The Competitiveness of Nations in a Global Knowledge-Based Economy

Robin Cowan (a), Paul A. David (b) & Dominique Foray (c)

The Explicit Economics of Knowledge: Codifcation and Tacitness


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1. Introduction: What’s All this Fuss over Tacit Knowledge About?

2. How the Tacit Dimension Found a Wonderful New Career in Economics

2.1 The Roots in the Sociology of Scientific Knowledge, and Cognitive Science

2.2 From Evolutionary Economics to Management Strategy and Technology Policy

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3. Codification and Tacitness Reconsidered

4. A Proposed Topography for Knowledge Activities

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5. Boundaries in the Re-mapped Knowledge Space and Their Significance

6. On the Value of This Re-mapping

6.1 On the Topography Itself

6.2 On Interactions with External Phenomena

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7. The Economic Determinants of Codification

7.1 The Endogeneity of the Tacitness - Codification Boundary

7.2 Costs, Benefits and the Knowledge Environment

7.3 Costs and Benefits in a Stable Context

7.4 Costs and Benefits in the Context of Change

8. Conclusions and the Direction of Further Work



HHC: Index added



Industrial and Corporate Change, 9 (2), 2000 , 211-253

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This paper attempts a greater precision and clarity of understanding concerning the nature and economic significance of knowledge and its variegated forms by presenting ‘the skeptical economist’s guide to “tacit knowledge’’.  It critically reconsiders the ways in which the concepts of tacitness and codification have come to be employed by economists and develops a more coherent re-conceptualization of these aspects of knowledge production and distribution activities.  It seeks also to show that a proposed alternative framework for the study of knowledge codification activities offers a more useful guide for further research directed to informing public policies for science, technological innovation and long-run economic growth.


1. Introduction: What’s All this Fuss over Tacit Knowledge About?

With increasing frequency these days references appear in the economics literature to ‘tacit knowledge’.  More often than not the meaning of this term itself is something that remains literally tacit - which is to say, those who employ it are silent as to its definition.  Something is suggested nevertheless by the common practice of juxtaposing mention of tacit knowledge and references to ‘codified knowledge’.  What is all this about?  Why has this distinction been made and what significance does it have for economists?

Polanyi (1958, 1967) introduced the term into modern circulation, by pointing to the existence of ‘the tacit dimension of knowledge’, a form or component of human knowledge distinct from, but complementary to, the knowledge explicit in conscious cognitive processes.  Polanyi illustrated this

(a) University of Maastricht, MERIT, Maastricht, The Netherlands, (b) Stanford University and All Souls College, Oxford, UK and  (c) University of Paris - Dauphine & IMRI (CNRS), France.


conceptualization by reference to a fact of common perception: we all are often aware of certain objects without being focused on them.  This, he maintained, did not make them the less important, as they form the context that renders focused perception possible, understandable and fruitful.  Reference to the findings of Gestalt psychology in regard to other perceptual phenomena formed another important aspect of Polanyi’s conceptualization of tacit knowledge: people appear to be perceptually (and/or intellectually) aware of some objects and things about the world only as entities - as illustrated by the identification of a particular human face or voice. [1]  Knowledge of this kind consists of holistic understandings, and thus is not completely amenable to purely reductionist analyses.

Subsequently, the term ‘tacit knowledge’ has come to be more widely applied to forms of personal knowledge that remain ‘UN-codified’ and do not belong in the category of ‘information’, which itself is thought of as an ideal-type good having peculiar economic features that differentiate it from other, conventional economic commodities. [2]  One may observe the growing practice among economists of juxtaposing ‘tacit’ and ‘codified’ knowledge, which casually applies the former term as a label for the entire (residual) category of knowledge that cannot be seen to be conveyed by means of codified, symbolic representations, i.e. transmitted as ‘information’.  In this process of inflating the usage of the term, the emphasis upon context and contextual understanding that was present in psychological references to the ‘tacit dimension’ of human knowledge has been largely discarded.  Tacit knowledge thus has come to signify an absolute type, namely: ‘not codified knowledge’.  Among economists it is used more and more in this way, without explicit definition, and therefore without further explication of the conditions that might underlie ‘tacitness’ or the resort to codification of knowledge.

