Nathan Rosenberg

It is no longer necessary for an economist to apologize when introducing the subject of technological change. That is, in itself, a (modest) cause for celebration, since the situation was very different as recently as forty years ago. At that time, economics had still not been awakened from its dogmatic slumber on the subject, and was content to treat - or perhaps a more appropriate operational verb would be “to dismiss” - technological change purely as an exogenous variable, one that had economic consequences but no visible economic antecedents. Although sympathetic readers of Marx and Schumpeter had learned to attach great importance to technological change as a major impulse - perhaps the major impulse - in generating long-term economic growth, such an awareness had not yet rubbed off on the dominant academic traditions of western economics.

Today, the economic importance of technological change is widely acknowledged. There cannot be many economists who would dissent from the view that the growth of technological knowledge is fundamental to the improvement of economic performance. In addition, it is widely accepted that, in advanced industrial economies, the growth in technological knowledge relies increasingly, although in ways that are never clearly specified, on science. [1]

I have had valuable discussions of the issues treated in this chapter with Stanley Engerman, William Parker, and Scott Stern. I owe a particular debt to Paul David for his gentle but persistent encouragement in formulating my thoughts about path-dependent phenomena. The chapter draws, occasionally, upon two earlier papers: “How Exogenous is Science?”, chapter 7 of Nathan Rosenberg, Inside the Black Box, Cambridge University Press, Cambridge, 1982, and Nathan Rosenberg, “The Commercialization of Science by American Industry,” in Kim Clark, Robert H. Hayes, and Christopher Lorenz (eds.), The Uneasy Alliance, Harvard Business School Press, Boston (MA), 1985.

1. An interesting index of this lack of clarity is that, for many years, the most valuable single source of quantitative information on technological matters was (and still is) the National Science Foundation’s biennial publication, Science Indicators. Only since the publication of the 1987 issue was it finally acknowledged in the title that the volume is at least equally concerned with matters pertaining to technology. Since that year it has borne the title Science and Engineering Indicators.

9 Index

Thus, it seems reasonable to pose two questions: what can be said about the manner in which the stock of technological knowledge grows over time? And, to what factors is it responsive, and in what ways?

In dealing with these questions I will argue that the main features of the stock of technological knowledge available at any given time can only be understood by a systematic examination of the earlier history out of which it emerged. There is, as I intend to show, a strong degree of path dependence, [2] in the sense that one cannot demonstrate the direction or path in the growth of technological knowledge merely by reference to certain initial conditions. Rather, the most probable directions for future growth in knowledge can only be understood within the context of the particular sequence of events which constitutes the history of the system.

Further, although I believe that economic factors have powerfully shaped the growth of that knowledge, I also believe that there is no prospect of adequately accounting for the content of that knowledge by any economic model. In this respect economic theory is not, and never can be, a substitute for history, although it is obviously an invaluable complement. Economic forces powerfully influence the decision to undertake a search process, but they do so in ways that do not predetermine the nature and the shape of the things that are found. The findings of scientific research, and their economic consequences, remain shrouded in uncertainty. They reflect certain properties of the physical universe that are uncovered by the search, and not the economic goals that were in the mind of decision-makers who allocated resources to the research process in the first place. [3]

2. The most rigorous formulation of path-dependent phenomena in terms of their relevance for history is by Paul David. See, in particular, “Path Dependence: Putting the Past Into the Future of Economics,” unpublished manuscript, Stanford University, July 1988. Elsewhere David has stated: “[I]t is sometimes not possible to uncover the logic (or illogic) of the world around us except by understanding how it got that way. A path-dependent sequence of economic changes is one in which important influences upon the eventual outcome can be exerted by temporally remote events, including happenings dominated by chance elements rather than systematic forces. Stochastic processes like that do not converge automatically to a fixed-point distribution of outcomes, and are called non-ergodic. In such circumstances ‘historical accidents’ can neither be ignored, nor neatly quarantined for the purposes of economic analysis; the dynamic process itself takes on an essentially historical character.” Paul David, “Understanding the Economics of QWERTY: The Necessity of History,” in William N. Parker (ed.), Economic History and the Modern Economist, Basil Blackwell, Oxford, 1986, p. 30. See also Brian Arthur, “Competing Technologies, Increasing Returns, and Lock-In by Historical Small Events,” Economic Journal, 99 (1989), pp. 116-131.

