surplus is accounted for by three areas: 1) P'de, or a conventional deadweight loss as- sociated with the output shortfall relative to competitive equilibriumat best practice cost; 2) cc'e, or excess production costs due to a gap between best and average practice; 3) CcdB associated with lower best practice due to inefficient industry R&D. If there are to be private incentives for innovative effort, there must be some degree of private property (at least de facto) in the fruits of such effort; practically speaking, this means that some social costs of the sorts identified above must be accepted. How should one then approach the problem of assessing the social gains from more industry R&D, so as to reach some conclusion regard- ing the suitability of the total R&D effort that private incentives would produce? In the first place, the analysis above points out that some key determinants of the social gains are themselves endogenously determined by, or codetermined with, market structure. For example, it matters whether R&D expendi- ture is efficiently allocated from a social point of view, and this is partly a function of market structure. But there are also im- portant exogenous considerations. The gains from a higher level of industry R&D spend- ing depend partly on the technological re- gime that governs R&D outcomes. For exam- ple, it matters whether diminishing returnsto industry R&D effort set in early and sharply, or whether marginal returns continue to be high at high levels of expenditure. In the former case, but not the latter, low levels of industry R&D expenditure may buy society much of what a significantly higher level would. C. WhatAre thePolicy Issues? While the discussion above identifies causal links between innovative opportuni- ties and the market structure that evolves, it offers no reason to expect that the social tradeoff might be optimized by the auto- matic functioning of those links. It is quite reasonable, therefore, for economists to be interested in policies that can influence structure. However, there seems to be a no- tion implicit in many studies that industry structureitself is the policy variable, and that structure should be chosen so as to optimize the tradeoff. Realistically, it is not clear how far the policy tools presently existing or pro- posed can go toward affecting structure in a durable way. Even if it could be argued that a lesser degree of concentration than prevails in an industry would promote innovation, and hence be desirable on both static and dynamic grounds, a deconcentration achieved by policy may get undone over time as the competitive struggle continues in the now more innovative environment. And repeated structural interventions would certainly lead to behavioral changes that might be costly in themselves. On occasion policy can influence structure directly if not durably, as by requiring dives- titures, or by forbidding or encouraging mergers, or entry. But much of policy aimed to influence industry performance operates through constraining or requiring certain behavior, and affects structure indirectly. Government policies, regarding for example, the rights and obligations of patent holders, influence whether imitation is hard or easy. Or, antitrust policy may or may not permit firms with a large market share and a strong technological position to exploit these advantages and take a larger market share. Such policies may influence performance di-
  6. 6. 118 THE AMERICAN ECONOMIC REVIEW MARCH 1982 rectly, and also indirectly by influencing the structure that evolves, which in turn de- termines performance. A further complica- tion of the policy task arises from the fact that structure is likely to respond rather slowly to feasible policy adjustments. It is at least reasonable to propose that the time patterns of social costs and benefits ought to receive some consideration when policy mea- sures affecting structure are analyzed. In sum, much of the discussion relating market structure to technological advance and static efficiency does not really connect with the policy instruments that are avail- able. A serious analysis of policy must start with recognition of the endogeneity of struc- ture. The modeling effort that follows is a step in this direction. II. A Model A. GeneralFormulation While the discussion above provides, we believe, a useful overview of the issues in- volved in analyzing the Schumpeterian argu- ments, it was quite general. In this section, the framework is greatly simplified and a model is presented that formalizes a number of the key relations.5 The model is of an industry in which a number of firms produce a single homoge- neous product for sale in a market char- acterized by a fixed downward-sloping de- mand curve. All techniques capable of producing this product are characterized by constant returns to scale and fixed input coefficients. Further, techniques differ by equiproportional amounts in all input coeffi- cients; productivity differences thus are "Hicks neutral." At any time each firm operates a single technique, the best it knows, to the maximal level permittedby its existing stock of capital, purchasing needed complementary inputs on factor markets. Input prices facing the in- dustry are constant, independent of total em- ployment or time. Thus, since all techniques have the same ratio of other inputs to capital, cost per unit of capital is constant across firms and over time. But the cost of a unit of output is a variable in the model, since out- put per unit of capital (and all other inputs) at any time will in general vary across firms, and will increase over time as better tech- niques are discovered and implemented. Analysis of the Schumpeterian tradeoffs would appear to require explicit recognition of two different kinds of R&D activity- innovative R&D which draws on a general fund of relevant technological knowledge, and imitative R&D which involves learning about and copying the techniques of other firms. We model firms as having R&D poli- cies that are defined in terms of innovative and imitative R&D spending per unit of capital. Thus, as firms grow or decline, so do their R&D expenditures on innovation and imitation. Firms may, and likely will, differ in terms of how much they spend on these two different kinds of R&D (per unit of capital). A central question to be explored is the conditions under which firms that em- phasize one or the other of these types of R&D expenditure prosper or survive. We model both kinds of R&D as a two- stage sampling process. Within a given period the probability that a firm may take a "draw" on the set of innovation possibilites, or the set of imitation possibilities, is proportional to the firm's spending on these activities. Hence, over a run of many periods, the real- ized average number of innovation and imi- tation draws per period is proportional to the firm's average expenditures per period on these kinds of R&D. In this model, there are no economies of scale of doing R&D. Big firms spend more on R&D than do small firms and thus have a greater chance each period of an R&D draw. But that increased chance is only just proportional to their greater spending. However, there are "ap- propriability advantages" of large firm size. Once a firm has acquired access to a new technique through either innovative or imita- tive R&D, it can apply that technique to its entire capacity without further costs. We set aside issues relating to the embodiment of technical advance, and assume away any possibilities that a large firm may be slower to adopt a new technique found through 5This is a more analytic formulation than that con- tained in our 1977 and 1978 articles. There also are certain small changes in terminology and notation be- tween this version and the earlier ones.