But, more than having become merely another overly vague bit of fashionable economic jargon, ‘tacit knowledge’ now is an increasingly ‘loaded’ buzzword, freighted with both methodological implications for micro-economic theory in general, and policy significance for the economics of science and technology, innovation, and economic growth.  Indeed, references to ‘tacitness’ have become a platform used by some economists to launch fresh attacks upon national policies of public subsidization for R&D activities, and

1. Polyani (1967, pp. 4-6): ‘I shall reconsider human knowledge by starting from the fact that we can know more than we can tell... Gestalt psychology has demonstrated that we may know a physiognomy by intergrating our awareness of its particulars without being able to identify these particulars ...’

2. Most significant, from the economist’s viewpoint, is the absence of super-additivity and the negligible marginal costs of transmitting information.  These properties and their implications are discussed more fully in the following text, but the canonical references are Nelson (1959) and Arrow (1962).


equally by other economists to construct novel rationales for governmental funding of science and engineering research and training programs.

The first-order result of all this would seem to have been the creation of a considerable amount of semantic and taxonomic confusion.  In and of itself, this might be both expected and tolerable as a transient phase in any novel conceptual development.  Unfortunately, one cannot afford to be so sanguine, because those very same confusions are being exploited to advance economic policy conclusions that claim to be grounded upon propositions that are well established (if only recently recognized in economics) about the existence of different kinds of knowledge pertinent to scientific, technological and organizational innovation.  In our view, however, such claims in many instances are neither analytically nor empirically warranted.

This essay responds to a felt need for greater precision and clarity of understanding concerning the nature and economic significance of knowledge and its variegated forms, by presenting what might be described as ‘the skeptical economist’s guide to “tacit knowledge”.  Our skepticism, however, does not extend to questioning the seriousness of the array of issues that economists and others have been discussing under the general rubric of tacit knowledge, which truly are important and deserving of careful consideration.  Furthermore, we acknowledge that some of the now-classic contributions to the economics of scientific and technological innovation, when regarded from an epistemological perspective, appear unwarrantedly simplistic in their handling of some subtle questions concerning ‘knowledge’ and ‘information’, and the relationship between the two.

Our immediate purposes in this paper are to critically reconsider the ways in which the concepts of tacitness and codification have come to be employed by economists, and to develop a more coherent re-conceptualization of these aspects of knowledge production and distribution activities.  We seek to show, further, that a proposed alternative framework for the study of knowledge codification activities - perhaps because it rests upon explicit microeconomic foundations - offers a more useful guide for further research directed to informing public policies for science, technological innovation and long-run economic growth.

The following section elaborates on our contention that the terminology and meaning of ‘tacitness’ in the economics literature, having drifted far from its original epistemological and psychological moorings, has become unproductively amorphous; indeed, that it now obscures more than it clarifies.  Among the matters that thereby have been hidden are some serious analytical and empirical flaws in the newly emerging critique of the old economics of R&D.  By the same token, we also can identify equally serious


flaws in the novel rationale that has recently been developed for continuing public support of R&D activities, based upon the alleged inherent tacitness of technological knowledge.

An explicit re-examination of some fundamental conceptual underpinnings in this area is therefore in order.  Although this requires that we re-open questions which readers coming to the topic from economics may feel are settled well enough for their purposes, a persuasive case can be made for doing so; and setting it forth in section 3, we seek to show that a new taxonomic framework would prove helpful in clearing away a number of the conceptual confusions that presently are impeding the progress of research in this area.  Such a framework is proposed in section 4, providing a topography of ‘knowledge transaction activities’, the salient features of which are discussed in section 5.  A number of advantages afforded by the novel conceptual structure (those that are discernible a priori) are indicated in section 6.  But, as the proof of any pudding of this kind is to be found only in the eating, we proceed to put it to practical use in section 7, where we consider the economic costs and benefits of codification activities in different knowledge environments, thereby exposing the main endogenous determinants of the dynamic path of the boundary between what is and is not codified in the existing state of knowledge.  Section 8 concludes with some brief comments indicating the implied directions for theoretical and empirical work needed to further explore this promising vein in the economics of knowledge.