3. As Arrow once succinctly put it: “European desire for spices in the fifteenth century may have had a good deal to do with motivating Columbus’ voyages, but the brute, though unknown, facts of geography determined what in fact was their economic results.” Kenneth Arrow, “Classificatory Notes on the Production and Transmission of Technological Knowledge,” American Economic Review Papers and Proceedings (May 1969), p. 35.

Index

II – The ‘D’ in R&D

Of course, it would not be quite correct to say that economic analysis has ever totally ignored the subject of technology. Rather, an explicit examination of technology and knowledge about technology has simply been suppressed by introducing certain assumptions, often only implicit, into the theory of the firm. Central to that theory, and therefore at the foundation of modern microeconomics, has been the assumption of a given set of tastes and some given stock of technological knowledge. This technological knowledge is (somehow) embedded in a set of production possibilities, a collection of known alternative combinations of factor inputs that may be employed in producing a given volume of output. Given this knowledge of tastes and technology, the firm then determines its optimal behavior, including the choice of technique, through the explicit consideration of factor prices. The implications for resource allocation of changes in technology or in factor prices can then be readily examined within this static equilibrium framework.

For many purposes this would seem to be quite enough to get the analytical ball rolling. [4] If one is interested only in exploring the implications of maximizing behavior, one is surely entitled to say that it is not a matter of primary concern to that analysis to know how any particular state of the world came to be that way. And exploring the implications of maximizing behavior subject to certain constraints is, obviously, a legitimate intellectual exercise.

I want to suggest that, even at this level, serious problems arise - not, of course, as a matter of pure logic, but as a matter of the potential explanatory usefulness of an analysis built on such premises. Moreover, the problems are not “merely” epistemological, but are central to the question of how to understand the level of technological competence that prevails in an economy at any particular time.

Why, to begin with, is it plausible to assume that a firm would know a range of technical options that are located far from the one that is presently employed? Once it is recognized that the acquisition of new technological knowledge is a costly process, why should resources be expended in acquiring knowledge that is not - or is not expected to be - economically useful?

One answer would rely on drawing a sharp distinction between the state of scientific knowledge and the state of technological knowledge. Such an answer might argue that a given level of scientific knowledge will always

4. Not for the purposes of Joseph Schumpeter, though. For a discussion of Schumpeter’s criticism of neoclassical economics, see chapter 3 of this book pp. 47-61.

11 Index

illuminate a wide spectrum of technological options, and that these are precisely the options represented on a production isoquant; that is, the production isoquant simply identifies the technological options that are made available by the existing stock of scientific knowledge. This is essentially the position that was argued by W.E.G. Salter in his valuable book, Productivity and Technical Change.

At one level this position is totally plausible. However difficult it may be to speak of the state of scientific knowledge as if it were some quantifiable magnitude, surely it is meaningful to say that the body of presently available scientific knowledge imposes certain constraints on what is technologically possible and also, by the same token, permits a range of technological alternatives to be taken up within the frontiers imposed by that knowledge. [5] As a statement about the scientific and technological realms, this is obviously useful. As a statement that has relevance for the economic realm, however, it is distinctly problematical.