  7. 7. VOL. 72 NO. 1 NELSONAND WINTER: SCHUMPETERIAN TRADEOFFS 119 R&D than a small firm. Similarly, require- ments to work with a new technique for a time to get it to operate effectively are ignored, as are the idiosyncratic characteris- tics of techniques honed through learning by doing. An imitation draw will, with certainty, enable a firm to copy prevailing best prac- tice. We exaggerate the distinction between innovation and imitation by disregardingthe costs, imperfections, and uncertainties that afflict the imitation process after a suitable target has been located. These are obviously quite restrictive assumptions about the im- plementation of innovation and imitation, and our analysis can only be suggestive of what might happen in other cases. We consider two different specifications of the distribution from which a firm samples if it has an innovation draw. These different regimes of technological change imply quite different relationships between industry pro- ductivity growth and industry R&D spend- ing. In one of these regimes, which we shall call the "science-based" case, we view the distribution sampled by an innovative R&D draw as improving over time as a result of events going on outside the industry, for example, advances in fundamental science occurring in universities. At any time, firms sample from a log-normal distribution of values of the average productivity of capital. (Recall that all other inputs are proportional to capital in all feasible techniques.) The mean (log) of this distribution increases over time at a rate we call the rate of growth of "latent productivity." Under this specifica- tion, what a firm finds today as a result of an innovation draw is independent of what it might have found last year or the year be- fore. And the population being sampled is better than the one sampled earlier. Innova- tive R&D by a firm can be interpreted as its efforts to keep up with a moving set of new technological possibilities created outside the industry. Less R&D by a firm or by the industry as a whole means that the time path of actual productivity tends to be lower in the range of exogenously determined possi- bilities.6 In the other regime, which we call the "cumulative technology" case, a firm in- novates by making incremental improve- ments in its own current technique-not by drawing on new knowledge created external to the industry. An innovation draw is, in effect, a draw on a constant distribution of proportional increments to its prevailing pro- ductivity level. Small increments are more likely than large ones. An innovative R&D success buys a firm not only a better tech- nique for the current period, but a higher platform for the next period's search. In the context of our model, "market structure" simply refers to (some statistical measure of) the degree of concentration of output or capital. There are no product dif- ferentiation issues to qualify that interpreta- tion, and we assume no new entry into the industry. Market structure and behavior af- fect the survivability of innovative R&D in two different ways. First, the sizes of the other firms in the industry, and their policies regarding spending on imitative R&D, in- fluence the amount of time a firm has after making an innovation before a large share of the rest of the industry is able to copy it. Also, market structure and behavior in- fluence the extent to which firms that have policies that are not profitable can survive. Market structure evolves endogenously. Given the capital stocks and techniques of the firms in a particular period, output for that period is determined. The demand curve then determinesprice, and productivity levels (given input prices) determine production cost. For each firm the ratio of price to unit production cost-which we call the price-cost margin-then is determined. (Given the as- sumptions that all inputs are proportioned to capital and all input prices constant, the rate of return on capital in production, i.e., ab- stracting R&D costs, is monotonically related to the price-cost margin.) We assume that a firm's desire to expand or contract is governed by its price-cost margin and its 6Stephen Homer's dissertation examines analytically some stochastic process models of technological change akin to that incorporated in our own model. Recent unpublished work by Katsuhito Iwai obtains interesting results regarding roughly the same range of problems with the aid of an assumption that the number of firms is very large. Both of these works are concerned in particular with examining the lag (or average ratio) between latent productivity and average realized pro- ductivity.
  8. 8. 120 THE AMERICAN ECONOMIC REVIEW MARCH 1982 prevailing market share, within constraints set by the assumed physical depreciation rate of capital and the firm's ability to finance investment. For firms of a given size, the greater the margin of price over production cost, the greater the desired proportional expansion. And, the greater the price-cost margin, the greater both retained earnings of the firm and its ability to persuade the capital market to provide finance. However, R&D expendi- ture, like production costs, acts as a drag on retained earnings. Firms with large market shares recognize that their expansion can spoil their own market. The larger a firm's current market share, the greater must be the price-cost margin needed to induce a given desired proportional expansion. By varying the shape of this relationship, a spectrum of possible patterns of investment behavior may be re- presented. In one extreme the expansion of the firm is not checked by market share at all; the firm responds as price taker regard- less of its share. Another investment strategy we call the "Cournot"strategy- a firm picks a target capital stock to maximize its profits under the assumption that all other firms in the industry keep their output constant. More formally, the model has the follow- ing structure: (1) Qit = AitKit- The output of firm i at time t equals its capital stock times the productivity of the technique it is employing. (2a) Qt = zQit = -AitKit; (2b) Pt= D(Qt). Industry output is the sum of individual firm outputs. Price is determined by industry out- put, given the product demand-price func- tion D(Qt). (3) 7rit= ( PtAit- c- rim -rin ) The profit on capital of that firm equals product price times output per unit of capital, minus production costs (including capital rental) per unit of capital, minus imitative and innovative R&D costs per unit of capital. A firm gets an innovation or imitation draw in period t with respective probabili- ties: (4) Pr(dint= 1)=- anrinKit; (5) Pr(dimt = I)=- amrimKit. (Parameters are chosen so that the upper- bound probability of one is not encountered.) If a firm gets an innovation draw, it samples from a distribution of technological oppor- tunities F(A; t, Ait). This distribution is a function of time and independent of a firm's prevailing technique in the science-based case. It is independent of time per se but dependent on the firm's prevailing technique in the cumulative technology case. If a firm does get an imitation draw, it then has the option of observing and copying industry best practice. For a firm that obtains both an imitation and innovation draw in the particularperiod, its following period's productivity level is given by (6) Ai + Max (Ait A't,~4ij Here At is the highest best practice produc- tivity level in the industry in period t, and Ait is a random variable that is the result of the innovation draw. Of course, the firm may fail to obtain an imitation draw, an innova- tion draw, or both, in which cases the menu from which next period productivity is drawn is shorter. A firm's desired expansion or contraction is determined by the ratio of price to produc- tion cost, P/(c/A), and its market share. But a firm's ability to finance its investment is constrained by its profitability, which is af- fected by its R&D outlays, as well as its production outlays. (7 i,t+ l = I(c(O,7it ) it
  9. 9. VOL. 72 NO. I NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 121 Here, 8 is the physical depreciation rate and the gross investment function I(-) is con- strained to be nonnegative; it is nonde- creasing in its first argumentand nonincreas- ing in the other two. Also, we assume 1imS01(l, s,0) 6, that is, a firm that has price equal to unit cost, negligible market share and zero R&D expense will have zero net investment. There are two key differences between our model and the other recent models of Schumpeterian competition cited in foot- note 1. The strategies or policies assumed of our firms are not derived from any maximi- zation calculations. And the industry is not assumed to be equilibrium. An essential aspect of real Schumpeterian competition is that firms do not know ex ante whetherit pays to try to be an innovator or an imitator, or what levels of R&D ex- penditures might be appropriate. Indeed, the answer to this question for any single firm depends on the choices made by other firms, and reality does not contain any provisions for firms to test out their policies before they actually commit to them. Thus there is little reason to expect equilibrium policy config- urations to arise. Only the course of events over time will determine and reveal what strategies are the better ones. And even the verdict of hindsight may be less than clear, for differences in luck will make the same policies brilliantly successful for some firms and dismal failures for others.7 To understand the process of industry evolution, we have chosen to focus initially on cases in which firm R&D policies are strictly constant over time. This might be defended as an approximation to empirical reality by some combination of arguments involving high set-up and adjustmentcosts in real R&D programs, bureaucratic sluggish- ness, and difficulties in distinguishing signal from noise in the feedback on a prevailing policy. But, in our view, a more fundamental justification for this approach is methodo- logical. If competition is aggressive enough and the profitability differences among poli- cies arelarge enough, differential firm growth will soon make the better policies dominate the scene, regardless of whether individual firms adjust or not. If, however, the model sets the stage for an evolutionary struggle that is quite protracted (as those in reality often are), then admitting policy change at the individual firm level is unlikely to change the generalindustryenvironment much-and it certainly complicates the task of under- standing the dynamic process. To forego the attempt to understand the process, on the other hand, is to leave open the question of the promptness and efficacy of the forces pressing the system toward equilibrium. It is also to overlook the shaping role of differen- tial firm growth as a determinant of the sort of equilibrium toward which the industry may be moving. While it would not be dif- ficult to augment the model by admitting adaptive R&D policies, in order to focus on the selection dimension of competition we have chosen not to do this. The model defines a stochastic dynamic system in which productivity levels tend to rise and unit production costs to fall over time as better technologies are found. As a result of these dynamic forces, price tends to fall and industry output tends to rise over time. Relatively profitable firms expand and unprofitable ones contract, and those that do innovative R&D may thrive or decline. In turn, their fate influences the flow of innova- tions. A number of conclusions about the behav- ior of the model sketched above can be de- rived by analytical methods. These relate, however, to special cases or to individual components of the model considered in iso- lation. We cannot, at this point, obtain by analytical methods conclusions about inter- esting features of the population of industry histories that the model implies. And, of course, analytic methods cannot be used to explore the implications of the assumption that all model parameters are in "plausible ranges." For these purposes we have turned to simulation. We exploit, however, the fact that the model is sufficiently transparent in 7The underlying theoretical viewpoint of the present paper is set forth in detail, and its application amply illustrated, in our forthcoming book on evolutionary economic theory.