2. How the Tacit Dimension Found a Wonderful New Career in Economics

To motivate this undertaking we begin with a closer look at the intellectual background of the increasing frequency with which the notion of tacit knowledge currently is entering economic policy discussions.  It is fair to say that economists have not had much preparation to deal with the recent debates that are emerging in their midst over the nature of knowledge and the significance of its tacit dimension.  This is understandable, because the popular social science career of this concept began elsewhere... long ago, and in a far distant discipline.

Must economists now prepare themselves to become ever more deeply involved in discussions of the nature of knowledge, and begin to care about the various categories into which other disciplines claim knowledge should be sorted?  Is this not something better left for epistemologists and others of similar philosophical inclination?  Although one might be tempted to answer the latter in the affirmative, it is now too late to ignore the very meaning of


something that a large and growing number economists and other social scientists seem bent upon discussing.  It seems helpful, therefore, to approach the subject with some background awareness of the historical path by which ‘tacitness’ made its way from the philosophical writings of Polanyi (1958, 1967) into widespread currency in the economic journals.


2.1 The Roots in the Sociology of Scientific Knowledge, and Cognitive Science

The pioneering role was taken by those who called themselves ‘sociologists of scientific knowledge’ (SSK), thereby distinguishing their purpose and approach from that of the then mainstream Mertonian school in the sociology of science.  Proponents of the SSK program were more interested in the role of social forces (under which both economic and political interests were subsumed) in shaping the cognitive aspects of scientific work.  The traditional approach in the sociology of knowledge had, by contrast, tended to focus attention upon the role of macro-institutional settings, reward structures and the like, in mobilizing resources for the pursuit of scientific knowledge and organizing the conduct of research.  By and large, it had thereby eschewed direct engagement with the epistemological issues that occupied philosophers of science, and so it appeared to accept if not endorse the latter’s formal accounts of ‘the scientific method’ as the ‘disinterested confrontation of logically derived propositions (theory) with empirical evidence (fact)’.

That picture, however, did not appear to square with the one found by some SSK-inspired students of contemporary scientific practice.  They observed that some kinds of knowledge deployed in scientific inquiry - most notably that relating to the assembly and operation of experimental apparatus and instrumentation, and the interpretation of the data which these generated - were not communicated as hypotheses or codified propositions, or by any means resembling the formalized modes of discourse with which philosophy of science traditionally had been preoccupied.  Rather, working scientists appeared to be more occupied with ‘craft knowledge’, and much of what seemed crucial to their research efforts was not being transmitted among them in the form of any explicit, fully codified statements.

Collins’s (1974) notably influential study in this vein examined the early construction of the TEA laser in a number of laboratories, and reported that none of the research teams which succeeded in building a working laser had done so without the participation of someone from another laboratory where a device of this type already had been put into operation.  For Collins, ‘[t]he major point is that the transmission of skills is not done through the medium


of written words’.  Subsequent contributors to the sociology of scientific and technological knowledge have read this and other, kindred observations as showing that ‘the diffusion of knowledge could not be reduced to the mere transmission of information’ (Callon, 1995).

A contrast thus was posed between the ‘algorithmic model’ of knowledge production, which is concerned exclusively with the generation of consistent propositions and the transmission of explicit declarative statements, on the one hand, and the so-called ‘enculturation model’ of scientific activities on the other.  This distinction was invoked primarily by philosophers and sociologists who sought to challenge the idea that science, and specifically the modern scientific method, was a source of ‘privileged’ statements.  The putative privilege in question derived from the implication that scientific statements could be stripped from the social contexts in which they had been formed and in which they had acquired meaning, and consequently could be promulgated as part of an authoritatively universal, ‘codified’ body of knowledge about the physical world.  Challengers of that view leaned heavily on the seeming importance of tacit knowledge in the actual conduct of scientific activities.