Perhaps it is helpful to invoke a distinction that Boswell offered to his readers in his Life of Johnson: “Knowledge,” he said, “is of two kinds. We know a subject ourselves, or we know where we can find information upon it.” Precisely. Science will often provide the capability to acquire information about technological alternatives that we do not presently possess, but science does not make the acquisition of this information costless. Indeed, it may for certain purposes be useful to think of science as a guide for exploring the technological realm, and it is also plausible to believe that, ceteris paribus, the greater the stock of scientific knowledge, the lower will be the cost of acquiring necessary, but presently unavailable, information concerning technological alternatives. But I suggest that the starting point for serious thinking about technological knowledge is the recognition that one cannot move costlessly to new points on the production isoquant, especially points that are a great technological distance from the present location of productive activities. There are, I believe, distinct limits to the usefulness of the notion of technological alternatives being “on-the-shelf.” Although we may indeed, as Boswell suggested, know where we can find information on the subject at hand, acquiring the information, in the detailed sense of being able to base productive activities upon it, may be, and surely often is, a very expensive activity. [6] And one need not belabor the

5. I put aside here the important consideration that technological progress can - and does - often go beyond the frontiers of what is understood in a scientific sense. The limited scientific understanding of the combustion process has not prevented the operation of blast furnaces or coal-fired electric power generating plants, and the absence of a theory of turbulence has not posed an impossible barrier to the design of reliable aircraft.

6. Even when certain blueprints are literally on the shelf, the technology may not be as “freely” available as might be assumed. Ken Arrow pointed out a number of years ago that “when the British in World War II supplied us with the plans for the jet engine, it took ten months to redraw them to conform to American usage.” Arrow, “Classificatory Notes,” p. 34.

point that the cost of alternative courses of action is precisely what economic analysis is all about.

One valuable perspective on the cost of acquiring information is offered by the available data on R&D expenditures. These data are additionally valuable in showing the extent to which the generation and diffusion of knowledge has become an endogenous economic activity. In the year 1991, according to Science and Engineering Indicators, total R&D spending in the United States was estimated to amount to $152 billion, of which private industry financed almost 56 percent. Of particular importance for present purposes is the fact that the great bulk of total R&D spending is for Development activities, not for Basic or Applied Research. Development expenditures accounted for approximately 67 percent of total R&D spending. These figures, at the very least, suggest great skepticism about the view that the state of scientific knowledge at any time illuminates a wide range of alternative techniques from which the firm may make cost-less, off-the-shelf selections. It thereby also encourages skepticism toward the notion that is so deeply embedded in the neoclassical theory of the firm, that one can draw a sharp and well-delineated distinction between technological change and factor substitution. Although it is essential to the argument of this paper that the D of R&D encompasses a wide range of diverse, information-acquiring activities, it also includes many expenditures that are essential to make possible what economists have in mind when speaking of factor substitution. [7]

The extent to which total R&D spending is dominated by the Development component calls attention to some critical aspects of the manner in which technological knowledge grows. At least in respect of “high-technology” products, it is misleading to speak of some as-yet-untried but on-the-shelf technologies as “known.” It is of the essence of these technologies that their designs need to undergo protracted periods of testing, redesign and modification, and retesting before their performance characteristics are well enough understood for them to be produced and sold in reasonable confidence. [8] Although these expensive and time-consuming Development activities are typically not of great interest for their specific scientific content, the information so acquired is absolutely essential from an economic point of view. Performance characteristics of high-technology products simply cannot be accurately predicted without extensive testing. A new jet-engine design, or airplane wing, or weapons system, or electronic switching system, or synthetic-fuel plant, or pharmaceutical product, may

7. This argument is pursued further in chapter 6 of this book, which argues that the relative abundance of natural resources within the United States (in addition to a host of other variables) affected the direction of American technological change throughout the first half of the nineteenth century.

8. Some of these issues are examined in Rosenberg, “Learning by Using,” Inside the Black Box, chapter 6.

13 Index

require an enormous amount of testing before its performance characteristics can be understood with a high enough degree of accuracy and reliability to warrant commercial introduction. A large part of the D of R&D is devoted precisely to acquiring such information. [9] It cannot be overemphasized that such information typically cannot be deduced from scientific principles. [10]

It is curious that whereas so much attention in the last few decades has been properly devoted to incorporating the effects of uncertainty into economic analysis, these effects should have been totally neglected in this particular realm - the determination of optimal design of specific products. Such uncertainties are of very limited interest from the point of view of academic science. But the essential economic point is that these uncertainties are extremely costly to reduce or resolve. When considering the possibility of technological alternatives that are so far only on the shelf, the reduction of design, cost, and performance uncertainties is of absolutely central economic importance. In fact, workable technological knowledge in highly industrialized societies today is, in considerable measure, the (eventual) product of Development activities. Much of the Development effort is, in effect, directed toward the progressive reduction of cost and performance uncertainties in product (and process) design.