  10. 10. 122 THE AMERICAN ECONOMIC REVIEW MARCH 1982 its logic so that it is easy to develop reason- able hypotheses about its behavior, hypothe- ses which can then be tested by reference to simulation results. B. SimulationExperiments We undertook three sets of simulation ex- periments. They have a common focus on the conditions for the survival of innovative R&D in an industry and on the interplay between innovation and industrial con- centration. These focal concerns are reflected in two aspects of the stylized evolutionary processes we have selected for study. First, in each experiment half of the firms do innova- tive R&D as well as imitative R&D, while the other half do imitative R&D only. (In this respect the design here is similar to that in our 1977 article. In our 1978 paper, all firms engaged in both innovative and imitative R&D.) Imitative R&D spending per unit capital is constant across all firms; similarly, spending on innovative R&D per unit capital is the same in all the firms that spend at a postive level. Secondly, we have in each case established initial conditions in which all firms are the same size, and the industry is approximately in equilibrium at the prevail- ing productivity level. We have also ruled out entry. These assumptions give us a clear innovator-imitator struggle and a definite reference point for the degree of concentra- tion that exists in the industry at any stage of the process. The numbers used to calibrate the model were chosen so that four simulation "periods" correspond to one calendar year. Under this interpretation, the average annual sales to capital ratio in the industry is in rough accord with that in technologically progressive industries. The rate of growth of latent pro- ductivity is roughly 2 percent per year in our slow growth condition; 6 percent per year in the fast growth condition. The ratio of innovative R&D spending to sales is 6 per- cent. In the "hard"imitation setting, a given probability of imitating best practice required twice as much imitative R&D as in the "easy" imitation setting. We ran the simulation for 101 periods, or twenty-five years after initial conditions. Within this framework, the three sets of experiments described below correspond to more specific contexts. In each set we explored the behavior of the system under different settings of particular parameter values. 1. Behavior and Performance in a Science- Based Oligopoly Our first set of experiments was concerned with the behavior of an industry that starts out initially highly concentrated, and in which the target markup formulas imputed to firms produce a regime of relatively restrainedcompetition. The industry involved is science based in that latent productivity evolves over time at a rate determined by outside forces. All firms start out with pro- ductivity levels close to then-prevailing latent productivity. In the context of this science-based oligop- oly, we wanted to explore the effect on behavior and performance of the following two variables: rate of growth of latent pro- ductivity, and ease of imitation. To what extent does the evolution of the industry, in particular the relative success of the firms that do innovative R&D and of those that just lay back, depend on the pace at which new R&D targets evolve over time? How does the ease with which one firm can im- itate the technology of another influence the evolution? The runs here are, we think, inter- esting in their own right. They also are inter- esting as a base of comparison for simulation experiments in which initially the industry is less concentrated. Understanding the results of the four-firm runs greatly facilitates one's ability to see through the workings of the model more generally, and thus to interpret what is going on in other contexts. Our past experience with running our model industry, starting it out with a quite concentrated structure, led us to predict the following.8 First, latent productivity will be trackedrelativelywell by the innovative firms because their level of innovative R&D spend- 8Of course, the relevance of our experience is partly attributable to the fact that we are working here with roughly the same values for the model's parameters that we have explored in the past.