At this juncture in the narrative a few remarks should be entered about the distinction observed here between ‘information’ and ‘knowledge’, terms that we shall continue to avoid using interchangeably.  We find it useful to operationally define an item of information as a message containing structured data, the receipt of which causes some action by the recipient agent - without implying that the nature of that action is determined solely and uniquely by the message itself.  Instead, it is the cognitive context afforded by the receiver that imparts meaning(s) to the information-message, and from the meaning(s) follow the specific nature of the induced action(s).  The term ‘knowledge’ is simply the label affixed to the state of the agent’s entire cognitive context. [3]

The algorithmic model to which reference has been made above, strictly interpreted, implies the absence of any meaningful distinction between information and knowledge.  Under this approach all the cognitive and behavioral capabilities of whatever human or non-human agent is being described must have been reduced to ‘code’, that is, to structured data and the necessary instructions for its processing.  Only in that way could a

3. From the foregoing it should be evident that we do not find it helpful to conflate ‘what humans learn in order to assimilate, digest and use information’ with the concept of ‘tacit knowledge’, and thereby to arrive at the glib but empty formalization: ‘information = codified knowledge’ and ‘knowledge’ = ‘tacit knowledge + ‘information’.  The definitions offered in the text make explicit the distinctive usage of the terms data, information and knowledge in Dasgupta and David (1994), David and Foray (1995) and Cowan and Foray (1997).  As we point out below, when knowledge is defined in this way, i.e. as an attribute of individual agents, some delicate conceptual issues arise when one tries to be precise in extending the notion by speaking of ‘social knowledge’ as the attribute of some collectivity of agents.


purely algorithmic actor generate further data and/or instructions for future actions - whether those were physical actions, or simply the processing, classification, storage, retrieval and transmission of information.  It is possible, therefore, to say that what an (algorithmic) economic agent ‘knows’ is nothing more nor less than ‘information’.

To stop there, of course, would be to ignore, inter alia, the manifest differences between intelligent human agents and computers.  Humans create new categories for the classification of information, and learn to assign meanings to (sensory) data inputs without the assistance of programmed instructions of which they are consciously aware.  Not surprisingly, then, the term ‘knowledge’ is applied in ordinary language when referring to human capacities that appear to be accessed without the intermediation of any formal code.  In other words, humans (and other living creatures) ‘know things’ that they have not acquired as ‘information’ and which, not having been reduced to symbolic representations (code), are held in forms that are not readily available for communication to others - at least not explicitly as ‘information-bearing’ messages.  At the same time, however, it is no less important to notice that the capacities of humans to ‘decode’, interpret, assimilate and find novel applications for particular items of information entail the use of still other items of information.  These latter equally are part (and may well form the critical part) of the ‘cognitive context’ within which the recipient of a given message assigns to it ‘meaning(s)’.  Moreover, there is nothing in this observation that would imply a lack of awareness on the part of the individual concerned about the pertinent ‘information context’, or any inability to transmit it to others.


2.2 From Evolutionary Economics to Management Strategy and Technology Policy

For some considerable time, economists took little if any interest in the question of separating the notion of knowledge from their idea of information, and scarcely noticed the sequel distinction that other disciplines had drawn between the algorithmic and enculturation models of learning and associated behaviors.  But things have moved on from that stage, and in several directions.  A parallel and related course of development also has been evident in the management studies literature, from the early formulations such as that provided by Winter (1987), to the recent wave of books on ‘knowledge management’, as exemplified by Leonard-Barton (1995), Nonaka and Keuchi (1995) and Davenport and Prusak (1998).

Thus, the importance of tacit knowledge as a strategy asset is ack-


nowledged today by students of rational management practices on the one hand, [4] and at the same time is cited as being crucial by critics of the ‘algorithmic’ approach of modern economic analysis of all aspects of human behavior.  In its latter manifestations, the concept of the inextricable tacitness of human knowledge forms the basis of arguments brought not only against the residue of behaviorist psychology which remains embedded in the neo-classical formulation of microeconomic analysis, but against virtually every construction of rational decision processes as the foundation for modeling and explaining the actions of individual human agents.