This observation concerning the importance of Development activities highlights an additional feature of the growth of technological knowledge. That is, most Development activities at any time are not devoted to the introduction of entirely new products, but rather to the improvement and modification of existing products. Although it is difficult to draw precise boundaries among the separate components of Development activities, undoubtedly the bulk of such activities, at any time, is devoted to efforts to improve existing products rather than to the introduction of entirely new products. In this respect, present activities are powerfully shaped by technological knowledge inherited from the past. Existing technologies commonly throw off signals and focusing devices indicating specific directions in which technological efforts can be usefully exercised. These internally generated pressures and compulsions play a large role in shaping day-to-day Development activities. [11] Such activities involve endless minor

9. The means of acquiring this information are themselves being transformed by new technologies. New aircraft designs are increasingly “tested” on supercomputers rather than in more traditional wind tunnels. Nevertheless, simulated testing, or other forms of laboratory testing, is often still very remote from actual operating conditions, and therefore of limited reliability.

10. For a full documentation of this point in the context of aeronautical engineering, see Walter Vincenti, What Engineers Know and How They Know It, The Johns Hopkins University Press, Baltimore (MD), 1991.

11. For further discussion of these themes, see Nathan Rosenberg, “The Direction of Technological Change: Inducement Mechanisms and Focusing Devices,” in Nathan Rosenberg, Perspectives on Technology, Cambridge University Press, Cambridge, 1976, [chapter 6. See also Paul A. David, Technical Choice, Innovation and Economic Growth, Cambridge University Press, Cambridge, 1975, introduction and chapter 1, for an illuminating analysis of the learning issues underlying the process of technological change.]

HHC: [bracketed] displayed on page 15 of original.

modifications and improvements in existing products, each of which is of small significance but which, cumulatively, are of major significance. Once the basic technology of generating electric power through the burning of fossil fuels had been introduced at the beginning of the twentieth century, it set the stage for several decades of minor plant improvements. This included a steady rise in operating temperatures and pressures, new alloys, modification of boiler design, etc. Although only specialists would be able to identify even a few of the associated improvements, the amount of coal required to generate a kilowatt-hour of electricity fell by almost an order of magnitude in the course of the following decades. More recently, by focusing upon a succession of individually small improvements, the semiconductor industry was able to move from products incorporating a single transistor on a chip to products incorporating more than a million such components. Similarly, in the computer industry the speed of computational capability has been increased, again by individually small increments, by many orders of magnitude.

Index

III – Soft Determinism

The instances of the electric power plant, the transistor, and the computer may be useful as a way of defining a major innovation. A major innovation is one that provides a framework for a large number of subsequent innovations, each of which is dependent upon, or complementary to, the original one. We can readily think of the framework established by the invention of the steam engine, machine tools, the internal combustion engine, electric power, or the vacuum tube in this context. But another way of expressing these connections is that each constitutes the initiation of a long sequence of path-dependent activities, typically extending over several decades, in which later developments cannot be understood except as part of a historical sequence.

There is commonly a certain logic in the sequence of some technological developments, a kind of, at least, “soft determinism,” in which one historical event did not rigidly prescribe certain subsequent technological developments, but at least made sequences of technological improvements in one direction easier - and hence both cheaper and more probable - than improvements in other directions. Technological knowledge is by nature cumulative: major innovations constitute new building blocks which provide a basis for subsequent technologies, but do so selectively and not randomly. The ability to generate and transmit electric power certainly did not make the invention of the vacuum tube inevitable, but it is difficult to

15 Index

think of the vacuum tube, and the transistor, without the prior development of some sort of electric-power generating capability. Again, sequences matter. Technological knowledge grows in distinctly path-dependent ways.