  11. 11. VOL. 72 NO. I NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 123 ing, given the other parametersof the model, is sufficiently high so that each samples the evolving distribution of new technological possibilities relatively frequently. Second, the imitating firms will trackthe innovating firms relatively closely because their level of imita- tive R&D expenditure is sufficiently high that, even under the setting that we define as difficult imitation, it is unlikely that many periods go by without their being able to imitate the best technology in practice. Third, under some circumstances firms that do not bear the costs of innovative R&D may tend to be slightly more profitable than the firms that do, but the innovative firms will never- theless survive, make money, and even grow relative to the imitators. The investment re- straint shown by the farge firms in this con- text limits the extent to which a firm more profitable than its competitors exploits that advantage by trying significantly to increase its market share. In this atmosphere of restrained competition, firms that are not maximallyprofitable are sheltered.And, even if they have a run of bad luck, their capaci- ties for recuperationare not quickly eroded. Regarding the social merit of the industry behavior considered in these runs, certain predictions are relatively obvious. One is that the industry will be characterized by high markups over production cost; thus, there will be significant "trianglelosses." A second is that, compared with a more competitive industry, there will be less waste due to the use of inferior technologies (there will be less of a gap between average and best practice). Third, R&D spending will be more efficient in the sense that less total R&D outlay is required to keep average practice moving at a given distance from potential best practice. Whether price will be higher or lower seems an open question. 2. A More Competitive Science-Based In- dustry In the second simulation experiment we started the industry initially with sixteen equal-sized firms, eight of these doing both innovative and imitative R&D, and eight doing only imitative R&D. As in the four-firm case, in some of our runs latent productivity advanced at a rapid rate, in others latent productivity grew more slowly, and we set imitation as "easy"in half of our runs, "hard" in the other half. One important difference between the range of experimental variation in the sixteen-firm runs and the four-firm runs is this: in some of the sixteen-firm runs we preserved the assumption, contained in the four-firmruns, that a firm's target margin over cost increases sharply with its market share; in others, we explored what would happen if a firm with a substantial market share continued to expand rapidly even if it only had a moderate gap between price and cost. There are several things that can be pre- dicted with some confidence about industry performance in the sixteen-firm case as con- trasted with the four-firm case. Above we discussed the expected differences in average price-cost margins, the gap between average practice and best practice, and R&D ef- ficiency. Another expected difference be- tween the four-firm and sixteen-firm runs is that, in the latter, the initial industry struc- ture will not prove to be stable. In general, concentration can be expected to increase under the force of Schumpeterian competi- tion. There is one particular competitive struggle we will want to watch attentively and to consider as a function of the various parameter settings-the performance and survivalof the firms that do innovative R&D, compared with those that do imitative R&D. A straightforwardconjecture is that the abil- ity of innovative firms to prosper and survive depends positively on the rate of latent pro- ductivity growth (which determines the aver- age advance achieved through innovative R&D) and negatively on the ease of imita- tion. 3. A CompetitiveIndustrywith a Cumulative Technology In this third set of simulation experiments we preserved the basic assumptions of the second set, except for the following. Instead of assuming that innovative R&D yielded random draws on a moving distribution of technological possibilities, we assumed that innovative R&D involved the incremental improvement of prevailing techniques. The probability distribution of innovative R&D
  12. 12. 124 THE AMERICAN ECONOMIC REVIEW MARCH 1982 successesfor a firm was clusteredon tech- niquescloseto its existingproductivitylevel. (Thistechnologicalregimewasnot studiedin our earlierpapers on Schumpeteriancom- petition. It is reminiscent, however, of our earlierevolutionarymodel of econom- ic growth, see our article with Herbert Schuette.) Thetwoassumptionsaboutrateof growth of latent productivitybuilt into the earlier twoexperimentswerereplacedin thisexperi- ment by the followingtwo. Under one as- sumption,thedistributionof innovativeR&D funds is packedclose aroundthe prevailing techniqueanda majoradvancein productiv- ity is unlikely.Under the otherassumption, the distributionis morespreadout, and the firmhasa largerchanceof comingup witha significantincreasein productivityfrom a singleinnovation.We attemptedin choosing the parametersettingsto bring about real- izedratesof productivitygrowthcomparable to thosein the science-basedcases. We variedthe parametersettingsregard- ing "easeof imitation"in the same way in theserunsas in the earliersixteen-firmruns. Wealsoexploredthedifferencethatit would makeif firmsas they grewlargedid, or did not, exertinvestmentrestraint. The most importantcontrastbetweenthis set of runsandthe earlierset of sixteen-firm runs, of course,is this: in the formercases thereis an exogenousforcepushingforward the frontiersof knowledge.If only a little innovativeR&Dis done in the industry,the averagesuccess from that effort will be a relativelyspectacularadvance,reflectingthe forward movement of latent productivity since the priorR&D success.In the present experimentalcontext, thereis no such out- side force. If industryinnovativeR&D is squeezeddown by the dynamicsof Schum- peteriancompetition,the rateof progressof best practice and averagepractice can be predictedto declinealso. III. TheResultsof theSimulationExperiments A. TightOligopoly We report,first,on our simulationexperi- mentwithanindustrystructureconsistingof four firms, initially of equal size, two of whichspendonly an imitativeR&D.Table 1 displaysrelevantdata from these runs.The firstrowdisplaysforperiod101the shareof industry capital held by firms that do innovativeR&D.In general,shareless than .50 indicatesthat firms that did innovative R&Dwerelessprofitablethanthosethatdid not; a shareslightlyover.50is, however,also compatiblewith inferior profitability.The secondrowshowsthe averagerateof excess return(i.e.,overandabovethecapitalrental rate) in the industryas a whole, as a per- centage per quarter. Rows 3 and 4 show, respectively,the percentagemarginbetween priceandaverageproductioncosts,P - C/C in the industry as a whole, and the per- centagemarginfor the firmwith best prac- ticetechnology. The next five rows display statistics of industryconcentrationin period 101: rows 5-8, the shareof industryoutputaccounted for by the firstand secondlargestinnovator and imitators;and row9, the inverseof the Hirschman/Herfindahlindex of (capital) concentration,that is, the "numbersequiva- lent"of thevalueof theconcentrationindex. The next blockof rowsoutlineproductiv- ity statistics:row 10, averageindustrypro- ductivity;rows 11, 12, averageproductivity of innovatorsand imitators,separately;row 13, best practice,all for the final periodof the run;and row 14, a measureof the aver- age relativegap over 101 periods between averageproductivityandlatentproductivity. Rows 15 and 16 show total industryex- penditureon innovativeR&D over the 101 periods,andthenumberof innovationdraws. The bottomrow, 17,is pricein period101. The symbolsS andF, andE andH, atop thecolumns,referto slowandfastgrowthof latent productivity,and easy and hardimi- tation.The two columnsundereachheading show the resultsof the differentruns with the sameparametersettings. The strikingcharacteristicof all the runs shown in Table 1 was that for the reasons explained above, the four firms tended to stay close togetherboth in productivityand in size. Under these circumstancesit is not surprisingthat the rate of growth of best practiceproductivity,and the rateof growth