Whether the emergence of these disparate intellectual developments can be said to constitute scientific ‘advance’, however, remains another matter.  Quite clearly, the challenge being brought against algorithmic representations of knowledge generation and acquisition goes much deeper than arguments for ‘bounded’ rationality following the work of Newell and Simon (1972); and it has been argued in far more sweepingly general terms by critics of the whole artificial intelligence (AI) program such as Hofstader (1979) and, more recently, Penrose (1989, 1997).  In those quarters, tacit knowledge has come to stand for the aspects of human intelligence that cannot be mimicked by any (computer) algorithm.

It may be remarked that were the latter rather nihilistic arguments against the quest for AI to be read as statements conditional on the presently available and foreseeable states of technology, rather than as absolute assertions of impossibility, this would leave room for the boundary between tacit knowledge and knowledge of other kinds to be other than inextricably fixed.  Instead, what was tacit, and correspondingly what was not, would be subject to some future readjustments by improvements in the performance of computer hardware and software - because increasing processing speeds, reduced access times, expanded storage and more efficient algorithm designs permitted the faithful reproduction of an increasing range of human capabilities. [5]  The resolution of debates over the mutability of this boundary would carry many implications for economics, but, as will be seen, the way in which the idea of tacitness has come to be used opens the possibility that still other,

4. The definition of knowledge given in the text (above) is broadly consonant with, albeit rather more spare than the way the term is being used in this literature, as may be seen by the following ‘working definition’ offered by Davenport and Prusak (1998, p. 5): ‘Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experience and information.  It originates and is applied in the minds of knowers.’

5. Balconi (1998) explores the effects of changes in the modern technology of steel product manufacturing upon the boundary between the codified and the tacit elements of the knowledge deemed relevant for production operations and worker training.  The relevance here is simply that this boundary is mutable under the influence of technological changes other than those in the domain information technology narrowly construed.


non-technological conditions also are influential in determining what knowledge is codified and what is not.

One may locate the seedbed of the modern flowering of economic discussions of tacit knowledge in the early attention that was directed to Polanyi’s writings by Nelson and Winter’s (1982) widely noticed critique of neoclassical analysis and initiation of a program of research in evolutionary economics. [6]  Their discussion of the parallels between individual human skills and organizational capacities (Nelson and Winter, 1982, ch. 4) gave particular prominence to the concept of tacit knowledge, and expanded upon its significance for economists concerned with the internal workings of the firm. [7]  Those passages remain as perceptive and stimulating, and as fresh and balanced today as when they first appeared, almost two decades ago, and it is a pity that a larger proportion of the economists who now talk about tacit knowledge and its implications do not appear to have acquainted themselves with this ‘local source’. [8]

What Nelson and Winter (1982) say about the nature and significance of tacitness in knowledge conveys not just one sharply defined concept, but a nexus of meanings, each carrying somewhat distinctive implications.  Their first reference to the term (1982, p. 73), for example, offers only a parenthetical clarification: ‘The knowledge that underlies skillful performance is in large measure tacit knowledge, in the sense that the performer is not fully aware of the details of the performance and finds it difficult or impossible to articulate a full account of those details’ (emphasis added).

Yet, as is made clear shortly following this statement, Nelson and Winter accept Polanyi’s (1967) account of such situations as being contextual, rather than absolute: ‘the aim of a skillful performance’ may ‘be achieved by the observance of a set of rules which are not known as such to the person following them’.  Reference then is made to Polanyi’s earlier philosophical work,

6. For the subsequent elaboration of a more thorough-going rejection of microeconomic optimization, in the evolutionary models of Schumpeterian competition, see e.g. Dosi (1988), Dosi et al. (1988) and Dosi and Egidi (1991).  Evolutionary modeling in economics now spans a wide range of practice in regard to how ‘bounded’ the bounded rationality of agents is assumed to be.  Anderson (1994) discusses this and other issues in the algorithmic representation of the general class of Nelson-Winter type models.