In all these ways, then, ongoing technological research is shaped by what has gone before. There is always a huge overhang of technological inheritance which exercises a powerful influence upon present and future technological possibilities. Much technological progress at any given time, therefore, has to be understood as the attempt to extend and further exploit certain trajectories of improvement that are made possible by the existing stock of technological knowledge. There are continuities of potential improvements which are generally well understood by engineers and product designers. Expert knowledge of the workings of the vacuum tube did not provide an adequate basis for a “discontinuous leap” to the transistor. However, once the transistor was invented, it created a set of opportunities for further improvement by pursuing a trajectory of miniaturization of components (including integrated circuitry) which has occupied the attention of technical personnel for nearly half a century.

So far the discussion of path dependence has been confined to its functioning within certain restricted technological spheres. However, it has also been important, historically, between fields that stood in some sort of complementary relationship to one another, and even between the realms of technology and science.

Scientific knowledge has been closely dependent upon progress within the technological realm. It would not be difficult to show, by drawing upon the long history of the microscope (starting from the simple screw-barrel type in the eighteenth century and proceeding through the compound microscope of the nineteenth century to the electron microscope of the twentieth century), the telescope (including the more recent radio telescope), and the recent histories of x-ray crystallography, the ultracentrifuge, the cyclotron, the various spectroscopies, chromatography, and the computer, how instrumentation possibilities have selectively distributed opportunities in ways that have pervasively affected both the rate and the direction of scientific progress. [12] At the same time, to leave the discussion at that level would constitute a rather crude sort of technological determinism. In fact, the relationship between technology and science is far more interactive (and dialectical) than such a determinism would imply . For the decision to push hard in the improvement of one specific class of instruments will often reflect a determination to advance a particular field of science as well as an expectation that the relevant instrumentation is ripe for improvement. Furthermore, instrumentation technologies differ enor-

12. An extended discussion of this phenomenon is taken up in the final chapter in this book.

mously in the range of their scientific impact. The linear accelerator and the ultracentrifuge are each relevant to a much narrower portion of the scientific spectrum than, say, the computer. The computer, in fact, has turned out to be a general-purpose research instrument, although it was certainly not visualized in that way by the scientists who invented it. Thus, different instruments may differ enormously in the specificity or generality of their impact upon fields of science. And, consequently, the rate and direction of progress in science is likely to be powerfully shaped by the peculiar characteristics of prior progress in scientific instruments.

At the same time, improvements in observational capabilities were, by themselves, of limited significance until concepts were developed and hypotheses formulated that imparted potential meaning to the observations offered by new instruments. The microscope had existed for over 200 years and many generations of curious observers had called attention to strange homunculi under the lens before Pasteur finally formulated a bacterial theory of disease, and the modern science of bacteriology, in the last third of the nineteenth century. The expansion of observational capability had to be complemented, in turn, by the formulation of meaningful interpretive concepts and testable hypotheses before the microscope could make large contributions to scientific progress. Thus, path dependence was critical in the sense that a particular body of science, bacteriology, had to await progress in an observational technology. But such progress was, of course, not sufficient for the scientific advance, only necessary.

Index

IV – Scientific Progress Dependent on Technological Capability

It is possible to accept everything that has been said so far but to argue that it is nevertheless restricted in significance. After all, much of what has been said can be captured within the summary observation that the technological trajectories that have been traversed in the past leave a profound imprint upon the present, and that they do so in a variety of ways. They serve to define technological possibilities by facilitating further progress in some directions but not in others. On the other hand, one might respond that the occurrence of major new scientific breakthroughs in effect opens up entirely new technological territories for exploration, thus liberating the economy from the constraints of the past.