  13. 13. VOL. 72 NO. I NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 125 TABLEI-FOUR-FIRM RUNS SE FE SH FH (1) (2) (1) (2) (1) (2) (1) (2) 1. Innovators'Capital Share .51 .49 .50 .50 .52 .50 .50 .51 2. Excess Return (%Qtr) 4.8 4.8 4.7 4.7 4.8 4.8 4.8 4.8 3. MarginoverAverageCost(%) 33.7 33.3 33.0 31.3 33.2 33.2 32.4 31.4 4. Marginover Best Practice (%) 33.7 33.3 33.0 31.3 33.2 33.2 47.8 38.0 5. Output Share: Largest Innovator .26 .25 .25 .26 .27 .25 .27 .28 6. Output Share: Second Innovator .26 .24 .25 .24 .24 .25 .25 .22 7. Output Share: Largest Imitator .26 .25 .25 .26 .25 .25 .25 .27 8. Output Share: Second Imitator .23 .25 .25 .25 .23 .25 .23 .22 9. Herfindahl Numbers Equivalent 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 10. Average Productivity .28 .28 .82 .94 .28 .29 .86 .75 11. Innovators'Average Productivity .28 .28 .82 .94 .28 .29 .89 .75 12. Imitators'Average Productivity .28 .28 .82 .94 .28 .29 .83 .75 13. Best Practice Productivity .28 .28 .82 .94 .28 .29 .96 .79 14. Average Productivity Gap .05 .04 .22 .18 .03 .08 .15 .17 15. Total Expenditureon Innov. R&D 156. 153. 156. 157. 156. 155. 153. 154. 16. Innovation Draws 77. 76. 79. 78. 72. 88. 62. 81. 17. Price .75 .76 .26 .22 .76 .73 .25 .28 Note: SE denotes the combination of slow latent productivity growth and easy imitation; FE denotes the combination of fast latent productivity growth and easy imitation; SH denotes the combination of slow latent productivity growth and hard imitation; FH denotes the combination of fast latent productivity growth and hard imitation. TABLE2-SIXTEEN-FIRM RUNS; SCIENCE-BASED,INVESTMENTRESTRAINT SE FE SH FH (1) (2) (1) (2) (1) (2) (1) (2) 1. Innovators'Capital Share .51 .55 .55 .50 .60 .61 .80 .70 2. Excess Return (%Qtr) .77 .77 .66 .77 .87 .92 .69 .81 3. Marginover Average Cost(%) 7.9 9.8 7.5 .83 .96 10.8 11.9 12.5 4. Marginover Best Practice(%) 12.1 13.7 27.4 12.3 13.6 15.1 37.9 35.5 5. Output Share: Largest Innovator .09 .11 .16 .11 .11 .14 .19 .27 6. Output Share: Second Innovator .09 .11 .09 .11 .10 .14 .16 .17 7. Output Share:Largest Imitator .08 .12 .15 .12 .11 .12 .09 .20 8. Output Share: Second Imitator .08 .12 .15 .11 .08 .07 .02 .04 9. Herfindahl Numbers Equivalent 13.7 11.8 11.3 12.2 12.4 11.1 8.0 7.3 10. Average Productivity .26 .28 .81 .82 .27 .27 .86 .83 11. Innovators'Average Productivity .26 .28 .81 .81 .27 .28 .91 .85 12. Imitators'Average Productivity .26 .28 .81 .82 .26 .26 .65 .79 13. Best Practice Productivity .27 .29 .96 .85 .28 .28 1.06 1.00 14. Average Productivity Gap .06 .06 .02 .12 .04 .03 .09 .05 15. Total Expenditure on Innov. R&D 196. 206. 197. 187. 200. 186. 242. 210. 16. Innovation Draws 87. 110. 78. 97. 104. 106. 137. 102. 17. Price .66 .63 .21 .21 .65 .65 .21 .22 Note: See Table 1. of averageproductivity(or theirlevelsat the end of the run)tendedto be functionsonly of the rateof growthof latentproductivity. These variableswereinsensitiveto the ease or difficultyof imitation. Table2 presentscomparabledataforruns that startedout with sixteenfirmsof equal size. Whatarethe interestingdifferencesbe- tween the sixteen- and four-firmruns, for comparableotherparametervalues? First,for a givenrateof growthof latent productivity,thereare not many noticeable differencesbetween the rate of growth of best practicein the four-firmruns as com-
  14. 14. 126 THEAMERICAN ECONOMIC REVIEW MARCH 1982 paredwith the sixteen-firmruns. However, averagepracticetendedto be higherin the four-firmrunsthanthe sixteen-firmruns.In thesenseof makingthemostwidespreaduse of the best availabletechnologies,as pre- dictedabove,themoreconcentratedindustry structurescored better than the less con- centratedone. Also, the more concentrated industrygenerallyachievedthis betterpro- ductivitygrowthperformancewith a smaller aggregatevolume of innovative R&D ex- penditure.The concentratedindustrystruc- turewasmoreefficientin its use of R&D. On the otherhand,averagemarkupsover variablecostsweresignificantlyhigherin the four-firmrunsthanin the sixteen-firmruns. The static triangleefficientlylosses, there- fore,weregreaterthere.And in the sixteen- firm/four-firmrun comparison,the higher markupsmorethanoffset thehigheraverage productivityin the former,so thatpricewas higherin theconcentratedindustrycasethan in thesixteen-firmcase.9 B. A More CompetitiveScience-Based Industry We havediscussedmanyof the important differencesbetween the four-firmand six- teen-firmrunsabove.A furthercontrastbe- tweenthefour-firmrunsandthesixteen-firm runswasa tendencyforindustrystructureto changesignificantlyin the latter,but not in the former.The initial distributionof firm sizes tended to be moderatelystable where latentproductivitygrowthwas slow (regard- less of theeaseof imitation),or wherelatent productivitygrowthwas rapidbut imitation easy, so long as profitablefirmsshowedre- straintregardingtheir output expansionas they grewlarge.In runswith thesesettings, the firmsthat did not engagein innovative R&Dtendedto be moreprofitablethanthose that did. In other words, innovativeR&D was not profitableto undertake.But given the output restraintshowedby the slightly more profitableimitators,competitionwas orderlyenoughso that innovatorswerenot drivenout of business. Where latent productivity growth was rapid,andimitationhard,the firmsthatdid innovativeR&Dfaredmuchbetter,and the firms that only imitated did much worse, even thoughprofitablefirmsshowedoutput growth restraint.In these runs it was ap- parentthattheimitatorsweregraduallybeing drivenout of businesseven though the in- novatorswereshowingconsiderablerestraint in pushingtheiradvantage.The asymmetry withthecaseabovewillbe explainedshortly. Table3 displaysstatisticsforrunsin which profitablefirmscontinuedto expandaggres- sivelyeven when they grewlargerelativeto the market.The comparisonbetweenTables 2 and 3 is quiteinteresting.Undercompara- ble conditions,in eachandeverycasewhere profitable firms did not show output re- straint,the fate of the innovatorswas less fortunaterelativeto the imitatorsthanwhen large profitablefirms did show output re- straint.In everyone of the runs of the ag- gressivecompetitioncase,by theendinnova- tors countedfor significantlyless than half of theindustrycapitalstock. Thecomparisonis particularlystrikingfor the caseof rapidgrowthof latentproductiv- ity and hard imitation. Under restrained competition,theinnovatorsclearlydominate and have 70 percent or more of industry capital by the end of the run. When firms have aggressiveinvestmentpolicies,the imi- tatorsprevail,and by much the same deci- sive margin.Althoughit would be easy to understandwhy the intensityof the struggle might affect the marginof "victory,"it is somethingof a surpriseto find that it also affects, quite systematically,the identityof the victor. Theexplanationforthisphenomenon,and for the asymmetryof resultsin the invest- ment restraintcase, residesin the following. In thismodelanimitatornevercanachievea higherproductivitylevelthanthebest of the 9In our 1977 article, we probed a wider and denser range of initial distribution of firm sizes. In the earlier experiment, as the current one, end-period price tended to be lower in the sixteen-firm case than in the four-firm case. However, the runs that initially started out with eight equal-size firms tended to have roughly the same end-period price as those that started out with sixteen equal-size firms. And the runs with thirty-two initially equal-size firms tended to have higher end-period prices than the runs that started out with eight and sixteen equal-size firms.