7. Skinner(1994, p. 11) points out that the use made of Polanyi’s concept by Nelson and Winter(1982) emphasized what cognitive scientists refer to as the granularity of the efficient mode of mental storage for learned skills and procedures (‘routines’), rather than for the storage of declarative statements.  Skinner suggests that, in developing the former theme, Nelson and Winter were influenced strongly by the previous work in cognitive science and AI, e.g. by Newell and Simon’s (1972) formulation of a production system model of information processing for ‘learning,’ and the idea of learned routines being holistically stored for recall (as ‘scripts’) - a concept due to Shank (1988), who was a leading figure in cognitive science and AI fields on the faculty of Yale University during the 1970s and 1980s (as were Nelson and Winter).

8. Had things been otherwise, it seems only reasonable to suppose that we would have been spared at least the more serious confusions and unwarranted generalizations that have become commonplace in the literature.


Persona/Knowledge (1958, p. 49), where an example is presented of a swimmer keeping himself buoyant by regulating respiration, yet remaining unconscious of doing so.  In this case the operant rule (‘never empty your lungs fully’) plainly is one that is articulable, could be known to another person, and so might be transmitted verbally by a swimming instructor - were the latter aware of the principle of buoyancy.  In other words, Nelson and Winter (1982, p. 77) do not insist, any more than did Polanyi, that tacitness implied ‘inarticulability’, even though the inarticulability of some (personal) knowledge logically implied that the latter would remain tacit.

On the question of ‘awareness’, Nelson and Winter (1982, p. 78) recognize that the skillful performer may have ‘subsidiary awareness’ of the rules that are being followed, while being ‘focally aware’ of some other - most probably novel - facet of the task in which she is engaged.  This reinforces an appreciation of the contextual boundaries within which knowledge will be tacit, rather than explicitly recognized and articulated.  Yet, if one can describe behavior in terms of ‘rule conformity’, then it is clear that the underlying knowledge is codifiable - and indeed may have previously been codified.

Most significant still, for what we shall say about the more recent strain of the literature on tacitness, is Nelson and Winter’s (1982, p. 78) acknowledgement that this quality is not inherent in the knowledge.  They write: ‘The same knowledge, apparently, is more tacit for some people than for others.  Incentives, too, clearly matter: when circumstances place a great premium on effective articulation, remarkable things can sometimes be accomplished’.  In amplification of this point, they offer the example of an expert pilot giving successful verbal instruction via radio to a complete novice as to how to land an airplane - even though the ‘expert’ had never had occasion previously to make explicit what was entailed in his successful performance of a landing.  Indeed, their section on ‘Skills and Tacit Knowledge’ concludes by emphasizing that

… costs matter.  Whether a particular bit of knowledge is in principle articulable or necessarily tacit is not the relevant question in most behavioral situations.  Rather, the question is whether the costs... are sufficiently high so that the knowledge in fact remains tacit (p. 80).

This important set of observations deserved much more attention and elaboration than it has been accord by the subsequent literature, and we have sought (in section 7, below) to begin the work of rectifying this oversight.

It is unfortunate that these more complicated aspects of the concept had been all but forgotten, were they ever widely grasped when ‘tacitness’ made


its debut on the economic policy stage.  Among the most notable of the uses to which the idea of tacit knowledge is being put on the more mundane levels at which most economists operate, and certainly the uses that have the greatest impact in economic policy circles, has been the qualification - and in some instances the outright rejection - of the practical policy conclusions drawn from the classic information-theoretic analysis of the economics of R&D activities.

Following the seminal work of Arrow (1955, 1962) and Nelson (1959), an entire generation of economists treated scientific and technological knowledge as ‘information’.  To that degree, they reasoned, the knowledge generated by research activities possessed certain generic properties of public goods.  Much of the case for government subsidization of science and engineering research, and for innovative activity more generally, came to be grounded on the proposition that qua information, such knowledge could not be optimally produced or distributed through the workings of competitive markets.