There is undoubtedly some truth in this observation. It can be argued that precisely because new scientific knowledge opens up new paths, such knowledge creates discontinuities that loosen the influence of the otherwise heavy hand of the past. In this sense, scientific research is a disrupter of technologically generated, path-dependent phenomena.

17 Index

I believe that this is, at best, only partially true. The possibility of important new scientific findings does not eliminate the impact of path-dependent forces of the kind that have been emphasized so far. In particular, it by no means eliminates the influence of inherited technological capabilities in shaping the future performance of the economy.

This is because the ability to exploit new scientific knowledge in a commercial context will depend directly and heavily upon the technological capabilities that are available within an economy. Consider the great excitement all over the world concerning the recent remarkable breakthroughs in superconductivity. As a purely scientific breakthrough, the excitement is well justified. Nevertheless, it may be decades before this is actually translated into better computers, magnetically levitated trains, the transmission of electricity without loss, or the storage of electricity. Achieving these outcomes is not primarily a matter of scientific research, although progress toward their achievement may draw very heavily upon scientific knowledge. Designing new products that exploit the knowledge of high-temperature superconductors, and then designing and making the technology that can produce these new products, are activities that draw primarily upon existing technological capabilities.

This brings us back again to the D of R&D: developing new product concepts, casting them in specific design forms, testing new prototypes, redesigning them, devising new manufacturing technologies that make it possible to achieve drastic reductions in cost, etc. In fact, one of the most forceful economic lessons of the post Second World War period - although there were ample prewar antecedents for those who were interested - is that the ability to achieve the commercial exploitation of new scientific knowledge is heavily dependent upon social capabilities that are remote from the realm of science. These capabilities involve skills in organization, management, and marketing in addition to those of a technological sort. But, in the context of the issues addressed in this chapter, it is inherited, path-dependent technological capabilities that have dominated the eventual commercial exploitation of new technologies whose underlying technological feasibility has been made possible by the advancement of science.

Thus, economic and technological considerations remain powerfully and inextricably involved in converting new scientific research findings into tangible human benefits. In some cases the new scientific understanding has been so limited, or so remote from a capability for exploiting it in an economically meaningful way, that an entirely new discipline had to be created to bring this about. Such was the case toward the end of the nineteenth century in chemistry, and the result was the development of the new discipline of chemical engineering early in the twentieth century. [13] At

13. For a discussion of the contemporary situation, see chapter 10 below.

about the same time, the achievement of heavier-than-air flight at the beginning of the twentieth century gave rise to the entirely new discipline of aeronautical engineering. Aeronautical engineering, as a discipline, had far less of a scientific base to draw upon than did chemical engineering. Indeed, to this day, aircraft design remains an activity that is less guided by a systematic scientific base and is therefore compelled to rely much more heavily upon experimentation and testing of prototypes.

I conclude that there are sharply defined limits to the extent to which new scientific knowledge can liberate an economy’s performance from the technological capabilities inherited from the particular path that it has traversed in arriving at its present state.

Index

V – Technological Determination of the Scientific Research Agenda

There are other ways in which prior developments in technology have shaped the progress of science and the economic consequences of science. A major development of the twentieth century is that the changing needs of the technological sphere have come to play a major role in shaping the agenda of science. In this sense, as well, scientific research itself has become increasingly dependent upon the path of technological change. Thus, I suggest that the formulation of the research agenda itself cannot be understood without paying attention to prior developments in the realm of technology.

This kind of dependence is not, of course, a uniquely twentieth century phenomenon. It can be seen in the spectacular developments in the iron-and-steel industry that began in the 1850s. In the cases of the three great innovations in the second half of the nineteenth century - the Bessemer converter, Siemens’ open-hearth furnace, and the Gilchrist-Thomas basic lining that made possible the exploitation of high phosphorus ores - none of them drew upon chemical knowledge that was less than half a century old. However, adoption of these innovations dramatically raised the payoffs resulting from acquisition of new scientific knowledge concerning the properties of steel.