  15. 15. VOL. 72 NO. ] NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 127 TABLE3-SIXTEEN-FIRMRUNS;SCIENCE-BASED,AGGRESSIVEINVESTMENT SE FE SH FH () (2) () (2) () (2) () (2) 1. Innovators'Capital Share .32 .31 .34 .32 .38 .36 .36 .32 2. Excess Return (%Qtr) .19 .19 .03 .09 .28 .21 -.01 -.15 3. Marginover Average cost(%) 2.3 2.9 1.1 1.8 4.7 4.9 .1 -1.1 4. Marginover Best Practice (%) 2.3 9.6 13.0 4.7 12.7 8.9 6.0 4.1 5. Output Share: Largest Innovator .06 .06 .13 .10 .07 .08 .11 .12 6. Output Share: Second Innovator .05 .05 .08 .06 .06 .07 .11 .05 7. Output Share: Largest Imitator .13 .12 .22 .23 .21 .18 .44 .54 8. Output Share: Second Imitator .12 .12 .16 .18 .13 .15 .15 .07 9. Herfindahl Numbers Equivalent 12.1 12.3 8.6 8.5 10.3 10.8 4.4 3.4 10. Average Productivity .26 .27 .85 .70 .26 .26 .71 .97 11. Innovators'Average Productivity .26 .27 .80 .69 .27 .26 .70 .96 12. Imitators'Average Productivity .26 .28 .87 .70 .26 .26 .72 .98 13. Best Practice Productivity .26 .29 .95 .72 .28 .27 .76 1.02 14. Average Productivity Gap .02 .04 .11 .10 .00 -.05 .05 .33 15. TotalExpenditureonInnov.R&D 162. 165. 174. 176. 174. 164. 175. 188. 16. Innovation Draws 72. 76. 99. 79. 85. 77. 92. 104. 17. Price .63 .60 .19 .23 .63 .65 .23 .16 Note: See Table 1. innovators.If he matchesaninnovator'spro- ductivityhe willhavehigherprofits,because he doesnot incurthe innovativeR&Dcosts. Butif he stopshis outputgrowthat reason- ablesize,pressureson theinnovatingfirmto contractarerelaxed,the R&Dbudgetis not eroded,andthereis a chanceof recoveryfor the innovatingfirm. (On the other hand, a largeinnovatorwill stochasticallyextendhis advantageovera smallimitator.)But,if the large imitatingfirm continues to grow, it forces the innovatingfirm to continue to contract. As the innovatorsR&D budget contracts, the chances of an innovative success that will spark recoverydiminish, and the expectedlead time before the big imitatorimitatesdiminishesas well. We think there is a phenomenonhere, albeit in stylized model form, well worth pondering.In our modelworld,an imitative strategymay, if supportedby luck earlyin the industry's evolution, be a runaway winner. And certainly imitators will have good luck at least some of the time. Is it reallysociallydesirablethattheyshouldpress theiradvantage?Earlierwe arguedthat the answermightdependon what a lowerlevel of innovativeR&Dcostssociety. In these simulationruns there was little tendencyforbestpracticeoraveragepractice in the last periodto be lowerin the aggres- sive investmentcase than in the restrained investmentcase.Inpartthisis because,while in thelattercasetheinnovatorsshareof total capitaltendedto be less, total industryout- put and capital tended to be greater,and total innovatiive R&D spending over the simulationrun not radicallydifferentthere- fore in the two cases. In part, the result pointsto therealsocialadvantageof a struc- ture dominatedby large imitators:once it exists, an innovationis rapidlyappliedto a largefractionof industrycapacity.(Note the high averageproductivityof imitatorsin the FH casesof Table3.) But the central reason is that relatively sharply diminishingreturns to innovative R&D are built into this model. A smaller R&D expendituremeans that the path of latent productivityis trackedless well, and that on averagethe differencebetweenin- dustrybest practiceand latent productivity is greater.Butevenan occasionalinnovative R&D hit suffices to keep the averagedis- tance from being very great. However,the social costs that occurif imitatorscome to dominatean industrymightbe significantly greaterin a regimewherethe opportunities for today's technicalchange are more in- fluencedby the industry'sown prior R&D
  16. 16. 128 THE AMERICAN ECONOMIC REVIEW MARCH 1982 efforts and less influenced by developments outside the industry than has been the case in the runs considered thus far. We now consider the simulation runs relating to this variant of our model. C. CumulativeTechnologicalAdvance In the simulation runs reported above, the firm's efforts at innovation were modeled as taking draws from a population of tech- niques, the average quality of which was expanding over time as a result of forces exogenous to the industry. A firm's prevail- ing technique had no influence on the distri- bution of possible outcomes of an innovative draw. In the simulation runs reported here, there was no outside augmentation of the set of possible innovations. And the outcome of any innovation draw was very much in- fluenced by the prevailing technique of the firm making that draw. In particular,techno- logical advance was cumulative in the sense defined above. Tables 4 and 5 display relevant industry statistics for a set of runs with a similar structureof parametersettings as in Tables 2 and 3. Many of the same relationships that held in the earliercases hold as well in these. The old results about the effect of the char- acter of innovation and imitation on in- dustry concentration obtain. The fast in- novation, hard imitation condition tends to lead to concentration. Where innovation is slow and imitation easy, such tendencies were far less marked. Regarding the competitive contest between innovators and imitators, innovators do well when the conditions per- mit fast technical advance and where imita- tion is difficult, provided large firms show restraint in further expanding output. The imitators do well, and the innovators poorly, in the opposite parameter settings. When firms continued to be aggressive in their output decisions even as they grew large, profits of both innovators and imitators were less than were more restrained behavior ob- tained. But it was especially the innovators whose fortunes were hurt. Thus the innova- tor/imitator asymmetry discussed earlier continues to have force under these different assumptions regarding the nature of innova- tion. What is different about these simulation runs is that aggressive competitive behavior has a clear negative effect on both best prac- tice productivity and average productivity. Under comparable conditions, each of the pair of best practice statistics (recall that we ran each parameter's setting twice) almost invariably was larger in the run where the firms restrained their output growth than in the runs where they did not. As in the earlier runs, aggressive