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Raninga, H. (2015). Economics Of Knowledge. PHILICA.COM Article number 494.

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Economics Of Knowledge

Himanshu Raningaunconfirmed user (Delhi University)

Published in econo.philica.com

Abstract
Knowledge and information appear in economic models in two different contexts. The mostfundamental assumption of standard microeconomics is that the economic system is based on rational choices made by individual agents. Thus, how much and what kind of information agents have about the world in which they operate and how powerful their ability to process the information is crucial issues.

Article body


 

Economics Of Knowledge

 

Knowledge and information appear in economic models in two different contexts. The mostfundamental assumption of standard microeconomics is that the economic system is based on rational choices made by individual agents. Thus, how much and what kind of information agents have about the world in which they operate and how powerful their ability to process the information is crucial issues.

The other major perspective is one in which knowledge is regarded as an asset. Here, knowledge may appear both as an input (competence) and output (innovation) in the production process. Under certain circumstances, it can be privately owned and/or bought and sold in the market as a commodity. The economics of knowledge is to a high degree about specifying the conditions for

Knowledge to appear as “a normal commodity”, i.e. as something similar a producible and reproducible tangible product.

One reason for the interest in this issue is that it is crucial for defining the role of government inknowledge production. If knowledge is a public good that can be accessed by anyone, there is noincentive for rational private agents to invest in its production. If it is less costly to imitate than to produce new knowledge, the social rate of return would be higher than the private rate of return and, again, private agents would invest too little. Nelson’s (1959) and Arrow’s (1962b) classical contributions demonstrated that, in such situations, there is a basis for government policy either to subsidise or to take charge directly of the production of knowledge. Public funding of schools and universities, as well as of generic technologies, has been motivated by this kind of reasoning, which also brings to the fore the protection of knowledge, for instance by patent systems.

 

 

 

Four different kinds of knowledge

Knowledge is here divided into four categories which in fact have ancient roots (Lundvall and Johnson, 1994).2

· Know-what

· Know-why

· Know-how

· Know-who

Know-what refers to knowledge about “facts”. How many people live in New York, what the ingredients in pancakes are, and when the battle of Waterloo took place are examples of this kind ofknowledge. Here, knowledge is close to what is normally called information – it can be broken down into bits and communicated as data. Know-why refers to knowledge about principles and laws of motion in nature, in the human mind and in society. This kind of knowledge has been extremely important for technological development

in certain science-based areas, such as the chemical and electric/electronic industries. Access to this kind of knowledge will often make advances in technology more rapid and reduce the frequency of errors in procedures involving trial and error. Know-how refers to skills – i.e. the ability to do something. It may be related to the skills of artisans and production workers, but, actually, it plays a key role in all important economic activities. The businessman judging the market prospects for a new product or the personnel manager selecting and training staff use their know-how. It would also be misleading to characterise know-how as practical rather than theoretical. One of the most interesting and profound analyses of the role and formation of know-how is actually about scientists’ need for skill formation and personal Knowledge (Polanyi, 1958/1978). Even finding the solution to complex mathematical problems is based on intuition and on skills related to pattern recognition which are rooted in experience-based learning rather than on the

 

 

mechanical carrying out of a series of distinct logical operations (Ziman, 1979, pp. 101-102).

Know-how is a kind of knowledge developed and kept within the borders of the individual firm or the single research team. As the complexity of the knowledge base increases, however, co-operation between organisations tends to develop. One of the most important reasons for industrial networks is the need for firms to be able to share and combine elements of know-how. Similar networks may,

for the same reasons, be formed between research teams and laboratories.

Most knowledge is neither strictly public nor strictly private

It is clear from what precedes that very little knowledge is “perfectly public”. Even information of the know-what type may be unavailable to those who are not connected to the right telecommunications or social networks. Moreover, the current state of information technology still limits access for those who are in fact connected. Scientific and other types of complex knowledge

may be perfectly accessible, in principle, but for effective access the user must have invested in building absorptive capacity. Know-how is never fully transferable since how a person does things reflects that individual’s personality (even organisations have a “personality” in this sense).

 

On tacitness and codification of knowledge

There is currently a lively debate among economists about the role of tacitness in knowledge. The reason for the interest is, of course, that tacitness relates to the transferability and to the public character of knowledge. It has been assumed that the more knowledge is tacit, the more difficult it is to share it between people, firms and regions. Specifically, markets might fail and other mediation mechanisms would have to be considered.

Tacit knowledge is knowledge that has not been documented and made explicit by the one who uses and controls it. The fact that a certain piece of knowledge is tacit does not rule out the possibility of making it explicit if incentives to do

 

 

so are strong enough. An important issue in this context is how much effort should be made to “codify” knowledge.

Knowledge written down in a code can be accessed only by those with access to that code. Two parties can share the knowledge or one party can sell the knowledge to another. Codified knowledge is potentially shared knowledge while non-codified knowledge remains individual, at least, until it can be learnt in direct interaction with the possessor. Sectors where the knowledge base is

dominated by non-codified but potentially codifiable knowledge, may be sectors where systematic progress towards more efficient practices is difficult.

 

An economic perspective on the production, mediation and use of knowledge

What is produced when firms produce knowledge?

Most authors using the concept of knowledge creation and knowledge production refer to technological knowledge and to technical innovation as the output of the process (Antonelli, 1999; Nonaka and Takeuchi, 1995). In the new growth theory, the output of the R&D sector is viewed either as a blueprint for a new production process that is more efficient than the previous one; it is

assumed that it can be protected by private property instruments such as patents; or as a production of new semi-manifactured goods that cannot easily be copied by competitors (Verspagen, 1992, p. 29-30).