The very success of the Bessemer process in lowering the price of steel and in introducing steel to a rapidly expanding array of new uses made it necessary to subject the inputs of the process to quantitative chemical analysis. This was because, as was quickly discovered, the quality of the output, and its structural integrity, was highly sensitive to even minute variations in the composition of the inputs. Sulfur and phosphorus content had an immediate and very deleterious effect upon the quality of the final product. The addition of even minute quantities of nitrogen from the air during the course of the Bessemer blast led eventually to serious and unexpected deterioration in the performance of the metal, although this

19 Index

causal relationship was not established until many years later. Indeed, it is fair to say that the modern science of metallurgy had its origins in the need to solve practical problems that were associated with the emergence of the modern steel industry.

I suggest that, even well into the twentieth century, metallurgy can be characterized as a sector in which the technologist typically “got there first,” that is, developed powerful technologies, or alloys, in advance of systematized guidance by science. The scientist was commonly confronted by the technologist with certain properties or performance characteristics that demanded a scientific explanation. A particularly fruitful area of research lay in trying to account for specific properties produced by certain technologies or exploiting particular inputs. Such phenomena as deterioration with age or the brittleness of metals made with a particular fuel were intriguing to scientifically trained people. At the same time, the economic payoff to the solution of such problems had become very high.

The increasing extent to which science became influenced by technology was, of course, greatly reinforced by one of the most important institutional innovations of the twentieth century: the emergence of a large number of industrial research laboratories - almost 12,000 in 1992. Research at these laboratories was obviously strongly shaped by the desire to improve the effectiveness of the technology upon which the firm depended. As these laboratories have matured, the best of them have not only applied scientific knowledge to industrial purposes; they have also been generating much of that knowledge. The recent award of Nobel Prizes to scientists working at IBM in Europe, and to scientists at Bell Labs in the United States, is an index of the quality of at least the best scientific research work that is conducted in industrial contexts where the research agenda is clearly shaped by a concern with specific advanced technological systems. The problems encountered by sophisticated industrial technologies, and the anomalous observations and unexpected difficulties that they have produced, have served as powerful stimuli to much fruitful scientific research, in the academic community as well as the industrial research laboratory. In these ways the responsiveness of scientific research to economic needs and technological opportunities has been powerfully reinforced. [14]

How else can one account for the fact that solid-state physics, presently the largest subdiscipline of physics, attracted the attention of only a few physicists before the advent of the transistor in 1948? [15] In fact, at that time there were many universities that did not even teach the subject. It was the

14 For further discussion, see Rosenberg, “How Exogenous is Science?”, in Inside the Black Box, chapter 7.

15. An extended discussion of the development of the transistor can be found in chapter 11 below.

development of the transistor that transformed that situation by dramatically upgrading the payoff to research in solid-state physics. Moreover, it is important to emphasize that the rapid mobilization of intellectual resources in research on the solid state occurred in the university as well as in private industry immediately after the momentous findings that were announced in 1948. The sequence of events is essential to my argument: transistor technology was not building upon a vast earlier commitment of resources to solid-state physics. Rather, it was the initial breakthrough of the transistor that gave rise to a subsequent large-scale commitment of scientific resources. Similarly, surface chemistry has become much more important for the same reason. More recently, and to oversimplify somewhat, the development of laser technology suggested the feasibility of using optical fibers for transmission purposes. This possibility naturally pointed to the field of optics, where advances in scientific knowledge could now be expected to have potentially high payoffs. As a result, optics as a field of scientific research has experienced a great resurgence in recent years. It has been converted by changed expectations, based upon past and prospective technological innovations, from a relatively quiet intellectual backwater of science to a burgeoning field of research. Under modern industrial conditions, therefore, technology shapes science in the most powerful of ways: it plays a major role in determining the research agenda of science.

One could examine these relationships in much finer detail by showing how, throughout the high technology sectors of the economy, shifts in the needs of industry have brought with them associated shifts in emphasis in scientific research. When the semiconductor industry moved from a reliance upon discrete circuits (transistors) to integrated circuits, there was also a shift from mechanical to chemical methods of fabrication. That shift brought with it an identifiable increase in chemical science and in the volume of resources devoted to that subject.