competitive behavior tends to generate a structure in which there is at least one large imitator who is capable of rather quickly mimicking any new innova- tion, and who operates with lower costs than the innovators. As the profitless innovating firms shrink, so does total industry innova- tive R&D. In all but two of the comparisons, industry innovative R&D was less in the aggressive competition case than in the re- strained competition case, and, in all but one, the number of innovation draws smaller. And, in contrast with the science-based cases, where such a cutback in industry innovative R&D and innovation draws had little effect on the time path of industry best practice (save to make it more jagged and somewhat lower), in these runs reduced industry in- novation shows up in a slower growth of best practice. As one would expect, there is less of an effect on average productivity than on best practice. Where there is one huge imitator comprising a large share of industry capital, average productivity is largely determined by his productivity, and his productivity stays close to best practice. Nonetheless, in most of the cases average productivity was lower in the aggressivecompetition case than in the comparable case of more restrained competi- tion. The effect of aggressivecompetition on the price of the industry product was more un- certain. To some extent lower markups over costs in the strong competition case offset higher costs in the case. Nonetheless, and in striking contradiction to textbook wisdom,
  17. 17. VOL. 72 NO. 1 NELSON AND WINTER: SCHUMPETERIA N TRADEOFFS 129 TABLE4-SIXTEEN-FIRM RUNS; CUMULATIVETECHNICALCHANGE,INVESTMENTRESTRAINT SE FE SH FH (1) (2) (1) (2) (1) (2) (1) (2) 1. Innovators'Capital Share .56 .50 .64 .49 .57 .50 .77 .68 2. Excess Return (%Qtr) .72 .74 .78 .75 .84 .78 .77 .83 3. Marginover Average Cost (%) 8.6 8.6 10.0 8.3 9.0 8.5 10.7 12.6 4. Marginover Best Practice(%) 13.1 12.9 32.4 22.3 13.7 13.2 31.1 31.4 5. Output Share: Largest Innovator .10 .11 .17 .19 .10 .10 .23 .20 6. Output Share: Second Innovator .10 .09 .14 .08 .10 .09 .19 .19 7. Output Share: Largest Imitator .08 .10 .17 .18 .10 .09 .07 .11 8. Output Share: Second Imitator .08 .10 .06 .17 .09 .08 .07 .08 9. Herfindahl Numbers Equivalent 14.3- 12.6 10.7 9.4 13.1 13.4 7.2 8.1 10. Average Productivity .24 .25 .59 .62 .23 .23 .83 .60 11. Innovators'Average Productivity .24 .25 .62 .61 .24 .23 .89 .64 12. Imitators'Average Productivity .24 .25 .55 .62 .23 .23 .64 .52 13. Best Practice Productivity .25 .26 .71 .70 .24 .24 .98 .70 14. Average Productivity Gap n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 15. Total Expenditureon Innov. R&D t97. 190. 210. 182. 206. 188. 235. 208. 16. Innovation Draws 99. 104. 105. 100. 102. 100. 130. 107. 17. Price .72 .69 .30 .28 .74 .75 .21 .30 Note: See Table 1. TABLE5-SIXTEEN-FIRM RUNS; CUMULATIVETECHNICALCHANGE,AGGRESSIVEINVESTMENT SE FE SH FH (1) (2) (1) (2) (1) (2) (1) (2) 1. Innovators' Capital Share .23 .29 .26 .40 .32 .35 .38 .48 2. Excess Return (%Qtr) .14 .17 .19 .19 .17 .20 .00 .02 3. Marginover Average Cost (%) 2.3 2.4 3.0 2.5 4.3 3.3 1.3 2.1 4. Marginover Best Practice(%) 2.3 2.4 9.1 9.3 9.5 7.8 6.2 5.7 5. Output Share: LargestInnovator .05 .06 .06 .10 .05 .07 .12 .14 6. Output Share: Second Innovator .04 .05 .05 .09 .05 .06 .08 .13 7. Output Share: Largest Imitator .13 .14 .24 .16 .14 .13 .20 .27 8. Output Share: Second Imitator .12 .14 .16 .14 .12 .12 .20 .14 9. Herfindahl Numbers Equivalent 11.4 10.9 8.5 11.2 12.1 11.8 7.1 7.0 10. Average Productivity .22 .24 .51 .75 .20 .23 .62 .56 11. Innovators'Average Productivity .21 .23 .50 .73 .20 .23 .61 .56 12. Imitators'Average Productivity .22 .24 .51 .77 .20 .22 .62 .55 13. Best Practice Productivity .22 .24 .54 .80 .21 .24 .65 .58 14. Average Productivity Gap n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 15. TotalExpenditureonInnov.R&D 148. 158. 146. 189. 156. 161. 192. 209. 16. Innovator Draws 80. 84. 83. 86. 64. 88. 99. 115. 17. Price .75 .69 .32 .22 .83 .73 .26 .29 Note: See Table 1. by and largeend-period industryprice tended to be higher in the aggressive competition case than in the restrained competition case. Furthermore,examination of price trends in the two contrasting settings suggest that if the simulation run were longer, price under aggressive competition would grow progres- sively higher than what price would be under more gentlemanly competition. Our 101 period simulation runs end with some in- novators continuing to exist with nontrivial capital stocks, but in retreat. As the retreat continues, industry innovative R&D expendi- tures should dry up. And ultimately the in-
  18. 18. 130 THE AMERICAN ECONOMIC REVIEW MARCH 1982 TABLE6-201 PERIODRUNS; SIXTEENFIRMS,FH Science Based Cumulative Aggressive Restrained Aggressive Restrained I. Innovators' Capital Share .23 .87 .30 .49 2. Excess Return (%Qtr) -.13 2.27 .03 3.68 3. Marginover Average Cost (%) .75 16.24 .13 28.36 4. Marginover Best Practice (%) 12.5 29.1 7.8 31.5 5. Output Share: Largest Innovator .20 .22 .22 .26 6. Output Share: Second Innovator .01 .20 .02 .22 7. Output Share: LargestImitator .73 .08 .55 .24 8. Output Share: Second Imitator .02 .01 .14 .23 9. Herfindahl Numbers Equivalent 1.71 6.25 2.81 4.41 10. Average Productivity 3.48 3.66 2.62 4.77 11. Innovators' Average Productivity 3.54 3.75 2.48 4.80 12. Imitators'Average Productivity 3.47 3.00 2.68 4.74 13. Best Practice Productivity 3.81 4.05 2.81 4.90 14. Average Productivity Gap .16 .15 n.a. n.a. 15. Total Expenditureon Innovative R&D 339.39 484.42 383.00 388.60 16. Innovation Draws 183. 254. 175. 185. 