Nowadays we are more and more frequently instructed otherwise.  In the newer understanding of science and technology as being pursuits inextricably involved with tacit knowledge, it is claimed that the old public policy rationales are exploded; the essential understandings are said to be the portion of knowledge that remains uncodified, and so deprived of the public goods properties that would result in informational spillovers and market failure.  Thus, as this argument concludes, the traditional economic case for subsidizing science and research in general collapses, as there is little or no basis for a presumption of market failure.  Similar arguments are advanced in the context of policy debates over the role of intellectual property rights in providing incentive for innovation: the claim is that the information presented (in codified form) in a patent is insufficient to allow others to actually make use of the patented invention, and it is the correlative ‘tacit knowledge’ that resides with the innovator that provides the real source of private, rent-appropriating (monopoly) power. [9]

But, at the same time, tacit knowledge is invoked by defenders of government subsidization of science as part of a strategic innovation policy.  A standard argument against public subsidy to science is that foreigners engaging in applied, commercially oriented R&D would free-ride (since information is a public good and travels freely) by exploiting the basic knowledge discoveries that our researchers vie to codify for disclosure in the

9. For further discussion see Arundel and Kabla (1999).  The force of such claims would seem restricted largely to the case of process patents rather than product patents.  Arundel (1996, 1997) reports that the CIS survey of EU firms found that 18-20% of respondent companies in the size range above 199 employees regarded process patents as a ‘very important or crucial’ source of competitive advantage, whereas in the case of product patents the corresponding figure was in the 30-40% range.


scientific journals and similar archival publications.  To this, the proponents of a strategic role for tacit knowledge reply, nations and regions, like individual enterprises undertaking R&D investments, can count on the benefits of ‘sticky data’ - to use von Hippel’s (1993) arresting term.  Knowledge does not travel freely, a condition that rests largely on the importance of tacit knowledge residing only in the heads of the scientists and engineers engaged in its production.  Codified knowledge may have low marginal costs of transmission and is thus slippery and hard to contain, but that is largely irrelevant if what one needs is its ‘sticky’, tacit counterpart. [10]

The inherent ‘stickiness’ of certain kinds of knowledge, consequently, enables business (or other) entities to protect their ability to appropriate the benefits derivable from their research investments fully, by controlling access to the repositories of uncodified knowledge.  For this, minimal recourse is required to the protection of intellectual property in the form of patents and copyrights; a mixture of trade secrecy law and labor law (master-servant relations) governing the behavior of current and former employees may be enough.  Thus, curious though it may seem, the tacit dimension of scientific and technological knowledge has found a new career for itself in science and technology policy debates: it is beginning to supplant its now dubious companion, ‘codified knowledge’, as the core of a new rationale for government research funding intended to build national and regional ‘competitiveness’ through innovation.

According to this application of the argument, even though the essential tacit knowledge concerning how to exploit what has been invented might be less than perfectly ‘sticky’, what this implies is that its economic benefits are only available to be captured locally.  In other, more formal, terms, it is asserted that the marginal costs of knowledge transmission rise very rapidly with ‘distance’ from the context in which such knowledge was generated.  Research by-products in the form of technological knowledge - being concerned with how best to get instrumentation involving chemical, mechanical, electrical and optical processes to work - are seen as inherently more strongly tacit in nature.  That is held to be particularly beneficial for would-be commercial developers who are able to situate closer to the locus of such discoveries (see e.g. Pavitt, 1987; Nelson, 1992; Patel and Pavitt, 1995).