Although the technological realm plays a role of growing importance in identifying research problems, the places where the eventual findings of science will have useful applications remain full of uncertainty. Consider information theory, a powerful intellectual tool developed since the Second World War. That this methodology should have been developed in the telephone industry, where channel capacity has been perhaps the most fundamental single constraint on the provision of the industry’s service, is hardly surprising. [16] Shannon’s analysis of how to determine the transmission capacity of a communication channel offered insights of critical importance to engineering design within the telephone system, where channel capacity is, of course, a dominating constraint. But, as is often the

16. Claude Shannon, “A Mathematical Theory of Communications,” Bell System Technical Journal (July 1948).

21 Index

case, a methodology that had been developed within a very specific context turned out to be capable of providing illuminating insights far from its place of origin. It has shaped the design of hardware and software in other communications media, including radio and television, as well as in data-processing technologies generally. But its uses have not been confined to the realm of engineering or the physical sciences; information theory has also been extensively employed in cryptography, linguistics, psychology, and economics.

Here again there have been highly important historical sequences that cannot, at least in any obvious way, be explained by recourse to economic (or other) logic. The specific needs of a particular technology - the telephone system - gave rise to a body of abstract theory that, in turn, had beneficial applications in numerous and remote contexts. Thus, although it can be explained why a telephone company was willing to support research in a particular direction (possible enlargement of channel capacity) economic factors are of little help in grasping the distinctive characteristics of what was learned as a result of the research.

Index

VI – Path Dependency of Economics, Science & Technology

The purpose of this chapter has been to describe the manner in which technological knowledge grows over time, and some of the determinants and consequences of this growth. A main aim has been to emphasize the extent to which technological change and scientific knowledge are responsive to underlying economic variables. This should not be too surprising, in view of the fact that the financing of R&D is generally undertaken with some explicit economic goal in mind. However, the peculiar nature of the information-acquisition process, especially the uncertainty of what will be found once a search has been undertaken, argues against adherence to a belief in a strict economic determinism. Even if one believes that technical change is propelled by economic forces, it does not follow that some simple functional form exists to describe the relationship between economic incentives and the qualitative nature of technical change. It is true that the transistor was the result of a search process that was set in motion for good economic reasons, that is, to reduce AT&T’s costly reliance on vacuum tubes for Long Lines switching. However, the disparate nature of the technological spillovers and social benefits that emerged from Bell Labs’ research effort is quite difficult to analyze without an appreciation of the sequence of events that transpired after the invention of the transistor. Ex ante analysis could not have predicted the transistor’s definitive role in reducing the cost of numerical calculation by many orders of magnitude through its central role in computer architecture. It is not simply that an

appropriate probability distribution of the transistor’s social benefits would be analytically daunting. The deeper point is that, at the point of invention, a well-defined and even marginally informative probability distribution simply could not be constructed.

Although modern economic analysis has, in recent years, paid some explicit attention to technological change, it has not dealt, in any depth, with its particular characteristics. The misreading of technological change, when viewed from a neoclassical perspective, should be apparent from the historical analysis offered in this chapter. Additional knowledge of new production possibilities is not costless, nor is the rate and direction of technological change exogenous.

Consequently, understanding the particular sequence of events that has shaped the knowledge of the technological frontier is crucial, not only to the historian, but to the economist as well. Technology and science, which are now generally acknowledged to be central to the achievement of economic growth, need to be understood as path-dependent phenomena. Indeed, it follows that economic growth itself needs to be understood in terms of path dependence. An economy’s history has left a large deposit of technological capabilities and possibilities on the shelf. The cost of taking items off that shelf is never known with any precision. Historical analysis, however, can allow us at least to narrow our estimates and thus to concentrate resources in directions that are more likely to have useful payoffs.

Index

The Competitiveness of Nations

in a Global Knowledge-Based Economy

May 2003

AAP Homepage