17. Price .04 .05 .06 .04 dustry should settle into something quite close to a competitive equilibrium with zero profits and static technology. Table 6 presents data for simulations we ran for 201 periods. In the science-based technology regime the innovators have in- deed shrunk further in the aggressive invest- ment behavior case, but best practice pro- ductivity is not badly affected. On the other hand, in the cumulative technology regime period 201 best practice is much lower in the aggressive competition case than in the case where competition is more restrained. The hidden hand has throttled the goose that lays the golden egg. IV. SomePolicyandEmpiricalImplications of theModel We referred in the introduction to the unsatisfactory character of the literature on the Schumpeterian arguments. Undoubtedly, the difficulties that beset the literature are in large part a reflection of the complexity and difficulty of the subject matter. But part of the problem has been that economists have not tried to confront those complexities with a model designed for that task. The model illustrates some policy conun- drums that may well be presented by eco- nomicreality.It leadsus to contemplatethe possibility that not only may a relatively concentratedindustryprovidea bettershelter for R&D than a more fragmentedindustry structure:productionand technicaladvance may alsobe moreefficientin sucha setting. Thus there are "tradeoffs"revealedby the model, but not necessarilythe same ones thathavebeenidentifiedpreviously.Further, the natureof the tradeoffsmaydifferacross industries.In an industrywheretechnology is "science based," greater concentration means highermarkups;it brings the com- pensatingadvantagesof a smallergap be- tween averageand best practiceand more efficient R&D (in the sense that a given productivitylevel is trackedmore cheaply), but does not buy a fasterrate of growthof productivity.However,in an industrywhose technologyis "cumulative,"a moresheltered competitiveenvironment,and the associated higher markups,does lead to more rapid productivitygrowth. Theresultsthatshowa tendencyfor firms that do innovativeR&D to lose out in a competitive struggle with skillful and ag- gressiveimitatorsare particularlyprovoca- tive, and illustratea possibility not much discussedin theeconomicliterature.Nor has there been much discussiondifferentiating
  19. 19. VOL. 72 NO. 1 NELSON AND WINTER: SCHUMPETERIAN TRADEOFFS 131 the kinds of regimes for technical progress under which the social costs are slight (sci- ence-based industries) or heavy (cumulative technology industries) of having firms that invest in innovative R&D driven to the wall or out of business. There are some fascinating predicted em- pirical relationships that flow from our model, but, as with the policy conundrums, they are somewhat more subtle than those many economists seem to think reside in Schumpeterian competition. Most of the re- ported empirical tests have rested on the supposition that the Schumpeterian argu- ments imply that large firms tend to spend relatively more on R&D than small firms. Under the stylized conditions of our experi- ments, such a correlation can arise only as a result of selection, that is, of differential growth of innovators relative to imitators. In experimental settings where innovative R&D is profitable, the firms that spend on innova- tive R&D (and hence have a higher ratio of total R&D to capital) do tend to grow rela- tive to the imitators, but in such a setting the small firms tend to be eliminated. Where innovative R&D is not profitable, but where market structure permits it to survive, the R&D intensive firms tend to be small. Our model does point to a prediction that industrieswith rapid technical progress ought to be markedby high average R&D intensity and, as the industry matures, a more con- centrated industry structure than industries where technical progress is slower. And, in- terestingly enough, various studies attempt- ing to explain cross-industry differences in productivity growth rates have identified roughly the above relationship. The model also suggests that concentration is likely to increase over time in a technologically pro- gressive industry, another relationship that seems to hold empirically."1 It should be clear that Schumpeter's ap- praisal of progressive capitalism continues to present an imposing challenge to theorists, econometricians, policy analysts, and schol- ars of technological change. We hope that this paper will prompt others to view the Schumpeterian arguments in a new light. REFERENCES Dasgupta,Parthaand Stiglitz, Joseph, (1980a) "Uncertainty, Industrial Structure,and the Speed of R&D," Bell Journal of Econom- ics, Spring 1980, 11, 1-28. and _ , (1980b) "Industrial Structure and the Nature of Innovative Activity," Economic Journal, June 1980, 90, 266-93. Flaherty,M. Therese,"Industry Structure and Cost Reducing Innovation," Economet- rica, July 1980, 48, 1187-211. Futia, Carl',"Schumpeterian Competition," QuarterlyJournal of Economics,June 1980, 94, 675-95. Horner,Stephen,"Stochastic Models of Tech- nology Diffusion," unpublished doctoral dissertation, Ann Arbor: The University of Michigan, 1977. Iwai,Katsuhito,"Towards Schumpeterian Dy- namics," mimeo., Yale University, 1980. Kamien,MortonandSchwartz,Nancy,"Market Structureand Innovation," Journal of Eco- nomic Literature, March 1975, 13, 1-37. Levin,Richard,"Toward an Empirical Model of SchumpeterianCompetition," Bell Jour- nal of Economics, forthcoming. Loury,Glenn,"Market Structure and Innova- tion," Quarterly Journal of Economics, August 1979, 93, 395-410. Nelson, Richard,Peck, MertonJ. andKalachek, Edward,Technology,Economic Growth,and Public Policy, Washington: Brookings In- stitution, 1967. ______ and Winter,Sidney,"DynamicCom- petition and Technical Progress," in Bella Balassa and Richard Nelson, eds., Eco- nomic Progress, Private Values and Public Policy: Essays in Honor of WilliamFellner, Amsterdam: North-Holland, 1977. ______ and _ , "Forces Generatingand Liniiting Concentration Under Schum- peterian Competition,"Bell Journal of Eco- nomics, Autumn 1978, 9, 524-48. ______ and _ , An Evolutionary Theory of Economic Change, Cambridge: Harvard University Press, forthcoming 1982. _ ,____ _ , and Schuette, Herbert, "Technical Change in an Evolutionary Model," QuarterlyJournal of Economics, February 1976, 90, 90-118.'0See, for example, Phillips and our 1978 article.
  20. 20. 132 THE AMERICAN ECONOMIC REVIEW MARCH 1982 Phillips, Almarin, Technology and Market Structure:A Study of theAircraftIndustry, Lexington, Mass.: D. C. Heath, 1971. Scherer, F. M., Industrial Market Structure and Economic Performance, 2d ed., Chicago: Rand McNally, 1980. Schumpeter,Joseph,"The Theoryof Economic Development, Cambridge: Harvard Uni- versitypress,1934. , Capitalism, Socialism, and Democ- racy,3d ed., New York: Harper& Row, 1950. Teece,David,"Economiesof Scope and the Scope of the Enterprise," Journal of Eco- nomic Behavior and Organization, Septem- ber 1980, 1, 223-47.