 

Innovation as one major outcome of knowledge production

There are two reasons for regarding innovation as an interesting outcome of knowledge production.

One is that innovation represents – by definition – something new and therefore adds to existing knowledge. The second is that innovation is – again by definition – knowledge that is in demand.

 

 

(Innovation is defined as an invention that has been introduced in the market and it thus represents knowledge that has proven its relevance for the market economy.)

On the other hand, it is important to note that innovation, as Schumpeter emphasised, is part of a process of “creative destruction”. An innovation may open up new markets and create the basis for

New firms and jobs, but it will, at the same time, close down some old markets and some firms and jobs will disappear. This has a parallel in the impact on the stock of know ledge used in the market economy. Moral depreciation of intellectual capital is the other side of innovation. For instance, the know how necessary to produce mechanical office equipment and the competencies of firms engaged in their production became obsolete when semi-conductors and computers were introduced.

 

Competence as the other major outcome of knowledge production

The change from a linear to an interactive view of innovation and knowledge production has also been a way to connect innovation and the further development of competence. As now understood, the innovation process may be described as a process of interactive learning in which those involved increase their competence while engaging in the innovation process.

 

Codification of knowledge and the mediation of knowledge

The process of codification of knowledge plays an ambivalent role in the mediation process. On the one hand, the production and use of highly specialised codes or codes using technical or local jargon would actually create an obstacle to appropriation of the knowledge by lay people and potential users of the knowledge. On the other hand, a lack of codification may constitute an

 

 

obstacle as users would not have access to sufficiently explicit knowledge. This ambivalence indicates the importance of designing and implementing

met codes or semi codes as mechanisms for developing compromises between the need to make knowledge more explicit and the need to avoid excessive technicalities and local jargons.

 

Conclusions on the contribution of economic analysis to the understanding of knowledge base

It may be argued that, in a sense, all economic theory is about information and knowledge. Problems of co-ordination have been at the core of economic theory since Adam Smith. Individual agents make choices independently on the basis of information offered by the market. Important differences between economic models and theories reflect differences in the assumptions made about what agents know and about the degree to which they learn anything from what they do. This separates neo-classical economics from Austrian economics; the former takes fully informed agents as the reference, whereas the latter emphasises ignorance as the starting point for learning (von

Hayek). It also separates those who assume hyper -rationality and rationality from those who assume limited rationality (Herbert A. Simon).

Modern economics is more than ever aware of the importance of knowledge and learning. New growth theory and new trade theory assume a strong link between the increase in the knowledge base and the rate of productivity growth. Austrian economists treat learning as a fundamental process in the analysis of market transactions. The last decades have witnessed an explosive growth

in institutional economics and the economics of innovation. In these new fields, knowledge and learning play a pivotal role in economic development. New theories of the firm focus on building capabilities and competencies. The management literature has made the concept of “learning organisations” central for theoretical developments and especially for practitioners.

 

However, in almost all of these contributions, the understanding of knowledge and learning remains narrow. In theories that form the core of standard

economics, it is assumed that rational agents make choices on the basis of a given amount of information. The only kind of learning allowed for is agents’ access to new bodies of information. The most recent developments within standard economics are contradictory and ambivalent in this respect. On the one hand, new growth theory and new trade theory focus on the importance of investments in education and research. On the other hand, some of the most fashionable developments in macroeconomics assume rational expectations and general equilibrium frameworks, thus operating with even more extreme assumptions, leaving no room for learning by agents. Recent developments outside standard economics have been less constrained. Research on the

economics of institutional and technical change has resulted in many new insights. Institutional economics, evolutionary economics, socio-economic research, industrial dynamics and the economics of innovation have typically been developed in close interaction with historical and empirical research programmes. This is why we now know much more than before about how

innovation takes place in different parts of the economy. When it comes to the other aspect of knowledge production, i.e. competence building and learning,

research is only now beginning to raise fundamental questions about who learns what and how learning takes place in the context of economic development. In this area, economists have a lot to learn from other disciplines and not least from education specialists who have developed a much more systematic and empirically based understanding of learning (Kolb, 1984). This reflects the fact that when economists begin to focus on learning, they face is sues for which their traditional toolbox is insufficient. Scholars in philosophy, psychology, education, anthropology and other disciplines have illuminated different aspects of these issues. The increasing division of labour in the production of knowledge – useful as it might have been for the rapid advance within special

 

fields - has had as a major negative consequence the lack of a deep and systematic understanding of the complex process of knowledge creation and learning.

 

REFERENCES

Arrow, K.J. (1962a), “The Economic Implications of Learning by Doing”, Review of Economic Studies, Vol.

XXIX, No. 80.

Arrow, K.J. (1962b) “Economic welfare and the allocation of resources for invention”, in Nelson, R.R. (ed.) The

rate and direction of inventive activity: Economic and social factors, Princeton, Princeton University Press.

Cohen, W.M. and Levinthal, D.A. (1990), “Absorptive capacity: A new perspective on learning and innovation”,

Administrative Science Quarterly 35, pp. 128-152.

Dasgupta, P. and David, P. (1994), “Towards a new economics of science”, Research Policy, Vol. 23

Freeman, C. (1987), Technology policy and economic performance: Lessons from Japan, London, Pinter

Publishers.

Freeman, C. (1991), “Networks of Innovators: a Synthesis of Research Issues”, Research Policy, Vol. 20, No. 5.

Marshall, A.P. (1923), Industry and trade, London, MacMillan.

Maskell, P. and Malmberg, A. (1999), “Localised learning and industrial competitiveness”, Cambridge Journal of

Economics 23 (2).



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Raninga, H. (2015). Economics Of Knowledge. PHILICA.COM Article number 494.


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