A broad policy implication following from this is that for an economy

10. In subsequent work, von Hippel (1994) generalizes the idea of stickiness so that it covers all situations in which there is an appreciable cost of transferring information, relevant for innovative activities.  In principle, at least, von Hippel’s use of the notion of ‘stickiness’ makes no distinction between transfer costs consisting of pure rents imposed by the owners of intellectual property rights, on the one hand, and real social resource costs such as those entailed in physically transporting an expert for the purpose of demonstrating the proper use of a novel product or process in a distant location.


to have a strong, innovative manufacturing sector, it is necessary also to have correspondingly strong applied and basic research activities situated in close proximity to the production operations themselves.  The new innovation strategy perspective that has now formed around the concept of tacitness in the business management literature is illustrated by the following passage (Kay, 1999, p. 13):

Since ‘knowledge that’ - the characteristic discoveries of natural science - is easily transmitted, one solution [to the problem of creating ‘knowledge-based competitive advantages’] is to continually innovate and stay one step ahead.  And that kind of innovative capacity depends on knowledge that isn’t ‘knowledge that’, but ‘knowledge how’ - i.e. tacit knowledge.  Tacit knowledge can take many forms, but it cannot be written down.  It is unique to an organization - and therefore cannot be copied... The benefits of such tacit knowledge arise only through a culture of trust and knowledge-sharing within an organization .

Such considerations apply not only to scientific and engineering know-how, but also, and perhaps more strongly, to marketing, and internal management knowledge pertaining to business organizations, all of which have strongly contextual elements that make them ‘natural’ contributors to what von Hippel (1994) refers to as ‘sticky information’.

Thus, a notion that took its origins in the psychology of visual perception and human motor skills has been wonderfully transmuted, first from an efficient mode of mental storage of knowledge into a putative epistemological category (having to do with the nature of the knowledge itself), from there into a phenomenon of inarticulable inter-organizational relationships and finally to one of the keys to corporate, and perhaps also national, competitive advantage!

A corollary of arguments in the latter vein is that the case for granting public subsidies and tax concessions to private companies that invest in R&D would seem to be much weakened, were it not for the difficulties caused these firms by the circulation of their research personnel. [11]  Scientific and engineering staff are able to carry critical tacit knowledge off to potential rival firms

11. We can observe that the more things change the more they stay the same.  We have moved from the view that the problem to be solved arises from the fact that a firm’s knowledge is easily appropriated by other firms.  Acknowledging the importance of tacit knowledge, and thus that the initial problem may not be so severe, we face a ‘new problem’ stemming from the fact that a firms knowledge workers are easily appropriated by other firms.  In both cases the general issue remains, however - fluidity of knowledge or information (whether transmitted through codified knowledge or labor mobility) is good for the economy but may be bad for the individual firm that bears the costs of enlightening its competitors.


that offer them better terms of employment, including equity ownership in ‘start ups’ of their own.  In the logic of this approach, recognition of the criticality of tacit knowledge argues for further strengthening of trade secrecy protections, to block those ‘leakages’ and altogether eliminate the market failure rationale for governmental support of the performance of R&D by the private sector. [12]  That leaves the way open for those who wish to mount an essentially ‘techno-mercantilist’ argument for R&D subsidies, grounded on the idea that the country can benefit from job-creation, etc., if its firms win the race to be first to launch new products in international markets.  It is, in effect, a new strategic trade policy argument, grounded on the claim that tacit knowledge permits national appropriation of the direct and indirect benefits of monopolizing international product niches by being ‘first to invest’.

We see a need to put the economics of tacit and codified knowledge on analytically firmer and terminologically more precise foundations than those upon which most of the recent literature presently rests.  The foregoing account of the wonderful career that has thrust ‘the tacit dimension’ into the science and technology policy limelight serves, at least, to identify a number of issues that are of sufficient importance to warrant much more careful consideration.  The notion that the economic case for public support of science and engineering should now be based upon the inherently tacit and ‘craft’ nature of research activities certainly is rather paradoxical.  Taken at face value, it would suggest that intellectual property protection is unjustified, since, in the ‘natural’ state of things, there are no ‘externalities’ of new knowledge.  By implication, the patent system’s exchange of monopoly of use for ‘disclosure’ allows the patentee to retain the tacit knowledge without which the information contained in the patent really is useless.

But, before rushing to discard everything we know about the economics of R&D, scientific skepticism instructs us to pause and ask whether the epistemological foundations of these new propositions really are all that solid.  We should therefore question whether the economic functioning and attributes of tacit and codified knowledge are well understood by those who would invoke those matters in the context of current innovation policy debates.

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